title: Multilabel classification with meta-level features in a learning-to-rank framework

abstract: Effective learning in multi-label classification (MLC) requires an appropriate level of abstraction for representing the relationship between each instance and multiple categories. Current MLC methods have focused on learning-to-map from instances to categories in a relatively low-level feature space, such as individual words. The fine-grained features in such a space may not be sufficiently expressive for learning to rank categories, which is essential in multi-label classification. This paper presents an alternative solution by transforming the conventional representation of instances and categories into meta-level features, and by leveraging successful learning-to-rank retrieval algorithms over this feature space. Controlled experiments on six benchmark datasets using eight evaluation metrics show strong evidence for the effectiveness of the proposed approach, which significantly outperformed other state-of-the-art methods such as Rank-SVM, ML-kNN (Multi-label kNN), IBLR-ML (Instance-based logistic regression for multi-label classification) on most of the datasets. Thorough analyses are also provided for separating the factors responsible for the improved performance.


title: A 6.3 mW high-SNR frame-rate scalable touch screen panel readout IC with column-parallel Σ-Δ ADC structure for mobile devices

abstract: This paper presents a capacitive touch screen panel (TSP) readout IC that provides highly enhanced signal-to-noise ratio (SNR) and scalable frame rate. To increase touch sensitivity and noise immunity, the proposed readout IC employs a differential sensing method. A column-parallel incremental sigma-delta (Σ-Δ) ADC structure is adopted to obtain high frame rate and its scalability. The readout IC is fabricated in 0.18-μm CMOS process and its active area is 2.2 mm2. The measured results show that the readout IC achieves 60-dB SNR and 200-Hz frame rate with 12 × 8 mutual capacitive TSP. The frame rate and SNR can be adjusted from 50 Hz to 1.6 kHz and from 50 dB to 67 dB, respectively. The power consumption is 6.3 mW with 3.3-V supply.


title: The Intricacies of Induced Lactation for Same-Sex Mothers of an Adopted Child

abstract: The definition of a modern family is changing. In this case study, we describe the breastfeeding experience of a child receiving human milk from all 3 of his mothers: his 2 adoptive mothers, who induced lactation to nurse him, and his birth mother, who shared in his early feeding during the open adoption process and continued to pump and send milk to him for several months. We review the lactation protocol used by his adoptive mothers and the unique difficulties inherent in this multi-mother family dynamic. Both adoptive mothers successfully induced moderate milk production using a combination of hormonal birth control, domperidone, herbal supplements, and a schedule of breast pumping. However, because of the increased complexity of the immediate postpartum period and concerns with defining parental roles in a same-sex marriage, maintenance of milk production was difficult.


title: Primary and Secondary Yield Losses Caused by Pests and Diseases: Assessment and Modeling in Coffee

abstract: The assessment of crop yield losses is needed for the improvement of production systems that contribute to the incomes of rural families and food security worldwide. However, efforts to quantify yield losses and identify their causes are still limited, especially for perennial crops. Our objectives were to quantify primary yield losses (incurred in the current year of production) and secondary yield losses (resulting from negative impacts of the previous year) of coffee due to pests and diseases, and to identify the most important predictors of coffee yields and yield losses. We established an experimental coffee parcel with full-sun exposure that consisted of six treatments, which were defined as different sequences of pesticide applications. The trial lasted three years (2013–2015) and yield components, dead productive branches, and foliar pests and diseases were assessed as predictors of yield. First, we calculated yield losses by comparing actual yields of specific treatments with the estimated attainable yield obtained in plots which always had chemical protection. Second, we used structural equation modeling to identify the most important predictors. Results showed that pests and diseases led to high primary yield losses (26%) and even higher secondary yield losses (38%). We identified the fruiting nodes and the dead productive branches as the most important and useful predictors of yields and yield losses. These predictors could be added in existing mechanistic models of coffee, or can be used to develop new linear mixed models to estimate yield losses. Estimated yield losses can then be related to production factors to identify corrective actions that farmers can implement to reduce losses. The experimental and modeling approaches of this study could also be applied in other perennial crops to assess yield losses.


title: Context-aware Mood Mining

abstract: Mood and emotion although used interchangeably, mood is significantly different from emotion in many aspects. It reflects the internal state of a person compared to a rather transient affective state shown by emotional expressions. Mood inference from voice can provide better performance, however, hardly any study exists that predicts mood from voice. In this paper, we propose a solution to this problem and present our work in progress.


title: In Situ Melanoma of the Nail Unit in Children: Report of Two Cases in Fair-Skinned Caucasian Children

abstract: Nail melanoma in children is rarely reported in the literature, and all of the published cases were diagnosed in dark-skinned phototypes or in Asians. We report two cases of in situ nail matrix melanoma presenting as longitudinal melanonychia (LM) in fair-skinned children of Italian origin. Nail plate dermatoscopy revealed a brown background with lines of irregular color, spacing, and thickness in both cases. Histopathology of the excised lesions showed melanoma in situ. Clinical, dermatoscopic, and pathological criteria that permit clear differentiation of benign melanocytic activation or proliferation from nail matrix melanoma are not established for children. The presence of a pigmented band of a single nail in a child usually represents a problem for clinicians, because the clinical and dermatoscopic features that are considered possible indicators of nail unit melanoma in adults are frequently observed in benign melanocytic hyperplasia and nevi in children. There is therefore the need to find parameters useful for clinical and dermatoscopic diagnosis in childhood nail pigmentation and to reach a consensus on management of children with a band of LM.


title: Biodegradation of bioplastics in natural environments

abstract: The extensive production of conventional plastics and their use in different commercial applications poses a significant threat to both the fossil fuels sources and the environment. Alternatives called bioplastics evolved during development of renewable resources. Utilizing renewable resources like agricultural wastes (instead of petroleum sources) and their biodegradability in different environments enabled these polymers to be more easily acceptable than the conventional plastics. The biodegradability of bioplastics is highly affected by their physical and chemical structure. On the other hand, the environment in which they are located, plays a crucial role in their biodegradation. This review highlights the recent findings attributed to the biodegradation of bioplastics in various environments, environmental conditions, degree of biodegradation, including the identified bioplastic-degrading microorganisms from different microbial communities.


title: Hierarchical Embeddings for Hypernymy Detection and Directionality

abstract: We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality. While previous embeddings have shown limitations on prototypical hypernyms, HyperVec represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym$-$hyponym distributional hierarchy. Moreover, our model is able to generalize over unseen hypernymy pairs, when using only small sets of training data, and by mapping to other languages. Results on benchmark datasets show that HyperVec outperforms both state$-$of$-$the$-$art unsupervised measures and embedding models on hypernymy detection and directionality, and on predicting graded lexical entailment.


title: AutoTest: A Tool for Automatic Test Case Generation in Spreadsheets

abstract: In this paper we present a system that helps users test their spreadsheets using automatically generated test cases. The system generates the test cases by backward propagation and solution of constraints on cell values. These constraints are obtained from the formula of the cell that is being tested when we try to execute all feasible DU associations within the formula. AutoTest generates test cases that execute all feasible DU pairs. If infeasible DU associations are present in the spreadsheet, the system is capable of detecting and reporting all of these to the user. We also present a comparative evaluation of our approach against the "Help Me Test" mechanism in Forms/3 and show that our approach is faster and produces test suites that give better DU coverage.


title: Automated conflict detection between medical care pathways

abstract: Clinical guidelines specify sequences of steps (care pathways) to treat patients with single conditions. Increasingly, many patients exhibit “multimorbidity,” several chronic conditions needing concurrent treatment. However, applying multiple guidelines in parallel can lead to conflicts, eg, between prescribed drugs, lifestyle intervention recommendations, or treatment schedules. In computer science, process languages used to design and reason about software development and business process management are similar to clinical pathways. Using formal model transformation, composition and analysis methods, models can be combined and conflicts detected and resolved. We propose BPMN+V, a data-driven formal model for clinical care pathways, as an extension of Business Process Model and Notation. We describe a method for conflict detection using a transformation of BPMN+V to Coloured Petri Nets and a state-space method for detection of conflict in composed models. We present results from a case study, showing that common conflicts are successfully detected, and propose extension to a complete framework for efficiently recommending resolutions to medical conflicts in composed care pathway models.


