title: A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval

abstract: Opinion retrieval is a task of growing interest in social life and academic research, which is to find relevant and opinionate documents according to a user's query. One of the key issues is how to combine a document's opinionate score (the ranking score of to what extent it is subjective or objective) and topic relevance score. Current solutions to document ranking in opinion retrieval are generally ad-hoc linear combination, which is short of theoretical foundation and careful analysis. In this paper, we focus on lexicon-based opinion retrieval. A novel generation model that unifies topic-relevance and opinion generation by a quadratic combination is proposed in this paper. With this model, the relevance-based ranking serves as the weighting factor of the lexicon-based sentiment ranking function, which is essentially different from the popular heuristic linear combination approaches. The effect of different sentiment dictionaries is also discussed. Experimental results on TREC blog datasets show the significant effectiveness of the proposed unified model. Improvements of 28.1% and 40.3% have been obtained in terms of MAP and p@10 respectively. The conclusion is not limited to blog environment. Besides the unified generation model, another contribution is that our work demonstrates that in the opinion retrieval task, a Bayesian approach to combining multiple ranking functions is superior to using a linear combination. It is also applicable to other result re-ranking applications in similar scenario.


title: SSS : A Hybrid Architecture Applied to Robot Navigation

abstract: Describes a three-layer architecture, SSS, for robot control. It combines a servo-control layer, a subsumption layer, and a symbolic layer in a way that allows the advantages of each technique to be fully exploited. The key to this synergy is the interface between the individual subsystems. The design of situation recognizers that bridge the gap between the servo and subsumption layers, and event detectors that link the subsumption layers and symbolic layers are discussed. The development of such a combined system is illustrated by a fully implemented indoor navigation example. The resulting robot was able to automatically map office building environments, and smoothly navigate through them at the rapid speed of 2.6 feet per second. >


title: Automated segmentation of multiple sclerosis lesions by model outlier detection

abstract: This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expect segmentations, and between expert and automatic measurements.


title: Fingerprint image quality analysis

abstract: Fingerprint image quality analysis is crucial in eliminating poor fingerprint images, which will affect the performance of the automatic fingerprint identification system. In this article, two types of new quality measures will be introduced: ridge and valley clarity and global orientation flow to calculate the overall image quality score that can be used to quantitatively determine the quality of the fingerprint image. In order to evaluate the performance of the proposed algorithm, the quality measure is used to rank the performance of a fingerprint recognition system and the ranking is compared with the quality measure rated manually. The result shows that the proposed scheme will return a score that ensures its reliability to indicate the quality of a given fingerprint image.


title: Windy trees: computing stress response for developmental tree models

abstract: We present a novel method for combining developmental tree models with turbulent wind fields. The tree geometry is created from internal growth functions of the developmental model and its response to external stress is induced by a physically-plausible wind field that is simulated by Smoothed Particle Hydrodynamics (SPH). Our tree models are dynamically evolving complex systems that (1) react in real-time to high-frequent changes of the wind simulation; and (2) adapt to long-term wind stress. We extend this process by wind-related effects such as branch breaking as well as bud abrasion and drying. In our interactive system the user can adjust the parameters of the growth model, modify wind properties and resulting forces, and define the tree's long-term response to wind. By using graphics hardware, our implementation runs at interactive rates for moderately large scenes composed of up to 20 tree models.


title: Automated Measurement and Prediction of Consciousness in Vegetative and Minimally Conscious Patients

abstract: Scientific advances in electrophysiology and clinical neuroscience have highlighted electroen-cephalography (EEG) biomarkers that allow to discriminate between different disorders of consciousness such as vegetative and minimally conscious state. In the current work, we present an automated approach to measuring consciousness disorders and guiding clinical diagnostics. This approach allows to extract scientifically validated biomarkers from incoming clinical EEG-recordings. Probabilistic predictions of an undi-agnosed patient's state of consciousness are then obtained by interrogating statistical models informed by database records of these biomark-ers. Predictions are subsequently summarized and deployed to the clinician in form of a self-contained HTML-report which supports interactive visualization and navigation. We additionally conducted replication and robustness analyses , which indicate that the EEG-biomarkers can be successfully employed in a wide range of practical contexts.


title: Encoding, fast and slow: low-latency video processing using thousands of tiny threads

abstract: We describe ExCamera, a system that can edit, transform, and encode a video, including 4K and VR material, with low latency. The system makes two major contributions. ::: ::: First, we designed a framework to run general-purpose parallel computations on a commercial "cloud function" service. The system starts up thousands of threads in seconds and manages inter-thread communication. ::: ::: Second, we implemented a video encoder intended for fine-grained parallelism, using a functional-programming style that allows computation to be split into thousands of tiny tasks without harming compression efficiency. Our design reflects a key insight: the work of video encoding can be divided into fast and slow parts, with the "slow" work done in parallel, and only "fast" work done serially.


title: Privacy-Preserving Smart Grid Tariff Decisions with Blockchain-Based Smart Contracts

abstract: The smart grid changes the way how energy and information are exchanged and offers opportunities for incentive-based load balancing. For instance, customers may shift the time of energy consumption of household appliances in exchange for a cheaper energy tariff. This paves the path towards a full range of modular tariffs and dynamic pricing that incorporate the overall grid capacity as well as individual customer demands. This also allows customers to frequently switch within a variety of tariffs from different utility providers based on individual energy consumption and provision forecasts. For automated tariff decisions it is desirable to have a tool that assists in choosing the optimum tariff based on a prediction of individual energy need and production. However, the revelation of individual load patterns for smart grid applications poses severe privacy threats for customers as analyzed in depth in literature. Similarly, accurate and fine-grained regional load forecasts are sensitive business information of utility providers that are not supposed to be released publicly. This paper extends previous work in the domain of privacy-preserving load profile matching where load profiles from utility providers and load profile forecasts from customers are transformed in a distance-preserving embedding in order to find a matching tariff. The embeddings neither reveal individual contributions of customers nor those of utility providers. Prior work requires a dedicated entity that needs to be trustworthy at least to some extent for determining the matches. In this paper we propose an adaption of this protocol, where we use blockchains and smart contracts for this matching process, instead. Blockchains are gaining widespread adaption in the smart grid domain as a powerful tool for public commitments and accountable calculations. The use of a blockchain for this protocol makes the calculations for tariff matching public, while still maintaining the privacy through embeddings. Further, such decentralized and trust-free blockchains improve the existing solution in terms of verifiability, reliability, and transparency.


title: Impact of Excipient Interactions on Drug Bioavailability from Solid Dosage Forms

abstract: Excipients are generally pharmacologically inert, but can interact with drugs in the dosage form and the physiological factors at the site of absorption to affect the bioavailability of a drug product. A general mechanistic understanding of the basis of these interactions is essential to design robust drug products. This paper focuses on drug-excipient interactions in solid dosage forms that impact drug bioavailability, the drug substance and drug product properties affected by excipients, and the impact of excipients on physiologic processes. The extent to which drug bioavailability is affected by these interactions would vary on a case-by-case basis depending upon factors such as the potency and dose of the drug, therapeutic window, site of absorption, rate limiting factor in drug absorption (e.g., permeability or solubility limited), or whether drug metabolism, efflux, complexation, or degradation at the site of absorption play a role in determining its bioavailability. Nonetheless, a mechanistic understanding of drug-excipient interactions and their impact on drug release and absorption can help develop formulations that exhibit optimum drug bioavailability.


Stability and traction optimization of reconfigurable vehicles . Application to an hybrid wheel-legged robot


title: Shifting the Baseline: Single Modality Performance on Visual Navigation&QA

abstract: We demonstrate the surprising strength of unimodal baselines in multimodal domains, and make concrete recommendations for best practices in future research. Where existing work often compares against random or majority class baselines, we argue that unimodal approaches better capture and reflect dataset biases and therefore provide an important comparison when assessing the performance of multimodal techniques. We present unimodal ablations on three recent datasets in visual navigation and QA, seeing an up to 29% absolute gain in performance over published baselines.


title: Combining low-cost Inertial Measurement Unit (IMU) and deep learning algorithm for predicting vehicle attitude

abstract: In the paper, we propose an acceleration-based and angular-velocity-based vehicle attitude recognition method by using a popular deep learning algorithm, i.e., Convolution Neural Network (CNN). We use an Inertial Measurement Unit (IMU) to collect six-axial signal of a vehicle. In particular, we construct a CNN model to learn the characteristics of six-axial IMU signal and the model can be used to predict vehicle attitudes. We constructed training data consists of 800 package from six attitudes. In addition, we preprocess the 800 package that each package will be broken down. Finally, our training data is 59200 sample-train. The experiment results show that the CNN works well, which can reach an average accuracy of 98% by the time of 1/5 of the overall action without any feature extraction methods. Because we use CNN model that it's the number of convolution kernel is less, we can reach real-time. Each estimated time is less than 0.5 sec based on the raspberry pi3.


