title: A Survey of Secure Routing Protocols in Multi-Hop Cellular Networks

abstract: Multi-hop networks are expected to be an important part of 5G mobile systems. In a multi-hop cellular network (MCN), the nodes assist each other in relaying packets towards their destinations. The structures and operations of multi-hop networks make them particularly vulnerable to certain types of attack. Hence, security measures to counter these attacks are necessary. In this paper, we provide an overview of the secure routing protocols for multi-hop networks and classify them into MCN Type-1 and Type-0 categories for device-to-device communications, and the Internet-of-Things category for machine-to-machine communications. Our focus is on the applied cryptographic techniques and the security mechanisms in secure routing protocols. We propose an evaluation framework incorporating different security aspects, vulnerabilities, and levels of deployability to compare a number of secure routing protocols. Moreover, we review the secure routing paths in software-defined networking as a solution for existing challenges in multi-hop networks. Some open research problems are highlighted as possible directions for future studies.


Hierarchical LSTMs with Joint Learning for Estimating Customer Satisfaction from Contact Center Calls.


title: Social network analysis for routing in disconnected delay-tolerant MANETs

abstract: Message delivery in sparse Mobile Ad hoc Networks (MANETs) is difficult due to the fact that the network graph is rarely (if ever) connected. A key challenge is to find a route that can provide good delivery performance and low end-to-end delay in a disconnected network graph where nodes may move freely. This paper presents a multidisciplinary solution based on the consideration of the so-called small world dynamics which have been proposed for economy and social studies and have recently revealed to be a successful approach to be exploited for characterising information propagation in wireless networks. To this purpose, some bridge nodes are identified based on their centrality characteristics, i.e., on their capability to broker information exchange among otherwise disconnected nodes. Due to the complexity of the centrality metrics in populated networks the concept of ego networks is exploited where nodes are not required to exchange information about the entire network topology, but only locally available information is considered. Then SimBet Routing is proposed which exploits the exchange of pre-estimated "betweenness' centrality metrics and locally determined social "similarity' to the destination node. We present simulations using real trace data to demonstrate that SimBet Routing results in delivery performance close to Epidemic Routing but with significantly reduced overhead. Additionally, we show that SimBet Routing outperforms PRoPHET Routing, particularly when the sending and receiving nodes have low connectivity.


title: Object Instance Segmentation and Fine-Grained Localization Using Hypercolumns

abstract: Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as a feature representation. However, the information in this layer may be too coarse spatially to allow precise localization. On the contrary, earlier layers may be precise in localization but will not capture semantics. To get the best of both worlds, we define the hypercolumn at a pixel as the vector of activations of all CNN units above that pixel. Using hypercolumns as pixel descriptors, we show results on three fine-grained localization tasks: simultaneous detection and segmentation, where we improve state-of-the-art from 49.7 mean $\mathrm{AP}^r$ to 62.4 , keypoint localization, where we get a 3.3 point boost over a strong regression baseline using CNN features, and part labeling, where we show a 6.6 point gain over a strong baseline.


Multitask learning for semantic sequence prediction under varying data conditions.


title: A Risk Assessment Framework for Automotive Embedded Systems

abstract: The automotive industry is experiencing a paradigm shift towards autonomous and connected vehicles. Coupled with the increasing usage and complexity of electrical and/or electronic systems, this introduces new safety and security risks. Encouragingly, the automotive industry has relatively well-known and standardised safety risk management practices, but security risk management is still in its infancy. In order to facilitate the derivation of security requirements and security measures for automotive embedded systems, we propose a specifically tailored risk assessment framework, and we demonstrate its viability with an industry use-case. Some of the key features are alignment with existing processes for functional safety, and usability for non-security specialists. The framework begins with a threat analysis to identify the assets, and threats to those assets. The following risk assessment process consists of an estimation of the threat level and of the impact level. This step utilises several existing standards and methodologies, with changes where necessary. Finally, a security level is estimated which is used to formulate high-level security requirements. The strong alignment with existing standards and processes should make this framework well-suited for the needs in the automotive industry.


title: Pitch Extraction and Fundamental Frequency: History and Current Techniques

abstract: Pitch extraction (also called fundamental frequency estimation) has been a popular topic in many fields of research since the age of computers. Yet in the course of some 50 years of study, current techniques are still not to a desired level of accuracy and robustness. When presented with a single clean pitched signal, most techniques do well, but when the signal is noisy, or when there are multiple pitch streams, many current pitch algorithms still fail to perform well. This report presents a discussion of the history of pitch detection techniques, as well as a survey of the current state of the art in pitch detection technology.


title: Tree ensembles for predicting structured outputs

abstract: In this paper, we address the task of learning models for predicting structured outputs. We consider both global and local predictions of structured outputs, the former based on a single model that predicts the entire output structure and the latter based on a collection of models, each predicting a component of the output structure. We use ensemble methods and apply them in the context of predicting structured outputs. We propose to build ensemble models consisting of predictive clustering trees, which generalize classification trees: these have been used for predicting different types of structured outputs, both locally and globally. More specifically, we develop methods for learning two types of ensembles (bagging and random forests) of predictive clustering trees for global and local predictions of different types of structured outputs. The types of outputs considered correspond to different predictive modeling tasks: multi-target regression, multi-target classification, and hierarchical multi-label classification. Each of the combinations can be applied both in the context of global prediction (producing a single ensemble) or local prediction (producing a collection of ensembles). We conduct an extensive experimental evaluation across a range of benchmark datasets for each of the three types of structured outputs. We compare ensembles for global and local prediction, as well as single trees for global prediction and tree collections for local prediction, both in terms of predictive performance and in terms of efficiency (running times and model complexity). The results show that both global and local tree ensembles perform better than the single model counterparts in terms of predictive power. Global and local tree ensembles perform equally well, with global ensembles being more efficient and producing smaller models, as well as needing fewer trees in the ensemble to achieve the maximal performance.


title: Lattice-Based Training of Bottleneck Feature Extraction Neural Networks

abstract: This paper investigates a method for training bottleneck (BN) features in a more targeted manner for their intended use in GMM-HMM based ASR. Our approach adds a GMM acoustic model activation layer to a standard BN feature extraction (FE) neural network and performs lattice-based MMI training on the resulting network. After training, the network is reverted back into a working BN FE network by removing the GMM activation layer, and we then train a GMM system on top of the bottleneck features in the normal way. Our results show that this approach can significantly improve recognition accuracy when compared to a baseline system that uses standard BN features. Further, we show that our approach can be used to perform unsupervised speaker adaptation, yielding significantly improved results compared to global cMLLR adaptation.


title: Kylix: A Sparse Allreduce for Commodity Clusters

abstract: Allreduce is a basic building block for parallel computing. Our target here is "Big Data" processing on commodity clusters (mostly sparse power-law data). Allreduce can be used to synchronize models, to maintain distributed datasets, and to perform operations on distributed data such as sparse matrix multiply. We first review a key constraint on cluster communication, the minimum efficient packet size, which hampers the use of direct all-to-all protocols on large networks. Our allreduce network is a nested, heterogeneous-degree butterfly. We show that communication volume in lower layers is typically much less than the top layer, and total communication across all layers a small constant larger than the top layer, which is close to optimal. A chart of network communication volume across layers has a characteristic "Kylix" shape, which gives the method its name. For optimum performance, the butterfly degrees also decrease down the layers. Furthermore, to efficiently route sparse updates to the nodes that need them, the network must be nested. While the approach is amenable to various kinds of sparse data, almost all "Big Data" sets show power-law statistics, and from the properties of these, we derive methods for optimal network design. Finally, we present experiments showing with Kylix on Amazon EC2 and demonstrating significant improvements over existing systems such as PowerGraph and Hadoop.


title: Aiding in the Treatment of Low Back Pain by a Fuzzy Linguistic Web System

abstract: Low back pain affects a large proportion of the adult population at some point in their lives and has a major economic and social impact. To soften this impact, one possible solution is to make use of recommender systems, which have already been introduced in several health fields. In this paper, we present TPLUFIB-WEB, a novel fuzzy linguistic Web system that uses a recommender system to provide personalized exercises to patients with low back pain problems and to offer recommendations for their prevention. This system may be useful to reduce the economic impact of low back pain, help professionals to assist patients, and inform users on low back pain prevention measures. A strong part of TPLUFIB-WEB is that it satisfies the Web quality standards proposed by the Health On the Net Foundation (HON), Official College of Physicians of Barcelona, and Health Quality Agency of the Andalusian Regional Government, endorsing the health information provided and warranting the trust of users.


