title: Estimation of absolute vehicle speed using fuzzy logic rule-based Kalman filter

abstract: Accurate knowledge on the absolute or true speed of a vehicle, if and when available, can be used to enhance advanced vehicle dynamics control systems such as anti-lock brake systems (ABS) and auto-traction systems (ATS) control schemes. Current conventional method uses wheel speed measurements to estimate the speed of the vehicle. As a result, indication of the vehicle speed becomes erroneous and, thus, unreliable when large slips occur between the wheels and terrain. This paper describes a fuzzy rule-based Kalman filtering technique which employs an additional accelerometer to complement the wheel-based speed sensor, and produce an accurate estimation of the true speed of a vehicle. We use the Kalman filters to deal with the noise and uncertainties in the speed and acceleration models, and fuzzy logic to tune the covariances and reset the initialization of the filter according to slip conditions detected and measurement-estimation condition. Experiments were conducted using an actual vehicle to verify the proposed strategy.


title: Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

abstract: Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.


title: Open access, readership, citations: a randomized controlled trial of scientific journal publishing.

abstract: Does free access to journal articles result in greater diffusion of scientific knowledge? Using a randomized controlled trial of open access publishing, involving 36 participating journals in the sciences, social sciences, and humanities, we report on the effects of free access on article downloads and citations. Articles placed in the open access condition (n=712) received significantly more downloads and reached a broader audience within the first year, yet were cited no more frequently, nor earlier, than subscription-access control articles (n=2533) within 3 yr. These results may be explained by social stratification, a process that concentrates scientific authors at a small number of elite research universities with excellent access to the scientific literature. The real beneficiaries of open access publishing may not be the research community but communities of practice that consume, but rarely contribute to, the corpus of literature.


title: Effect of compression ratio on the performance, combustion and emission of DI diesel engine fueled with ethanol – Diesel blend

abstract: The present work investigates the influence of compression ratio on the performance, combustion and emission characteristics of a single cylinder four stroke direct injections naturally aspirated diesel engine. Compression ratios such as 17.5:1, 18.5:1 and 19.5:1 were considered and varied by altering the dimensions of piston bowl by keeping the standard stroke volume. Tests were performed with five different blends of ethanol [E0, E10, E20, E30 and E40]. To maintain homogeneity and to prevent the phase separation, 1% of Ethyl Acetate plus 1% diethyl carbonate are added to ethanol diesel blend. The brake thermal efficiency found to increase at full load with higher percentage of ethanol at maximum compression ratio. The increase in compression ratio and ethanol blended fuel improves combustion and improves the performance of engine. Further there is a significant decrease in hydrocarbon (HC), Carbon monoxide (CO) and smoke emissions however there is an increase in Nitrogen oxides (NOx).


title: Steady-State Simulation and Optimization of Class-E Power Amplifiers With Extended Impedance Method

abstract: This paper proposes a new iteration-free steady-state analysis to the Class-E power amplifier (PA) in frequency-domain by extending the scope of electrical impedance. Owning to this extension, the impedances of different circuit components in the Class-E PA circuits, including the active switches, can be expressed in matrix form. Based on the conventional circuit laws, e.g., the series and parallel laws and the Ohm's law, the steady-state characteristics of a whole Class-E PA circuit can be obtained by vector and matrix manipulation. The number of harmonics involved in the calculation have effects on the computational efficiency and accuracy. The influences of some circuit conditions towards the selection of harmonic number are discussed. The proposed formulation enables fast mapping of some performance indices, e.g., the output power and conversion efficiency, under steady state, so that the changing trends of these indices with respect to the variations of specified circuit parameters can be estimated. This feature can be utilized to carry out offline optimization by tuning some passive components, or online optimization by tuning two in situ adjustable parameters, i.e., the switch driving frequency and duty cycle. The later scheme provides preliminary knowledge on the in situ tuning of Class-E PA in the load varying applications, e.g., electric process heating and wireless power link.


title: Automatically Identifying Fake News in Popular Twitter Threads

abstract: Information quality in social media is an increasingly important issue, but web-scale data hinders experts' ability to assess and correct much of the inaccurate content, or "fake news," present in these platforms. This paper develops a method for automating fake news detection on Twitter by learning to predict accuracy assessments in two credibility-focused Twitter datasets: CREDBANK, a crowdsourced dataset of accuracy assessments for events in Twitter, and PHEME, a dataset of potential rumors in Twitter and journalistic assessments of their accuracies. We apply this method to Twitter content sourced from BuzzFeed's fake news dataset and show models trained against crowdsourced workers outperform models based on journalists' assessment and models trained on a pooled dataset of both crowdsourced workers and journalists. All three datasets, aligned into a uniform format, are also publicly available. A feature analysis then identifies features that are most predictive for crowdsourced and journalistic accuracy assessments, results of which are consistent with prior work. We close with a discussion contrasting accuracy and credibility and why models of non-experts outperform models of journalists for fake news detection in Twitter.


title: Ranking outlier nodes in subspaces of attributed graphs

abstract: Outlier analysis is an important data mining task that aims to detect unexpected, rare, and suspicious objects. Outlier ranking enables enhanced outlier exploration, which assists the user-driven outlier analysis. It overcomes the binary detection of outliers vs. regular objects, which is not adequate for many applications. Traditional outlier ranking techniques focus on either vector data or on graph structures. However, many of today's databases store both, multi dimensional numeric information and relations between objects in attributed graphs. An open challenge is how outlier ranking should cope with these different data types in a unified fashion. In this work, we propose a first approach for outlier ranking in subspaces of attributed graphs. We rank graph nodes according to their degree of deviation in both graph and attribute properties. We describe novel challenges induced by this combination of data types and propose subspace analysis as important method for outlier ranking on attributed graphs. Subspace clustering provides a selected subset of nodes and its relevant attributes in which deviation of nodes can be observed. Our graph outlier ranking (GOutRank) introduces scoring functions based on these selected subgraphs and subspaces. In addition to this technical contribution, we provide an attributed graph extracted from the Amazon marketplace. It includes a ground truth of real outliers labeled in a user experiment. In order to enable sustainable and comparable research results, we publish this database on our website1 as benchmark for future publications. Our experiments on this graph demonstrate the potential and the capabilities of outlier ranking in subspaces of attributed graphs.


Elements of information theory (2. ed.).


Resource Allocation in D 2 D Communication – A Game Theoretic Approach


title: Law Hum Behav DOI 10.1007/s10979-009-9188-6 ORIGINAL ARTICLE Police-Induced Confessions: Risk Factors and Recommendations

abstract: Recent DNA exonerations have shed light on the problem that people sometimes confess to crimes they did not commit. Drawing on police practices, laws concerning the admissibility of confession evidence, core principles of psychology, and forensic studies involving multiple methodologies, this White Paper summarizes what is known about police-induced confessions. In this review, we identify suspect characteristics (e.g., adolescence; intellectual disability; mental illness; and certain personality traits), interrogation tactics (e.g., excessive interrogation time; presentations of false evidence; and minimization), and the phenomenology of innocence (e.g., the tendency to waive Miranda rights) that influence confessions as well as their effects on judges and juries. This article concludes with a strong recommendation for the mandatory electronic recording of interrogations and considers other possibilities for the reform of interrogation practices and the protection of vulnerable suspect populations.


title: Deep learning based root-soil segmentation from X-ray tomography images

abstract: One of the most challenging computer vision problem in plant sciences is the segmentation of root and soil from X-ray tomography. So far, this has been addressed from classical image analysis methods. In this paper, we address this root/soil segmentation problem from X-ray tomography using a new deep learning classification technique. The robustness of this technique, tested for the first time on this plant science problem, is established with root/soil presenting a very low contrast in X-ray tomography. We also demonstrate the possibility to segment efficiently root from soil while learning on purely synthetic soil and root.


title: A 75-GHz Phase-Locked Loop in 90-nm CMOS Technology

abstract: The design and experimental verification of a 75-GHz phase-locked loop (PLL) fabricated in 90-nm CMOS technology is presented. The circuit incorporates a three-quarter wavelength oscillator to achieve high-frequency operation and a novel phase-frequency detector (PFD) based on SSB mixers to suppress the reference feedthrough. The PLL demonstrates an operation range of 320 MHz and reference sidebands of less than -72 dBc while consuming 88 mW from a 1.45-V supply.