title: Weakly Semi-Supervised Deep Learning for Multi-Label Image Annotation

abstract: In this paper, we study leveraging both weakly labeled images and unlabeled images for multi-label image annotation. Motivated by the recent advance in deep learning, we propose an approach called we akly se mi-supervised d eep learning for multi-label image annotation (WeSed). In WeSed, a novel weakly weighted pairwise ranking loss is effectively utilized to handle weakly labeled images, while a triplet similarity loss is employed to harness unlabeled images. WeSed enables us to train deep convolutional neural network (CNN) with images from social networks where images are either only weakly labeled with several labels or unlabeled. We also design an efficient algorithm to sample high-quality image triplets from large image datasets to fine-tune the CNN. WeSed is evaluated on benchmark datasets for multi-label annotation. The experiments demonstrate the effectiveness of our proposed approach and show that the leverage of the weakly labeled images and unlabeled images leads to a significantly better performance.


title: Sample sealing approaches for Mars Sample Return caching

abstract: Sealing methods for encapsulating samples in 1 cm diameter thin-walled sample tubes applicable to future proposed Mars Sample Return missions were investigated based on power requirements, operation in the Martian environment, dust tolerance, shock and vibration, maintenance of sample integrity, hermeticity, packaging requirements, mission risk, and ability for autonomy. Techniques implemented include a spring energized Teflon sleeve plug, a crimped tube seal, a heat-activated shape memory alloy plug, a shape memory alloy activated cap, a solder-based plug, and a solder-based cap. Testing will be performed on the prototypes to determine their sensitivity to particle contamination along the sealing surface, Helium leak rate, and survivability over a range of temperatures to help recommend sealing techniques for the proposed Mars Sample Return campaign.


title: Wearable Audio and IMU Based Shot Detection in Racquet Sports

abstract: Wearables like smartwatches which are embedded with sensors and powerful processors, provide a strong platform for development of analytics solutions in sports domain. To analyze players' games, while motion sensor based shot detection has been extensively studied in sports like Tennis, Golf, Baseball; Table Tennis and Badminton are relatively less explored due to possible less intense hand motion during shots. In our paper, we propose a novel, computationally inexpensive and real-time system for shot detection in table tennis, based on fusion of Inertial Measurement Unit (IMU) and audio sensor data embedded in a wrist-worn wearable. The system builds upon our presented methodology for synchronizing IMU and audio sensor input in time using detected shots and achieves 95.6% accuracy. To our knowledge, it is the first fusion-based solution for sports analysis in wearables. Shot detectors for other racquet sports as well as further analytics to provide features like shot classification, rally analysis and recommendations, can easily be built over our proposed solution.


title: Variational Inference in Nonconjugate Models

abstract: Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm. When the model is conditionally conjugate, the coordinate updates are easily derived and in closed form. However, many models of interest---like the correlated topic model and Bayesian logistic regression---are nonconjuate. In these models, mean-field methods cannot be directly applied and practitioners have had to develop variational algorithms on a case-by-case basis. In this paper, we develop two generic methods for nonconjugate models, Laplace variational inference and delta method variational inference. Our methods have several advantages: they allow for easily derived variational algorithms with a wide class of nonconjugate models; they extend and unify some of the existing algorithms that have been derived for specific models; and they work well on real-world datasets. We studied our methods on the correlated topic model, Bayesian logistic regression, and hierarchical Bayesian logistic regression.


title: Syntactic Annotations for the Google Books NGram Corpus

abstract: We present a new edition of the Google Books Ngram Corpus, which describes how often words and phrases were used over a period of five centuries, in eight languages; it reflects 6% of all books ever published. This new edition introduces syntactic annotations: words are tagged with their part-of-speech, and head-modifier relationships are recorded. The annotations are produced automatically with statistical models that are specifically adapted to historical text. The corpus will facilitate the study of linguistic trends, especially those related to the evolution of syntax.


title: A Comparative Study of Classification Algorithms using Data Mining: Crime and Accidents in Denver City the USA

abstract: In the last five years, crime and accidents rates have increased in many cities of America. The advancement of new technologies can also lead to criminal misuse. In order to reduce incidents, there is a need to understand and examine emerging patterns of criminal activities. This paper analyzed crime and accident datasets from Denver City, USA during 2011 to 2015 consisting of 372,392 instances of crime. The dataset is analyzed by using a number of Classification Algorithms. The aim of this study is to highlight trends of incidents that will in return help security agencies and police department to discover precautionary measures from prediction rates. The classification of algorithms used in this study is to assess trends and patterns that are assessed by BayesNet, NaiveBayes, J48, JRip, OneR and Decision Table. The output that has been used in this study, are correct classification, incorrect classification, True Positive Rate (TP), False Positive Rate (FP), Precision (P), Recall (R) and F-measure (F). These outputs are captured by using two different test methods: k-fold cross-validation and percentage split. Outputs are then compared to understand the classifier performances. Our analysis illustrates that JRip has classified the highest number of correct classifications by 73.71% followed by decision table with 73.66% of correct predictions, whereas OneR produced the least number of correct predictions with 64.95%. NaiveBayes took the least time of 0.57 sec to build the model and perform classification when compared to all the classifiers. The classifier stands out producing better results among all the classification methods. This study would be helpful for security agencies and police department to discover data patterns and analyze trending criminal activity from prediction rates.


Response Latencies and Probabilities


title: Automated discovery, interaction and composition of Semantic Web Services

abstract: In this paper we introduce a vision for Semantic Web Services, which combine the growing Web services architecture and the Semantic Web and we will propose DAML-S as a prototypical example of an ontology for describing Semantic Web services.


title: A novel multipurpose smart wheelchair

abstract: In this paper we propose an intelligent wheelchair which can assist physically handicapped, visually impaired as well as elderly people. Elderly and physically challenged people often find themselves unable to do their daily routine work without other's help. In today's fast world where people don't have enough time for others, elderly and physically challenged people need to be independent. Our wheelchair is developed with a view to serve the purpose. It consists of a navigation system which makes use of accelerometer and magnetometer, the system contains a navigation pad which can be held in hand or tied to the head for navigating the chair. It also has obstacle avoidance system comprising of four ultrasonic sensors, real time location tracking system which makes use of RFID for tracking the chair inside a building and voice guidance system to assist the visually impaired. Thus, it is a multipurpose intelligent wheelchair which is cost-effective and can assist people in their daily work.


title: Metal systems as tools for soil remediation

abstract: Abstract Soil is a vital resource that must be preserved and possibly restored to a pristine state after pollution. Thus, sustainability of remediation processes is an important issue. In this review the state of the art of research aimed at soil remediation with metal systems is presented. Both inorganic and organic pollutants were considered. Heterogeneous as well as homogeneous systems are discussed, with attention to in field applications, when available. Intrinsic difficulties of in situ applications are evidenced. Literature is covered to the end of 2015.


title: Pathwise uniqueness of non-uniformly elliptic SDEs with rough coefficients

abstract: In this paper, we review and improve pathwise uniqueness results for some types of one-dimensional stochastic differential equations (SDE) involving the local time of the unknown process. The diffusion coefficient of the SDEs we consider is allowed to vanish on a set of positive measure and is not assumed to be smooth. As opposed to various existing results, our arguments are mainly based on the comparison theorem for local time and the occupation time formula. We apply our pathwise uniqueness results to derive strong existence and other properties of solutions for SDEs with rough coefficients.


title: The fragile communities of Antikythera

abstract: While many Mediterranean islands have been subjected to archaeological survey methods of one kind or another, until now few if any have been covered in both a comprehensive and intensive manner. In this article the authors describe a survey on the Greek island of Antikythera (the Antikythera Survey Project – ASP) and demonstrate how full investigation of a tiny, remote and very sparsely populated island offers distinct analytical advantages for archaeologists. Some of the resulting benefits are methodological, relating to simplified sampling procedures, while others relate to the archaeology itself and include the documentation of rollercoaster demographies, changing connections with the wider world and the development of idiosyncratic insular lifestyles.


title: Exploring the evolution of NoC-based Spiking Neural Networks on FPGAs

abstract: Bio-inspired paradigms such as Spiking Neural Networks (SNNs) offer the potential to emulate the repairing and adaptive ability of the brain. This paper presents EMBRACE-FPGA, a scalable, configurable Network on Chip (NoC)-based SNN architecture, implemented on Xilinx Virtex II-Pro FPGA hardware. In association with a Genetic Algorithm-based hardware evolution platform, EMBRACE-FPGA provides a computing platform for intrinsic hardware evolution, which can be used to explore the evolution and adaptive capabilities of hardware SNNs. Results demonstrate the application of the hardware SNN evolution platform to solve the XOR benchmark problem.


title: Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning

abstract: We present an improved three-step pipeline for the stereo matching problem and introduce multiple novelties at each stage. We propose a new highway network architecture for computing the matching cost at each possible disparity, based on multilevel weighted residual shortcuts, trained with a hybrid loss that supports multilevel comparison of image patches. A novel post-processing step is then introduced, which employs a second deep convolutional neural network for pooling global information from multiple disparities. This network outputs both the image disparity map, which replaces the conventional winner takes all strategy, and a confidence in the prediction. The confidence score is achieved by training the network with a new technique that we call the reflective loss. Lastly, the learned confidence is employed in order to better detect outliers in the refinement step. The proposed pipeline achieves state of the art accuracy on the largest and most competitive stereo benchmarks, and the learned confidence is shown to outperform all existing alternatives.


title: Models of user engagement

abstract: Our research goal is to provide a better understanding of how users engage with online services, and how to measure this engagement. We should not speak of one main approach to measure user engagement --- e.g. through one fixed set of metrics --- because engagement depends on the online services at hand. Instead, we should be talking of models of user engagement. As a first step, we analysed a number of online services, and show that it is possible to derive effectively simple models of user engagement, for example, accounting for user types and temporal aspects. This paper provides initial insights into engagement patterns, allowing for a better understanding of the important characteristics of how users repeatedly interact with a service or group of services.