title: High-Frequency-Link-Based Grid-Tied PV System With Small DC-Link Capacitor and Low-Frequency Ripple-Free Maximum Power Point Tracking

abstract: This paper proposes a grid-tied photovoltaic (PV) system consisting of modular current-fed dual-active-bridge (CF-DAB) dc–dc converter with cascaded multilevel inverter. The proposed converter allows a small dc-link capacitor in the three-phase wye-connected PV system; therefore, the system reliability can be improved by replacing electrolytic capacitors with film capacitors. The low-frequency ripple-free maximum power point tracking (MPPT) is also realized in the proposed converter. First of all, to minimize the influence resulting from reduced capacitance, a dc-link voltage synchronizing control is developed. Then, a detailed design of power mitigation control based on CF-DAB dynamic model is presented to prevent the large low-frequency voltage variation propagating from the dc-link to PV side. Finally, a novel variable step-size MPPT algorithm is proposed to ensure not only high MPPT efficiency, but also fast maximum power extraction under rapid irradiation change. A downscaled 5-kW PV converter module with a small dc-link capacitor was built in the laboratory with the proposed control and MPPT algorithm, and experimental results are given to validate the converter performance.


title: Automatic parallel parking with geometric continuous-curvature path planning

abstract: This paper presents the experimental results of our path planning for a passenger vehicle in parallel parking problems. The generation of the path planning consists in two parts: create a simple geometric path for the parallel parking in one or more maneuvers by circle arcs and then transform it into a continuous-curvature path with the use of clothoids. Experimental results of the parking and of the localization of the vehicle confirm the chosen approach.


title: A Survey on Big Data Market: Pricing, Trading and Protection

abstract: Big data is considered to be the key to unlocking the next great waves of growth in productivity. The amount of collected data in our world has been exploding due to a number of new applications and technologies that permeate our daily lives, including mobile and social networking applications, and Internet of Thing-based smart-world systems (smart grid, smart transportation, smart cities, and so on). With the exponential growth of data, how to efficiently utilize the data becomes a critical issue. This calls for the development of a big data market that enables efficient data trading. Via pushing data as a kind of commodity into a digital market, the data owners and consumers are able to connect with each other, sharing and further increasing the utility of data. Nonetheless, to enable such an effective market for data trading, several challenges need to be addressed, such as determining proper pricing for the data to be sold or purchased, designing a trading platform and schemes to enable the maximization of social welfare of trading participants with efficiency and privacy preservation, and protecting the traded data from being resold to maintain the value of the data. In this paper, we conduct a comprehensive survey on the lifecycle of data and data trading. To be specific, we first study a variety of data pricing models, categorize them into different groups, and conduct a comprehensive comparison of the pros and cons of these models. Then, we focus on the design of data trading platforms and schemes, supporting efficient, secure, and privacy-preserving data trading. Finally, we review digital copyright protection mechanisms, including digital copyright identifier, digital rights management, digital encryption, watermarking, and others, and outline challenges in data protection in the data trading lifecycle.


title: Social VR: How Personal Space is Affected by Virtual Agents' Emotions

abstract: Personal space (PS), the flexible protective zone maintained around oneself, is a key element of everyday social interactions. It, e.g., affects people's interpersonal distance and is thus largely involved when navigating through social environments. However, the PS is regulated dynamically, its size depends on numerous social and personal characteristics and its violation evokes different levels of discomfort and physiological arousal. Thus, gaining more insight into this phenomenon is important. We contribute to the PS investigations by presenting the results of a controlled experiment in a CAVE, focusing on German males in the age of 18 to 30 years. The PS preferences of 27 participants have been sampled while they were approached by either a single embodied, computer-controlled virtual agent (VA) or by a group of three VAs. In order to investigate the influence of a VA's emotions, we altered their facial expression between angry and happy. Our results indicate that the emotion as well as the number of VAs approaching influence the PS: larger distances are chosen to angry VAs compared to happy ones; single VAs are allowed closer compared to the group. Thus, our study is a foundation for social and behavioral studies investigating PS preferences.


title: AlkylVM: A Virtual Machine for Smart Contract Blockchain Connected Internet of Things

abstract: Blockchain technology and the application of smart contracts allow for automation of verifiable digital processes between any number of parties. The Internet of Things (IoT) has seen great potential in the past decade to revolutionise our day-to-day lives with the aim of automating physical processes by incorporating Internet-connected devices into commodities. By integrating the IoT with blockchain systems and smart contracts it is possible to provide verifiable automation of physical processes involving different parties. The challenge lies in that due to resource constraints, many of the computational devices used within the IoT are not capable of directly interacting with blockchain implementations. In this paper, we describe and give a reference design and implementation of a split-virtual machine, AlkylVM, which allows for resource constrained IoT devices to interact with blockchain systems.


Heat Conduction Effects of the Chamber Wall on the Air Pressure Dynamics


title: Performance of Fractal Image Compression for Medical Images: A Comprehensive Literature Review

abstract: Generally the fractal image compression is a technique based on the presentation of an image by a contractive transform, on the space of image, for which the fixed point is close to the original image. The fractal image compression is rapidly growing principle covers a wide variety of coding scheme in various domains. A large number of theoretical aspects of this concept are available. However, a few concentrations have been given to the image encoding model which justifies its proper implementation. Most fractal base schemes are not competitive with the current state of the art like JPEG, JPEG2000. To identify the performance of fractal image compression, specifically for the medical images, a comprehensive review of worldwide contributions from 1990 to 2013 has been carried out. As a result, the fractal image compression techniques are classified into four domains, i.e., spatial, frequency, soft computing, and hybrid. It is found that, fractal theory in spatial domain, fractal theory along with discrete cosine transform and wavelet transform in frequency domain, fractal theory along with fuzzy logic and neural network in soft computing domain, and combination of fractal theory along with discrete cosine transform, wavelet transform, fuzzy logic and neural network in hybrid domain are often used by the contributors. It is found that the fractal dimension and fuzzy logic approach on regular image processing are sufficiently suitable for image texture analysis. Consequently fuzzy logic is successfully applied in regular image and found outstanding results. On the basis of these facts it is anticipated that the fractal dimension and fuzzy logic could be a suitable approach in medical image compression using fractal theory as a future expansion is discussed in this review article.


ANTLRWorks: an ANTLR grammar development environment


title: Abnormal deep grey matter development following preterm birth detected using deformation-based morphometry

abstract: Preterm birth is a leading risk factor for neurodevelopmental and cognitive impairment in childhood and adolescence. The most common known cerebral abnormality among preterm infants at term equivalent age is a diffuse white matter abnormality seen on magnetic resonance (MR) images. It occurs with a similar prevalence to subsequent impairment, but its effect on developing neural systems is unknown. MR images were obtained at term equivalent age from 62 infants born at 24–33 completed weeks gestation and 12 term born controls. Tissue damage was quantified using diffusion-weighted imaging, and deformation-based morphometry was used to make a non-subjective survey of the whole brain to identify significant cerebral morphological alterations associated with preterm birth and with diffuse white matter injury. Preterm infants at term equivalent age had reduced thalamic and lentiform volumes without evidence of acute injury in these regions (t = 5.81, P < 0.05), and these alterations were more marked with increasing prematurity (t = 7.13, P < 0.05 for infants born at less than 28 weeks) and in infants with diffuse white matter injury (t = 6.43, P < 0.05). The identification of deep grey matter growth failure in association with diffuse white matter injury suggests that white matter injury is not an isolated phenomenon, but rather, it is associated with the maldevelopment of remote structures. This could be mediated by a disturbance to corticothalamic connectivity during a critical period in cerebral development. Deformation-based morphometry is a powerful tool for modelling the developing brain in health and disease, and can be used to test putative aetiological factors for injury.


title: An efficient and simple under-sampling technique for imbalanced time series classification

abstract: Imbalanced time series classification (TSC) involving many real-world applications has increasingly captured attention of researchers. Previous work has proposed an intelligent-structure preserving over-sampling method (SPO), which the authors claimed achieved better performance than other existing over-sampling and state-of-the-art methods in TSC. The main disadvantage of over-sampling methods is that they significantly increase the computational cost of training a classification model due to the addition of new minority class instances to balance data-sets with high dimensional features. These challenging issues have motivated us to find a simple and efficient solution for imbalanced TSC. Statistical tests are applied to validate our conclusions. The experimental results demonstrate that this proposed simple random under-sampling technique with SVM is efficient and can achieve results that compare favorably with the existing complicated SPO method for imbalanced TSC.