title: Improving Performance in Neural Networks Using a Boosting Algorithm

abstract: A boosting algorithm converts a learning machine with error rate less than 50% to one with an arbitrarily low error rate. However, the algorithm discussed here depends on having a large supply of independent training samples. We show how to circumvent this problem and generate an ensemble of learning machines whose performance in optical character recognition problems is dramatically improved over that of a single network. We report the effect of boosting on four databases (all handwritten) consisting of 12,000 digits from segmented ZIP codes from the United State Postal Service (USPS) and the following from the National Institute of Standards and Testing (NIST): 220,000 digits, 45,000 upper case alphas, and 45,000 lower case alphas. We use two performance measures: the raw error rate (no rejects) and the reject rate required to achieve a 1% error rate on the patterns not rejected. Boosting improved performance in some cases by a factor of three.


title: Operant conditioning in invertebrates

abstract: Learning to anticipate future events on the basis of past experience with the consequences of one's own behavior (operant conditioning) is a simple form of learning that humans share with most other animals, including invertebrates. Three model organisms have recently made significant contributions towards a mechanistic model of operant conditioning, because of their special technical advantages. Research using the fruit fly Drosophila melanogaster implicated the ignorant gene in operant conditioning in the heat-box, research on the sea slug Aplysia californica contributed a cellular mechanism of behavior selection at a convergence point of operant behavior and reward, and research on the pond snail Lymnaea stagnalis elucidated the role of a behavior-initiating neuron in operant conditioning. These insights demonstrate the usefulness of a variety of invertebrate model systems to complement and stimulate research in vertebrates.


title: Characterization of the equivalence of robustification and regularization in linear, median, and matrix regression

abstract: Sparsity is a key driver in modern statistical problems, from linear regression via the Lasso to matrix regression with nuclear norm penalties in matrix completion and beyond. In stark contrast to sparsity motivations for such problems, it is known in the field of robust optimization that a variety of vector regression problems, such as Lasso which appears as a loss function plus a regularization penalty, can arise by simply immunizing a nominal problem (with only a loss function) to uncertainty in the data. Such a robustification offers an explanation for why some linear regression methods perform well in the face of noise, even when these methods do not produce reliably sparse solutions. In this paper we deepen and extend the understanding of the connection between robustification and regularization in regression problems. Specifically, (a) in the context of linear regression, we characterize under which conditions on the model of uncertainty used and on the loss function penalties robustification and regularization are equivalent; (b) we show how to tractably robustify median regression problems; and (c) we extend the characterization of robustification and regularization to matrix regression problems (matrix completion and Principal Component Analysis).


title: General Graph Data De-Anonymization: From Mobility Traces to Social Networks

abstract: When people utilize social applications and services, their privacy suffers a potential serious threat. In this article, we present a novel, robust, and effective de-anonymization attack to mobility trace data and social data. First, we design a Unified Similarity (US) measurement, which takes account of local and global structural characteristics of data, information obtained from auxiliary data, and knowledge inherited from ongoing de-anonymization results. By analyzing the measurement on real datasets, we find that some data can potentially be de-anonymized accurately and the other can be de-anonymized in a coarse granularity. Utilizing this property, we present a US-based De-Anonymization (DA) framework, which iteratively de-anonymizes data with accuracy guarantee. Then, to de-anonymize large-scale data without knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework. By smartly working on two core matching subgraphs, ADA achieves high de-anonymization accuracy and reduces computational overhead. Finally, we examine the presented de-anonymization attack on three well-known mobility traces: St Andrews, Infocom06, and Smallblue, and three social datasets: ArnetMiner, Google+, and Facebook. The experimental results demonstrate that the presented de-anonymization framework is very effective and robust to noise. The source code and employed datasets are now publicly available at SecGraph [2015].


Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management


title: Model checking and abstraction

abstract: We describe a method for using abstraction to reduce the complexity of temporal-logic model checking. Using techniques similar to those involved in abstract interpretation, we construct an abstract model of a program without ever examining the corresponding unabstracted model. We show how this abstract model can be used to verify properties of the original program. We have implemented a system based on these techniques, and we demonstrate their practicality using a number of examples, including a program representing a pipelined ALU circuit with over 10 1300 states.


title: Understanding online social network usage from a network perspective

abstract: Online Social Networks (OSNs) have already attracted more than half a billion users. However, our understanding of which OSN features attract and keep the attention of these users is poor. Studies thus far have relied on surveys or interviews of OSN users or focused on static properties, e. g., the friendship graph, gathered via sampled crawls. In this paper, we study how users actually interact with OSNs by extracting clickstreams from passively monitored network traffic. Our characterization of user interactions within the OSN for four different OSNs (Facebook, LinkedIn, Hi5, and StudiVZ) focuses on feature popularity, session characteristics, and the dynamics within OSN sessions. We find, for example, that users commonly spend more than half an hour interacting with the OSNs while the byte contributions per OSN session are relatively small.


title: Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples

abstract: This paper provides methodology for fully automated model-based image segmentation. All information necessary to perform image segmentation is automatically derived from a training set that is presented in a form of segmentation examples. The training set is used to construct two models representing the objects-shape model and border appearance model. A two-step approach to image segmentation is reported. In the first step, an approximate location of the object of interest is determined. In the second step, accurate border segmentation is performed. The shape-variant Hough transform method was developed that provides robust object localization automatically. It finds objects of arbitrary shape, rotation, or scaling and can handle object variability. The border appearance model was developed to automatically design cost functions that can be used in the segmentation criteria of edge-based segmentation methods. The authors' method was tested in five different segmentation tasks that included 489 objects to be segmented. The final segmentation was compared to manually defined borders with good results [rms errors in pixels: 1.2 (cerebellum), 1.1 (corpus callosum), 1.5 (vertebrae), 1.4 (epicardial), and 1.6 (endocardial) borders]. Two major problems of the state-of-the-art edge-based image segmentation algorithms were addressed: strong dependency on a close-to-target initialization, and necessity for manual redesign of segmentation criteria whenever new segmentation problem is encountered.


title: Natively Unfolded Nucleoporins Gate Protein Diffusion across the Nuclear Pore Complex

abstract: Summary Nuclear pore complexes (NPCs) form aqueous conduits in the nuclear envelope and gate the diffusion of large proteins between the cytoplasm and nucleoplasm. NPC proteins (nucleoporins) that contain phenylalanine-glycine motifs in filamentous, natively unfolded domains (FG domains) line the diffusion conduit of the NPC, but their role in the size-selective barrier is unclear. We show that deletion of individual FG domains in yeast relaxes the NPC permeability barrier. At the molecular level, the FG domains of five nucleoporins anchored at the NPC center form a cohesive meshwork of filaments through hydrophobic interactions, which involve phenylalanines in FG motifs and are dispersed by aliphatic alcohols. In contrast, the FG domains of four peripherally anchored nucleoporins are generally noncohesive. The results support a two-gate model of NPC architecture featuring a central diffusion gate formed by a meshwork of cohesive FG nucleoporin filaments and a peripheral gate formed by repulsive FG nucleoporin filaments.


title: Computational Models to Assign Biopharmaceutics Drug Disposition Classification from Molecular Structure

abstract: Purpose ::: We applied in silico methods to automatically classify drugs according to the Biopharmaceutics Drug Disposition Classification System (BDDCS).