ROOM SHAPE AND SIZE ESTIMATION USING DIRECTIONAL IMPULSE RESPONSE MEASUREMENTS


title: A 40-Gb/s Clock and Data Recovery Circuit in

abstract: A phase-locked clock and data recovery circuit incorporates a multiphase LC oscillator and a quarter-rate bang-bang phase detector. The oscillator is based on differential excitation of a closed-loop transmission line at evenly spaced points, providing half-quadrature phases. The phase detector employs eight flip-flops to sample the input every 12.5 ps, detecting data transitions while retiming and demultiplexing the data into four 10-Gb/s outputs. Fabricated in 0.18m CMOS technology, the circuit produces a clock jitter of 0.9 psrms and 9.67 pspp with a PRBS of 2 1 while consuming 144 mW from a 2-V supply.


title: Color-based road detection in urban traffic scenes

abstract: Road detection is a key issue for autonomous driving in urban traffic. In this paper, after a brief overview of existing methods, we present a road-area detection algorithm based on color images. This algorithm is composed of two modules: boundaries are first estimated based on the intensity image and road areas are subsequently detected based on the full color image. In the first module, an edge image of the scene is analyzed to obtain the candidates for the left and right road borders and to delimit the area that will subsequently be used to compute the mean and variance of the Gaussian distribution, assumed to be obeyed by the color components of road surfaces. The second module effectively extracts the road area and reinforces boundaries that most appropriately fit the road-extraction result. The combination of these modules can overcome basic problems due to inaccuracies in edge detection based on the intensity image alone and due to the computational complexity of segmentation algorithms based on color images. Experimental results on real road scenes have substantiated the effectiveness of the proposed method.


title: The role of domain ontologies in database design: An ontology management and conceptual modeling environment

abstract: Database design is difficult because it involves a database designer understanding an application and translating the design requirements into a conceptual model. However, the designer may have little or no knowledge about the application or task for which the database is being designed. This research presents a methodology for supporting database design creation and evaluation that makes use of domain-specific knowledge about an application stored in the form of domain ontologies. The methodology is implemented in a prototype system, the Ontology Management and Database Design Environment. Initial testing of the prototype illustrates that the incorporation and use of ontologies is effective in creating entity-relationship models.


title: Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies

abstract: After an historical review of evaluation methods, we describe an emerging research method called Multi-dimensional In-depth Long-term Case studies (MILCs) which seems well adapted to study the creative activities that users of information visualization systems engage in. We propose that the efficacy of tools can be assessed by documenting 1) usage (observations, interviews, surveys, logging etc.) and 2) expert users' success in achieving their professional goals. We summarize lessons from related ethnography methods used in HCI and provide guidelines for conducting MILCs for information visualization. We suggest ways to refine the methods for MILCs in modest sized projects and then envision ambitious projects with 3-10 researchers working over 1-3 years to understand individual and organizational use of information visualization by domain experts working at the frontiers of knowledge in their fields.


title: The truth about lying in online dating profiles

abstract: Online dating is a popular new tool for initiating romantic relationships, although recent research and media reports suggest that it may also be fertile ground for deception. Unlike previous studies that rely solely on self-report data, the present study establishes ground truth for 80 online daters' height, weight and age, and compares ground truth data to the information provided in online dating profiles. The results suggest that deception is indeed frequently observed, but that the magnitude of the deceptions is usually small. As expected, deceptions differ by gender. Results are discussed in light of the Hyperpersonal model and the self-presentational tensions experienced by online dating participants.


title: Implementing function spreadsheets

abstract: A large amount of end-user development is done with spreadsheets. The spreadsheet metaphor is attractive because it is visual and accommodates interactive experimentation, but as observed by Peyton Jones, Blackwell and Burnett, the spreadsheet metaphor does not admit even the most basic abstraction: that of turning an expression into a named function. Hence they proposed a way to define a function in terms of a worksheet with designated input and output cells; we shall call it a function sheet. The goal of our work is to develop implementations of function sheets and study their application to realistic examples. Therefore, we are also developing a simple yet comprehensive spreadsheet core implementation for experimentation with this technology. Here we report briefly on our experiments with function sheets as well as other uses of our spreadsheet core implementation.


title: Automatic music transcription: challenges and future directions

abstract: Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects.


title: Face model compression by distilling knowledge from neurons

abstract: The recent advanced face recognition systems were built on large Deep Neural Networks (DNNs) or their ensembles, which have millions of parameters. However, the expensive computation of DNNs make their deployment difficult on mobile and embedded devices. This work addresses model compression for face recognition, where the learned knowledge of a large teacher network or its ensemble is utilized as supervision to train a compact student network. Unlike previous works that represent the knowledge by the soften label probabilities, which are difficult to fit, we represent the knowledge by using the neurons at the higher hidden layer, which preserve as much information as the label probabilities, but are more compact. By leveraging the essential characteristics (domain knowledge) of the learned face representation, a neuron selection method is proposed to choose neurons that are most relevant to face recognition. Using the selected neurons as supervision to mimic the single networks of DeepID2+ and DeepID3, which are the state-of-the-art face recognition systems, a compact student with simple network structure achieves better verification accuracy on LFW than its teachers, respectively. When using an ensemble of DeepID2+ as teacher, a mimicked student is able to outperform it and achieves 51.6× compression ratio and 90× speed-up in inference, making this cumbersome model applicable on portable devices.


title: Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm

abstract: Nowadays with a growing number of online controlling systems in the organization and also a high demand of monitoring and stats facilities that uses data streams to log and control their subsystems, data stream mining becomes more and more vital. Hoeffding Trees (also called Very Fast Decision Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for classification and regression problems showed good performance in handling facing challenges and making the possibility of any-time prediction. Although these methods outperform other methods e.g. Artificial Neural Networks (ANN) and Support Vector Regression (SVR), they suffer from high latency in adapting with new concepts when the statistical distribution of incoming data changes. In this article, we introduced a new algorithm that can detect and handle concept drift phenomenon properly. This algorithms also benefits from fast startup ability which helps systems to be able to predict faster than other algorithms at the beginning of data stream arrival. We also have shown that our approach will overperform other controversial approaches for classification and regression tasks.


title: The Antitumor Activity of a Lead Thioxanthone is Associated with Alterations in Cholesterol Localization

abstract: The search for novel anticancer small molecules and strategies remains a challenge. Our previous studies have identified TXA1 (1-{[2-(diethylamino)ethyl]amino}-4-propoxy-9H- thioxanthen-9-one) as a hit compound, with in vitro antitumor potential by modulating autophagy and apoptosis in human tumor cell lines. In the present study, the mechanism of action and antitumor potential of the soluble salt of this molecule (TXA1.HCl) was further investigated using in vitro and mouse xenograft tumor models of NSCLC. Our results showed that TXA1.HCl affected steroid biosynthesis, increased RagD expression, and caused abnormal cellular cholesterol localization. In addition, TXA1.HCl treatment presented no toxicity to nude mice and significantly reduced the growth of human NSCLC cells xenografts in mice. Overall, this work provides new insights into the mechanism of action of TXA1, which may be relevant for the development of anticancer therapeutic strategies, which target cholesterol transport.


title: A new approach for increasing the performance of the industrial Ethernet system PROFINET

abstract: In todaypsilas realtime Ethernet systems two approaches can be differentiated regarding the organisation of the frame transmission: On the one hand there is a summation frame approach in which one frame can supply several nodes with data at the same time. On the other hand, there is the approach of providing data with individual frames for each node. The two most relevant factors influencing the performance in terms of the minimum achievable update time are the propagation delay and the frame transfer time. In this paper we introduce a new concept for reducing the minimum possible update time for individual frame based systems like PROFINET IRT. The performance evaluation compares the results of our concept with the summation frame based EtherCAT System.


title: VMCTune: A Load Balancing Scheme for Virtual Machine Cluster Using Dynamic Resource Allocation

abstract: This paper designs and implements a load balancing scheme based on dynamic resource allocation policy for virtual machine cluster, which are running under para-virtualization mode on a cluster of physical machines (PM) in shared storage architecture. It monitors the real-time resources utilization of VMs and PMs, including CPU, memory and network, then uses instant resource reallocation for virtual machines (VM) running on same PM to achieve local VMs’ load balancing, while uses live migration of VMs among PMs to achieve global VMs’ load balancing. It optimize the resource allocation of VMs to achieve global load balancing of virtual machine cluster. The resource utilization of physical machines will be improved as well. Compared to traditional load balancing schemes based on task scheduling, it is application independent and works seamless on VMs hosting different kinds of applications.