title: Real-Time Stream Processing in Java

abstract: This paper presents a streaming data framework for the Real-Time Specification for Java, with the goal of levering as much as possible the Java 8 Stream processing framework whilst delivering bounded latency. Our approach is to buffer the incoming streaming data into micro batches which are then converted to collections for processing by the Java 8 infrastructure which is configured with a real-time ForkJoin thread pool. Deferrable servers are used to limit the impact of stream processing activity on hard real-time activities.


title: Disentangling the independently controllable factors of variation by interacting with the world

abstract: It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors, and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.


title: Efficient Ray Tracing Through Aspheric Lenses and Imperfect Bokeh Synthesis

abstract: We present an efficient ray-tracing technique to render bokeh effects produced by parametric aspheric lenses. Contrary to conventional spherical lenses, aspheric lenses do generally not permit a simple closed-form solution of ray-surface intersections. We propose a numerical root-finding approach, which uses tight proxy surfaces to ensure a good initialization and convergence behavior. Additionally, we simulate mechanical imperfections resulting from the lens fabrication via a texture-based approach. Fractional Fourier transform and spectral dispersion add additional realism to the synthesized bokeh effect. Our approach is well-suited for execution on graphics processing units (GPUs) and we demonstrate complex defocus-blur and lens-flare effects.


title: Topic Modeling Based Multi-modal Depression Detection

abstract: Major depressive disorder is a common mental disorder that affects almost 7% of the adult U.S. population. The 2017 Audio/Visual Emotion Challenge (AVEC) asks participants to build a model to predict depression levels based on the audio, video, and text of an interview ranging between 7-33 minutes. Since averaging features over the entire interview will lose most temporal information, how to discover, capture, and preserve useful temporal details for such a long interview are significant challenges. Therefore, we propose a novel topic modeling based approach to perform context-aware analysis of the recording. Our experiments show that the proposed approach outperforms context-unaware methods and the challenge baselines for all metrics.


title: Stress and Type 2 Diabetes: A Review of How Stress Contributes to the Development of Type 2 Diabetes

abstract: Current policy and research around type 2 diabetes (T2D) interventions largely invoke a behavioral model. We suggest that activation of the physiologic stress response (PSR) from chronic exposure to stressors, low socioeconomic status (SES), severe mental health problems, or aggressive behavior increases the risk of T2D. This article is a comprehensive review of the literature on the link between T2D and psychosocial factors focusing on prospective studies of the risk for developing diabetes. The review found an increased risk for T2D in people: exposed to stressful working conditions or traumatic events; with depression; with personality traits or mental health problems that put them in conflict with others; of low SES, either currently or in childhood; and in racial/ethnic minority populations, independent of current SES. This review suggests that T2D prevention research would be more effective if (a) the PSR to psychosocial factors (especially social disparities) was recognized and (b) intervention programs evaluated reduction in social disparities as part of a comprehensive approach.


title: The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

abstract: State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.,,,,,, Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.,,,,,, In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets.


title: ESC: Dataset for Environmental Sound Classification

abstract: One of the obstacles in research activities concentrating on environmental sound classification is the scarcity of suitable and publicly available datasets. This paper tries to address that issue by presenting a new annotated collection of 2000 short clips comprising 50 classes of various common sound events, and an abundant unified compilation of 250000 unlabeled auditory excerpts extracted from recordings available through the Freesound project. The paper also provides an evaluation of human accuracy in classifying environmental sounds and compares it to the performance of selected baseline classifiers using features derived from mel-frequency cepstral coefficients and zero-crossing rate.


title: Joint Optimization for Chinese POS Tagging and Dependency Parsing

abstract: Dependency parsing has gained more and more interest in natural language processing in recent years due to its simplicity and general applicability for diverse languages. Previous work demonstrates that part-of-speech (POS) is an indispensable feature in dependency parsing since pure lexical features suffer from serious data sparseness problem. However, due to little morphological changes, Chinese POS tagging has proven to be much more challenging than morphology-richer languages such as English (94% vs. 97% on POS tagging accuracy). This leads to severe error propagation for Chinese dependency parsing. Our experiments show that parsing accuracy drops by about 6% when replacing manual POS tags of the input sentence with automatic ones generated by a state-of-the-art statistical POS tagger. To address this issue, this paper proposes a solution by jointly optimizing POS tagging and dependency parsing in a unique model. We propose for our joint models several dynamic programming based decoding algorithms which can incorporate rich POS tagging and syntactic features. Then we present an effective pruning strategy to reduce the search space of candidate POS tags, leading to significant improvement of parsing speed. Experimental results on two Chinese data sets, i.e. Penn Chinese Treebank 5.1 and Penn Chinese Treebank 7, demonstrate that our joint models significantly improve both the state-of-the-art tagging and parsing accuracies. Detailed analysis shows that the joint method can help resolve syntax-sensitive POS ambiguities {\ssrNN,\ssrVV}. In return, the POS tags become more reliable and helpful for parsing since the syntactic features are used in POS tagging. This is the fundamental reason for the performance improvement.


title: Fuzzy Logic Based Method for Improving Text Summarization

abstract: Text summarization can be classified into two approaches: extraction and abstraction. This paper focuses on extraction approach. The goal of text summarization based on extraction approach is sentence selection. One of the methods to obtain the suitable sentences is to assign some numerical measure of a sentence for the summary called sentence weighting and then select the best ones. The first step in summarization by extraction is the identification of important features. In our experiment, we used 125 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, removing stop word, and word stemming. Then, we used 8 important features and calculate their score for each sentence. We proposed text summarization based on fuzzy logic to improve the quality of the summary created by the general statistic method. We compared our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the best average precision, recall, and fmeasure for the summaries were obtained by fuzzy method.


title: Z-touch: an infrastructure for 3d gesture interaction in the proximity of tabletop surfaces

abstract: Sensing the depth (distance from the surface) of fingers/hands near a tabletop is very important. It allows us to use three-dimensional (3D) gesture interaction in multi-touch applications as we do in the real world. We introduce Z-touch, a multi-touch table that can sense the approximate postures of fingers or hands in the proximity of the tabletop's surface. Z-touch uses a vision-based posture sensing system. Multilayered infrared (IR) laser planes are synchronized with shutter signals from a high-speed camera, which captures each layer of the laser images. A depth map is obtained by using the captured image. Our prototype works at ~30 fps. Z-touch not only uses with the finger/hand contact points but also the angle of the hovering fingers. The interaction with the finger angles is unique and allows users to control multiple parameters by using a single finger. In this study, we introduce the principle of the method of finger detection and its applications (e.g., drawing, map zooming viewer, Bezier curve control).


title: Face Biometrics Under Spoofing Attacks: Vulnerabilities, Countermeasures, Open Issues, and Research Directions

abstract: Among tangible threats and vulnerabilities facing current biometric systems are spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access and advantages. Recently, an increasing attention has been given to this research problem. This can be attested by the growing number of articles and the various competitions that appear in major biometric forums. We have recently participated in a large consortium (TABULARASA) dealing with the vulnerabilities of existing biometric systems to spoofing attacks with the aim of assessing the impact of spoofing attacks, proposing new countermeasures, setting standards/protocols, and recording databases for the analysis of spoofing attacks to a wide range of biometrics including face, voice, gait, fingerprints, retina, iris, vein, electro-physiological signals (EEG and ECG). The goal of this position paper is to share the lessons learned about spoofing and anti-spoofing in face biometrics, and to highlight open issues and future directions.