Learning and evaluating Boltzmann Machines


title: Empirical comparison of ab initio repeat finding programs

abstract: Identification of dispersed repetitive elements can be difficult, especially when elements share little or no homology with previously described repeats. Consequently, a growing number of computational tools have been designed to identify repetitive elements in an ab initio manner, i.e. without using prior sequence data. Here we present the results of side-by-side evaluations of six of the most widely used ab initio repeat finding programs. Using sequence from rice chromosome 12, tools were compared with regard to time requirements, ability to find known repeats, utility in identifying potential novel repeats, number and types of repeat elements recognized and compactness of family descriptions. The study reveals profound differences in the utility of the tools with some identifying virtually their entire substrate as repetitive, others making reasonable estimates of repetition, and some missing almost all repeats. Of note, even when tools recognized similar numbers of repeats they often showed marked differences in the nature and number of repeat families identified. Within the context of this comparative study, ReAS and RepeatScout showed the most promise in analysis of sequence reads and assembled genomic regions, respectively. Our results should help biologists identify the program(s), if any, that is best suited for their needs.


title: Ray Casting of Trimmed NURBS Surfaces on the GPU

abstract: We propose a conceptual extension of the standard triangle-based graphics pipeline by an additional intersection stage. The corresponding intersection program performs ray-object intersection tests for each fragment of an object's bounding volume. The resulting hit fragments are transfered to the fragment shading stage for computing the illumination and performing further fragment operations. Our approach combines the efficiency of the standard hardware graphics pipeline with the advantages of ray casting such as pixel accurate rendering and exact normals as well as early ray termination. This concept serves as a framework for the implementation of an interactive ray casting system for trimmed NURBS surfaces. We show how to realize an iterative ray-object intersection method for NURBS primitives as an intersection program. Convex hulls are used as tight bounding volumes for the NURBS patches to minimize the number of fragments to be processed. In addition, we developed a trimming algorithm for the GPU that works with an exact representation of the trimming curves. First experiments with our implementation show that real-time rendering of medium complex scenes is possible on current graphics hardware.


The school-level factors associated with internet addiction among adolescents: A Cross-Sectional study in Bangladesh


title: Learning Where to Fixate on Foveated Images

abstract: Foveation, the ability to sequentially acquire high-acuity regions of a scene viewed initially at low-acuity, is a key property of biological vision systems. In a computer vision system, foveation is also desired to increase data efficiency and derive task-relevant features. Yet, most existing deep learning models lack the ability to foveate. In this paper, we propose a deep reinforcement learning-based foveation model, DRIFT, and apply it to challenging fine-grained classification tasks. Training of DRIFT requires only image-level category labels and encourages fixations to contain discriminative information while maintaining data efficiency. Specifically, we formulate foveation as a sequential decision-making process and train a foveation actor network with a novel Deep Deterministic Policy Gradient by Conditioned Critic and Coaching (DDPGC3) algorithm. In addition, we propose to shape the reward to provide informative feedback after each fixation to better guide the RL training. We demonstrate the effectiveness of our method on five fine-grained classification benchmark datasets, and show that the proposed approach achieves state-of-the-art performance using an order-of-magnitude fewer pixels.


Load Disaggregation of Industrial Machinery Power Consumption Monitoring Using Factorial Hidden Markov Models


title: Current Perspectives on Biotechnological Cannabinoid Production in Plants

abstract: The plant Cannabis sativa contains a number of psychoactive chemical compounds, the cannabinoids, which possess a significant pharmaceutical potential. Recently, the usage of Cannabis for medicinal purposes was legalized in many countries. Thus, the study on the influence of different cannabinoids in combination with other Cannabis-derived compounds with respect to the treatment of various diseases becomes increasingly important. Besides the production of distinct cannabinoids in a heterologous host, like tobacco or yeast, transgenic Cannabis plants would be a suitable alternative to modify and therefore optimize the cannabinoid profile. This perspective highlights the current efforts on Cannabis cell culture systems, in vitro propagation, and transformation of the plant and reveals the resulting opportunities concerning biotechnological production of cannabinoids. Furthermore, alternative platform organisms for the heterologous production of cannabinoids, like tobacco, are considered and evaluated.


title: Critical review of the e-loyalty literature: a purchase-centred framework

abstract: Over the last few years, the concept of online loyalty has been examined extensively in the literature, and it remains a topic of constant inquiry for both academics and marketing managers. The tremendous development of the Internet for both marketing and e-commerce settings, in conjunction with the growing desire of consumers to purchase online, has promoted two main outcomes: (a) increasing numbers of Business-to-Customer companies running businesses online and (b) the development of a variety of different e-loyalty research models. However, current research lacks a systematic review of the literature that provides a general conceptual framework on e-loyalty, which would help managers understand their customers better, take advantage of industry-related factors, and improve their service quality. The present study is an attempt to critically synthesize results from multiple empirical studies on e-loyalty. Our findings illustrate that 62 instruments for measuring e-loyalty---with two or more items--are currently in use, influenced predominantly by Zeithaml et al. (J. Marketing 60(2):31---46, 1996) and Oliver (Satisfaction: a behavioral perspective on the consumer. New York: McGraw Hill, 1997). Additionally, we propose a new general conceptual framework, which leads to e-loyalty dividing antecedents into prepurchase, during-purchase and after-purchase factors, based on the act of purchase. To conclude, a number of managerial implementations are suggested in order to help marketing managers increase their customers' e-loyalty by making crucial changes in each purchase stage.


title: Airport management: taxi planning

abstract: The Taxi Planning studies the aircraft routing and scheduling on the airport ground. This is a dynamic problem, which must be updated almost every time that a new aircraft enters or exits the system. Taxi Planning has been modelled using a linear multicommodity flow network model with side constraints and binary variables. The flow capacity constraints are used to represent the conflicts and competence between aircrafts using a given airport capacity. The “Branch and Bound” and “Fix and Relax” methodologies have been used. The computational tests have been run at the Madrid-Barajas airport, using actual data from the airport traffic.


title: Traffic accident detection through a hydrodynamic lens

abstract: In this paper we present a novel method for automatic traffic accident detection, based on Smoothed Particles Hydrodynamics (SPH). In our method, a motion flow field is obtained from the video through dense optical flow extraction. Then a thermal diffusion process (TDP) is exploited to turn the motion flow field into a coherent motion field. Approximating the moving particles to individuals, their interaction forces, represented as endothermic reactions, are computed using the enthalpy measure, thus obtaining the potential particles of interest. Furthermore, we exploit SPH that accumulates the contribution of each particle in a weighted form, based on a kernel function. The experimental evaluation is conducted on a set of video sequences collected from Youtube, and the obtained results are compared against a state of the art technique.


title: Analysis of TPC-DS: the first standard benchmark for SQL-based big data systems

abstract: The advent of Web 2.0 companies, such as Facebook, Google, and Amazon with their insatiable appetite for vast amounts of structured, semi-structured, and unstructured data, triggered the development of Hadoop and related tools, e.g., YARN, MapReduce, and Pig, as well as NoSQL databases. These tools form an open source software stack to support the processing of large and diverse data sets on clustered systems to perform decision support tasks. Recently, SQL is resurrecting in many of these solutions, e.g., Hive, Stinger, Impala, Shark, and Presto. At the same time, RDBMS vendors are adding Hadoop support into their SQL engines, e.g., IBM's Big SQL, Actian's Vortex, Oracle's Big Data SQL, and SAP's HANA. Because there was no industry standard benchmark that could measure the performance of SQL-based big data solutions, marketing claims were mostly based on "cherry picked" subsets of the TPC-DS benchmark to suit individual companies strengths, while blending out their weaknesses. In this paper, we present and analyze our work on modifying TPC-DS to fill the void for an industry standard benchmark that is able to measure the performance of SQL-based big data solutions. The new benchmark was ratified by the TPC in early 2016. To show the significance of the new benchmark, we analyze performance data obtained on four different systems running big data, traditional RDBMS, and columnar in-memory architectures.


title: fuzzyPSM: A New Password Strength Meter Using Fuzzy Probabilistic Context-Free Grammars

abstract: To provide timely feedbacks to users, nearly every respectable Internet service now imposes a password strength meter (PSM) upon user registration or password change. It is a rare bit of good news in password research that well-designed PSMs do help improve the strength of user-chosen passwords. However, leading PSMs in the industrial world (e.g., Zxcvbn, KeePSM and NIST PSM) are mainly composed of simple heuristic rules and found to be highly inaccurate, while state-of-the-art PSMs from academia (e.g., probabilistic context-free grammar based ones and Markov-based ones) are still far from satisfactory, especially incompetent at gauging weak passwords. As preventing weak passwords is the primary goal of any PSM, this means that existing PSMs largely fail to serve their purpose. To fill this gap, in this paper we propose a novel PSM that is grounded on real user behavior. Our user survey reveals that when choosing passwords for a new web service, most users (77.38%) simply retrieve one of their existing passwords from memory and then reuse (or slightly modify) it. This is in vast contrast to the seemingly intuitive yet unrealistic assumption (often implicitly) made in most of the existing PSMs that, when user registers, a whole new password is constructed by mixing segments of letter, digit and/or symbol or by combining n-grams. To model users' realistic behaviors, we use passwords leaked from a less sensitiveservice as our base dictionary and another list of relatively strong passwords leaked from a sensitive service as our training dictionary, and determine how mangling rules are employed by users to construct passwords for new services. This process automatically creates a fuzzy probabilistic context-free grammar (PCFG) and gives rise to our fuzzy-PCFG-based meter, fuzzyPSM. It can react dynamically to changes in how users choose passwords and is evaluated by comparisons with five representative PSMs. Extensive experiments on 11 real-world password lists show that fuzzyPSM, in general, outperforms all its counterparts, especially accurate in telling apart weak passwords and suitable for services where online guessing attacks prevail.