Path Planning with Phased Array SLAM


title: Dielectric properties of liquid ethanol. A computer simulation study

abstract: Static and dynamic dielectric properties of liquidethanol have been studied as a function of the wave-vector number by computer simulation.Molecular dynamics simulations at room temperature have been performed using the optimized potentials for liquid simulations (OPLS) potential model proposed by Jorgensen [J. Phys. Chem. 90, 1276 (1986)]. The time dependent correlation functions of the longitudinal and transverse components of the dipole density as well as the individual and total dipole moment autocorrelation functions have been calculated. The infrared spectra and the dielectric relaxation of the liquid have been also analyzed. Results have been compared with the available experimental data. Special attention has been dedicated to investigate the molecular origin of the different analyzed properties.


Cluster-based Algorithms for Filling Missing Values


title: Gradient boosted feature selection

abstract: A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of features and dimensions; allow the incorporation of known sparsity structure. In this work we propose a novel feature selection algorithm, Gradient Boosted Feature Selection (GBFS), which satisfies all four of these requirements. The algorithm is flexible, scalable, and surprisingly straight-forward to implement as it is based on a modification of Gradient Boosted Trees. We evaluate GBFS on several real world data sets and show that it matches or outperforms other state of the art feature selection algorithms. Yet it scales to larger data set sizes and naturally allows for domain-specific side information.


title: Detecting recent positive selection in the human genome from haplotype structure

abstract: The ability to detect recent natural selection in the human population would have profound implications for the study of human history and for medicine. Here, we introduce a framework for detecting the genetic imprint of recent positive selection by analysing long-range haplotypes in human populations. We first identify haplotypes at a locus of interest (core haplotypes). We then assess the age of each core haplotype by the decay of its association to alleles at various distances from the locus, as measured by extended haplotype homozygosity (EHH). Core haplotypes that have unusually high EHH and a high population frequency indicate the presence of a mutation that rose to prominence in the human gene pool faster than expected under neutral evolution. We applied this approach to investigate selection at two genes carrying common variants implicated in resistance to malaria: G6PD and CD40 ligand. At both loci, the core haplotypes carrying the proposed protective mutation stand out and show significant evidence of selection. More generally, the method could be used to scan the entire genome for evidence of recent positive selection.


title: A Study of Various Approaches and Tools on Ontology

abstract: The increasing volume and unstructured nature of data available on the World Wide Web (WWW) makes information retrieval a tedious and mechanical task. Lots of this information is not semantic driven, and hence not machine process able, but its only in human readable form. The WWW is designed to builds up a source of reference for web of meaning. Ontology information on different subjects spread globally is made available at one place. The Semantic Web (SW), moreover as an extension of WWW is designed to build as a foundation of vocabularies and effective communication of Semantics. The promising area of Semantic Web is logical and lexical semantics. Ontology plays a major role to represent information more meaningfully for humans and machines for its later effective retrieval. This paper constitutes the requisite with a unique approach for a representation and reasoning with ontology for semantic analysis of various type of document and also surveys multiple approaches for ontology learning that enables reasoning with uncertain, incomplete and contradictory information in a domain context.


title: Evaluation of atmosphere–land interactions in an LES from the perspective of heterogeneity propagation

abstract: Atmosphere–land interactions simulated by an LES model are evaluated from the perspective of heterogeneity propagation by comparison with airborne measurements. It is found that the footprints of surface heterogeneity, though as 2D patterns can be dissipated quickly due to turbulent mixing, as1D projections can persist and propagate to the top of the atmospheric boundary layer. Direct comparison and length scale analysis show that the simulated heterogeneity patterns are comparable to the observation. The results highlight the model’s capability in simulating the complex effects of surface heterogeneity on atmosphere–land interactions.


title: The convex grating with high efficiency for hyperspectral remote sensing

abstract: The convex grating is one of the key elements in Offner imaging spectrometers. In this paper the diffraction efficiency of the convex grating is investigated by using rigorous coupled-wave analysis (RCWA), which indicates that within the visible and near infrared broadband, the first-order diffraction efficiency can be over 40% through controlling the blaze angle of blazed convex grating. The blazed convex grating with the period of 6.17μm in the center, the blaze angle about 2.9 degree, the anti-blaze angle about 21 degree, and the ruled area – a convex substrate with its radius 36.31mm and aperture 23.6mm has been fabricated by holographic lithography - scan ion beam etching. Experimental measurements show that the first-order diffraction efficiency is more than 30%, and the first-order diffraction efficiency at blazed wavelength can reach 62%.


Adolescents' experiences of familial involvement in their peer relations and school attendance


title: Learning Feature Pyramids for Human Pose Estimation

abstract: Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens. Although pyramid methods are widely used to handle scale changes at inference time, learning feature pyramids in deep convolutional neural networks (DCNNs) is still not well explored. In this work, we design a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs. Given input features, the PRMs learn convolutional filters on various scales of input features, which are obtained with different subsampling ratios in a multibranch network. Moreover, we observe that it is inappropriate to adopt existing methods to initialize the weights of multi-branch networks, which achieve superior performance than plain networks in many tasks recently. Therefore, we provide theoretic derivation to extend the current weight initialization scheme to multi-branch network structures. We investigate our method on two standard benchmarks for human pose estimation. Our approach obtains state-of-the-art results on both benchmarks. Code is available at https://github.com/bearpaw/PyraNet.


title: A fast shadow algorithm for area light sources using backprojection

abstract: The fast identification of shadow regions due to area light sources is necessary for realistic rendering and for discontinuity meshing for global illumination. A new shadow-determination algorithm is presented that uses a data structure, called a backprojection , to represent the visible portion of a light source from any point in the scene. A complete discontinuity meshing algorithm is described for polyhedral scenes and area light sources, which includes an important class of light/geometry interactions that have not been implemented before. A fast incremental algorithm for computing backprojections is also described. The use of spatial subdivision, and heuristics based on computed statistics of typical scenes, results in efficient mesh and backprojection computation. Results of the implementation show that the use of the backprojection and discontinuity meshing permits accelerated high-quality rendering of shadows using both ray-casting and polygon-rendering with interpolants.


title: Wearable Medical Sensor-Based System Design: A Survey

abstract: Wearable medical sensors (WMSs) are garnering ever-increasing attention from both the scientific community and the industry. Driven by technological advances in sensing, wireless communication, and machine learning, WMS-based systems have begun transforming our daily lives. Although WMSs were initially developed to enable low-cost solutions for continuous health monitoring, the applications of WMS-based systems now range far beyond health care. Several research efforts have proposed the use of such systems in diverse application domains, e.g., education, human-computer interaction, and security. Even though the number of such research studies has grown drastically in the last few years, the potential challenges associated with their design, development, and implementation are neither well-studied nor well-recognized. This article discusses various services, applications, and systems that have been developed based on WMSs and sheds light on their design goals and challenges. We first provide a brief history of WMSs and discuss how their market is growing. We then discuss the scope of applications of WMS-based systems. Next, we describe the architecture of a typical WMS-based system and the components that constitute such a system, and their limitations. Thereafter, we suggest a list of desirable design goals that WMS-based systems should satisfy. Finally, we discuss various research directions related to WMSs and how previous research studies have attempted to address the limitations of the components used in WMS-based systems and satisfy the desirable design goals.