Automatic audio sentiment extraction using keyword spotting


title: Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks

abstract: Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environment of the engine is harsh and the sensors will not work in high temperature, at high rotation speed, or under high pressure. Thus, there is not enough sensory data from the working engine to diagnose potential failures using existing approaches. In this paper, we consider the problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deep belief networks based on information entropy, IE-DBNs, for engine fault diagnosis. We first introduce several information entropies and propose joint complexity entropy based on single signal entropy. Second, the deep belief networks (DBNs) is analyzed and a logistic regression layer is added to the output of the DBNs. Then, information entropy is used in fault diagnosis and as the input for the DBNs. Comparison between the proposed IE-DBNs method and state-of-the-art machine learning approaches shows that the IE-DBNs method achieves higher accuracy.


title: Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

abstract: The recent advances in mobile technologies have resulted in Internet of Things (IoT)-enabled devices becoming more pervasive and integrated into our daily lives. The security challenges that need to be overcome mainly stem from the open nature of a wireless medium, such as a Wi-Fi network. An impersonation attack is an attack in which an adversary is disguised as a legitimate party in a system or communications protocol. The connected devices are pervasive, generating high-dimensional data on a large scale, which complicates simultaneous detections. Feature learning, however, can circumvent the potential problems that could be caused by the large-volume nature of network data. This paper thus proposes a novel deep-feature extraction and selection (D-FES), which combines stacked feature extraction and weighted feature selection. The stacked autoencoding is capable of providing representations that are more meaningful by reconstructing the relevant information from its raw inputs. We then combine this with modified weighted feature selection inspired by an existing shallow-structured machine learner. We finally demonstrate the ability of the condensed set of features to reduce the bias of a machine learner model as well as the computational complexity. Our experimental results on a well-referenced Wi-Fi network benchmark data set, namely, the Aegean Wi-Fi Intrusion data set, prove the usefulness and the utility of the proposed D-FES by achieving a detection accuracy of 99.918% and a false alarm rate of 0.012%, which is the most accurate detection of impersonation attacks reported in the literature.


title: Linking organizational resources and work engagement to employee performance and customer loyalty: the mediation of service climate.

abstract: This study examined the mediating role of service climate in the prediction of employee performance and customer loyalty. Contact employees (N = 342) from 114 service units (58 hotel front desks and 56 restaurants) provided information about organizational resources, engagement, and service climate. Furthermore, customers (N = 1,140) from these units provided information on employee performance and customer loyalty. Structural equation modeling analyses were consistent with a full mediation model in which organizational resources and work engagement predict service climate, which in turn predicts employee performance and then customer loyalty. Further analyses revealed a potential reciprocal effect between service climate and customer loyalty. Implications of the study are discussed, together with limitations and suggestions for future research.


title: Virtual network security: threats, countermeasures, and challenges

abstract: Network virtualization has become increasingly prominent in recent years. It enables the creation of network infrastructures that are specifically tailored to the needs of distinct network applications and supports the instantiation of favorable environments for the development and evaluation of new architectures and protocols. Despite the wide applicability of network virtualization, the shared use of routing devices and communication channels leads to a series of security-related concerns. It is necessary to provide protection to virtual network infrastructures in order to enable their use in real, large scale environments. In this paper, we present an overview of the state of the art concerning virtual network security. We discuss the main challenges related to this kind of environment, some of the major threats, as well as solutions proposed in the literature that aim to deal with different security aspects.


title: A Logic-Based Semantic Approach To Recognizing Textual Entailment

abstract: This paper proposes a knowledge representation model and a logic proving setting with axioms on demand successfully used for recognizing textual entailments. It also details a lexical inference system which boosts the performance of the deep semantic oriented approach on the RTE data. The linear combination of two slightly different logical systems with the third lexical inference system achieves 73.75% accuracy on the RTE 2006 data.


title: Towards SVC-based Adaptive Streaming in information centric networks

abstract: HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services. In HAS, each video is segmented and stored in different qualities. The client can dynamically select the most appropriate quality level to download, allowing it to adapt to varying network conditions. As the Internet was not designed to deliver such applications, optimal support for multimedia delivery is still missing. Information Centric Networking (ICN) is a recently proposed disruptive architecture that could solve this issue, where the focus is given to the content rather than to end-to-end connectivity. Due to the bandwidth unpredictability typical of ICN, standard AVC-based HAS performs quality selection sub-optimally, thus leading to a poor Quality of Experience (QoE). In this article, we propose to overcome this inefficiency by using Scalable Video Coding (SVC) instead. We individuate the main advantages of SVC-based HAS over ICN and outline, both theoretically and via simulation, the research challenges to be addressed to optimize the delivered QoE.


title: HEMAN: Health monitoring and nous: An IoT based e-health care system for remote telemedicine

abstract: Any country with Social, technological and economical advancement needs enhanced healthcare system. Telemedicine healthcare system provides the provision of medical treatment from a remote distance. The telemedicine research and product development has embarked prodigious growth during the past decade primarily due to tremendous technological advancement in ICT and automation. The aim of IoT-based health care system is to ensure and increase the welfare of patients and the quality of life in rural areas. In this paper we present a low cost Health sensor platform for rural health monitoring with a well-structured and secure interface between medical experts and Remote centers for sharing of important medical parameters. In our proposed and implemented model we developed separate interface for medical experts and remote Centre's and introduced a new algorithm for implementation. Features like live video streaming, chat boxes, automatic prescription generation and push notification are included. The prototype is used for trial under the supervision of medical experts and the data are compared with standard test done in pathological laboratory. The result is satisfactory with good level of acceptance.


title: Interaction-aware occupancy prediction of road vehicles

abstract: A crucial capability of autonomous road vehicles is the ability to cope with the unknown future behavior of surrounding traffic participants. This requires using non-deterministic models for prediction. While stochastic models are useful for long-term planning, we use set-valued non-determinism capturing all possible behaviors in order to verify the safety of planned maneuvers. To reduce the set of solutions, our earlier work considers traffic rules; however, it neglects mutual influences between traffic participants. This work presents the first solution for establishing interaction within set-based prediction of traffic participants. Instead of explicitly modeling dependencies between vehicles, we trim reachable occupancy regions to consider interaction, which is computationally much more efficient. The usefulness of our approach is demonstrated by experiments from the CommonRoad benchmark repository.


title: Brain connectivity and psychiatric comorbidity in adolescents with Internet gaming disorder

abstract: Prolonged Internet video game play may have multiple and complex effects on human cognition and brain development in both negative and positive ways. There is not currently a consensus on the principle effects of video game play neither on brain development nor on the relationship to psychiatric comorbidity. In this study, 78 adolescents with Internet gaming disorder (IGD) and 73 comparison subjects without IGD, including subgroups with no other psychiatric comorbid disease, with major depressive disorder and with attention deficit hyperactivity disorder (ADHD), were included in a 3 T resting state functional magnetic resonance imaging analysis. The severity of Internet gaming disorder, depression, anxiety and ADHD symptoms were assessed with the Young Internet Addiction Scale, the Beck Depression Inventory, the Beck Anxiety Inventory and the Korean ADHD rating scales, respectively. Patients with IGD showed an increased functional correlation between seven pairs of regions, all satisfying q < 0.05 False discovery rates in light of multiple statistical tests: left frontal eye field to dorsal anterior cingulate, left frontal eye field to right anterior insula, left dorsolateral prefrontal cortex (DLPFC) to left temporoparietal junction (TPJ), right DLPFC to right TPJ, right auditory cortex to right motor cortex, right auditory cortex to supplementary motor area and right auditory cortex to dorsal anterior cingulate. These findings may represent a training effect of extended game play and suggest a risk or predisposition in game players for over-connectivity of the default mode and executive control networks that may relate to psychiatric comorbidity.


title: Learning Controllers for Reactive and Proactive Behaviors in Human–Robot Collaboration

abstract: Designed to safely share the same workspace as humans and assist them in a variety of tasks, the new collaborative robots are targeting manufacturing and service applications that once were considered unattainable. The large diversity of tasks to carry out, the unstructured environments and the close interaction with humans call for collaborative robots to seamlessly adapt their behaviors so as to cooperate with the users successfully under different and possibly new situations (characterized, for example, by positions of objects/landmarks in the environment, or by the user pose). This paper investigates how controllers capable of reactive and proactive behaviors in collaborative tasks can be learned from demonstrations. The proposed approach exploits the temporal coherence and dynamic characteristics of the task observed during the training phase to build a probabilistic model that enables the robot to both react to the user actions and lead the task when needed. The method is an extension of the Hidden Semi-Markov Model where the duration probability distribution is adapted according to the interaction with the user. This Adaptive Duration Hidden Semi-Markov Model (ADHSMM) is used to retrieve a sequence of states governing a trajectory optimization that provides the reference and gain matrices to the robot controller. A proof-of-concept evaluation is first carried out in a pouring task. The proposed framework is then tested in a collaborative task using a 7 DOF backdrivable manipulator.