Cognitive inhibition across psychopathologies


title: The lone-actor terrorist and the TRAP-18.

abstract: An open source sample of 111 lone actor terrorists from the United States and Europe were studied through the lens of the Terrorist Radicalization Assessment Protocol (TRAP-18). This investigative template consists of 8 proximal warning behaviors and 10 distal characteristics for active risk management or active monitoring, respectively, by national security threat assessors. Several aspects of criterion validity were tested in this known outcome sample. Seventy per cent of the terrorists were positive for at least half or more of the indicators. When the sample was divided into Islamic extremists, right wing extremists, and single issue terrorists, there were no significant differences across all eighteen indicators except for four. When the sample was divided according to successful vs. thwarted attackers, the successful attackers were significantly more fixated, creative and innovative, and failed to have a prior sexually intimate pair bond. They were significantly less likely to have displayed pathway warning behavior and be dependent on a virtual community of likeminded true believers. Effect sizes were small to medium (phi = 0.190-0.317). The TRAP-18 appears to have promise as an eventual structured professional judgment risk assessment instrument according to some of the individual terrorist content domains outlined by Monahan (2012, 2016).


title: Dense Subgraph Extraction with Application to Community Detection

abstract: This paper presents a method for identifying a set of dense subgraphs of a given sparse graph. Within the main applications of this “dense subgraph problem,” the dense subgraphs are interpreted as communities, as in, e.g., social networks. The problem of identifying dense subgraphs helps analyze graph structures and complex networks and it is known to be challenging. It bears some similarities with the problem of reordering/blocking matrices in sparse matrix techniques. We exploit this link and adapt the idea of recognizing matrix column similarities, in order to compute a partial clustering of the vertices in a graph, where each cluster represents a dense subgraph. In contrast to existing subgraph extraction techniques which are based on a complete clustering of the graph nodes, the proposed algorithm takes into account the fact that not every participating node in the network needs to belong to a community. Another advantage is that the method does not require to specify the number of clusters; this number is usually not known in advance and is difficult to estimate. The computational process is very efficient, and the effectiveness of the proposed method is demonstrated in a few real-life examples.


title: Genetic Algorithm Rule-Based Intrusion Detection System (GAIDS)

abstract: This study examines the detection of attacks or network intrusion by users referred to as hackers (whose aim is to gain illegal entry as well as access to a network system and resources. Network and data security has become a pertinent issue with the advent of the Internet; though the Internet comes with a lot of merits on its own. Traditional used methods for data security includes the use of passwords, cryptography to mention few. The approach considered here is Intrusion Detection System, which is a software, driver or device used to prevent an unauthorized or illegal access to data in a networked system. Most of the existing IDS are implemented via rule-based systems where new attacks are not detectable. This study thus, presents a genetic algorithm based approach (with its driver implementation), which employs a set of classification rule derived from network audit data and the support-confidence framework, utilized as fitness function to judge the quality of each rule. The software implementation is aimed at improving system security in networked settings allowing for confidentiality, integrity and availability of system resources.


title: Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers

abstract: Future planetary exploration missions will require wheeled mobile robots ("rovers") to traverse very rough terrain with limited human supervision. Wheel-terrain interaction plays a critical role in rough-terrain mobility. In this paper, an online estimation method that identifies key terrain parameters using on-board robot sensors is presented. These parameters can be used for traversability prediction or in a traction control algorithm to improve robot mobility and to plan safe action plans for autonomous systems. Terrain parameters are also valuable indicators of planetary surface soil composition. The algorithm relies on a simplified form of classical terramechanics equations and uses a linear-least squares method to compute terrain parameters in real time. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for various soil types.


title: An improved K-nearest-neighbor algorithm for text categorization

abstract: Text categorization is a significant tool to manage and organize the surging text data. Many text categorization algorithms have been explored in previous literatures, such as KNN, Naive Bayes and Support Vector Machine. KNN text categorization is an effective but less efficient classification method. In this paper, we propose an improved KNN algorithm for text categorization, which builds the classification model by combining constrained one pass clustering algorithm and KNN text categorization. Empirical results on three benchmark corpora show that our algorithm can reduce the text similarity computation substantially and outperform the-state-of-the-art KNN, Naive Bayes and Support Vector Machine classifiers. In addition, the classification model constructed by the proposed algorithm can be updated incrementally, and it has great scalability in many real-word applications.


title: Contactless and Partial 3D Fingerprint Recognition using Multi-view Deep Representation

abstract: Abstract Contactless 3D fingerprint identification has gained significant attentions in recent years as it can offer more hygienic, accurate and ubiquitous personal identification. Despite such advantages, contactless 3D imaging often results in partial 3D fingerprints as it requires relatively higher cooperation from users during the contactless 3D imaging. Such contactless 3D fingerprint images significantly degrade matching accuracy due to partial 3D fingerprint imaging. This paper proposes an end-to-end contactless 3D fingerprint representation learning model based on convolutional neural network (CNN). The proposed model includes one fully convolutional network for fingerprint segmentation and three Siamese networks to learn multi-view 3D fingerprint feature representation. Contactless partial 3D fingerprint identification is a more challenging problem due to its high degree of freedom during contactless 3D fingerprint acquisition and is also addressed by using proposed model. We therefore investigate multi-view 3D fingerprint recognition and partial 3D fingerprint using proposed approach. Comparative experimental results, presented in this paper using state-of-the-art 3D fingerprint recognition method, demonstrate the effectiveness of the proposed multi-view approach and illustrate a significant improvement of state-of-the-art 3D fingerprint recognition methods.


title: STCT: Sequentially Training Convolutional Networks for Visual Tracking

abstract: Due to the limited amount of training samples, finetuning pre-trained deep models online is prone to overfitting. In this paper, we propose a sequential training method for convolutional neural networks (CNNs) to effectively transfer pre-trained deep features for online applications. We regard a CNN as an ensemble with each channel of the output feature map as an individual base learner. Each base learner is trained using different loss criterions to reduce correlation and avoid over-training. To achieve the best ensemble online, all the base learners are sequentially sampled into the ensemble via important sampling. To further improve the robustness of each base learner, we propose to train the convolutional layers with random binary masks, which serves as a regularization to enforce each base learner to focus on different input features. The proposed online training method is applied to visual tracking problem by transferring deep features trained on massive annotated visual data and is shown to significantly improve tracking performance. Extensive experiments are conducted on two challenging benchmark data set and demonstrate that our tracking algorithm can outperform state-of-the-art methods with a considerable margin.


Hidden Softmax Sequence Model for Dialogue Structure Analysis


title: Triple Band Annular Ring Loaded Stacked Circular Patch Microstrip Antenna

abstract: In this paper a novel structure of annular ring loaded stacked circular patch microstrip antenna is theoretically analysed to observe various parameters such as return loss, input impedance, gain, directivity and radiation pattern. It is found that antenna possess three band of operation which signify the compactness and multiband operation of antenna. The antenna is resonating at three operating frequencies 1.720, 2.950, 3.060 GHz. The proposed theory is verified by simulation using Ansoft's HFSS and theoretical results are in good agreement with simulated results. The antenna is useful for multi-services operations such as WLAN, GSM, UMTS, and WiMAX services.


title: Subspace Alignment Based Domain Adaptation for RCNN Detector

abstract: In this paper, we propose subspace alignment based domain adaptation of the state of the art RCNN based object detector. The aim is to be able to achieve high quality object detection in novel, real world target scenarios without requiring labels from the target domain. While, unsupervised domain adaptation has been studied in the case of object classification, for object detection it has been relatively unexplored. In subspace based domain adaptation for objects, we need access to source and target subspaces for the bounding box features. The absence of supervision (labels and bounding boxes are absent) makes the task challenging. In this paper, we show that we can still adapt sub- spaces that are localized to the object by obtaining detections from the RCNN detector trained on source and applied on target. Then we form localized subspaces from the detections and show that subspace alignment based adaptation between these subspaces yields improved object detection. This evaluation is done by considering challenging real world datasets of PASCAL VOC as source and validation set of Microsoft COCO dataset as target for various categories.


title: NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets

abstract: This paper describes state-of-the-art statistical systems for automatic sentiment analysis of tweets. In a Semeval-2014 shared task (Task 9), our submissions obtained highest scores in the term-level sentiment classification subtask on both the 2013 and 2014 tweets test sets. In the message-level sentiment classification task, our submissions obtained highest scores on the LiveJournal blog posts test set, sarcastic tweets test set, and the 2013 SMS test set. These systems build on our SemEval-2013 sentiment analysis systems (Mohammad et al., 2013) which ranked first in both the termand message-level subtasks in 2013. Key improvements over the 2013 systems are in the handling of negation. We create separate tweet-specific sentiment lexicons for terms in affirmative contexts and in negated contexts.


title: Expanding and reprogramming the genetic code

abstract: A review of the recent developments in reprogramming the genetic code of cells and organisms to include non-canonical amino acids in precisely engineered proteins.


title: Semantic Object Parsing with Graph LSTM

abstract: By taking the semantic object parsing task as an exemplar application scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network, which is the generalization of LSTM from sequential data or multi-dimensional data to general graph-structured data. Particularly, instead of evenly and fixedly dividing an image to pixels or patches in existing multi-dimensional LSTM structures (e.g., Row, Grid and Diagonal LSTMs), we take each arbitrary-shaped superpixel as a semantically consistent node, and adaptively construct an undirected graph for each image, where the spatial relations of the superpixels are naturally used as edges. Constructed on such an adaptive graph topology, the Graph LSTM is more naturally aligned with the visual patterns in the image (e.g., object boundaries or appearance similarities) and provides a more economical information propagation route. Furthermore, for each optimization step over Graph LSTM, we propose to use a confidence-driven scheme to update the hidden and memory states of nodes progressively till all nodes are updated. In addition, for each node, the forgets gates are adaptively learned to capture different degrees of semantic correlation with neighboring nodes. Comprehensive evaluations on four diverse semantic object parsing datasets well demonstrate the significant superiority of our Graph LSTM over other state-of-the-art solutions.