title: Two-Step Quantization for Low-bit Neural Networks

abstract: Every bit matters in the hardware design of quantized neural networks. However, extremely-low-bit representation usually causes large accuracy drop. Thus, how to train extremely-low-bit neural networks with high accuracy is of central importance. Most existing network quantization approaches learn transformations (low-bit weights) as well as encodings (low-bit activations) simultaneously. This tight coupling makes the optimization problem difficult, and thus prevents the network from learning optimal representations. In this paper, we propose a simple yet effective Two-Step Quantization (TSQ) framework, by decomposing the network quantization problem into two steps: code learning and transformation function learning based on the learned codes. For the first step, we propose the sparse quantization method for code learning. The second step can be formulated as a non-linear least square regression problem with low-bit constraints, which can be solved efficiently in an iterative manner. Extensive experiments on CIFAR-10 and ILSVRC-12 datasets demonstrate that the proposed TSQ is effective and outperforms the state-of-the-art by a large margin. Especially, for 2-bit activation and ternary weight quantization of AlexNet, the accuracy of our TSQ drops only about 0.5 points compared with the full-precision counterpart, outperforming current state-of-the-art by more than 5 points.


title: The Impact of Gender Diversity on the Performance of Business Teams: Evidence from a Field Experiment

abstract: This paper reports about a field experiment conducted to estimate the impact of the share of women in business teams on their performance. Teams consisting of undergraduate students in business studies start up a venture as part of their curriculum. We manipulated the gender composition of teams and assigned students, conditional on their gender, randomly to teams. We find that teams with an equal gender mix perform better than male-dominated and female-dominated teams in terms of sales, profits and earnings per share. We explore various mechanisms and find that mutual monitoring is more intense in mixed teams than in homogeneous teams. This partially explains the better results of mixed teams.


title: Personalized video recommendation through tripartite graph propagation

abstract: The rapid growth of the number of videos on the Internet provides enormous potential for users to find content of interest to them. Video search, such as Google, Youtube, Bing, is a popular way to help users to find desired videos. However, it is still very challenging to discover new video contents for users. In this paper, we address the problem of providing personalized video suggestions for users. Rather than only exploring the user-video graph that is formulated using the click-through information, we also investigate other two useful graphs, the user-query graph indicating if a user ever issues a query, and the query-video graph indicating if a video appears in the search result of a query. The two graphs act as a bridge to connect users and videos, and have a large potential to improve the recommendation as the queries issued by a user essentially imply his interest. As a result, we reach a tripartite graph over (user, video, query). We develop an iterative propagation scheme over the tripartite graph to compute the preference information of each user. Experimental results on a dataset of 2,893 users, 23,630 queries and 55,114 videos collected during Feb. 1-28, 2011 demonstrate that the proposed method outperforms existing state-of-the-art approaches, co-views and random walks on the user-video bipartite graph.


title: Analysis of a linear series-fed rectangular microstrip antenna array

abstract: A new method is proposed to analyze a linear series-fed microstrip antenna array which maintains accuracy while not requiring a full-wave analysis of the entire structure. The method accounts for mutual coupling between patches as well as the patch excitations by the feed lines.


SURREAL: SecURe coveRt peEr communicAtion over BitTorrent protocoL


title: Graph Drawing by Force-directed Placement

abstract: SUMMARY We present a modification of the spring-embedder model of Eades [ Congresses Numerantium, 42, 149–160, (1984)] for drawing undirected graphs with straight edges. Our heuristic strives for uniform edge lengths, and we develop it in analogy to forces in natural systems, for a simple, elegant, conceptuallyintuitive, and efficient algorithm.


title: Work Environment Characteristics and Teacher Well-Being: The Mediation of Emotion Regulation Strategies

abstract: Based on an adjusted Job Demands-Resources (JD-R) model that considers the mediation of personal resources, this study examined the relationships between two characteristics of teachers’ work environment (i.e., emotional job demands and trust in colleagues) and two indicators of teachers’ well-being (i.e., teaching satisfaction and emotional exhaustion). In particular, the study focused on how emotion regulation strategies (i.e., reappraisal and suppression) mediate these relationships. Data collected from a questionnaire survey of 1115 primary school teachers in Hong Kong was analyzed to test the hypothesized relationships. The results of structural equation modeling indicated that: (1) the emotional job demands of teaching were detrimental to teacher well-being, whereas trust in colleagues was beneficial; (2) both emotion regulation strategies mediated the relationships between both emotional job demands and trust in colleagues and teacher well-being; and (3) teachers who tend to use more reappraisal may be psychologically healthier than those tend to adopt more suppression. These findings support the applicability of the JD-R model to school settings and highlight the role of teachers’ emotion regulation in teachers’ well-being. Implications for the improvement of school environments and teachers’ well-being are identified.


title: Examining Intrinsic Versus Extrinsic Exercise Goals: Cognitive, Affective, and Behavioral Outcomes

abstract: Grounded in self-determination theory (SDT), this study had two purposes: (a) examine the associations between intrinsic (relative to extrinsic) exercise goal content and cognitive, affective, and behavioral outcomes; and (b) test the mediating role of psychological need satisfaction in the Exercise Goal Content ➞ Outcomes relationship. Using a sample of 410 adults, hierarchical regression analysis showed relative intrinsic goal content to positively predict physical self-worth, self-reported exercise behavior, psychological well-being, and psychological need satisfaction and negatively predict exercise anxiety. Except for exercise behavior, the predictive utility of relative intrinsic goal content on the dependent variables of interest remained significant after controlling for participants’ relative self-determined exercise motivation. Structural equation modeling analyses showed psychological need satisfaction to partially mediate the effect of relative intrinsic goal content on the outcome variables. Our findings support further investigation of exercise goals commensurate with the goal content perspective advanced in SDT.


title: The Mixture of Textrank and Lexrank Techniques of Single Document Automatic Summarization Research in Tibetan

abstract: Today is an era of knowledge economy and information dominated. Automatic summarization is an important research in the field of natural language processing, its purpose to explore human obtain valuable information from natural language texts. As the Tibetan information processing technology is backward, and the achievements of automatic summarization have not been publicly reported in Tibetan. This paper references the existed Chinese and English automatic summarization technology in domestic and foreign, and proposes a method of Tibetan automatic summarization. Combination with the advantage of keyword processing based on TextRank and processing of the relationship between sentences based on LexRank algorithm. Take full account of the frequency, part of speech, word position, word length, content and position of a sentence. In particular, the generated summarization considering the similarity of candidate sentences. Experiments analysis three summarization methods based on TextRank, based on LexRank and based on LexRank+TextRank respectively, and using the ROUGE value to evaluate the effect of summarization. Experimental results show that, the effect of the mixture of TextRank and LexRank techniques of single document automatic summarization in Tibetan is better and accuracy reached 80%.


title: Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

abstract: With the rapid development of the World Wide Web, electronic word-of-mouth interaction has made consumers active participants. Nowadays, a large number of reviews posted by the consumers on the Web provide valuable information to other consumers. Such information is highly essential for decision making and hence popular among the internet users. This information is very valuable not only for prospective consumers to make decisions but also for businesses in predicting the success and sustainability. In this paper, a Gini Index based feature selection method with Support Vector Machine (SVM) classifier is proposed for sentiment classification for large movie review data set. The results show that our Gini Index method has better classification performance in terms of reduced error rate and accuracy.


title: Morphometric study of the greater palatine canal: cone-beam computed tomography

abstract: Purpose ::: To analyze greater palatine canal (GPC) dimensions using cone-beam computed tomography (CBCT) images, and to evaluate the position of the greater palatine foramen (GPF) with respect to various landmarks selected in relation to dental status.