title: Steganalysis of LSB Matching Based on Local Binary Patterns

abstract: Least significant bit (LSB) matching is a well-known steganographic method, which can embed large payload into cover data with good visual and statistical imperceptibility. However, it disturbs the correlation of adjacent pixels in smooth image regions as it randomly modifies half of the payload pixels by 1. Local binary patterns (LBPs) are first proposed as texture features, and can summarize local image structures efficiently by comparing each pixel with its neighbors. In this paper, we propose to utilize LBPs to detect LSB matching steganography. In brief, multi-scaled rotation invariant LBPs are extracted from smooth pixels as distinctive features, and the features are trained and classified using linear support vector machine. Extensive experiments are conducted to compare our method with some state-of-the-art targeted steganalyzer, and the results show the superiority of our method with a higher detection accuracy.


title: A nanowatt cascadable delay element for compact power-on-reset (POR) circuits

abstract: A cascadable power-on-reset (POR) delay element consuming nanowatt of peak power was developed to be used in very compact power-on-reset pulse generator (POR-PG) circuits. Operation principles and features of the POR delay element were presented in this paper. The delay element was designed, and fabricated in a 0.5µm 2P3M CMOS process. It was determined from simulation as well as measurement results that the delay element works wide supply voltage ranges between 1.8 volt and 5 volt and supply voltage rise times between 100nsec and 1msec allowing wide dynamic range POR-PG circuits. It also has very small silicon footprint. Layout size of a single POR delay element was 35µm x 55µm in 0.5µm CMOS process.


title: Automatic generation of question answer pairs from noisy case logs

abstract: In a customer support scenario, a lot of valuable information is recorded in the form of ‘case logs’. Case logs are primarily written for future references or manual inspections and therefore are written in a hasty manner and are very noisy.


title: A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users

abstract: Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.


title: Analytics of deep neural network in change detection

abstract: Recently, deep neural networks (DNNs) have demonstrated excellent performance for change detection. The DNN-based background subtraction automatically discovers background features from datasets and outperforms traditional background modeling based on handcraft features and/or subtraction strategies. Most researchers mainly discuss the accuracy of foreground detection and do not analyze how and why the DNN works well for change detection tasks. It is necessary to understand what the DNN learns as background features in order to discuss the potential of the DNN in background subtraction. In this paper, we focus on the filters in the first convolution layer and the activations of neurons in the last fully connected layer to understand the behavior of the DNN. From the experiment, we found that 1) the first layer performs the role of background subtraction using several filters, and 2) the last layer categorizes some background changes into a group without supervised signals. These findings suggest the possibility of a new background modeling strategy based on data-driven extracted features.


'...the deep sleep of forgetfulness: reflecting on disremembering


title: The Helmholtz Machine

abstract: Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.


title: A novel overlay architecture for Information Centric Networking

abstract: Information Centric Networks (ICN) has emerged as a new Internet paradigm where the users are being provided seamless access to contents rather than the host as in classical approach. For ICN realization, the current Internet requires architectural changes. These changes must be incorporated such that existing applications continue running without any difficulties. In this paper, we introduce a novel ICN architecture, called Overlay Information Centric Networks (O-ICN), which separates the data plane from the control plane activities of the network. Unlike the usual ICN architectures, in our approach naming and routing (control plane) functionalities have been segregated from the caching (data plane) functionality at each ICN router. We introduce a new module called an ICN Manager which is an extended version of Domain Name System (DNS) server and performs name resolution along with routing functionalities for ICN type of requests as well as the normal DNS resolution for non-ICN type of requests. The O-ICN architecture can be deployed incrementally over the current Internet and can operate both as ICN and non-ICN/legacy network simultaneously. This can also be implemented both in Software Defined Networks (SDN) as well as non-SDN networks without much modifications. The proposed architecture provides benefits in terms of simplification of network management, vendor independent solution, better use of network resources in terms of bandwidth and delay, and reduction of deployment/operational cost of the network.


title: An administration concept for the enterprise role-based access control model

abstract: Using an underlying role-based model for the administration of roles has proved itself to be a successful approach. This paper sets out to describe the enterprise role-based access control model (ERBAC) in the context of SAM Jupiter, a commercial enterprise security management software.We provide an overview of the role-based conceptual model underlying SAM Jupiter. Having established this basis, we describe how the model is used to facilitate a role-based administration approach. In particular, we discuss our notion of 'scopes', which describe the objects over which an administrator has authority. The second part provides a case study based on our real-world experiences in the implementation of role-based administrative infrastructures. Finally, a critical evaluation and comparison with current approaches to administrative role-based access control is provided.


title: Enabling Real-Time Adaptivity in MOOCs with a Personalized Next-Step Recommendation Framework

abstract: In this paper, we demonstrate a first-of-its-kind adaptive intervention in a MOOC utilizing real-time clickstream data and a novel machine learned model of behavior. We detail how we augmented the edX platform with the capabilities necessary to support this type of intervention which required both tracking learners' behaviors in real-time and dynamically adapting content based on each learner's individual clickstream history. Our chosen pilot intervention was in the category of adaptive pathways and courseware and took the form of a navigational suggestion appearing at the bottom of every non-forum content page in the course. We designed our pilot intervention to help students more efficiently navigate their way through a MOOC by predicting the next page they were likely to spend significant time on and allowing them to jump directly to that page. While interventions which attempt to optimize for learner achievement are candidates for this adaptive framework, behavior prediction has the benefit of not requiring causal assumptions to be made in its suggestions. We present a novel extension of a behavioral model that takes into account students' time spent on pages and forecasts the same. Several approaches to representing time using Recurrent Neural Networks are evaluated and compared to baselines without time, including a basic n-gram model. Finally, we discuss design considerations and handling of edge cases for real-time deployment, including considerations for training a machine learned model on a previous offering of a course for use in a subsequent offering where courseware may have changed. This work opens the door to broad experimentation with adaptivity and serves as a first example of delivering a data-driven personalized learning experience in a MOOC.


title: EEG Correlates of Self-Referential Processing

abstract: Self-referential processing has been principally investigated using functional magnetic resonance imaging (fMRI). However, understanding of the brain functioning is not possible without careful comparison of the evidence coming from different methodological domains. This paper aims to review electroencephalographic (EEG) studies of self-referential processing and to evaluate how they correspond, complement, or contradict the existing fMRI evidence. There are potentially two approaches to the study of EEG correlates of self-referential processing. Firstly, because simultaneous registration of EEG and fMRI has become possible, the degree of overlap between these two signals in brain regions related to self-referential processing could be determined. Second and more direct approach would be the study of EEG correlates of self-referential processing per se. In this review, I discuss studies, which employed both these approaches and show that in line with fMRI evidence, EEG correlates of self-referential processing are most frequently found in brain regions overlapping with the default network, particularly in the medial prefrontal cortex. In the time domain, the discrimination of self- and others-related information is mostly associated with the P300 ERP component, but sometimes is observed even earlier. In the frequency domain, different frequency oscillations have been shown to contribute to self-referential processing, with spontaneous self-referential mentation being mostly associated with the alpha frequency band.


title: MobileNets for flower classification using TensorFlow

abstract: Classification of objects into their specific classes is always been significant tasks of machine learning. As the study of flower, categorizing specific class of flower is important subject in the field of Botany but the similarity between the diverse species of flowers, texture and color of flowers, and the dissimilarities amongst the same species of flowers, there still are some challenges in the recognition of flower images. Existing recent Google's inception-v3 model comparatively takes more time and space for classification with high accuracy. In this paper, we have shown experimental performance of MobileNets model on TensorFlow platform to retrain the flower category datasets, which can greatly minimize the time and space for flower classification compromising the accuracy slightly.