title: Drawing graphs with non-uniform vertices

abstract: The vertices of most graphs that appear in real applications are non-uniform. They can be circles, ellipses, rectangles, or other geometric elements of varying shapes and sizes. Unfortunately, current force directed methods for laying out graphs are suitable mostly for graphs whose vertices are zero-sized and dimensionless points. It turns out that naively extending these methods to handle non-uniform vertices results in serious deficiencies in terms of output quality and performance. In this paper we try to remedy this situation by identifying the special characteristics and problematics of such graphs and offering several algorithms for tackling them. The algorithms can be viewed as carefully constructed extensions of force-directed methods, and their output quality and performance are similar.


title: An analytical model for software defined networking: A network calculus-based approach

abstract: Software defined networking (SDN) and OpenFlow as the outcome of recent research and development efforts provided unprecedented access into the forwarding plane of networking elements. This is achieved by decoupling the network control out of the forwarding devices. This separation paves the way for a more flexible and innovative networking. While SDN concept and OpenFlow find their ways into commercial deployments, performance evaluation of the SDN concept and its scalability, delay bounds, buffer sizing and similar performance metrics are not investigated in recent researches. In spite of usage of benchmark tools (like OFlops and Cbench), simulation studies and very few analytical models, there is a lack of analytical models to express the boundary condition of SDN deployment. In this work we present a model based on network calculus theory to describe the functionality of an SDN switch and controller. To the best of our knowledge, this is for the first time that network calculus framework is utilized to model the behavior of an SDN switch in terms of delay and queue length boundaries and the analysis of the buffer length of SDN controller and SDN switch. The presented model can be used for network designers and architects to get a quick view of the overall SDN network deployment performance and buffer sizing of SDN switches and controllers.


title: High-level visual features for underwater place recognition

abstract: This paper reports on a method to perform robust visual relocalization between temporally separated sets of underwater images gathered by a robot. The place recognition and relocalization problem is more challenging in the underwater environment mainly due to three factors: 1) changes in illumination; 2) long-term changes in the visual appearance of features because of phenomena like biofouling on man-made structures and growth or movement in natural features; and 3) low density of visually salient features for image matching. To address these challenges, a patch-based feature matching approach is proposed, which uses image segmentation and local intensity contrast to locate salient patches and HOG description to make correspondences between patches. Compared to traditional point-based features that are sensitive to dramatic appearance changes underwater, patch-based features are able to encode higher level information such as shape or structure which tends to persist across years in underwater environments. The algorithm is evaluated on real data, from multiple years, collected by a Hovering Autonomous Underwater Vehicle for ship hull inspection. Results in relocalization performance across missions from different years are compared to other traditional methods.


title: VoR-Tree: R-trees with Voronoi Diagrams for Efficient Processing of Spatial Nearest Neighbor Queries

abstract: A very important class of spatial queries consists of nearest-neighbor (NN) query and its variations. Many studies in the past decade utilize R-trees as their underlying index structures to address NN queries efficiently. The general approach is to use R-tree in two phases. First, R-tree's hierarchical structure is used to quickly arrive to the neighborhood of the result set. Second, the R-tree nodes intersecting with the local neighborhood (Search Region) of an initial answer are investigated to find all the members of the result set. While R-trees are very efficient for the first phase, they usually result in the unnecessary investigation of many nodes that none or only a small subset of their including points belongs to the actual result set. ::: ::: On the other hand, several recent studies showed that the Voronoi diagrams are extremely efficient in exploring an NN search region, while due to lack of an efficient access method, their arrival to this region is slow. In this paper, we propose a new index structure, termed VoR-Tree that incorporates Voronoi diagrams into R-tree, benefiting from the best of both worlds. The coarse granule rectangle nodes of R-tree enable us to get to the search region in logarithmic time while the fine granule polygons of Voronoi diagram allow us to efficiently tile or cover the region and find the result. Utilizing VoR-Tree, we propose efficient algorithms for various Nearest Neighbor queries, and show that our algorithms have better I/O complexity than their best competitors.


title: Introduction to rain and fog attenuation on automotive surround sensors

abstract: Surround sensors in automated vehicles use electromagnetic waves to perceive their local environment. Due to atmospheric phenomena such as rain and fog, the waves are attenuated and limit sensor performance. As already small errors in sensor data measurements could lead to severe accidents, involved sensors should be able to detect measurement errors caused by adverse weather conditions and react accordingly. Rain drops as well as fog drops absorb and scatter electromagnetic waves depending on different properties. Studying the interaction of electromagnetic waves and hydrometeors has a long history in meteorology and radio engineering. This paper gives an overview of the basic knowledge with focus on automotive applications. We focus on camera, lidar, and radar sensors and describe the attenuation in visible, near infrared, and millimeter frequency ranges. For simulating automotive sensors, different disturbance models must be developed for each sensor type. This work should serve as a quick introduction to the field of rain and fog attenuation, highlights potential problems, and discusses possible solutions.


title: COBANETS: A new paradigm for cognitive communications systems

abstract: In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this position paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS we propose to combine the learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared to past and current research efforts in this area, the technical approach depicted in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication networks.


title: A file comparison program

abstract: SUMMARY This paper presents a simple method for computing a shortest sequence of insertion and deletion commands that converts one given file to another. The method is particularly efficient when the difference between the two files is small compared to the files' lengths. In experiments performed on typical files, the program often ran four times faster than the UNIX diff command.


title: Keyword Search on External Memory Data Graphs

abstract: Keyword search on graph structured data has attracted a lot of attention in recent years. Graphs are a natural "lowest common denominator" representation which can combine relational, XML and HTML data. Responses to keyword queries are usually modeled as trees that connect nodes matching the keywords. ::: ::: In this paper we address the problem of keyword search on graphs that may be significantly larger than memory. We propose a graph representation technique that combines a condensed version of the graph (the "supernode graph") which is always memory resident, along with whatever parts of the detailed graph are in a cache, to form a multi-granular graph representation. We propose two alternative approaches which extend existing search algorithms to exploit multigranular graphs; both approaches attempt to minimize IO by directing search towards areas of the graph that are likely to give good results. We compare our algorithms with a virtual memory approach on several real data sets. Our experimental results show significant benefits in terms of reduction in IO due to our algorithms.


title: Combining Language and Vision with a Multimodal Skip-gram Model

abstract: We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based word representations by learning to predict linguistic contexts in text corpora. However, for a restricted set of words, the models are also exposed to visual representations of the objects they denote (extracted from natural images), and must predict linguistic and visual features jointly. The MMSKIP-GRAM models achieve good performance on a variety of semantic benchmarks. Moreover, since they propagate visual information to all words, we use them to improve image labeling and retrieval in the zero-shot setup, where the test concepts are never seen during model training. Finally, the MMSKIP-GRAM models discover intriguing visual properties of abstract words, paving the way to realistic implementations of embodied theories of meaning.


title: Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs

abstract: Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.


title: Automatic irrigation system using Arduino UNO

abstract: Now a days its a challenge to improve development of plant in respect of its growth and to reduce costs which leads to an innovative idea of using an automated irrigation system which will further help in better management of water and human resources. An automated irrigation system have been developed using sensors technology with Arduino to efficiently utilize water for irrigation purpose. The system has soil moisture sensor inserted into the soil of the plants and a water level sensor placed in a water container from where water will be pumped to plants for irrigation. An algorithm has been build out with threshold values of soil moisture sensor to control the water quantity in soil and also a water level sensor has been implemented to measure the water level in tank. This project requires Arduino board having inbuilt ATMega328 microcontroller. This project is need of the hour to convert manual irrigation into an automated irrigation which with the help of soil moisture sensor will detect dankness content of soil leading to turn ON/OFF of pumping motor. Human efforts can be reduced using this technique and increase saving of water by efficiently irrigating the plants. The design has been made with better resource management and low power consumption. This project brings into play a micro-controller which is of 8051 family, this programmable micro-controller collects the input signals converted into values of moisture in the soil via soil moisture sensors. As the microcontroller starts obtaining the signals, it creates an output that forces a relay for running the water pumping motor. An LCD screen is also linked to the micro-controller to show moisture conditions of the soil and water pump. The water level sensor is used to detect the level of tank so that tank contains efficient water to transfer into crops.