Philosophical Issues that Divide Liberals: Omniscience or Omni-nescience about the Future?


Information-processing analysis of perceptual processes in problem solving.


title: Portal flow and hepatic function after splenectomy and esophagogastric devascularization.

abstract: We studied 27 patients who had mansonic schistosomiasis and portal hypertension with previous episodes of digestive bleeding from esophageal varices. According to the routine of the Liver and Portal Hypertension Unit of the Faculty of Medical Sciences-Santa Casa de Sao Paulo, these patients underwent surgical treatment that consisted of azygoportal disconnection and splenectomy. The goal of this study was to analyze the developmental profile of portal flow and hepatic function using Doppler ultrasound and the Child-Turcotte-Pugh criteria, respectively, in the pre- and postoperatory stages, in a 3-year period. The results showed a significant 27% reduction of the portal blood flow in the recent postoperatory period and up to 37% in the first to the second year after surgery, remaining stable after this period. We observed that all patients presented with good hepatic functional reserve in the preoperatory period, with no hepatic alteration in the postoperatory period. We conclude that, despite the reduction of portal flow in response to surgery, there was no deterioration of hepatic function.


title: Emotion, gender, and gender typical identity in autobiographical memory.

abstract: ABSTRACTGender differences in the emotional intensity and content of autobiographical memory (AM) are inconsistent across studies, and may be influenced as much by gender identity as by categorical gender. To explore this question, data were collected from 196 participants (age 18–40), split evenly between men and women. Participants narrated four memories, a neutral event, high point event, low point event, and self-defining memory, completed ratings of emotional intensity for each event, and completed four measures of gender typical identity. For self-reported emotional intensity, gender differences in AM were mediated by identification with stereotypical feminine gender norms. For narrative use of affect terms, both gender and gender typical identity predicted affective expression. The results confirm contextual models of gender identity (e.g., Diamond, 2012. The desire disorder in research on sexual orientation in women: Contributions of dynamical systems theory. Archives of Sexual Behavior, 41, 73–83...


title: A High Performance Implementation of Spectral Clustering on CPU-GPU Platforms

abstract: Spectral clustering is one of the most popular graph clustering algorithms, which achieves the best performance for many scientific and engineering applications. However, existing implementations in commonly used software platforms such as Matlab and Python do not scale well for many of the emerging Big Data applications. In this paper, we present a fast implementation of the spectral clustering algorithm on a CPU-GPU heterogeneous platform. Our implementation takes advantage of the computational power of the multi-core CPU and the massive multithreading and SIMD capabilities of GPUs. Given the input as data points in high dimensional space, we propose a parallel scheme to build a sparse similarity graph represented in a standard sparse representation format. Then we compute the smallest k eigenvectors of the Laplacian matrix by utilizing the reverse communication interfaces of ARPACK software and cuSPARSE library, where k is typically very large. Moreover, we implement a very fast parallelized k-means algorithm on GPUs. Our implementation is shown to be significantly faster compared to the best known Matlab and Python implementations for each step. In addition, our algorithm scales to problems with a very large number of clusters.


title: Reconstructing the Extended Nasal Tip Defect

abstract: Reconstruction of large facial defects requires surgical skill, an understanding of engineering principles, an artistic eye, and patience to design the most elegant solution for each patient. Extended nasal tip defects, which may involve additional facial subunits, require even more thoughtful analysis and planning. Reconstructive surgeons need to be aware of the pros and cons of various options for flaps, the use of a delay stage, and sequencing and scheduling of staged operations to achieve an optimal outcome.


title: Adaptive sequential offloading game for multi-cell Mobile Edge Computing

abstract: Mobile Edge Computing (MEC) provides computation capabilities within the radio access network and in close proximity to mobile users, so as to enrich user experience. In this paper, we consider a multi-cell MEC scenario, where the small cells are densely deployed and serve multiple mobile users. However, the finite computation resources of MEC server and severe inter-cell interference limit the scalability of offloading. We propose an adaptive sequential offloading game approach to resolve these problems, where the mobile users sequentially make offloading decisions based on the current interference environment and available computation resources. Besides, we adjust the number of offloaded users adaptively to avoid the unexpected queuing delay due to the competition for limited computation resources among offloaded users. Numerical results show that our proposed algorithm can achieve efficient performance in terms of both the experienced latency and energy consumption and scale well as the network system size increases.


title: Image Enhancement in Encrypted Domain over Cloud

abstract: Cloud-based multimedia systems are becoming increasingly common. These systems offer not only storage facility, but also high-end computing infrastructure which can be used to process data for various analysis tasks ranging from low-level data quality enhancement to high-level activity and behavior identification operations. However, cloud data centers, being third party servers, are often prone to information leakage, raising security and privacy concerns. In this article, we present a Shamir's secret sharing based method to enhance the quality of encrypted image data over cloud. Using the proposed method we show that several image enhancement operations such as noise removal, antialiasing, edge and contrast enhancement, and dehazing can be performed in encrypted domain with near-zero loss in accuracy and minimal computation and data overhead. Moreover, the proposed method is proven to be information theoretically secure.


title: A Facial Pore Aided Detection System Using CNN Deep Learning Algorithm

abstract: Many people are concerned about their facial skin maintenance. Rough pore is one of the facial skin problems which annoyed many people. The size of facial pore is tiny, and it has various shapes. Therefore, it is difficult to recognize facial pore by using traditional image processing. In this paper, we propose an approach based on convolutional neural networks (CNNs) to develop a facial pore aided detection system. We use the LeNet−5 model as our benchmark architecture, and investigate the performance of different depths network on our facial pore dataset. The facial pore aided detection system will help people understand more about their facial skin problems and properly keep their facial skin well.


title: Image detail enhancement with spatially guided filters

abstract: In recent years, enhancing image details without introducing artifacts has been attracting much attention in image processing community. Various image filtering methods have been proposed to achieve this goal. However, existing methods usually treat all pixels equally during the filtering process without considering the relationship between filtering strengths and image contents. In this paper, we address this issue by spatially distributing the filtering strengths with simple low-level features. First, to determine the pixel-wise filtering strength, we construct a spatially guided map, which exploits the spatial influence of image details based on the edge response of an image. Then, we further integrate this guided map into two state-of-the-art image filters and apply the improved filters to the task of image detail enhancement. Experiments demonstrate that our results generate better content-specific visual effects and introduce much less artifacts. We construct a spatially guided map exploiting spatial influences of edge features.We improve two state-of-the-art filters by integrating our spatially guided map.Better filtering results are obtained with more naturalness and less artifacts.


title: Size reduction of cavity-backed slot antennas

abstract: A technique for reducing the dimensions of the cavity of a cavity-backed slot antenna (CBSA) is presented, which facilitates proper fabrication and integration of miniaturized slot antennas on multilayer substrates. This is accomplished by replacing the solid metal around the traditional slot antennas with a specific metallic pattern that can be viewed as a series of parallel strip lines placed around the slot antenna. This metallic pattern is then modified by designing the parallel strips in a compact fashion to reduce the overall antenna dimensions and obtain a reduced-size CBSA. It is shown that, for a simple straight slot antenna, the overall occupied volume of the modified cavity backing the slot antenna can be reduced by more than 65% without affecting the high radiation efficiency of the antenna. A number of traditional cavity backed slot antennas and the proposed modified CBSAs are designed, fabricated, and measured. The reduced-size CBSAs show a very low input VSWR, low cross-polarized radiation levels, and high radiation efficiency. Despite their small ground plane size, the proposed cavity backed slot antennas have front-to-back ratio (FTBR) values in the range of 6-7 dB


title: Modeling topic specific credibility on twitter

abstract: This paper presents and evaluates three computational models for recommending credible topic-specific information in Twitter. The first model focuses on credibility at the user level, harnessing various dynamics of information flow in the underlying social graph to compute a rating. The second model applies a content-based strategy to compute a finer-grained credibility score for individual tweets. Lastly, we discuss a third model which combines facets from both models in a hybrid method, using both averaging and filtering hybrid strategies. To evaluate our novel credibility models, we perform an evaluation on 7 topic specific data sets mined from the Twitter streaming API, with specific focus on a data set of 37K users who tweeted about the topic "Libya". Results show that the social model outperfoms hybrid and content-based prediction models in terms of predictive accuracy over a set of manually collected credibility ratings on the "Libya" dataset.