MetaMind Neural Machine Translation System for WMT 2016


title: Product Quality Control Method for Extended Enterprises Based on Holonic

abstract: Based on holons theory, the product quality control holonic structure for extended enterprises is constructed by the similarity characteristic of quality control function extended enterprises. In the structure, the quality control holons that has autonomy and cooperation ability are established, and by the analysis of holon cooperation operation, by means of the definition and description the cooperation relations among holons, the structure can complete complex cooperation in internal and external cooperation holons to improve the ability and efficiency of product quality control. The implementation strategy of the product quality control holonic structure is constructed by finite state markov chains to dynamically describe cooperation relationship, and forecast the dynamic cooperation tendency to serve for quality decision. Finally, the implementation strategy can be demonstrated and validated the proposed idea by Flexsim software. It shows that the product quality control holonic structure for extended enterprises can satisfy the agility, distribution, and robustness requirements the quality control among enterprises.


title: Microwave Doherty Power Amplifier for High Efficiency and Linearity

abstract: This paper explains the basic circuit configuration of microwave Doherty amplifier and additional design methods for wide bandwidth and high power applications. This paper also presents the recent experimental results of our group using the uneven power drive. Most of the previous works about microwave Doherty amplifier have employed identical input drives. In this works, two 1:1(2-way) and a 1:2(3-way) Doherty amplifiers are implemented at 2.14GHz center frequency using LDMOS with a peak envelope power of 180 watt and 285 watt, respectively. The 2-way Doherty amplifiers are optimized at 25 watt average output power for high efficiency and linearity and the 3-way Doherty amplifier at 16 watt average output power for high linearity. The amplifiers are tested using WCDMA 4FA signal. The performances of the 2-way Doherty amplifier are compared with that of the comparable class AB amplifier and the Doherty amplifier with even power drive. The performances of the 3-way Doherty amplifiler are compared with that of the comparable class AB amplifier. The experimental results show the superior performances of our Doherty amplifiers.


title: Integrating crowd intelligence into software

abstract: The knowledge resources available on the Internet are increasingly being used to support software both at development time and at execution time. These take the form of conventional services as well as human knowledge work both through crowd sourcing and information stored directly on the World Wide Web. But while these resources are vast and rich, they are also unreliable. In this paper, we propose a novel software development pattern called contributional implementation (CI) inspired by the way humans mitigate this unreliability: sources are treated as opinion providers with varying amounts of trust and aggregating multiple opinions from different sources helps improve the quality of the answers. We sketch some detailed examples of how a CI could be coded, discuss issues related to the realization of CI's in practice and outline plans for an evaluation of the approach.


title: Designing the whyline: a debugging interface for asking questions about program behavior

abstract: Debugging is still among the most common and costly of programming activities. One reason is that current debugging tools do not directly support the inquisitive nature of the activity. Interrogative Debugging is a new debugging paradigm in which programmers can ask why did and even why didn't questions directly about their program's runtime failures. The Whyline is a prototype Interrogative Debugging interface for the Alice programming environment that visualizes answers in terms of runtime events directly relevant to a programmer's question. Comparisons of identical debugging scenarios from user tests with and without the Whyline showed that the Whyline reduced debugging time by nearly a factor of 8, and helped programmers complete 40% more tasks.


title: Some Correlates of Electronic Health Information Management System Success in Nigerian Teaching Hospitals

abstract: Nowadays, an electronic health information management system (EHIMS) is crucial for patient care in hospitals. This paper explores the aspects and elements that contribute to the success of EHIMS in Nigerian teaching hospitals. The study adopted a survey research design. The population of study comprised 442 health information management personnel in five teaching hospitals that had implemented EHIMS in Nigeria. A self-developed questionnaire was used as an instrument for data collection. The findings revealed that there is a positive, close relationship between all the identified factors and EHIMS’s success: technical factors (r = 0.564, P < 0.05); social factors (r = 0.616, P < 0.05); organizational factors (r = 0.621, P < 0.05); financial factors (r = 0.705, P < 0.05); and political factors (r = 0.589, P < 0.05). We conclude that consideration of all the identified factors was highly significant for the success of EHIMS in Nigerian teaching hospitals.


title: Parallel Personalized Pagerank on Dynamic Graphs

abstract: Personalized PageRank (PPR) is a well-known proximity measure in graphs. To meet the need for dynamic PPR maintenance, recent works have proposed a local update scheme to support incremental computation. Nevertheless, sequential execution of the scheme is still too slow for highspeed stream processing. Therefore, we are motivated to design a parallel approach for dynamic PPR computation. First, as updates always come in batches, we devise a batch processing method to reduce synchronization cost among every single update and enable more parallelism for iterative parallel execution. Our theoretical analysis shows that the parallel approach has the same asymptotic complexity as the sequential approach. Second, we devise novel optimization techniques to effectively reduce runtime overheads for parallel processes. Experimental evaluation shows that our parallel algorithm can achieve orders of magnitude speedups on GPUs and multi-core CPUs compared with the state-of-the-art sequential algorithm.


title: Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering

abstract: It is traditionally a challenge for home buyers to understand, compare and contrast the investment values of real estates. While a number of estate appraisal methods have been developed to value real property, the performances of these methods have been limited by the traditional data sources for estate appraisal. However, with the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of estates for enhancing estate appraisal. Indeed, the geographic dependencies of the value of an estate can be from the characteristics of its own neighborhood (individual), the values of its nearby estates (peer), and the prosperity of the affiliated latent business area (zone). To this end, in this paper, we propose a geographic method, named ClusRanking, for estate appraisal by leveraging the mutual enforcement of ranking and clustering power. ClusRanking is able to exploit geographic individual, peer, and zone dependencies in a probabilistic ranking model. Specifically, we first extract the geographic utility of estates from geography data, estimate the neighborhood popularity of estates by mining taxicab trajectory data, and model the influence of latent business areas via ClusRanking. Also, we use a linear model to fuse these three influential factors and predict estate investment values. Moreover, we simultaneously consider individual, peer and zone dependencies, and derive an estate-specific ranking likelihood as the objective function. Finally, we conduct a comprehensive evaluation with real-world estate related data, and the experimental results demonstrate the effectiveness of our method.


title: Augmented Reality Mobile Application of Balinese Hindu Temples: DewataAR

abstract: Augmented reality is a visual technology which combines virtual objects into the real environment in real time. E-tourism in Bali needs to be optimized, so that information technology can help tourists and provide new experiences when traveling. Generally, tourists wish for gaining information in an attractive way about visiting tourism objects. Nowadays, mobile-based application programs that provide information about tourism objects in Bali are rarely found. Therefore, it is important to develop an application which provides information system about tourism objects, especially about the Tanah Lot temple. By implementing augmented reality technology, which grows rapidly all over the world, the application of DewataAR can show 3 dimensional objects, video, and audio information of the temples. The application works by scanning brochure of tourism object by using an Android smartphone or tablet, then it can display 3 dimensional objects, video, and audio information about those tourism objects. Hence, augmented reality can be alternative media for promoting tourism object attractively for tourists and also be able to develop tourism in Bali.


title: Face recognition using PCA and SVM

abstract: Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security applications or video indexing. Face recognition is an important and very challenging technique to automatic people recognition. Up to date, there is no technique that provides a robust solution to all situations and different applications that face recognition may encounter. In general, we can make sure that performance of a face recognition system is determined by how to extract feature vector exactly and to classify them into a group accurately. It, therefore, is necessary for us to closely look at the feature extractor and classifier. In this paper, Principle Component Analysis (PCA) is used to play a key role in feature extractor and the SVMs are used to tackle the face recognition problem. Support Vector Machines (SVMs) have been recently proposed as a new classifier for pattern recognition. We illustrate the potential of SVMs on the Cambridge ORL Face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. The SVMs that have been used included the Linear (LSVM), Polynomial (PSVM), and Radial Basis Function (RBFSVM) SVMs. We provide experimental evidence which show that Polynomial and Radial Basis Function (RBF) SVMs performs better than Linear SVM on the ORL Face Dataset when both are used with one against all classification. We also compared the SVMs based recognition with the standard eigenface approach using the Multi-Layer Perceptron (MLP) Classification criterion.


title: False Positives Reduction Techniques in Intrusion Detection Systems-A Review

abstract: Summary During the last decade with the growth of cyber attacks, information safety has become an important issue all over the world. Intrusion detection systems (IDSs) are an essential element for network security infrastructure and play a very important role in detecting large number of attacks. Although there are different types of intrusion detection systems, all these systems suffer a common problem which is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. The aim of conducted research in the field is to propose different techniques to handle the alerts, reduce them and distinguish real attacks from false positives and low importance events. This manuscript is a survey paper that represents a review of the current research related to the false positives problem. The focus will be on data mining techniques of alert reduction. This paper reviews more than 30 related studies during the last decade with the hope of providing a reference for further research in this area. Several open issues have also been addressed in this paper.


title: Stronger Data Poisoning Attacks Break Data Sanitization Defenses

abstract: Machine learning models trained on data from the outside world can be corrupted by data poisoning attacks that inject malicious points into the models' training sets. A common defense against these attacks is data sanitization: first filter out anomalous training points before training the model. Can data poisoning attacks break data sanitization defenses? In this paper, we develop three new attacks that can all bypass a broad range of data sanitization defenses, including commonly-used anomaly detectors based on nearest neighbors, training loss, and singular-value decomposition. For example, our attacks successfully increase the test error on the Enron spam detection dataset from 3% to 24% and on the IMDB sentiment classification dataset from 12% to 29% by adding just 3% poisoned data. In contrast, many existing attacks from the literature do not explicitly consider defenses, and we show that those attacks are ineffective in the presence of the defenses we consider. Our attacks are based on two ideas: (i) we coordinate our attacks to place poisoned points near one another, which fools some anomaly detectors, and (ii) we formulate each attack as a constrained optimization problem, with constraints designed to ensure that the poisoned points evade detection. While this optimization involves solving an expensive bilevel problem, we explore and develop three efficient approximations to this problem based on influence functions; minimax duality; and the Karush-Kuhn-Tucker (KKT) conditions. Our results underscore the urgent need to develop more sophisticated and robust defenses against data poisoning attacks.