title: Task-Driven Feature Pooling for Image Classification

abstract: Feature pooling is an important strategy to achieve high performance in image classification. However, most pooling methods are unsupervised and heuristic. In this paper, we propose a novel task-driven pooling (TDP) model to directly learn the pooled representation from data in a discriminative manner. Different from the traditional methods (e.g., average and max pooling), TDP is an implicit pooling method which elegantly integrates the learning of representations into the given classification task. The optimization of TDP can equalize the similarities between the descriptors and the learned representation, and maximize the classification accuracy. TDP can be combined with the traditional BoW models (coding vectors) or the recent state-of-the-art CNN models (feature maps) to achieve a much better pooled representation. Furthermore, a self-training mechanism is used to generate the TDP representation for a new test image. A multi-task extension of TDP is also proposed to further improve the performance. Experiments on three databases (Flower-17, Indoor-67 and Caltech-101) well validate the effectiveness of our models.


title: A New Markov Model for Non-Saturated 802.11 Networks

abstract: In this work, we introduce a new Markov model for the distributed coordination function (DCF) protocol which accurately predicts the network throughput of 802.11 networks under realistic traffic load. Specifically, we provide an improved treatment of the post backoff, the probability of immediate transmission after DIFS, and the probability of having a new packet for transmission after the post backoff. A key feature of our model is the ability to predict the optimal network throughput and the network capacity (in terms of number of stations with acceptable QoS). Knowledge of the network capacity aids to network planning and QoS for multimedia applications. Another feature is that the model converges to a saturated model when the number of stations becomes large. Based on this feature, we introduce a new method to estimate the lower bound of network capacity.


title: Recurrent Highway Networks

abstract: Many sequential processing tasks require complex nonlinear transition functions from one step to the next. However, recurrent neural networks with 'deep' transition functions remain difficult to train, even when using Long Short-Term Memory (LSTM) networks. We introduce a novel theoretical analysis of recurrent networks based on Gersgorin's circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell. Based on this analysis we propose Recurrent Highway Networks, which extend the LSTM architecture to allow step-to-step transition depths larger than one. Several language modeling experiments demonstrate that the proposed architecture results in powerful and efficient models. On the Penn Treebank corpus, solely increasing the transition depth from 1 to 10 improves word-level perplexity from 90.6 to 65.4 using the same number of parameters. On the larger Wikipedia datasets for character prediction (text8 and enwik8), RHNs outperform all previous results and achieve an entropy of 1.27 bits per character.


title: SIT: A Lightweight Encryption Algorithm for Secure Internet of Things

abstract: The Internet of Things (IoT) being a promising technology of the future is expected to connect billions of devices. The increased number of communication is expected to generate mountains of data and the security of data can be a threat. The devices in the architecture are essentially smaller in size and low powered. Conventional encryption algorithms are generally computationally expensive due to their complexity and requires many rounds to encrypt, essentially wasting the constrained energy of the gadgets. Less complex algorithm, however, may compromise the desired integrity. In this paper we propose a lightweight encryption algorithm named as Secure IoT (SIT). It is a 64-bit block cipher and requires 64-bit key to encrypt the data. The architecture of the algorithm is a mixture of feistel and a uniform substitution-permutation network. Simulations result shows the algorithm provides substantial security in just five encryption rounds. The hardware implementation of the algorithm is done on a low cost 8-bit micro-controller and the results of code size, memory utilization and encryption/decryption execution cycles are compared with benchmark encryption algo-rithms. The MATLAB code for relevant simulations is available online at https://goo.gl/Uw7E0W.


title: Live Prefetching for Mobile Computation Offloading

abstract: Mobile computation offloading refers to techniques for offloading computation intensive tasks from mobile devices to the cloud so as to lengthen the formers’ battery lives and enrich their features. The conventional designs fetch (transfer) user-specific data from mobiles to the cloud prior to computing, called offline prefetching . However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching , which seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks, is proposed in this paper. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies enable real-time control of the prefetched-data sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based structure, selecting candidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design close-to-optimal prefetching policies to fast fading channels. Compared with fetching without prediction, live prefetching is shown theoretically to always achieve reduction on mobile energy consumption.


title: On the meaning of work: A theoretical integration and review

abstract: Abstract The meaning of work literature is the product of a long tradition of rich inquiry spanning many disciplines. Yet, the field lacks overarching structures that would facilitate greater integration, consistency, and understanding of this body of research. Current research has developed in ways that have created relatively independent domains of study that exist in silos organized around various sources of meaning and meaningfulness. In this paper, we review the meaning of work literature in order to propose new frameworks within which to classify existing work and to seed new work. Our review is organized by the major sources of the meaning of work on which extant research has focused, and by the mechanisms through which work is proposed to become meaningful. We analyze the evolution and current state of meaning of work research, identifying core patterns and assumptions that have defined research in this area to date, and offer a theoretical framework based on this body of research that illuminates the main pathways to meaningful work. Throughout, we outline several promising directions for future research that we hope will stimulate further generative inquiry in this rich area of study.


Etiology of the Psychopathic Serial Killer: An Analysis of Antisocial Personality Disorder, Psychopathy, and Serial Killer Personality and Crime Scene Characteristics


title: Optimum Design of a Wideband Two-Way Gysel Power Divider With Source to Load Impedance Matching

abstract: A two-way Gysel power divider is proposed as a modification of the common five-port Gysel divider, which has several advantages such as high power-handling capability and suitability for multi-staging to increase the frequency bandwidth. The even- and odd-mode technique is used to analyze its performance and the method of least squares is used to devise a procedure for its design and optimization. The source to load impedance matching is also incorporated into the design procedure and the input power is divided equally between the two outputs to provide an equal-split power divider. A method is proposed for the initial values of the widths and lengths of strips as an initial design of the two-way Gysel divider. The performance of the method of least squares design and optimization procedure is verified by the available simulation software packages and fabrication and measurements of two prototypes.


title: Psycholinguistic Approaches to SLA

abstract: These are exciting times for research into the psychological processes underlying second language acquisition (SLA). In the 1970s, SLA emerged as a field of inquiry in its own right (Brown 1980), and in the 1980s, a number of different approaches to central questions in the field began to develop in parallel and in relative isolation (McLaughlin and Harrington 1990). In the 1990s, however, these different approaches began to confront one another directly. Now we are entering a period reminiscent, in many ways, of the intellectually turbulent times following the Chomskyan revolution (Chomsky 1957; 1965). Now, as then, researchers are debating basic premises of a science of mind, language, and learning. Some might complain, not entirely without reason, that we are still debating the same issues after 30–40 years. However, there are now new conceptual and research tools available to test hypotheses in ways previously thought impossible. Because of this, many psychologists believe there will soon be significant advancement on some SLA issues that have resisted closure for decades. We outline some of these developments and explore where the field may be heading. More than ever, it appears possible that psychological theory and SLA theory are converging on solutions to common issues.


title: Fine-Grained Visual Classification of Aircraft

abstract: This paper introduces FGVC-Aircraft, a new dataset containing 10,000 images of aircraft spanning 100 aircraft models, organised in a three-level hierarchy. At the finer level, differences between models are often subtle but always visually measurable, making visual recognition challenging but possible. A benchmark is obtained by defining corresponding classification tasks and evaluation protocols, and baseline results are presented. The construction of this dataset was made possible by the work of aircraft enthusiasts, a strategy that can extend to the study of number of other object classes. Compared to the domains usually considered in fine-grained visual classification (FGVC), for example animals, aircraft are rigid and hence less deformable. They, however, present other interesting modes of variation, including purpose, size, designation, structure, historical style, and branding.


title: Design and Analysis of a Series-Fed Aperture-Coupled Antenna Array With Wideband and High-Efficient Characteristics

abstract: In this paper, a new wideband series-fed antenna array is proposed and analyzed. The antenna array structure consists of two antennas with two ports, two antennas with one port, and a wideband 180° coupler. A portion of power is propagated by the antenna with two ports, and the rest is propagated by the antenna with one port. A new wideband 180° coupler is designed in order to make the feed structure as compact as possible and to be integrated with the antenna array. Due to using elements with wideband structure, the overall antenna is wideband with measured bandwidth of 40% with reflection coefficient of 10 dB and 3 dB gain deviation from 3.85 to 5.75 GHz. The measured peak gain of 13.4 dBi has been achieved for the proposed $1\times 4$ antenna array. The measured results shows that the efficiency is better than 87% and side lobe level at the frequencies of 4.6 and 5.4 GHz are, respectively, nearly −14 and −12 dB.