title: Low power chopper amplifier without LPF

abstract: Chopping technique is an efficient approach to decrease the 1/f noise and low-frequency offset of CMOS amplifiers, but conventional chopper amplifier consumes large power because it required a wide-band amplifier exceed a chopping frequency and a post low pass filter (LPF) for eliminating modulation noise. In this paper, an improved chopper amplifier for reducing power consumption is presented which is composed of a two-stage amplifier. The high output impedance of the first stage folded cascode amplifier and the equivalent Miller capacitance constitute together a LPF to filter out the modulation noise, so the chopper amplifier need not the post LPF, which can reduce the power consumption. The circuit of the presented chopper amplifier is designed in TSMC 0.18µm CMOS technology. The chopper amplifier consumes 20 µA current and 36 µW power at 1.8V supply. The equivalent input noise voltage is 73nV/√Hz @1Hz and the input noise voltage integrated from 0.1Hz to 150Hz is 0.68µV rms.


title: Task-Based Robot Grasp Planning Using Probabilistic Inference

abstract: Grasping and manipulating everyday objects in a goal-directed manner is an important ability of a service robot. The robot needs to reason about task requirements and ground these in the sensorimotor information. Grasping and interaction with objects are challenging in real-world scenarios, where sensorimotor uncertainty is prevalent. This paper presents a probabilistic framework for the representation and modeling of robot-grasping tasks. The framework consists of Gaussian mixture models for generic data discretization, and discrete Bayesian networks for encoding the probabilistic relations among various task-relevant variables, including object and action features as well as task constraints. We evaluate the framework using a grasp database generated in a simulated environment including a human and two robot hand models. The generative modeling approach allows the prediction of grasping tasks given uncertain sensory data, as well as object and grasp selection in a task-oriented manner. Furthermore, the graphical model framework provides insights into dependencies between variables and features relevant for object grasping.


title: Proliferative and Nonproliferative Lesions of the Rat and Mouse Respiratory Tract

abstract: The INHAND Project (International Harmonization of Nomenclature and Diagnostic Criteria for Lesions in Rats and Mice) is a joint initiative of the Societies of Toxicologic Pathology from Europe (ESTP), Great Britain (BSTP), Japan (JSTP) and North America (STP) to develop an internationally-accepted nomenclature for proliferative and non-proliferative lesions in laboratory animals. The purpose of this publication is to provide a standardized nomenclature for classifying microscopic lesions observed in the respiratory tract of laboratory rats and mice, with color photomicrographs illustrating examples of some lesions. The standardized nomenclature presented in this document is also available electronically on the internet (http://www.goreni.org/). Sources of material included histopathology databases from government, academia, and industrial laboratories throughout the world. Content includes spontaneous developmental and aging lesions as well as lesions induced by exposure to test materials. A widely accepted and utilized international harmonization of nomenclature for respiratory tract lesions in laboratory animals will decrease confusion among regulatory and scientific research organizations in different countries and provide a common language to increase and enrich international exchanges of information among toxicologists and pathologists.


title: Predefined pattern detection in large time series

abstract: Predefined pattern detection from time series is an interesting and challenging task. In order to reduce its computational cost and increase effectiveness, a number of time series representation methods and similarity measures have been proposed. Most of the existing methods focus on full sequence matching, that is, sequences with clearly defined beginnings and endings, where all data points contribute to the match. These methods, however, do not account for temporal and magnitude deformations in the data and result to be ineffective on several real-world scenarios where noise and external phenomena introduce diversity in the class of patterns to be matched. In this paper, we present a novel pattern detection method, which is based on the notions of templates, landmarks, constraints and trust regions. We employ the Minimum Description Length (MDL) principle for time series preprocessing step, which helps to preserve all the prominent features and prevents the template from overfitting. Templates are provided by common users or domain experts, and represent interesting patterns we want to detect from time series. Instead of utilising templates to match all the potential subsequences in the time series, we translate the time series and templates into landmark sequences, and detect patterns from landmark sequence of the time series. Through defining constraints within the template landmark sequence, we effectively extract all the landmark subsequences from the time series landmark sequence, and obtain a number of landmark segments (time series subsequences or instances). We model each landmark segment through scaling the template in both temporal and magnitude dimensions. To suppress the influence of noise, we introduce the concept of trust region, which not only helps to achieve an improved instance model, but also helps to catch the accurate boundaries of instances of the given template. Based on the similarities derived from instance models, we introduce the probability density function to calculate a similarity threshold. The threshold can be used to judge if a landmark segment is a true instance of the given template or not. To evaluate the effectiveness and efficiency of the proposed method, we apply it to two real-world datasets. The results show that our method is capable of detecting patterns of temporal and magnitude deformations with competitive performance.


title: Applications of cloud computing in education

abstract: In this paper, we provide an overview of the cloud computing model and discuss its applications for collaboration between academic institutions. Cloud computing is considered a typical paradigm that provides suitable, efficient network login to an appropriate pool of computing resources which can be provided and released with just nominal assiduity and service providers reciprocity. However, many organizations understand cloud computing in different ways. We briefly analyse the cloud computing applications in this paper, and describe some current and accomplished educational and research products. Then, we go on to evaluate the successful applications of cloud computing models at educational institutions, and the different ways to implement cloud computing. Finally, we present different education applications for education infrastructures which are implemented for academic use.


An Exploration of the Personal Software Process ( PSP )


title: EKG-based key agreement in Body Sensor Networks

abstract: Preserving a person's privacy in an efficient manner is very important for critical, life-saving infrastructures like body sensor networks (BSN). This paper presents a novel key agreement scheme which allows two sensors in a BSN to agree to a common key generated using electrocardiogram (EKG) signals. This EKG-based key agreement (EKA) scheme aims to bring the "plug-n-play" paradigm to BSN security whereby simply deploying sensors on the subject can enable secure communication, without requiring any form of initialization such as pre-deployment. Analysis of the scheme based on real EKG data (obtained from MIT PhysioBank database) shows that keys resulting from EKA are: random, time variant, can be generated based on short-duration EKG measurements, identical for a given subject and different for separate individuals.


title: Word Alignment Modeling with Context Dependent Deep Neural Network

abstract: In this paper, we explore a novel bilingual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in various machine learning tasks (Collobert et al., 2011). We describe in detail how we adapt and extend the CD-DNNHMM (Dahl et al., 2012) method introduced in speech recognition to the HMMbased word alignment model, in which bilingual word embedding is discriminatively learnt to capture lexical translation information, and surrounding words are leveraged to model context information in bilingual sentences. While being capable to model the rich bilingual correspondence, our method generates a very compact model with much fewer parameters. Experiments on a large scale EnglishChinese word alignment task show that the proposed method outperforms the HMM and IBM model 4 baselines by 2 points in F-score.


title: Detecting visually similar Web pages: Application to phishing detection

abstract: We propose a novel approach for detecting visual similarity between two Web pages. The proposed approach applies Gestalt theory and considers a Web page as a single indivisible entity. The concept of supersignals, as a realization of Gestalt principles, supports our contention that Web pages must be treated as indivisible entities. We objectify, and directly compare, these indivisible supersignals using algorithmic complexity theory. We illustrate our approach by applying it to the problem of detecting phishing scams. Via a large-scale, real-world case study, we demonstrate that 1) our approach effectively detects similar Web pages; and 2) it accuractely distinguishes legitimate and phishing pages.


title: Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms

abstract: Computer aided diagnosis (CADx) systems for digitized mammograms solve the problem of classification between benign and malignant tissues while studies have shown that using only a subset of features generated from the mammograms can yield higher classification accuracy. To this end, we propose a mutual information-based Support Vector Machine Recursive Feature Elimination (SVM-RFE) as the classification method with feature selection in this paper. We have conducted extensive experiments on publicly available mammographic data and the obtained results indicate that the proposed method outperforms other SVM and SVM-RFE-based methods.


title: Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification Theory and Case Study

abstract: In this paper, we propose a new methodology based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence, specifically, Artificial Immune Systems to identify takeover targets. Although considerable research based on customary statistical techniques and some contemporary Computational Intelligence techniques have been devoted to identify takeover targets, most of the existing studies are based upon multiple previous mergers and acquisitions. Contrary to previous research, the novelty of this proposal lies in its ability to suggest takeover targets for novice firms that are at the beginning of their merger and acquisition spree. We first discuss the theoretical perspective and then provide a case study with details for practical implementation, both capitalizing from unique generalization capabilities of artificial immune systems algorithms.