title: Modeling and Simulation of Photovoltaic module using MATLAB/Simulink

abstract: This paper presents modeling of Photovoltaic (PV) module using MATLAB/Simulink. The model is developed based on the mathematical model of the PV module. Two particular PV modules are selected for the analysis of developed model. The essential parameters required for modeling the system are taken from datasheets. I-V and P-V characteristics curves are obtained for the selected modules with the output power of 60W and 64W from simulation and compared with the curves provided by the datasheet. The results obtained from the simulation model are well matched with the datasheet information.


title: Time Series Analysis with R

abstract: Abstract A brief overview of the R statistical computing and programming environment is given that explains why many time series researchers in both applied and theoretical research may find R useful. The core features of R for basic time series analysis are outlined. Some intermediate level and advanced topics in time series analysis that are supported in R are discussed such as including state-space models, structural change, generalized linear models, threshold models, neural nets, co-integration, GARCH, wavelets, and stochastic differential equations. Numerous examples of beautiful graphs constructed using R for time series are shown. R code for reproducing all the graphs and tables is given on my homepage.


Full-thickness skin necrosis following cryolipolysis to the upper arm


title: Detection of Malicious and Low Throughput Data Exfiltration Over the DNS Protocol

abstract: In the presence of security countermeasures, a malware designed for data exfiltration must do so using a covert channel to achieve its goal. Among existing covert channels stands the domain name system (DNS) protocol. Although the detection of covert channels over the DNS has been thoroughly studied in the last decade, previous research dealt with a specific subclass of covert channels, namely DNS tunneling. While the importance of tunneling detection is not undermined, an entire class of low throughput DNS exfiltration malware remained overlooked. The goal of this study is to propose a method for detecting both tunneling and low-throughput data exfiltration over the DNS. Towards this end, we propose a solution composed of a supervised feature selection method, and an interchangeable, and adjustable anomaly detection model trained on legitimate traffic. In the first step, a one-class classifier is applied for detecting domain-specific traffic that does not conform with the normal behavior. Then, in the second step, in order to reduce the false positive rate resulting from the attempt to detect the low-throughput data exfiltration we apply a rule-based filter that filters data exchange over DNS used by legitimate services. Our solution was evaluated on a medium-scale recursive DNS server logs, and involved more than 75,000 legitimate uses and almost 2,000 attacks. Evaluation results shows that while DNS tunneling is covered with at least 99% recall rate and less than 0.01% false positive rate, the detection of low throughput exfiltration is more difficult. While not preventing it completely, our solution limits a malware attempting to avoid detection with at most a 1kb/h of payload under the limitations of the DNS syntax (equivalent to five credit cards details, or ten user credentials per hour) which reduces the effectiveness of the attack.


Recommender systems


title: Observation of long-lived interlayer excitons in monolayer MoSe2–WSe2 heterostructures

abstract: Van der Waals bound heterostructures constructed with two-dimensional materials, such as graphene, boron nitride and transition metal dichalcogenides, have sparked wide interest in device physics and technologies at the two-dimensional limit. One highly coveted heterostructure is that of differing monolayer transition metal dichalcogenides with type-II band alignment, with bound electrons and holes localized in individual monolayers, that is, interlayer excitons. Here, we report the observation of interlayer excitons in monolayer MoSe2-WSe2 heterostructures by photoluminescence and photoluminescence excitation spectroscopy. We find that their energy and luminescence intensity are highly tunable by an applied vertical gate voltage. Moreover, we measure an interlayer exciton lifetime of ~1.8 ns, an order of magnitude longer than intralayer excitons in monolayers. Our work demonstrates optical pumping of interlayer electric polarization, which may provoke further exploration of interlayer exciton condensation, as well as new applications in two-dimensional lasers, light-emitting diodes and photovoltaic devices.


title: Compressive imaging for video representation and coding

abstract: Compressive Sensing is an emerging field based on the revelation that a small group of nonadaptive linear projections of a compressible signal contains enough information for reconstruction and processing. In this paper, we propose algorithms and hardware to support a new theory of Compressive Imaging. Our approach is based on a new digital image/video camera that directly acquires random projections of the light field without first collecting the pixels/voxels. Our camera architecture employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Its hallmarks include the ability to obtain an image with a single detection element while measuring the image/video fewer times than the number of pixels/voxels; this can significantly reduce the computation required for video acquisition/encoding. Since our system relies on a single photon detector, it can also be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers. We are currently testing a prototype design for the camera and include experimental results.


An Adversarial and Densely Dilated Network for Connectomes Segmentation


title: Blood pressure-lowering effect of Shinrin-yoku (Forest bathing): a systematic review and meta-analysis

abstract: BACKGROUND ::: Shinrin-yoku (experiencing the forest atmosphere or forest bathing) has received increasing attention from the perspective of preventive medicine in recent years. Some studies have reported that the forest environment decreases blood pressure. However, little is known about the possibility of anti-hypertensive applications of Shinrin-yoku. This study aimed to evaluate preventive or therapeutic effects of the forest environment on blood pressure. ::: ::: ::: METHODS ::: We systematically reviewed the medical literature and performed a meta-analysis.Four electronic databases were systematically searched for the period before May 2016 with language restriction of English and Japanese. The review considered all published, randomized, controlled trials, cohort studies, and comparative studies that evaluated the effects of the forest environment on changes in systolic blood pressure. A subsequent meta-analysis was performed. ::: ::: ::: RESULTS ::: Twenty trials involving 732 participants were reviewed. Systolic blood pressure of the forest environment was significantly lower than that of the non-forest environment. Additionally, diastolic blood pressure of the forest environment was significantly lower than that of the non-forest environment. ::: ::: ::: CONCLUSIONS ::: This systematic review shows a significant effect of Shinrin-yoku on reduction of blood pressure.


title: Robust salient motion detection in non-stationary videos via novel integrated strategies of spatio-temporal coherency clues and low-rank analysis

abstract: Salient motion detection is vital for security surveillance, pattern and motion recognition, traffic control, human-computer interaction, etc. Although such a subject has been very well investigated for analysis of stationary videos, many technical challenges still prevail when correctly handling and analyzing non-stationary videos recorded by hand-hold and pan-tilt-zoom cameras. To ameliorate, this paper develops a novel and robust salient motion detection method (especially valuable for quantitative analysis of non-stationary videos) by employing new computational strategies, including low-rank analysis aided by the divide-and-conquer approach, and exploration of the space-time semantic coherency. The key idea in our new approach is to respectively conduct multi-purpose low-rank analysis over a temporal series of well-decomposed frame-batches that have relatively-consistent backgrounds. First, we conduct bilateral random projection (BRP)-based low-rank analysis to accurately keep track of short-term stable-background observations, which consist of frames with similar global appearance and small local variations. Then, to eliminate the side effects due to visual variations induced by view angle changes, we incorporate the low-rank background prior into previous short-term observation to guide robust principal component analysis (RPCA) low-rank revealing based robust salient motion detection over current short-term observation. Meanwhile, a series of saliency clues extracted from the stabilized short-term observations are leveraged to expedite the proper updating of the low-rank background information, which enables us to effectively combat several obstinate problems. Finally, we conduct comprehensive experiments on the public CD2014 benchmark and other five non-stationary videos recorded from the hand-hold camera, and make extensive and quantitative evaluations with six state-of-the-art methods. Experimental results indicate that our method not only outperforms all other methods in the case of non-stationary videos but also obtains outstanding performance for stationary videos. HighlightsWe propose a versatile and robust background tracking method.We integrate low-rank background prior with coherency revealing based on the aligned RPCA low-rank analysis.We define a series of saliency clues via online exploration of the spatio-temporal coherency.


title: Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

abstract: This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.


title: Analysis of direct action fuzzy PID controller structures

abstract: The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the Mamdani-type controller. By expressing the fuzzy rules in different forms, each PLD structure is distinctly identified. For purpose of analysis, a linear-like fuzzy controller is defined. A simple analytical procedure is developed to deduce the closed form solution for a three-input fuzzy inference. This solution is used to identify the fuzzy PID action of each structure type in the dissociated form. The solution for single-input-single-output nonlinear fuzzy inferences illustrates the effect of nonlinearity tuning. The design of a fuzzy PID controller is then treated as a two-level tuning problem. The first level tunes the nonlinear PID gains and the second level tunes the linear gains, including scale factors of fuzzy variables. By assigning a minimum number of rules to each type, the linear and nonlinear gains are deduced and explicitly presented. The tuning characteristics of different fuzzy PID structures are evaluated with respect to their functional behaviors. The rule decoupled and one-input rule structures proposed in this paper provide greater flexibility and better functional properties than the conventional fuzzy PHD structures.