Minimal Time Dynamic Task Allocation for a Swarm of Robots


title: Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies

abstract: As infrastructure-as-a-service clouds become more popular, cloud providers face the complicated problem of maximizing their resource utilization by handling the dynamics of user demand. Auction-based pricing, such as Amazon EC2 spot pricing, provides an option for users to use idle resources at highly reduced yet dynamic prices; under such a pricing scheme, users place bids for cloud resources, and the provider chooses a threshold “spot” price above which bids are admitted. In this paper, we propose a nonlinear dynamical system model for the time-evolution of the spot price as a function of latent states that characterize user demand in the spot and on-demand markets. This model enables us to adaptively predict future spot prices given past spot price observations, allowing us to derive user bidding strategies for heterogeneous cloud resources that minimize the cost to complete a job with negligible probability of interruption. Along the way, the model also yields novel, empirically verifiable insights into cloud provider behavior. We experimentally validate our model and bidding strategy on two months of Amazon EC2 spot price data and find that our proposed bidding strategy is up to 4 times closer to the optimal strategy in hindsight compared to a baseline regression approach while incurring the same negligible probability of interruption.


title: Visual data mining in software archives

abstract: Software archives contain historical information about the development process of a software system. Using data mining techniques rules can be extracted from these archives. In this paper we discuss how standard visualization techniques can be applied to interactively explore these rules. To this end we extended the standard visualization techniques for association rules and sequence rules to also show the hierarchical order of items. Clusters and outliers in the resulting visualizations provide interesting insights into the relation between the temporal development of a system and its static structure. As an example we look at the large software archive of the MOZILLA open source project. Finally we discuss what kind of regularities and anomalies we found and how these can then be leveraged to support software engineers.


title: Intelligent control of the regenerative braking in an induction motor drive

abstract: This work deal with the problem related to the energy recovered in an induction motor drive. During deceleration, a regenerative braking control strategy allows the recovery of the mechanical energy stored in an inertial mass. This process involves the conversion of the kinetic energy into electric energy. The parts that compose the system are a squirrel cage induction motor variable frequency drive and controller and the mechanical system (flywheel) representing the inertia of the automobile. In order to test the performance of the intelligent control, it is compared with a conventional control scheme. The conventional control scheme is used to generate the samples of the input and output signals, which allows the identification of the dynamics of the system (plant). Also, it is used as a reference for the performance of the intelligent control.


title: Taking advantage of LVDS input buffers to implement sigma-delta A/D converters in FPGAs

abstract: This paper describes the implementation of a sigma-delta (/spl Sigma//spl Delta/) A/D converter within an FPGA, with minimal use of external analog components. The approach takes advantage of existing low-voltage differential signalling (LVDS) I/O pads; this allows the implementation of low-cost ADCs into existent FPGAs, even though such digital devices do not possess analog interfacing capabilities at first. The converter was implemented in an actual FPGA and had its performance evaluated.


title: Cost Minimization Algorithms for Data Center Management

abstract: Due to the increasing usage of cloud computing applications, it is important to minimize energy cost consumed by a data center, and simultaneously, to improve quality of service via data center management. One promising approach is to switch some servers in a data center to the idle mode for saving energy while to keep a suitable number of servers in the active mode for providing timely service. In this paper, we design both online and offline algorithms for this problem. For the offline algorithm, we formulate data center management as a cost minimization problem by considering energy cost, delay cost (to measure service quality), and switching cost (to change servers’s active/idle mode). Then, we analyze certain properties of an optimal solution which lead to a dynamic programming based algorithm. Moreover, by revising the solution procedure, we successfully eliminate the recursive procedure and achieve an optimal offline algorithm with a polynomial complexity. For the online algorithm, We design it by considering the worst case scenario for future workload. In simulation, we show this online algorithm can always provide near-optimal solutions.


1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs.


title: Petri Net Modeling of Cyber-Physical Attacks on Smart Grid

abstract: This paper investigates the use of Petri nets for modeling coordinated cyber-physical attacks on the smart grid. Petri nets offer more flexibility and expressiveness than traditional attack trees to represent the actions of simultaneous attackers. However, Petri net models for attacks on very large critical infrastructures such as the smart grid require a great amount of manual effort and detailed expertise in cyber-physical threats. To overcome these obstacles, we propose a novel hierarchical method to construct large Petri nets from a number of smaller Petri nets that can be created separately by different domain experts. The construction method is facilitated by a model description language that enables identical places in different Petri nets to be matched. The new modeling approach is described for an example attack on smart meters, and its efficacy is demonstrated by a proof-of-concept Python program.


title: Mining behavioral economics to design persuasive technology for healthy choices

abstract: Influence through information and feedback has been one of the main approaches of persuasive technology. We propose another approach based on behavioral economics research on decision-making. This approach involves designing the presentation and timing of choices to encourage people to make self-beneficial decisions. We applied three behavioral economics persuasion techniques - the default option strategy, the planning strategy, and the asymmetric choice strategy - to promote healthy snacking in the workplace. We tested the strategies in three experimental case studies using a human snack deliverer, a robot, and a snack ordering website. The default and the planning strategies were effective, but they worked differently depending on whether the participants had healthy dietary lifestyles or not. We discuss designs for persuasive technologies that apply behavioral economics.


title: 2D Feature Selection by Sparse Matrix Regression

abstract: For many image processing and computer vision problems, data points are in matrix form. Traditional methods often convert a matrix into a vector and then use vector-based approaches. They will ignore the location of matrix elements and the converted vector often has high dimensionality. How to select features for 2D matrix data directly is still an uninvestigated important issue. In this paper, we propose an algorithm named sparse matrix regression (SMR) for direct feature selection on matrix data. It employs the matrix regression model to accept matrix as input and bridges each matrix to its label. Based on the intrinsic property of regression coefficients, we design some sparse constraints on the coefficients to perform feature selection. An effective optimization method with provable convergence behavior is also proposed. We reveal that the number of regression vectors can be regarded as a tradeoff parameter to balance the capacity of learning and generalization in essence. To examine the effectiveness of SMR, we have compared it with several vector-based approaches on some benchmark data sets. Furthermore, we have also evaluated SMR in the application of scene classification. They all validate the effectiveness of our method.


title: Queer Diagnoses: Parallels and Contrasts in the History of Homosexuality, Gender Variance, and the Diagnostic and Statistical Manual

abstract: The American Psychiatric Association (APA) is in the process of revising its Diagnostic and Statistical Manual (DSM), with the DSM-V having an anticipated publication date of 2012. As part of that ongoing process, in May 2008, APA announced its appointment of the Work Group on Sexual and Gender Identity Disorders (WGSGID). The announcement generated a flurry of concerned and anxious responses in the lesbian, gay, bisexual, and transgender (LGBT) community, mostly focused on the status of the diagnostic categories of Gender Identity Disorder (GID) (for both children and adolescents and adults). Activists argued, as in the case of homosexuality in the 1970s, that it is wrong to label expressions of gender variance as symptoms of a mental disorder and that perpetuating DSM-IV-TR’s GID diagnoses in the DSM-V would further stigmatize and cause harm to transgender individuals. Other advocates in the trans community expressed concern that deleting GID would lead to denying medical and surgical care for transgender adults. This review explores how criticisms of the existing GID diagnoses parallel and contrast with earlier historical events that led APA to remove homosexuality from the DSM in 1973. It begins with a brief introduction to binary formulations that lead not only to linkages of sexual orientation and gender identity, but also to scientific and clinical etiological theories that implicitly moralize about matters of sexuality and gender. Next is a review of the history of how homosexuality came to be removed from the DSM-II in 1973 and how, not long thereafter, the GID diagnoses found their way into DSM-III in 1980. Similarities and differences in the relationships of homosexuality and gender identity to psychiatric and medical thinking are elucidated. Following a discussion of these issues, the author recommends changes in the DSM-V and some internal and public actions that the American Psychiatric Association should take.


title: Digital forensics investigations in the Cloud

abstract: The essentially infinite storage space offered by Cloud Computing is quickly becoming a problem for forensics investigators in regards to evidence acquisition, forensic imaging and extended time for data analysis. It is apparent that the amount of stored data will at some point become impossible to practically image for the forensic investigators to complete a full investigation. In this paper, we address these issues by determining the relationship between acquisition times on the different storage capacities, using remote acquisition to obtain data from virtual machines in the cloud. A hypothetical case study is used to investigate the importance of using a partial and full approach for acquisition of data from the cloud and to determine how each approach affects the duration and accuracy of the forensics investigation and outcome. Our results indicate that the relation between the time taken for image acquisition and different storage volumes is not linear, owing to several factors affecting remote acquisition, especially over the Internet. Performing the acquisition using cloud resources showed a considerable reduction in time when compared to the conventional imaging method. For a 30GB storage volume, the least time was recorded for the snapshot functionality of the cloud and dd command. The time using this method is reduced by almost 77 percent. FTK Remote Agent proved to be most efficient showing an almost 12 percent reduction in time over other methods of acquisition. Furthermore, the timelines produced with the help of the case study, showed that the hybrid approach should be preferred to complete approach for performing acquisition from the cloud, especially in time critical scenarios.


title: Accelerating Join Operation for Relational Databases with FPGAs

abstract: In this paper, we investigate the use of field programmable gate arrays (FPGAs) to accelerate relational joins. Relational join is one of the most CPU-intensive, yet commonly used, database operations. Hashing can be used to reduce the time complexity from quadratic (naive) to linear time. However, doing so can introduce false positives to the results which must be resolved. We present a hash-join engine on FPGA that performs hashing, conflict resolution, and joining on a PCIe-attached system, achieving greater than 11x speedup over software.