title: Underwater sensor networks: applications, advances and challenges

abstract: This paper examines the main approaches and challenges in the design and implementation of underwater wireless sensor networks. We summarize key applications and the main phenomena related to acoustic propagation, and discuss how they affect the design and operation of communication systems and networking protocols at various layers. We also provide an overview of communications hardware, testbeds and simulation tools available to the research community.


title: Feature selection for efficient gender classification

abstract: The study presents an efficient gender classification technique. The gender of a facial image is the most prominent feature, and improvement in the existing gender classification methods will result in the high performance of the face retrieval and classification methods for large repositories. In this paper a new efficient gender classification method is proposed. First, the face part of the image is segmented using Viola and Jones face detection technique which excludes unwanted area from the image, so reducing image size. Histogram equalization is performed to normalize the illumination effect. Discrete Cosine Transform (DCT) is employed for feature extraction and sorting the features with high variance. K-nearest neighbor classifier (KNN) is used for classification. The face images used in this study were obtained from the Stanford university medical student (SUMS) frontal facial images database. The experimental results on the SUMS face database indicate that the proposed approach achieves as high as 99.3% gender classification accuracy.


title: A Game Theory Approach to Multi-Agent Decentralized Energy Management of Autonomous Polygeneration Microgrids

abstract: Energy management systems are essential and indispensable for the secure and optimal operation of autonomous polygeneration microgrids which include distributed energy technologies and multiple electrical loads. In this paper, a multi-agent decentralized energy management system was designed. In particular, the devices of the microgrid under study were controlled as interactive agents. The energy management problem was formulated here through the application of game theory, in order to model the set of strategies between two players/agents, as a non-cooperative power control game or a cooperative one, according to the level of the energy produced by the renewable energy sources and the energy stored in the battery bank, for the purpose of accomplishing optimal energy management and control of the microgrid operation. The Nash equilibrium was used to compromise the possible diverging goals of the agents by maximizing their preferences. The proposed energy management system was then compared with a multi-agent decentralized energy management system where all the agents were assumed to be cooperative and employed agent coordination through Fuzzy Cognitive Maps. The results obtained from this comparison, demonstrate that the application of game theory based control, in autonomous polygeneration microgrids, can ensure operational and financial benefits over known energy management approaches incorporating distributed intelligence.


title: Analysis of simplified variants of SHA-256 ∗

abstract: In this paper we analyse the role of some of the building blocks of SHA-256. We show that the disturbance-correction strategy is applicable to the SHA-256 architecture and we prove that functions Σ, σ are vital for the security of SHA-256 by showing that for a variant without them it is possible to find collisions with complexity 2^64 hash operations. As a step towards an analysis of the full function, we present the results of our experiments on Hamming weights of expanded messages for different variants of the message expansion and show that there exist low-weight expanded messages for XOR-linearised variants.


Thoughtful Surprise Generation as a Computational Creativity Challenge


Two-Phase Flow Patterns and Frictional Pressure Gradients in a Small Rectangular Channel : A Comparison between Two Horizontal Orientations


title: CPU transparent protection of OS kernel and hypervisor integrity with programmable DRAM

abstract: Increasingly, cyber attacks (e.g., kernel rootkits) target the inner rings of a computer system, and they have seriously undermined the integrity of the entire computer systems. To eliminate these threats, it is imperative to develop innovative solutions running below the attack surface. This paper presents MGuard, a new most inner ring solution for inspecting the system integrity that is directly integrated with the DRAM DIMM devices. More specifically, we design a programmable guard that is integrated with the advanced memory buffer of FB-DIMM to continuously monitor all the memory traffic and detect the system integrity violations. Unlike the existing approaches that are either snapshot-based or lack compatibility and flexibility, MGuard continuously monitors the integrity of all the outer rings including both OS kernel and hypervisor of interest, with a greater extendibility enabled by a programmable interface. It offers a hardware drop-in solution transparent to the host CPU and memory controller. Moreover, MGuard is isolated from the host software and hardware, leading to strong security for remote attackers. Our simulation-based experimental results show that MGuard introduces no speed overhead, and is able to detect nearly all the OS-kernel and hypervisor control data related rootkits we tested.


title: Towards a PHP webshell taxonomy using deobfuscation-assisted similarity analysis

abstract: The abundance of PHP-based Remote Access Trojans (or web shells) found in the wild has led malware researchers to develop systems capable of tracking and analysing these shells. In the past, such shells were ably classified using signature matching, a process that is currently unable to cope with the sheer volume and variety of web-based malware in circulation. Although a large percentage of newly-created webshell software incorporates portions of code derived from seminal shells such as c99 and r57, they are able to disguise this by making extensive use of obfuscation techniques intended to frustrate any attempts to dissect or reverse engineer the code. This paper presents an approach to shell classification and analysis (based on similarity to a body of known malware) in an attempt to create a comprehensive taxonomy of PHP-based web shells. Several different measures of similarity were used in conjunction with clustering algorithms and visualisation techniques in order to achieve this. Furthermore, an auxiliary component capable of syntactically deobfuscating PHP code is described. This was employed to reverse idiomatic obfuscation constructs used by software authors. It was found that this deobfuscation dramatically increased the observed levels of similarity by exposing additional code for analysis.


title: Time course of the development of motor behaviors in the zebrafish embryo.

abstract: The development and properties of locomotor behaviors in zebrafish embryos raised at 28.5 degrees C were examined. When freed from the chorion, embryonic zebrafish showed three sequential stereotyped behaviors: a transient period of alternating, coiling contractions followed by touch-evoked rapid coils, then finally, organized swimming. The three different behaviors were characterized by video microscopy. Spontaneous, alternating contractions of the trunk appeared suddenly at 17 h postfertilization (hpf), with a frequency of 0.57 Hz, peaked at 19 hpf at 0.96 Hz, and gradually decreased to <0.1 Hz by 27 hpf. Starting at 21 hpf, touching either the head or the tail of the embryos resulted in vigorous coils. The coils accelerated with development, reaching a maximum speed of contraction before 48 hpf, which is near the time of hatching. After 27 hpf, touching the embryos, particularly on the tail, could induce partial coils (instead of full coils). At this time, embryos started to swim in response to a touch, preferentially to the tail. The swim cycle frequency gradually increased with age from 7 Hz at 27 hpf to 28 Hz at 36 hpf. Lesions of the central nervous system rostral to the hindbrain had no effect on the three behaviors. Lesioning the hindbrain eliminated swimming and touch responses, but not the spontaneous contractions. Our observations suggest that the spontaneous contractions result from activation of a primitive spinal circuit, while touch and swimming require additional hindbrain inputs to elicit mature locomotor behaviors.


Surround-screen projection-based virtual reality: the design and implementation of the CAVE


title: Sketchplore: Sketch and Explore with a Layout Optimiser

abstract: This paper studies a novel concept for integrating real-time design optimisation to a sketching tool. Although optimisation methods can attack very complex design problems, their insistence on precise objectives and a point optimum is a poor fit with sketching practices. Sketchplorer is a multitouch sketching tool that uses a real-time layout optimiser. It automatically infers the designer's task to search for both local improvements to the current design and global (radical) alternatives. Using predictive models of sensorimotor performance and perception, these suggestions steer the designer toward more usable and aesthetic layouts without overriding the designer or demanding extensive input.


title: DeepDGA: Adversarially-Tuned Domain Generation and Detection

abstract: Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections. While there are many methods to pseudorandomly generate domains, we focus in this paper on detecting (and generating) domains on a per-domain basis which provides a simple and flexible means to detect known DGA families. Recent machine learning approaches to DGA detection have been successful on fairly simplistic DGAs, many of which produce names of fixed length. However, models trained on limited datasets are somewhat blind to new DGA variants. In this paper, we leverage the concept of generative adversarial networks to construct a deep learning based DGA that is designed to intentionally bypass a deep learning based detector. In a series of adversarial rounds, the generator learns to generate domain names that are increasingly more difficult to detect. In turn, a detector model updates its parameters to compensate for the adversarially generated domains. We test the hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs. We detail solutions to several challenges in training this character-based generative adversarial network. In particular, our deep learning architecture begins as a domain name auto-encoder (encoder + decoder) trained on domains in the Alexa one million. Then the encoder and decoder are reassembled competitively in a generative adversarial network (detector + generator), with novel neural architectures and training strategies to improve convergence.