title: Routing, Disjoint Paths, and Classification

abstract: In this thesis, we study three problems: routing, edge disjoint paths, and classification. ::: In hierarchical routing, we obtain "near-optimal" routing table size and path stretch through a randomized hierarchical decomposition scheme in the metric space induced by a graph. We say that a metric (X, d) has doubling dimension dim(X) at most α if every set of diameter D can be covered by 2α sets of diameter D/2. (A doubling metric is one whose doubling dimension dim(X) is a constant.) For a connected graph G, whose shortest path distances dG induce the doubling metric (X, dG), we show how to perform (1 + τ)-stretch routing on G for any 0 < τ ≤ 1 with routing tables of size at most (α/τ) O(α) logΔlogδ bits with only (α/τ)O(α) logΔ entries, where Δ is the diameter of G and δ is the maximum degree of G. ::: The Edge Disjoint Paths (EDP) problem in undirected graphs refers to the following: Given a graph G with n nodes and a set T of pairs of terminals, connect as many terminal pairs as possible using paths that are mutually edge disjoint. This leads to a variety of classic NP-complete problems, for which approximability is not well understood. We show a polylogarithmic approximation algorithm for the undirected EDP problem in general graphs with a moderate restriction on graph connectivity: we require the global minimum cut of G to be Ω(log5 n). Our algorithm extends previous techniques in that it applies to graphs with high diameters and asymptotically large minors. ::: In the classification problem, we are given a set of 2N diploid individuals from population P1 and P2 (with no admixture), and a small amount of multilocus genotype data from the same set of K loci for all 2N individuals, and we aim to partition P 1 and P2 perfectly. In our model, given the population of origin of each individual, the genotypes are assumed to be generated by drawing alleles independently at random across the K loci, each form its own distribution. We show several results for this problem.


title: Observations: SAM: The Self-Assessment Manikin An Efficient Cross-Cultural Measurement Of Emotional Response 1

abstract: Although consumer research began focusing on emotional response to advertising during the 1980s (Goodstein, Edell, and Chapman Moore. 1990; Burke and Edell, 1989; Aaker, Stayman, and Vezina, 1988; Holbrook and Batra, 1988), perhaps one of the most practical measures of affective response has only recently emerged. Part of the difficulty in developing measures of emotional response stems from the complexity of emotion itself (Plummer and Leckenby, 1985). Researchers have explored several different measurement formats including: verbal self-reports (adjective checklists), physiological techniques, photodecks, and dial-turning instruments.


title: Design of an innovative prosthetic hand with compact shape memory alloy actuators

abstract: Shape memory alloy actuation is one of the emerging technologies that shows up remarkable advantages compared to conventional actuation. In this research work, this technology is exploited in driving an innovative prosthetic hand designed to meet most of the requirements of the upper limb amputees. Compact and totally silent linear actuation units are developed and integrated within a lightweight housing closely replicating the shape and size of the average human hand. In addition, a tendon-driven underactuated mechanism provides the necessary dexterity without compromising the simplicity of the device. The physical implementation of the hand chassis is accomplished using rapid prototyping techniques. Experiments with a single-finger testbed are conducted in order to evaluate speed performance and investigate the feasibility of a resistance feedback control scheme.


title: Maximum likelihood voice conversion based on gmm with straight mixed excitation

abstract: INTERSPEECH2006: the 9th International Conference on Spoken Language Processing (ICSLP), September 17-21, 2006, Pittsburgh, Pennsylvania, USA.


title: Efficient resource provisioning in compute clouds via VM multiplexing

abstract: Resource provisioning in compute clouds often require an estimate of the capacity needs of Virtual Machines (VMs). The estimated VM size is the basis for allocating resources commensurate with workload demand. In contrast to the traditional practice of estimating the VM sizes individually, we propose a joint-VM sizing approach in which multiple VMs are consolidated and provisioned, based on an estimate of their aggregate capacity needs. This new approach exploits statistical multiplexing among the workload patterns of multiple VMs, i.e., the peaks and valleys in one workload pattern do not necessarily coincide with the others. Thus, the unused resources of a low utilized VM can be directed to the other co-located VMs with high utilization. Compared to individual VM based provisioning, joint-VM sizing and provisioning may lead to much higher resource utilization. This paper presents three design modules to enable the concept in practice. Specifically, a performance constraint describing the capacity need of a VM for achieving a certain level of application performance; an algorithm for estimating the size of jointly provisioning VMs; a VM selection method that seeks to find good VM combinations for being provisioned together. We showcase that the proposed three modules can be seamlessly plugged into existing applications such as resource provisioning, and providing resource guarantees for VMs. The proposed algorithms and applications are evaluated by monitoring data collected from about 16 thousand VMs in commercial data centers. These evaluations reveal more than 45% improvements in terms of the overall resource utilization.


title: Security operation center implementation on OpenStack

abstract: Information security management is a complicated task in cloud environment. Cloud service layers and multi-tenant architecture have created a complicated environment for developing and managing a monitoring and incident response environment in organizations. The main goal of this paper is to receive and analyze events from OpenStack environment. Events and system logs are received from OpenStack environment. The organization SOC must be customized to receive and detect cloud specific attacks. In this paper, we customize SOC for OpenStack environment to detect cloud specific attacks. The customized SOC receives and normalizes OpenStack alerts. We have developed new correlation rules and response scenarios for these alerts. We have presented evaluation results of customized SOC development in OpenStack. The customized SOC can detect OpenStack and hypervisor based attacks.


title: AddressSanitizer : A Fast Address Sanity Checker

abstract: Memory access bugs, including buffer overflows and uses of freed heap memory, remain a serious problem for programming languages like C and C++. Many memory error detectors exist, but most of them are either slow or detect a limited set of bugs, or both. ::: ::: This paper presents AddressSanitizer, a new memory error detector. Our tool finds out-of-bounds accesses to heap, stack, and global objects, as well as use-after-free bugs. It employs a specialized memory allocator and code instrumentation that is simple enough to be implemented in any compiler, binary translation system, or even in hardware. ::: ::: AddressSanitizer achieves efficiency without sacrificing comprehensiveness. Its average slowdown is just 73% yet it accurately detects bugs at the point of occurrence. It has found over 300 previously unknown bugs in the Chromium browser and many bugs in other software.


title: frequencydomain approach to watermarking 3D shapes

abstract: This paper presents a robust watermarking algorithm with informed detection for 3D polygonal meshes. The algorithm is based on our previous algorithm [22] that employs mesh-spectral analysis to modify mesh shapes in their transformed domain. This paper presents extensions to our previous algorithm so that (1) much larger meshes can be watermarked within a reasonable time, and that (2) the watermark is robust against connectivity alteration (e.g., mesh simplification), and that (3) the watermark is robust against attacks that combine similarity transformation with such other attacks as cropping, mesh simplification, and smoothing. Experiment showed that our new watermarks are resistant against mesh simplification and remeshing combined with resection, similarity transformation, and other operations..


title: Playability heuristics for mobile games

abstract: Expert evaluation is a widely used method for evaluating the usability of software products. When evaluating games, traditional usability heuristics lack comprehension and cannot be directly applied. In this paper, we introduce playability heuristics that are specifically designed for evaluating mobile games. Heuristics form a core model that can be used in any mobile game evaluation. The model consists of three modules: Game Usability, Mobility, and Gameplay. The mobile context has some unique characteristics, which require special attention during the evaluation. These characteristics are described in mobility heuristics. Mobile devices also set some of their own requirements for general usability and these issues are described along with game usability heuristics. These heuristics have been developed by using an iterative design process of a mobile game. In addition, we have validated the heuristics and evaluated five mobile games by using them with the expert evaluation method. The results indicate that playability problems, which violate game usability or mobility heuristics, are quite easy to identify. Gameplay problems are harder to find, but gameplay heuristics help in evaluation and focus on different aspects of the gameplay.


title: Simpler core vector machines with enclosing balls

abstract: The core vector machine (CVM) is a recent approach for scaling up kernel methods based on the notion of minimum enclosing ball (MEB). Though conceptually simple, an efficient implementation still requires a sophisticated numerical solver. In this paper, we introduce the enclosing ball (EB) problem where the ball's radius is fixed and thus does not have to be minimized. We develop efficient (1 + e)-approximation algorithms that are simple to implement and do not require any numerical solver. For the Gaussian kernel in particular, a suitable choice of this (fixed) radius is easy to determine, and the center obtained from the (1 + e)-approximation of this EB problem is close to the center of the corresponding MEB. Experimental results show that the proposed algorithm has accuracies comparable to the other large-scale SVM implementations, but can handle very large data sets and is even faster than the CVM in general.