title: Learning Multi-frame Visual Representation for Joint Detection and Tracking of Small Objects

abstract: Deep convolutional and recurrent neural networks have delivered significant advancements in object detection and tracking. However, current models handle detection and tracking through separate networks, and deep-learning-based joint detection and tracking has not yet been explored despite its potential benefits to both tasks. In this study, we present an integrated neural model called the Recurrent Correlational Network for joint detection and tracking, where the two tasks are performed over multi-frame representation learned through a single, trainable, and end-to-end network. Detection is benefited by the tracker because of the stabilized trajectories and tracking is aided by the enhanced representation afforded by the training of the detector. We show that recently developed convolutional long short-term memory networks can learn multi-frame, multi-task representation, which is useful for both tasks. In experiments, we tackled the detection of small flying objects, such as birds and unmanned aerial vehicles, that can be challenging for single-frame-based detectors. We found that there was consistent improvement in detection performance by the proposed model in comparison with deep single-frame detectors and currently used motion-based detectors.


title: Localising surface defects in random colour textures using multiscale texem analysis in image eigenchannels

abstract: A novel method is presented to detect defects in random colour textures which requires only a very few normal samples for unsupervised training. We decorrelate the colour image by generating three eigenchannels in each of which the surface texture image is divided into overlapping patches of various sizes. Then, a mixture model and EM is applied to reduce groupings of patches to a small number of textural exemplars, or texems. Localised defect detection is achieved by comparing the learned texems to patches in the unseen image eigenchannels.


title: Texture Classification by Wavelet Packet Signatures

abstract: This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twenty-five natural textures were classified without error by a simple two-layer network classifier. An analyzing function of large regularity (D/sub 20/) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (D/sub 6/) In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twenty-five textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture. >


title: Fine-Grained Age Estimation in the wild with Attention LSTM Networks

abstract: Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the global features, while neglecting the fine-grained features of age-sensitive areas. We propose a novel method based on our attention long short-term memory (AL) network for fine-grained age estimation in the wild, inspired by the fine-grained categories and the visual attention mechanism. This method combines the residual networks (ResNets) or the residual network of residual network (RoR) models with LSTM units to construct AL-ResNets or AL-RoR networks to extract local features of age-sensitive regions, which effectively improves the age estimation accuracy. First, a ResNets or a RoR model pretrained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images. To extract the local features of age-sensitive regions, the LSTM unit is then presented to obtain the coordinates of the age-sensitive region automatically. Finally, the age group classification is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and 15/16LAP datasets. By combining the global and the local features, we obtain our final prediction results. Experimental results illustrate the effectiveness and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the wild, where it achieves better state-of-the-art performance than all other convolutional neural network (CNN) methods on the Adience, MORPH Album 2, FG-NET and 15/16LAP datasets.


title: Distributed Machine Learning: Foundations, Trends, and Practices

abstract: In recent years, artificial intelligence has achieved great success in many important applications. Both novel machine learning algorithms (e.g., deep neural networks), and their distributed implementations play very critical roles in the success. In this tutorial, we will first review popular machine learning algorithms and the optimization techniques they use. Second, we will introduce widely used ways of parallelizing machine learning algorithms (including both data parallelism and model parallelism, both synchronous and asynchronous parallelization), and discuss their theoretical properties, strengths, and weakness. Third, we will present some recent works that try to improve standard parallelization mechanisms. Last, we will provide some practical examples of parallelizing given machine learning algorithms in online application (e.g. Recommendation and Ranking) by using popular distributed platforms, such as Spark MlLib, DMTK, and Tensorflow. By listening to this tutorial, the audience can form a clear knowledge framework about distributed machine learning, and gain some hands-on experiences on parallelizing a given machine learning algorithm using popular distributed systems.


title: Effects of domain on measures of semantic relatedness

abstract: Measures of semantic relatedness have been used in a variety of applications in information retrieval and language technology, such as measuring document similarity and cohesion of text. Definitions of such measures have ranged from using distance-based calculations over WordNet or other taxonomies to statistical distributional metrics over document collections such as Wikipedia or the Web. Existing measures do not explicitly consider the domain associations of terms when calculating relatedness: This article demonstrates that domain matters. We construct a data set of pairs of terms with associated domain information and extract pairs that are scored nearly identical by a sample of existing semantic-relatedness measures. We show that human judgments reliably score those pairs containing terms from the same domain as significantly more related than cross-domain pairs, even though the semantic-relatedness measures assign the pairs similar scores. We provide further evidence for this result using a machine learning setting by demonstrating that domain is an informative feature when learning a metric. We conclude that existing relatedness measures do not account for domain in the same way or to the same extent as do human judges.


title: Quick response barcode deblurring via doubly convolutional neural network

abstract: Various image preprocessing applications for two dimensional (2D) barcode involve reversing the degradation operations (e.g. deblurring). Most of the previously proposed deblurring approaches focus on the construction of suitable deconvolution models, which have shown significant performance at laboratory level. However, the model-based image deblurring solutions might not work well in practical scenarios. To deal with this problem, we propose a convolutional neural network (CNN) based framework to tackle the parameter-free situation for 2D barcode deblurring. The proposed solution leverages the deep learning technique to bridge the gap between traditional model-based methods and requirement of reversing the blurry 2D barcode images. Experiments on practically blurred quick response (QR) barcode images demonstrate that the proposed approach achieves the superior performance in comparison with state-of-the-art model-based image deblurring approaches.


title: Planar scan matching using incident angle

abstract: The main contribution of this paper is a planar scan matching algorithm that makes use of the incident angle of a scan point as a feature to enhance the robustness to large relative transformations, particularly in orientation. A new definition of the incident angle is introduced and its consistency with respect to relative transformation between scans is demonstrated. A method that uses the Radial distance and Incident Angle (RIA) for scan alignment, that can efficiently estimate the relative transformation between two scans, is proposed. This approach is designed as a preprocessor for fine scan matching and it is able to pre-align scans that have large angular but limited translational displacement. Scans collected with aggressive robot motion in two typical indoor environments as well as benchmark datasets, are used to demonstrate the effectiveness of RIA when compared with a range of scan matching algorithms reported in the literature.