title: Moving ERP Systems to the Cloud - Data Security Issues

abstract: This paper brings to light data security issues and concerns for organizations by moving their Enterprise Resource Planning (ERP) systems to the cloud. Cloud computing has become the new trend of how organizations conduct business and has enabled them to innovate and compete in a dynamic environment through new and innovative business models. The growing popularity and success of the cloud has led to the emergence of cloud-based Software-as-a-Service (SaaS) ERP systems, a new alternative approach to traditional on-premise ERP systems. Cloud-based ERP has a myriad of benefits for organizations. However, infrastructure engineers need to address data security issues before moving their enterprise applications to the cloud. Cloud-based ERP raises specific concerns about the confidentiality and integrity of the data stored in the cloud. Such concerns that affect the adoption of cloud-based ERP are based on the size of the organization. Small to medium enterprises (SMEs) gain the maximum benefits from cloud-based ERP as many of the concerns around data security are not relevant to them. On the contrary, larger organizations are more cautious in moving their mission critical enterprise applications to the cloud. A hybrid solution where organizations can choose to keep their sensitive applications on-premise while leveraging the benefits of the cloud is proposed in this paper as an effective solution that is gaining momentum and popularity for large organizations.


title: Dry EEG Electrodes

abstract: Electroencephalography (EEG) emerged in the second decade of the 20th century as a technique for recording the neurophysiological response. Since then, there has been little variation in the physical principles that sustain the signal acquisition probes, otherwise called electrodes. Currently, new advances in technology have brought new unexpected fields of applications apart from the clinical, for which new aspects such as usability and gel-free operation are first order priorities. Thanks to new advances in materials and integrated electronic systems technologies, a new generation of dry electrodes has been developed to fulfill the need. In this manuscript, we review current approaches to develop dry EEG electrodes for clinical and other applications, including information about measurement methods and evaluation reports. We conclude that, although a broad and non-homogeneous diversity of approaches has been evaluated without a consensus in procedures and methodology, their performances are not far from those obtained with wet electrodes, which are considered the gold standard, thus enabling the former to be a useful tool in a variety of novel applications.


title: Comparing Penile Measurements in Normal and Erectile Dysfunction Subjects

abstract: ABSTRACT Introduction With the increase in penile augmentation procedures it becomes important to assess what is the normal erect penile size in both potent men and men with erectile dysfunction (ED). Aim The aim of this work is to define the average stretched penile size in normal men and ED patients. Main Outcome Measures Penile length and girth. Methods This study included 1,027 adult men presenting to a university hospital outpatient clinic. Two groups of patients were included in this research work. Group I comprises normal adult men (949) and Group II, ED patients (78). There were no differences of race, age, height, and weight. Penile length and girth were measured using a tape measure and rigid ruler in the fully stretched states in both groups. All penile measurements were performed by the same physician. Results In normal men (Group I) the mean of the fully stretched length was 12.9 ± 1.9 cm and the mean of the fully stretched girth was 8.9 ± 0.9 cm. In ED patients (Group II), the mean of the fully stretched length was 11.2 ± 1.5 cm and the mean of fully stretched girth was 8.8 ± 0.8 cm. Comparing the mean of fully stretched penile lengths in both groups revealed statistical significant difference ( P P = 0.474) between them. There were significant positive correlations between fully stretched penile lengths and fully stretched penile girths in both groups. Conclusion The average of fully stretched penile length in normal potent men is 12.9 cm, whereas the patients with ED tend to have significantly shorter penises (11.2 ± 1.5 cm). Kamel I, Gadalla A, Ghanem H, and Oraby M. Comparing penile measurements in normal and erectile dysfunction subjects. J Sex Med 2009;6:2305–2310.


SIMULATED ENTROPY AND GLOBAL MINIMIZATION


title: A 10 nW–1 µW Power Management IC With Integrated Battery Management and Self-Startup for Energy Harvesting Applications

abstract: This paper presents a 10 nW–1 $\upmu{\rm{W}}$ power management IC with 3.2 nW quiescent power consumption for solar energy harvesting applications. The chip integrates a switch matrix that can be configured as a buck or a boost dc–dc converter using a single inductor as well as output voltage regulation logic, battery management block, and self-startup. The control circuit of the converter is designed in an asynchronous fashion that scales the effective switching frequency of the converter with the level of power transferred. The on-time of the converter switches adapts dynamically to the input and output voltages for peak-current control and zero-current switching. For input power of 500 nW, the proposed chip achieves an efficiency of 82%, including the control circuit overhead, while charging the energy storage device at 3 V from 0.5 V input. In buck mode, it achieves a peak efficiency of 87% and maintains efficiency greater than 80% for output power of 50 nW–1 $\upmu{\rm{W}}$ with input voltage of 3 V and output voltage of 1 V.


title: Lift: Multi-Label Learning with Label-Specific Features

abstract: Multi-label learning deals with the problem where each training example is represented by a single instance while associated with a set of class labels. For an unseen example, existing approaches choose to determine the membership of each possible class label to it based on identical feature set, i.e. the very instance representation of the unseen example is employed in the discrimination processes of all labels. However, this commonly-used strategy might be suboptimal as different class labels usually carry specific characteristics of their own, and it could be beneficial to exploit different feature sets for the discrimination of different labels. Based on the above reflection, we propose a new strategy to multi-label learning by leveraging label-specific features, where a simple yet effective algorithm named LIFT is presented. Briefly, LIFT constructs features specific to each label by conducting clustering analysis on its positive and negative instances, and then performs training and testing by querying the clustering results. Extensive experiments across sixteen diversified data sets clearly validate the superiority of LIFT against other well-established multi-label learning algorithms.


title: Numerical modeling of induction heating of paramagnetic billets

abstract: This paper deals with numerical modeling of industrial induction heating of paramagnetic billets for hot forming applications using ANSYS Multiphysics. Its solution is solved numerically in the quasi-coupled formulation respecting all important nonlinearities. The computations are carried out in ANSYS APDL code, based on finite element method. Results were verified experimentally on an industrial inductive installation ITP2 3k150. The goal of numerical modeling was to investigate the thermal dynamic characteristic generated by the through-heater system, in order to have an uniform temperature distribution of heated material for hot forming. Obtained results of the numerical simulation were plotted along with the results from the experimental validation.


title: Social Media Effects on Young Women’s Body Image Concerns: Theoretical Perspectives and an Agenda for Research

abstract: Although there is a voluminous literature on mass media effects on body image concerns of young adult women in the U.S., there has been relatively little theoretically-driven research on processes and effects of social media on young women’s body image and self-perceptions. Yet given the heavy online presence of young adults, particularly women, and their reliance on social media, it is important to appreciate ways that social media can influence perceptions of body image and body image disturbance. Drawing on communication and social psychological theories, the present article articulates a series of ideas and a framework to guide research on social media effects on body image concerns of young adult women. The interactive format and content features of social media, such as the strong peer presence and exchange of a multitude of visual images, suggest that social media, working via negative social comparisons, transportation, and peer normative processes, can significantly influence body image concerns. A model is proposed that emphasizes the impact of predisposing individual vulnerability characteristics, social media uses, and mediating psychological processes on body dissatisfaction and eating disorders. Research-based ideas about social media effects on male body image, intersections with ethnicity, and ameliorative strategies are also discussed.


title: A Literature Review of Privacy Research on Social Network Sites

abstract: With the development of social network sites, security protection of private information online has been a serious and important research topic. Research status quo on privacy of social network sites has been investigated and reviewed in this paper. Two main ways of obtaining private information on social network sites were summarized as based on privacy disclosure and attack technique. In each category, actions to threaten privacy were brought forth. A framework of dangers, which users facing on social network sites, was proposed, and according to privacy risks, protection methods were also discussed. Finally, new topics about privacy research directions on social network sites, privacy-preserving collaborative social network and business model of privacy protection, which need further research, were presented and discussed.


title: Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems

abstract: Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.


title: MASCOT: Memory-efficient and Accurate Sampling for Counting Local Triangles in Graph Streams

abstract: How can we estimate local triangle counts accurately in a graph stream without storing the whole graph? The local triangle counting which counts triangles for each node in a graph is a very important problem with wide applications in social network analysis, anomaly detection, web mining, etc. In this paper, we propose MASCOT, a memory-efficient and accurate method for local triangle estimation in a graph stream based on edge sampling. To develop MASCOT, we first present two naive local triangle counting algorithms in a graph stream: MASCOT-C and MASCOT-A. MASCOT-C is based on constant edge sampling, and MASCOT-A improves its accuracy by utilizing more memory spaces. MASCOT achieves both accuracy and memory-efficiency of the two algorithms by an unconditional triangle counting for a new edge, regardless of whether it is sampled or not. In contrast to the existing algorithm which requires prior knowledge on the target graph and appropriately set parameters, MASCOT requires only one simple parameter, the edge sampling probability. Through extensive experiments, we show that for the same number of edges sampled, MASCOT provides the best accuracy compared to the existing algorithm as well as MASCOT-C and MASCOT-A. Thanks to MASCOT, we also discover interesting anomalous patterns in real graphs, like core-peripheries in the web and ambiguous author names in DBLP.