title: Transitive Invariance for Self-Supervised Visual Representation Learning

abstract: Learning visual representations with self-supervised learning has become popular in computer vision. The idea is to design auxiliary tasks where labels are free to obtain. Most of these tasks end up providing data to learn specific kinds of invariance useful for recognition. In this paper, we propose to exploit different self-supervised approaches to learn representations invariant to (i) inter-instance variations (two objects in the same class should have similar features) and (ii) intra-instance variations (viewpoint, pose, deformations, illumination, etc). Instead of combining two approaches with multi-task learning, we argue to organize and reason the data with multiple variations. Specifically, we propose to generate a graph with millions of objects mined from hundreds of thousands of videos. The objects are connected by two types of edges which correspond to two types of invariance:"different instances but a similar viewpoint and category"and"different viewpoints of the same instance". By applying simple transitivity on the graph with these edges, we can obtain pairs of images exhibiting richer visual invariance. We use this data to train a Triplet-Siamese network with VGG16 as the base architecture and apply the learned representations to different recognition tasks. For object detection, we achieve 63.2% mAP on PASCAL VOC 2007 using Fast R-CNN (compare to 67.3% with ImageNet pre-training). For the challenging COCO dataset, our method is surprisingly close (23.5%) to the ImageNet-supervised counterpart (24.4%) using the Faster R-CNN framework. We also show that our network can perform significantly better than the ImageNet network in the surface normal estimation task.


title: Skintillates: Designing and Creating Epidermal Interactions

abstract: Skintillates is a wearable technology that mimics tattoos - the oldest and most commonly used on-skin displays in human culture. We demonstrate that by fabricating electrical traces and thin electronics on temporary tattoo paper, a wide array of displays and sensors can be created. Just like the traditional temporary tattoos often worn by children and adults alike, Skintillates flex naturally with the user's skin. Our simple fabrication technique also enables users to freely design and print with a full range of colors to create application-specific customized designs. We demonstrate the technical capabilities of Skintillates as sensors and as expressive personal and private displays through a series of application examples. Finally, we detail the results of a set of user studies that highlight the user experience, comfort, durability, acceptability, and application potential for Skintillates.


title: Tableau Methods for Modal and Temporal Logics

abstract: Modal and temporal logics are finding new and varied applications in Computer Science in fields as diverse as Artificial Intelligence [Marek et al.,1991], Models for Concurrency [Stirling, 1992] and Hardware Verification [Nakamura et al.,1987]. Often the eventual use of these logics boils down to the task of deducing whether a certain formula of a logic is a logical consequence of a set of other formula of the same logic. The method of semantic tableaux is now well established in the field of Automated Deduction [Oppacher and Suen, 1988; Baumgartner et al., 1995; Beckert and Possega, 1995] as a viable alternative to the more traditional methods based on resolution [Chang and Lee, 1973]. In this chapter we give a systematic and unified introduction to tableau methods for automating deduction in modal and temporal logics. We concentrate on the propositional fragments restricted to a two-valued (classical) basis and assume some prior knowledge of modal and temporal logic, but give a brief overview of the associated Kripke semantics to keep the chapter self-contained.


title: Growth Inhibiting Effects of Three Different Plant Extracts on Tribolium castaneum (Herbst) (Tenebrionidae: Coleoptera)

abstract: Petroleum ether, acetone and ethanol extracts of neem, sweet flag, and turmeric were evaluated for their growth inhibiting effects on the Tribolium castaneum at the application rate of 1000, 500, 250 and 125 μg/g food media. Petroleum ether extracts showed the lowest number of larvae produced (34.00) in 1000 μg/g neem extract. Acetone extract of sweet flag showed minimum and significantly lower number of larvae produced (8.00) at 1000 μg/g, whereas ethanol extracts of turmeric showed minimum numbers of larvae (106.30) at 1000 μg/g application rate. Maximum inhibition ratio of pupation was found as 81.18 % at 1000 μg/g dosage of petroleum ether extract of sweet flag resulting into 8.75 pupae. Acetone extract of sweet flag showed minimum numbers of pupae produced (8.00) at 1000 μg/g of sweet flag, whereas ethanol extract of sweet flag showed minimum numbers of pupae (41.75) at 1000 μg/g. Petroleum ether extract of sweet flag showed a maximum inhibition ratio (40.00 %) resulting into 5.25 adults produced at 1000 μg/g. Acetone extract of sweet flag showed the lowest numbers of adults produced (8.00) at 1000 μg/g. Ethanol extract of neem showed lower numbers of adults (35.25) at 1000 μg/g dosage, followed by 38.50 in sweet flag and 52.75 in turmeric at the same application rate.


title: What could move? Finding cars, pedestrians and bicyclists in 3D laser data

abstract: This paper tackles the problem of segmenting things that could move from 3D laser scans of urban scenes. In particular, we wish to detect instances of classes of interest in autonomous driving applications - cars, pedestrians and bicyclists - amongst significant background clutter. Our aim is to provide the layout of an end-to-end pipeline which, when fed by a raw stream of 3D data, produces distinct groups of points which can be fed to downstream classifiers for categorisation. We postulate that, for the specific classes considered in this work, solving a binary classification task (i.e. separating the data into foreground and background first) outperforms approaches that tackle the multi-class problem directly. This is confirmed using custom and third-party datasets gathered of urban street scenes. While our system is agnostic to the specific clustering algorithm deployed we explore the use of a Euclidean Minimum Spanning Tree for an end-to-end segmentation pipeline and devise a RANSAC-based edge selection criterion.


title: Multichannel bipotentiostat system for cellular monitoring platforms

abstract: A versatile 4-channel bipotentiostat system for biochemical sensing is presented. A 1pA current resolution and 8kHz bandwidth are suited for amperometric detection of neurotransmitters released by cells, monitored in a smart microfluidic culture chamber. Multiple electrochemical measurements can be carried out on arrays of microelectrodes. Key design issues are here discussed along with the results of extensive electrochemical experiments (cyclic voltammetry, chronoamperometry, redox recycling and potentiometry).


title: A Green Computing Based Architecture Comparison and Analysis

abstract: With rising energy cost and growing environmental concerns, green computing is receiving more and more attention. Software and system architectures ( in terms of concurrency patterns) play a crucial role in both computing and telecommunication systems, and they have been analyzed for performance, reliability, maintainability, and security. Yet, little work on analysis based on the amount of energy that the CPU/processor will consume has been reported. Since most communication systems have to run 24/7 (e.g., most server farms, servers in a cloud computing infrastructure), the energy consumption of a system based on a specific software architecture is of great importance. For example, high energy consuming always leads to higher operational cost of the system. High energy consumption also implies more heat produced, thus, more power is required for cooling-down. In this paper, we propose a model that can be used to analyze the energy consumption of a software architecture, given the CPU/processor that will be used. Thus, this model gives one an additional dimension to compare competing software architectures. We present a case study using a generic sender/receiver communication system to evaluate two architectural alternatives with emphasis on energy consumption


title: A Semantic Web Methodology for Situation-Aware Curative Food Service Recommendation System

abstract: Recently curative food is becoming more popular, as more people realize its benefits. Based on the theory of Chinese medicine, food itself is medicine. The curative food which is an ideal nutritious food can help to loss weight, increase immunity and is also good for curative effects in patients. In this paper, we propose a new Curative Food Service (CFS) recommendation system, the designed Situation-aware Curative Food Service Recommendation System (SCFSRS), which provides the cooperation web-based platform for all related Mobile Users (MUs) and Curative Food Service Providers (CFSPs), could strengthen the ability of CFS suggestion. SCFSRS is a five-tier system composed of the MUs, UDDI Registries (UDDIRs), CFSPs, Curative Food Services Server (CFSS), and Database Server (DS).


title: Perspective: Behavioural addictions matter

abstract: More research, and dedicated funding, is needed to understand and successfully treat compulsive habits, says Marc Potenza.


title: Sleep inspires insight

abstract: Insight denotes a mental restructuring that leads to a sudden gain of explicit knowledge allowing qualitatively changed behaviour1,2. Anecdotal reports on scientific discovery suggest that pivotal insights can be gained through sleep3. Sleep consolidates recent memories4,5,6 and, concomitantly, could allow insight by changing their representational structure. Here we show a facilitating role of sleep in a process of insight. Subjects performed a cognitive task requiring the learning of stimulus–response sequences, in which they improved gradually by increasing response speed across task blocks. However, they could also improve abruptly after gaining insight into a hidden abstract rule underlying all sequences. Initial training establishing a task representation was followed by 8 h of nocturnal sleep, nocturnal wakefulness, or daytime wakefulness. At subsequent retesting, more than twice as many subjects gained insight into the hidden rule after sleep as after wakefulness, regardless of time of day. Sleep did not enhance insight in the absence of initial training. A characteristic antecedent of sleep-related insight was revealed in a slowing of reaction times across sleep. We conclude that sleep, by restructuring new memory representations, facilitates extraction of explicit knowledge and insightful behaviour.