abstract: One-shot learning of generative speech concepts Brenden M. Lake* Chia-ying Lee* James R. Glass Joshua B. Tenenbaum Brain and Cognitive Sciences MIT CSAIL MIT CSAIL MIT Brain and Cognitive Sciences MIT Abstract 2007). Related computational work has investigated other factors that contribute to learning word meaning, including learning-to-learn which features are important (Colunga & Smith, 2005; Kemp et al., 2007) and cross-situational word learning (Smith & Yu, 2008; Frank, Goodman, & Tenen- baum, 2009). But by any account, the acquisition of mean- ing is only possible because the child can also learn the spo- ken word as a category, mapping all instances (and exclud- ing non-instances) of a word like “elephant” to the same phonological representation, regardless of speaker identify and other sources of acoustic variability. This is the focus of the current paper. Previous work has shown that chil- dren can do one-shot spoken word learning (Carey & Bartlett, 1978). When children (ages 3-4) were asked to bring over a “chromium” colored object, they seemed to flag the sound as a new word; some even later produced their own approxima- tion of the word “chromium.” Furthermore, acquiring new spoken words remains an important problem well into adult- hood whether its learning a second language, a new name, or a new vocabulary word. The goal of our work is twofold: to develop one-shot learn- ing tasks that can compare people and models side-by-side, and to develop a computational model that performs well on these tasks. Since the tasks must contain novel words for both people and algorithms, we tested English speakers on their ability to learn Japanese words. This language pairing also offers an interesting test case for learning-to-learn through the transfer of phonetic structure, since the Japanese analogs to English phonemes fall roughly within a subset of English phonemes (Ohata, 2004). Can the recent progress on models of one-shot learning be leveraged for learning new spoken words from raw speech? How could a generative model of a word be learned from just one example? Recent behavioral and computational work suggests that compositionality, combined with Hierarchical Bayesian modeling, can be a powerful way to build a “gen- erative model for generative models” that supports one-shot learning (Lake, Salakhutdinov, & Tenenbaum, 2012; Lake et al., 2013). This idea was applied to the one-shot learning of handwritten characters, a similarly high-dimensional do- main of natural concepts, using an “analysis-by-synthesis” approach. Given a raw image of a novel character, the model learns to represent it by a latent dynamic causal process, com- posed of pen strokes and their spatial relations (Fig. 1a). The sharing of stochastic motor primitives across concepts (Fig. 1a-i) provides a means of synthesizing new generative mod- els out of pieces of existing ones (Fig. 1a-iii). Compositional generative models are well-suited for the problem of spoken word acquisition, as they relate to classic One-shot learning – the human ability to learn a new concept from just one or a few examples – poses a challenge to tradi- tional learning algorithms, although approaches based on Hi- erarchical Bayesian models and compositional representations have been making headway. This paper investigates how chil- dren and adults readily learn the spoken form of new words from one example – recognizing arbitrary instances of a novel phonological sequence, and excluding non-instances, regard- less of speaker identity and acoustic variability. This is an es- sential step on the way to learning a word’s meaning and learn- ing to use it, and we develop a Hierarchical Bayesian acoustic model that can learn spoken words from one example, utiliz- ing compositions of phoneme-like units that are the product of unsupervised learning. We compare people and computa- tional models on one-shot classification and generation tasks with novel Japanese words, finding that the learned units play an important role in achieving good performance. Keywords: one-shot learning; speech recognition; category learning; exemplar generation Introduction People can learn a new concept from just one or a few ex- amples, making meaningful generalizations that go far be- yond the observed data. Replicating this ability in machines has been challenging, since standard learning algorithms re- quire tens, hundreds, or thousands of examples before reach- ing a high level of classification performance. Nonetheless, recent interest from cognitive science and machine learning has advanced our computational understanding of “one-shot learning,” and several key themes have emerged. Proba- bilistic generative models can predict how people general- ize from just one or a few examples, as shown for data ly- ing in a low-dimensional space (Shepard, 1987; Tenenbaum & Griffiths, 2001). Another theme has developed around learning-to-learn, the idea that one-shot learning itself de- velops from previous learning with related concepts, and Hi- erarchical Bayesian (HB) models can learn-to-learn by high- lighting the dimensions or features that are most important for generalization (Fei-Fei, Fergus, & Perona, 2006; Kemp, Perfors, & Tenenbaum, 2007; Salakhutdinov, Tenenbaum, & Torralba, 2012). In this paper, we study the problem of learning new spoken words, an essential ingredient for language development. By one estimate, children learn an average of ten new words per day from the age of one to the end of high school (Bloom, 2000). For learning to proceed at such an astounding rate, children must be learning new words from very little data. Previous computational work has focused on the problem of learning the meaning of words from a few examples; for in- stance, upon hearing the word “elephant” paired with an ex- emplar, the child must decide which objects belong to the set of “elephants” and which do not (e.g., Xu & Tenenbaum, * The first two authors contributed equally to this work.


title: A general perspective of Big Data: applications, tools, challenges and trends

abstract: Big Data has become a very popular term. It refers to the enormous amount of structured, semi-structured and unstructured data that are exponentially generated by high-performance applications in many domains: biochemistry, genetics, molecular biology, physics, astronomy, business, to mention a few. Since the literature of Big Data has increased significantly in recent years, it becomes necessary to develop an overview of the state-of-the-art in Big Data. This paper aims to provide a comprehensive review of Big Data literature of the last 4 years, to identify the main challenges, areas of application, tools and emergent trends of Big Data. To meet this objective, we have analyzed and classified 457 papers concerning Big Data. This review gives relevant information to practitioners and researchers about the main trends in research and application of Big Data in different technical domains, as well as a reference overview of Big Data tools.


title: Computer aided detection of anemia-like pallor

abstract: Paleness or pallor is a manifestation of blood loss or low hemoglobin concentrations in the human blood that can be caused by pathologies such as anemia. This work presents the first automated screening system that utilizes pallor site images, segments, and extracts color and intensity-based features for multi-class classification of patients with high pallor due to anemia-like pathologies, normal patients and patients with other abnormalities. This work analyzes the pallor sites of conjunctiva and tongue for anemia screening purposes. First, for the eye pallor site images, the sclera and conjunctiva regions are automatically segmented for regions of interest. Similarly, for the tongue pallor site images, the inner and outer tongue regions are segmented. Then, color-plane based feature extraction is performed followed by machine learning algorithms for feature reduction and image level classification for anemia. In this work, a suite of classification algorithms image-level classifications for normal (class 0), pallor (class 1) and other abnormalities (class 2). The proposed method achieves 86% accuracy, 85% precision and 67% recall in eye pallor site images and 98.2% accuracy and precision with 100% recall in tongue pallor site images for classification of images with pallor. The proposed pallor screening system can be further fine-tuned to detect the severity of anemia-like pathologies using controlled set of local images that can then be used for future benchmarking purposes.


title: Recursive neural networks for processing graphs with labelled edges : theory and applications

abstract: In this paper, we introduce a new recursive neural network model able to process directed acyclic graphs with labelled edges. The model uses a state transition function which considers the edge labels and is independent both from the number and the order of the children of each node. The computational capabilities of the new recursive architecture are assessed. Moreover, in order to test the proposed architecture on a practical challenging application, the problem of object detection in images is also addressed. In fact, the localization of target objects is a preliminary step in any recognition system. The proposed technique is general and can be applied in different detection systems, since it does not exploit any a priori knowledge on the particular problem. Some experiments on face detection, carried out on scenes acquired by an indoor camera, are reported, showing very promising results.


title: Continuous Patient Monitoring With a Patient Centric Agent: A Block Architecture

abstract: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.


title: A model-free mapless navigation method for mobile robot using reinforcement learning

abstract: In this paper, a simple and efficient method is proposed using one of the basic reinforcement learning tool Q-learning. This method is to achieve the aim of mobile robot maples navigation with collision-free. Thus, an end-to-end navigation model is built, which uses lidar sensor massage as input and moving command of mobile robot as output, so as to simplify the process of environmental perception and decision making on action. Through simulation and experiments, its effectiveness is proved. After trained by this method, mobile robot can safely reach the navigation target in an unknown environment without any prior demonstration. In addition, an extensive quantitative and qualitative evaluation of this method is presented by the comparison with traditional path planning method based on foregone global environment map.


title: Dialogue Modeling Via Hash Functions

abstract: We propose a novel dialogue modeling framework which uses binary hashcodes as compressed text representations, allowing for efficient similarity search, and a novel lower bound on mutual information between the hashcodes of the two dialog agents, which serves as a model selection criterion for optimizing those representations towards better alignment between the dialog participants and higher predictability of one response from another, facilitating better dialog generation. Empirical evaluation on several datasets, from depression therapy sessions to Larry King TV show interviews and Twitter data, demonstrate that our hashing-based approach is competitive with state-of-art neural network based dialogue generation systems, often significantly outperforming them in terms of response quality and computational efficiency, especially on relatively small datasets.


title: Visual servoing of UAV using cuboid model with simultaneous tracking of multiple planar faces

abstract: Pose estimation is a key component for robot navigation. An Unmanned Aerial Vehicle (UAV) that is instructed to reach certain location requires a way of measuring its pose. This article presents a method for UAV visual servoing that uses the 3D pose of the drone as controller feedback. A remote monocular camera observes the tracked UAV while it moves and rotates in a 3D space. Pose is obtained from a 3D tracking process based on a cuboid model. In particular, a simultaneous face tracking strategy where 3D pose estimations from different faces are combined is introduced. Face combination was validated using a robotic arm with a cuboid at the final joint. For the UAV control, hover and path following tasks were tested. Results show that the proposed method correctly handles changes in pose, even though no face is always visible. Also, the UAV maintained a low speed in order to satisfy the small inter-frame displacement constraint imposed by visual tracking algorithm.


title: Business process modelling: Review and framework

abstract: Abstract A business process is the combination of a set of activities within an enterprise with a structure describing their logical order and dependence whose objective is to produce a desired result. Business process modelling enables a common understanding and analysis of a business process. A process model can provide a comprehensive understanding of a process. An enterprise can be analysed and integrated through its business processes. Hence the importance of correctly modelling its business processes. Using the right model involves taking into account the purpose of the analysis and, knowledge of the available process modelling techniques and tools. The number of references on business modelling is huge, thus making it very time consuming to get an overview and understand many of the concepts and vocabulary involved. The primary concern of this paper is to make that job easier, i.e. review business process modelling literature and describe the main process modelling techniques. Also a framework for classifying business process-modelling techniques according to their purpose is proposed and discussed.