title: From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains

abstract: The most successful 2D object detection methods require a large number of images annotated with object bounding boxes to be collected for training. We present an alternative approach that trains on virtual data rendered from 3D models, avoiding the need for manual labeling. Growing demand for virtual reality applications is quickly bringing about an abundance of available 3D models for a large variety of object categories. While mainstream use of 3D models in vision has focused on predicting the 3D pose of objects, we investigate the use of such freely available 3D models for multicategory 2D object detection. To address the issue of dataset bias that arises from training on virtual data and testing on real images, we propose a simple and fast adaptation approach based on decorrelated features. We also compare two kinds of virtual data, one rendered with real-image textures and one without. Evaluation on a benchmark domain adaptation dataset demonstrates that our method performs comparably to existing methods trained on large-scale real image domains.


title: Automatic Taxonomy Construction for Eye Colors Data without Using Context Information

abstract: Taxonomy Learning is one of the challenge subtasks of Ontology Learning. A taxonomy describes hyponymy between concepts, and it plays an important role in information systems and categorization tasks. Works in this field prefer to utilize context of sentences to obtain a precise analysis of the corpus, since context can provide more semantic information for building a precise interpretation of the semantic classes. In this paper, we propose a method, called SenseRefined, for automatic taxonomy construction without using context information. It's a part work of one of our project. The proposed method is specially applied for eye colors data, and all the domain phrases are extracted out before building taxonomy. Word senses are extracted out as a way to provide semantic information, because we find the senses of words are more like the definitions of the concepts, which often contain hypernym concepts in. Experiments were carried out using SenseRefined method and Substring Matching method, where the latter one is baseline. Experimental results show the effectiveness of our approach.


title: Sequential fraud detection for prepaid cards using hidden Markov model divergence

abstract: Abstract Stored-value cards, or prepaid cards, are increasingly popular. Like credit cards, their use is vulnerable to fraud, costing merchants and card processors millions of dollars. Prior techniques to automate fraud detection rely on a priori rules or specialized learned models associated with the customer. Mostly, these techniques do not consider fraud sequences or changing behavior, which can lead to false alarms. This study demonstrates how a transaction model can be dynamically created and updated, and fraud can be automatically detected for prepaid cards. A card processing company creates models of the store terminals rather than the customers, in part, because of the anonymous nature of prepaid cards. The technique automatically creates, updates, and compares hidden Markov models (HMM) of merchant terminals. We present fraud detection and experiments on real transactional data, showing the efficiency and effectiveness of the approach. In the fraud test cases, derived from known fraud cases, the technique has a good F-score. The technique can detect fraud in real-time for merchants, as card transactions are processed by a modern transaction processing system.


title: Across the Ovulatory Cycle: Naturalistic and Laboratory Task-Based Evidence

abstract: The authors tested the prediction that women prefer clothing that is more revealing and sexy when fertility is highest within the ovulatory cycle. Eighty-eight women reported to the lab twice: once on a low-fertility day of the cycle and once on a high-fertility day (confirmed using hormone tests). In each session, participants posed for full-body photographs in the clothing they wore to the lab, and they drew illustrations to indicate an outfit they would wear to a social event that evening. Although each data source supported the prediction, the authors found the most dramatic changes in clothing choice in the illustrations. Ovulatory shifts in clothing choice were moderated by sociosexuality, attractiveness, relationship status, and relationship satisfaction. Sexually unrestricted women, for example, showed greater shifts in preference for revealing clothing worn to the laboratory near ovulation. The authors suggest that clothing preference shifts could reflect an increase in female—female competition ...


title: A Survey on Region Extractors from Web Documents

abstract: Extracting information from web documents has become a research area in which new proposals sprout out year after year. This has motivated several researchers to work on surveys that attempt to provide an overall picture of the many existing proposals. Unfortunately, none of these surveys provide a complete picture, because they do not take region extractors into account. These tools are kind of preprocessors, because they help information extractors focus on the regions of a web document that contain relevant information. With the increasing complexity of web documents, region extractors are becoming a must to extract information from many websites. Beyond information extraction, region extractors have also found their way into information retrieval, focused web crawling, topic distillation, adaptive content delivery, mashups, and metasearch engines. In this paper, we survey the existing proposals regarding region extractors and compare them side by side.


title: Design procedure for two-stage CMOS opamp with flexible noise-power balancing scheme

abstract: This paper presents a basic two-stage CMOS opamp design procedure that provides the circuit designer with a means to strike a balance between two important characteristics in electronic circuit design, namely noise performance and power consumption. It is shown in this paper that, unlike the previously reported design procedures, the proposed design step allows opamp designers to trade between noise performance and power consumption with greater flexibility. In order to verify the viability of the proposed design step, SPICE simulation results of the opamp designed by the proposed procedure, under a variety of temperature and process conditions, are given.


title: Security Analysis on One-to-Many Order Preserving Encryption-Based Cloud Data Search

abstract: For ranked search in encrypted cloud data, order preserving encryption (OPE) is an efficient tool to encrypt relevance scores of the inverted index. When using deterministic OPE, the ciphertexts will reveal the distribution of relevance scores. Therefore, Wang et al. proposed a probabilistic OPE, called one-to-many OPE, for applications of searchable encryption, which can flatten the distribution of the plaintexts. In this paper, we proposed a differential attack on one-to-many OPE by exploiting the differences of the ordered ciphertexts. The experimental results show that the cloud server can get a good estimate of the distribution of relevance scores by a differential attack. Furthermore, when having some background information on the outsourced documents, the cloud server can accurately infer the encrypted keywords using the estimated distributions.


title: Does the Triple Helical Domain of Type I Collagen Encode Molecular Recognition and Fiber Assembly while Telopeptides Serve as Catalytic Domains? EFFECT OF PROTEOLYTIC CLEAVAGE ON FIBRILLOGENESIS AND ON COLLAGEN-COLLAGEN INTERACTION IN FIBERS

abstract: Abstract Over the last several decades, it has been established that proteolytic removal of short, non-helical terminal peptides (telopeptides) from type I collagen significantly alters the kinetics of in vitro fibrillogenesis. However, it has also been observed that the protein is still capable of forming fibers even after complete removal of telopeptides. This study focuses on the characterization of this fibrillogenesis competency of collagen. We have combined traditional kinetic and thermodynamic assays of fibrillogenesis efficacy with direct measurements of interaction between collagen molecules in fibers by osmotic stress and x-ray diffraction. We found that telopeptide cleavage by pepsin or by up to 20 h of Pronase treatment altered fiber assembly kinetics, but the same fraction of the protein still assembled into fibers. Small-angle x-ray diffraction showed that these fibers have normal, native-like D-stagger. Force measurements indicated that collagen-collagen interactions in fibers were not affected by either pepsin or Pronase treatment. In contrast, prolonged (>20 h) Pronase treatment resulted in cleavage of the triple helical domain as indicated by SDS-polyacrylamide gel electrophoresis. The triple-helix cleavage correlated with the observed decrease in the fraction of protein capable of forming fibers and with the measured loss of attraction between helices in fibers. These data suggest that telopeptides play a catalytic role, whereas the information necessary for proper molecular recognition and fiber assembly is encoded in the triple helical domain of collagen.


title: Feature Selection Based on the Shapley Value

abstract: We present and study the Contribution-Selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the Multiperturbation Shapley Analysis, a framework which relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. Empirical comparison with several other existing feature selection methods shows that the backward eliminati-nation variant of CSA leads to the most accurate classification results on an array of datasets.


title: Convolutional neural network feature maps selection based on LDA

abstract: Convolutional neural network (CNN), as widely applied to vision and speech, has developed lager and lager network size in last few years. In this paper, we propose a CNN feature maps selection method which can simplify CNN structure on the premise of stabilize the classifier performance. Our approach aims to cut the feature map number of the last subsampling layer and achieves shortest runtime on the basis of Linear Discriminant Analysis (LDA). We rebuild feature maps selection formula based on the between-class scatter matrix and within-class scatter matrix, because LDA can lead to information loss in the dimension-reduction process. Our experiments measure on two standard datasets and a dataset made by ourselves. According to the separability value of each feature map, we suggest the least number of feature maps which can keep the classifier performance. Furthermore, we prove that separability value is an effective indicator for reference to select feature maps.


title: Relations between components of visual attention

abstract: This chapter reviews recent work on visual attention in children. The review is not intended to be comprehensive, but rather to provide a framework in which questions concerning the relations between various components of attention can be explored. A taxonomy of visual attention is suggested, developmental data on each of the taxonomy components is summarized, and a paradigm for examining the relations between components is presented. Examinations of the relations between several components show that all but one (overt visual search) appear to share mechanisms. This finding has important implications for theories of attention and cognitive development.


title: Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

abstract: When observing the actions of others, humans carry out inferences about why the others acted as they did, and what this implies about their view of the world. Humans also use the fact that their actions will be interpreted in this manner when observed by others, allowing them to act informatively and thereby communicate efficiently with others. Although learning algorithms have recently achieved superhuman performance in a number of two-player, zero-sum games, scalable multi-agent reinforcement learning algorithms that can discover effective strategies and conventions in complex, partially observable settings have proven elusive. We present the Bayesian action decoder (BAD), a new multi-agent learning method that uses an approximate Bayesian update to obtain a public belief that conditions on the actions taken by all agents in the environment. Together with the public belief, this Bayesian update effectively defines a new Markov decision process, the public belief MDP, in which the action space consists of deterministic partial policies, parameterised by deep neural networks, that can be sampled for a given public state. It exploits the fact that an agent acting only on this public belief state can still learn to use its private information if the action space is augmented to be over partial policies mapping private information into environment actions. The Bayesian update is also closely related to the theory of mind reasoning that humans carry out when observing others' actions. We first validate BAD on a proof-of-principle two-step matrix game, where it outperforms traditional policy gradient methods. We then evaluate BAD on the challenging, cooperative partial-information card game Hanabi, where in the two-player setting the method surpasses all previously published learning and hand-coded approaches.