title: An Enhanced Convolutional Neural Network Model for Answer Selection

abstract: Answer selection is an important task in question answering (QA) from the Web. To address the intrinsic difficulty in encoding sentences with semantic meanings, we introduce a general framework, i.e., Lexical Semantic Feature based Skip Convolution Neural Network (LSF-SCNN), with several optimization strategies. The intuitive idea is that the granular representations with more semantic features of sentences are deliberately designed and estimated to capture the similarity between question-answer pairwise sentences. The experimental results demonstrate the effectiveness of the proposed strategies and our model outperforms the state-of-the-art ones by up to 3.5% on the metrics of MAP and MRR.


title: Piezoelectric RF resonant voltage amplifiers for IoT applications

abstract: This paper reports the design and development of aluminum nitride (AlN) piezoelectric RF resonant voltage amplifiers for Internet of Things (IoT) applications. These devices can provide passive and highly frequency selective voltage gain to RF backends with a capacitive input to drastically enhance sensitivity and to reduce power consumption of the transceiver. Both analytical and finite element models (FEM) have been utilized to identify the optimal designs. Consequently, an AlN voltage amplifier with an open circuit gain of 7.27 and a fractional bandwidth (FBW) of 0.11 % has been demonstrated. This work provides a material-agnostic framework for analytically optimizing piezoelectric voltage amplifiers.


title: Finished Annotated Genome Sequence of Burkholderia pseudomallei Strain Bp1651, a Multidrug-Resistant Clinical Isolate

abstract: ABSTRACT Burkholderia pseudomallei strain Bp1651, a human isolate, is resistant to all clinically relevant antibiotics. We report here on the finished genome sequence assembly and annotation of the two chromosomes of this strain. This genome sequence may assist in understanding the mechanisms of antimicrobial resistance for this pathogenic species.


title: Towards Force-aware Robot Collectives for On-site Construction

abstract: Due to the irregular and variable environments in which most construction projects take place, the topic of on-site automation has previously been largely neglected in favor of off-site prefabrication. While prefabrication has certain obvious economic and schedule benefits, a number of potential applications would benefit from a fully autonomous robotic construction system capable of building without human supervision or intervention; for example, building in remote environments, or building structures whose form changes over time. Previous work using a swarm approach to robotic assembly generally neglected to consider forces acting on the structure, which is necessary to guarantee against failure during construction. In this paper we report on key findings for how distributed climbing robots can use local force measurements to assess aspects of global structural state. We then chart out a broader trajectory for the affordances of distributed on-site construction in the built environment and position our contributions within this research agenda. The principles explored in simulation are demonstrated in hardware, including solutions for force-sensing as well as a climbing robot.


title: Detecting Satire in the News with Machine Learning

abstract: We built models with Logistic Regression and linear Support Vector Machines on a large dataset consisting of regular news articles and news from satirical websites, and showed that such linear classifiers on a corpus with about 60,000 articles can perform with a precision of 98.7% and a recall of 95.2% on a random test set of the news. On the other hand, when testing the classifier on"publication sources"which are completely unknown during training, only an accuracy of 88.2% and an F1-score of 76.3% are achieved. As another result, we showed that the same algorithm can distinguish between news written by the news agency itself and paid articles from customers. Here the results had an accuracy of 99%.


title: RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach

abstract: BACKGROUND ::: RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation. ::: ::: ::: RESULTS ::: In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in iDeep is also able to automatically capture the interpretable binding motifs for RBPs. Large-scale experiments demonstrate that these mined binding motifs agree well with the experimentally verified results, suggesting iDeep is a promising approach in the real-world applications. ::: ::: ::: CONCLUSION ::: The iDeep framework not only can achieve promising performance than the state-of-the-art predictors, but also easily capture interpretable binding motifs. iDeep is available at http://www.csbio.sjtu.edu.cn/bioinf/iDeep.


title: A POMDP Formulation of Preference Elicitation Problems

abstract: Preference elicitation is a key problem facing the deployment of intelligent systems that make or recommend decisions on the behalf of users. Since not all aspects of a utility function have the same impact on object-level decision quality, determining which information to extract from a user is itself a sequential decision problem, balancing the amount of elicitation effort and time with decision quality. We formulate this problem as a partially-observable Markov decision process (POMDP). Because of the continuous nature of the state and action spaces of this POMDP, standard techniques cannot be used to solve it. We describe methods that exploit the special structure of preference elicitation to deal with parameterized belief states over the continuous state space, and gradient techniques for optimizing parameterized actions. These methods can be used with a number of different belief state representations, including mixture models.


From Theories to Queries : Active Learning in Practice


title: An EDA-friendly protection scheme against side-channel attacks

abstract: This paper introduces a generic and automated methodology to protect hardware designs from side-channel attacks in a manner that is fully compatible with commercial standard cell design flows. The paper describes a tool that artificially adds jitter to the clocks of the sequential elements of a cryptographic unit, which increases the non-determinism of signal timing, thereby making the physical device more difficult to attack. Timing constraints are then specified to commercial EDA tools, which restore the circuit functionality and efficiency while preserving the introduced randomness. The protection scheme is applied to an AES-128 hardware implementation that is synthesized using both ASIC and FPGA design flows.


title: Automated Checking of Conformance to Requirements Templates Using Natural Language Processing

abstract: Templates are effective tools for increasing the precision of natural language requirements and for avoiding ambiguities that may arise from the use of unrestricted natural language. When templates are applied, it is important to verify that the requirements are indeed written according to the templates. If done manually, checking conformance to templates is laborious, presenting a particular challenge when the task has to be repeated multiple times in response to changes in the requirements. In this article, using techniques from natural language processing (NLP), we develop an automated approach for checking conformance to templates. Specifically, we present a generalizable method for casting templates into NLP pattern matchers and reflect on our practical experience implementing automated checkers for two well-known templates in the requirements engineering community. We report on the application of our approach to four case studies. Our results indicate that: (1) our approach provides a robust and accurate basis for checking conformance to templates; and (2) the effectiveness of our approach is not compromised even when the requirements glossary terms are unknown. This makes our work particularly relevant to practice, as many industrial requirements documents have incomplete glossaries.


title: A Review of the Application of Meta-Heuristic Algorithms to 2D Strip Packing Problems

abstract: This paper is a review of the approaches developed to solve 2D packing problems with meta-heuristic algorithms. As packing tasks are combinatorial problems with very large search spaces, the recent literature encourages the use of meta-heuristic search methods, in particular genetic algorithms. The objective of this paper is to present and categorise the solution approaches in the literature for 2D regular and irregular strip packing problems. The focus is hereby on the analysis of the methods involving genetic algorithms. An overview of the methods applying other meta-heuristic algorithms including simulated annealing, tabu search, and artificial neural networks is also given.


2 A 28 GHz 32-Element Phased-Array Transceiver IC with Concurrent Dual Polarized Beams and 1 . 4 Degree Beam-Steering Resolution for 5 G Communication


title: JML: notations and tools supporting detailed design in Java

abstract: is a notation for specifying the detailed design of Java classes and interfaces. JML's assertions are stated using a slight extension of Java's expression syntax. This should make it easy to use. Tools for JML aid in static analysis, verification, and run-time debugging of Java code.


title: Load Balancing of Heterogeneous Workloads in Memcached Clusters

abstract: Web services, large and small, use in-memory caches like memcached to lower database loads and quickly respond to user requests. These cache clusters are typically provisioned to support peak load, both in terms of request processing capabilities and cache storage size. This kind of worst-case provisioning can be very expensive (e.g., Facebook reportedly uses more than 10,000 servers for its cache cluster) and does not take advantage of the dynamic resource allocation and virtual machine provisioning capabilities found in modern public and private clouds. Further, there can be great diversity in both the workloads running on a cache cluster and the types of nodes that compose the cluster, making manual management difficult. This paper identifies the challenges in designing large-scale self-managing caches. Rather than requiring all cache clients to know the key to server mapping, we propose an automated load balancer that can perform line-rate request redirection in a far more dynamic manner. We describe how stream analytic techniques can be used to efficiently detect key hotspots. A controller then guides the load balancer’s key mapping and replication level to prevent overload, and automatically starts additional servers when needed.