Understanding the Dynamics of Motivation in Sport and Physical Activity: An Achievement Goal Interpretation


title: Shelf detection via vanishing point and radial projection

abstract: Retailers need to monitor products on store shelves in order to maintain adequate stocking and in many cases to comply with a placement arrangement defined by a planogram. It is possible that such inspections be performed automatically or semi-automatically using computer vision techniques. Towards this goal, we propose an algorithm to detect shelves from images captured with a handheld digital camera. First, we identify the vanishing point and the associated line segments corresponding to the shelves. Second, we divide the image into equal-angle wedges centered at the vanishing point and project the associated line segments into the wedges. Finally, we identify shelves by analyzing the projections.


title: Review: Cloud computing service composition: A systematic literature review

abstract: The increasing tendency of network service users to use cloud computing encourages web service vendors to supply services that have different functional and nonfunctional (quality of service) features and provide them in a service pool. Based on supply and demand rules and because of the exuberant growth of the services that are offered, cloud service brokers face tough competition against each other in providing quality of service enhancements. Such competition leads to a difficult and complicated process to provide simple service selection and composition in supplying composite services in the cloud, which should be considered an NP-hard problem. How to select appropriate services from the service pool, overcome composition restrictions, determine the importance of different quality of service parameters, focus on the dynamic characteristics of the problem, and address rapid changes in the properties of the services and network appear to be among the most important issues that must be investigated and addressed. In this paper, utilizing a systematic literature review, important questions that can be raised about the research performed in addressing the above-mentioned problem have been extracted and put forth. Then, by dividing the research into four main groups based on the problem-solving approaches and identifying the investigated quality of service parameters, intended objectives, and developing environments, beneficial results and statistics are obtained that can contribute to future research.


title: Edge enhancement by local deconvolution

abstract: In this paper a new approach for blurred image restoration is presented. Our algorithm is based on human vision which zooms back and forth in the image in order to identify global structures or details. Deconvolution parameters are estimated by an edge detection and correspond to the ones of a chosen edge detection model. The segmentation is obtained by merging multiscale information provided by multiscale edge detection. The edge detection is achieved by using a derivative approach following a generalization of Canny-Deriche filtering. This multiscale analysis performs an efficient edge detection in noisy blurred images. The merging leads to the best local representation of edge information across scales. The algorithm deals with a mixed (coarse-to-fine/fine-to-coarse) approach and searches for candidate edge points through the scales. Edge characteristics are estimated by the merging algorithm for the chosen model. Scale, direction and amplitude informations allow a local deconvolution of the original image. The noise problem is not considered in this work since it does not disturb the process. Results show that this method allows non-uniformly blurred image restoration. An implementation of the whole algorithm in an intelligent camera (DSP) has been performed.


Coupled Harmonic Admittance Identification Based on Least Square Estimation


title: Improved Arabic Dialect Classification with Social Media Data

abstract: Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach to improving Arabic dialect classification with semi-supervised learning: multiple classifiers are trained with weakly supervised, strongly supervised, and unsupervised data. Their combination yields significant and consistent improvement on two different test sets. The dialect classification accuracy is improved by 5% over the strongly supervised classifier and 20% over the weakly supervised classifier. Furthermore, when applying the improved dialect classifier to build a Modern Standard Arabic (MSA) language model (LM), the new model size is reduced by 70% while the English-Arabic translation quality is improved by 0.6 BLEU point.


title: Cyber-Attack Modeling Analysis Techniques: An Overview

abstract: Cyber attack is a sensitive issue in the world of Internet security. Governments and business organisations around the world are providing enormous effort to secure their data. They are using various types of tools and techniques to keep the business running, while adversaries are trying to breach security and send malicious software such as botnets, viruses, trojans etc., to access valuable data. Everyday the situation is getting worse because of new types of malware emerging to attack networks. It is important to understand those attacks both before and after they happen in order to provide better security to our systems. Understanding attack models provide more insight into network vulnerability, which in turn can be used to protect the network from future attacks. In the cyber security world, it is difficult to predict a potential attack without understanding the vulnerability of the network. So, it is important to analyse the network to identify top possible vulnerability list, which will give an intuitive idea to protect the network. Also, handling an ongoing attack poses significant risk on the network and valuable data, where prompt action is necessary. Proper utilisation of attack modelling techniques provide advance planning, which can be implemented rapidly during an ongoing attack event. This paper aims to analyse various types of existing attack modelling techniques to understand the vulnerability of the network, and the behaviour and goals of the adversary. The ultimate goal is to handle cyber attack in efficient manner using attack modelling techniques.


title: Personal Data Lake with Data Gravity Pull

abstract: This paper presents Personal Data Lake, a unified storage facility for storing, analyzing and querying personal data. A data lake stores data regardless of format and thus provides an intuitive way to store personal data fragments of any type. Metadata management is a central part of the lake architecture. For structured/semi-structured data fragments, metadata may contain information about the schema of the data so that the data can be transformed into queryable data objects when required. For unstructured data, enabling gravity pull means allowing third-party plugins so that the unstructured data can be analyzed and queried.


title: A layered naming architecture for the internet

abstract: Currently the Internet has only one level of name resolution, DNS, which converts user-level domain names into IP addresses. In this paper we borrow liberally from the literature to argue that there should be three levels of name resolution: from user-level descriptors to service identifiers; from service identifiers to endpoint identifiers; and from endpoint identifiers to IP addresses. These additional levels of naming and resolution (1) allow services and data to be first class Internet objects (in that they can be directly and persistently named), (2) seamlessly accommodate mobility and multi-homing and (3) integrate middleboxes (such as NATs and firewalls) into the Internet architecture. We further argue that flat names are a natural choice for the service and endpoint identifiers. Hence, this architecture requires scalable resolution of flat names, a capability that distributed hash tables (DHTs) can provide.


Reflection of Indonesian Economic Development Model Approach Through the Economic and Social Dynamics Ibnu Khaldun


title: Constraint propagation algorithms for temporal reasoning

abstract: This paper considers computational aspects of several temporal representation languages. It investigates an interval-based representation, and a point-based one. Computing the consequences of temporal assertions is shown to be computationally intractable in the interval-based representation, but not in the point-based one. However, a fragment of the interval language can be expressed using the point language and benefits from the tractability of the latter.


title: Embodiment of Wearable Augmented Reality Technology in Tourism Experiences

abstract: The increasing use of wearable devices for tourism purposes sets the stage for a critical discussion on technological mediation in tourism experiences. This article provides a theoretical reflection on the phenomenon of embodiment relation in technological mediation and then assesses the embodiment of wearable augmented reality technology in a tourism attraction. The findings suggest that technology embodiment is a multidimensional construct consisting of ownership, location, and agency. These support the concept of technology withdrawal, where technology disappears as it becomes part of human actions, and contest the interplay of subjectivity and intentionality between humans and technology in situated experiences such as tourism. It was also found that technology embodiment affects enjoyment and enhances experience with tourism attractions.


title: Environmental effect assessment on performance of solar PV panel

abstract: The objective of this research was to study the effect of different environmental factors on performance of solar photovoltaic panel. The photovoltaic solar power represents one of the most promising energy in the world. It is also the cleanest form of energy. But the implementation of a PV system has shown that their reliability and efficiency depend upon many factors. The output of solar PV system is mainly affected by different environmental factors like dust, color, irradiance, shading, etc. Because in all the cases, the output power and efficiency are more rather than affected condition. Here the number of experiment has been conducted to verify the change in i-v and p-v characteristics of the system. The experimental data were used to calculate the efficiency and power output of the system. The result shows that the output power and efficiency are affected by environmental factors.