What 'gaps'? Reply to Grush and Churchland


title: Stability of the Baseline Holder in Readout Circuits for Radiation Detectors

abstract: Baseline holder (BLH) circuits are used widely to stabilize the analog output of application-specific integrated circuits (ASICs) for high-count-rate applications. The careful design of BLH circuits is vital to the overall stability of the analog-signal-processing chain in ASICs. Recently, we observed self-triggered fluctuations in an ASIC in which the shaping circuits have a BLH circuit in the feedback loop. In fact, further investigations showed that methods of enhancing small-signal stabilities cause an even worse situation. To resolve this problem, we used large-signal analyses to study the circuit's stability. We found that a relatively small gain for the error amplifier and a small current in the non-linear stage of the BLH are required to enhance stability in large-signal analysis, which will compromise the properties of the BLH. These findings were verified by SPICE simulations. In this paper, we present our detailed analysis of the BLH circuits, and propose an improved version of them that have only minimal self-triggered fluctuations. We summarize the design considerations both for the stability and the properties of the BLH circuits.


title: Enhanced AES cryptosystem by using genetic algorithm and neural network in S-box

abstract: Cryptography based on block ciphers use Key-dependent ciphers for encryption and decryption. The efficiency of these systems depends on the security and the speed of the algorithm. The encryption process needs to be adaptive and dynamic in order to face any cryptanalytic attacks. Increasing the complexity of the algorithm is one way to prevent the attacks. The introduced complexity increases the execution time of the algorithm which leads to timing attacks. This paper attempts to propose two enhanced AES cryptosystem by employing Genetic algorithm (GA) in SPboxes and modification of AES by implementing nonlinear neural network (NN) in SP network to increase the security against timing attack and reduce the computational time of the proposed system. Both GA and NN are used in key expansion and key distribution of the AES algorithm.


title: Behavior planning for character animation

abstract: This paper explores a behavior planning approach to automatically generate realistic motions for animated characters. Motion clips are abstracted as high-level behaviors and associated with a behavior finite-state machine (FSM) that defines the movement capabilities of a virtual character. During runtime, motion is generated automatically by a planning algorithm that performs a global search of the FSM and computes a sequence of behaviors for the character to reach a user-designated goal position. Our technique can generate interesting animations using a relatively small amount of data, making it attractive for resource-limited game platforms. It also scales efficiently to large motion databases, because the search performance is primarily dependent on the complexity of the behavior FSM rather than on the amount of data. Heuristic cost functions that the planner uses to evaluate candidate motions provide a flexible framework from which an animator can control character preferences for certain types of behavior. We show results of synthesized animations involving up to one hundred human and animal characters planning simultaneously in both static and dynamic environments.


title: The Papez Circuit in First-Episode, Treatment-Naive Adults with Major Depressive Disorder: Combined Atlas-Based Tract-Specific Quantification Analysis and Voxel-Based Analysis

abstract: Previous findings suggest that the Papez Circuit may have a role in major depressive disorders. We used atlas-based tract-specific quantification analysis and voxel-based analysis to examine the integrity of white matter tracts involved in mood regulation (including tracts in the Papez Circuit). Diffusion tensor imaging acquired from 35 first-episode, treatment-naive adults with major depressive disorders and 34 healthy adult controls were compared. Our statistical approach compared structural integrity of 11 major white matter tracts between the major depressive disorder and adult controls, as well as illness duration influence in patients. Fractional anisotropy was decreased in the hippocampal cingulum and in the anterior thalamic radiation according to both analytical approaches, all of which were important tracts included in the Papez Circuit. Our results support the role of the Papez Circuit in major depressive disorders with the minimal probability of false positive due to similar findings in both analyses that have complementary advantages. Dysfunction of the Papez Circuit may be a potential marker for studying the pathogenesis of major depressive disorders.


title: Text Classification using String Kernels

abstract: We introduce a novel kernel for comparing two text documents. The kernel is an inner product in the feature space consisting of all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguously. The subsequences are weighted by an exponentially decaying factor of their full length in the text, hence emphasising those occurrences which are close to contiguous. A direct computation of this feature vector would involve a prohibitive amount of computation even for modest values of k, since the dimension of the feature space grows exponentially with k. The paper describes how despite this fact the inner product can be efficiently evaluated by a dynamic programming technique. A preliminary experimental comparison of the performance of the kernel compared with a standard word feature space kernel [6] is made showing encouraging results.


title: Telco Big Data: Current State & Future Directions

abstract: A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce and mobility patterns and user service experience. The ability to perform analytics on the generated big data within tolerable elapsed time and share it with key smart city enablers (e.g., municipalities, public services, startups, authorities, and companies), elevates the role of Telcos in the realm of future smart cities from pure network access providers to information providers. In this talk, we overview the state-of-the-art in Telco big data analytics by focusing on a set of basic principles, namely: (i) real-time analytics and detection; (ii) experience, behavior and retention analytics; (iii) privacy; and (iv) storage. We also present experiences from developing an innovative such architecture and conclude with open problems and future directions.


title: A Simple and Faster Branch-and-Bound Algorithm for Finding a Maximum Clique with Computational Experiments

abstract: SUMMARY Many problems can be formulated as maximum clique problems. Hence, it is highly important to develop algorithms that can find a maximum clique very fast in practice. We propose new approximate coloring and other related techniques which markedly improve the run time of the branch-and-bound algorithm MCR (J. Global Optim., 37, pp.95–111, 2007), previously shown to be the fastest maximum-clique-finding algorithm for a large number of graphs. The algorithm obtained by introducing these new techniques in MCR is named MCS. It is shown that MCS is successful in reducing the search space quite efficiently with low overhead. Extensive computational experiments confirm the superiority of MCS over MCR and other existing algorithms. It is faster than the other algorithms by orders of magnitude for several graphs. In particular, it is faster than MCR for difficult graphs of very high density and for very large and sparse graphs, even though MCS is not designed for any particular type of graph. MCS can be faster than MCR by a factor of more than 100,000 for some extremely dense random graphs. This paper demonstrates in detail the effectiveness of each new techniques in MCS, as well as the overall contribution.


title: Big Data Analytics in Bioinformatics: A Machine Learning Perspective

abstract: Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big data using the distributed and parallel computing technologies. ::: Usually big data tools perform computation in batch-mode and are not optimized for iterative processing and high data dependency among operations. In the recent years, parallel, incremental, and multi-view machine learning algorithms have been proposed. Similarly, graph-based architectures and in-memory big data tools have been developed to minimize I/O cost and optimize iterative processing. ::: However, there lack standard big data architectures and tools for many important bioinformatics problems, such as fast construction of co-expression and regulatory networks and salient module identification, detection of complexes over growing protein-protein interaction data, fast analysis of massive DNA, RNA, and protein sequence data, and fast querying on incremental and heterogeneous disease networks. This paper addresses the issues and challenges posed by several big data problems in bioinformatics, and gives an overview of the state of the art and the future research opportunities.


title: CYBERLEGS Beta-Prosthesis active knee system

abstract: The addition of active components to prostheses has the potential to extend the capabilities and reduce metabolic energy consumption of users when compared to current prosthetic technology. The CYBERLEGs Beta-Prosthesis is a new active transfemoral prosthesis that builds upon the passive principles of the CYBERLEGs Alpha-Prosthesis. The prosthesis has two active degrees of freedom, one in the ankle and one in the knee. The knee actuator is a totally new design consisting of a Weight Acceptance mechanism, Active knee drive that incorporates a primarily passive Baseline Spring system, and Energy Transfer mechanism. The use of low power motors in the knee has the potential to lead to a prosthesis that is electrically energy efficient for normal gait that is also capable of providing power for intensive actions such as stair climbing and sit-to-stand motions. Preliminary trials have shown basic walking functionality and results are described. Although this study focused on the usability of the prosthesis and general overall function, it is hoped that with better control the device will eventually be able to lead to an electrically efficient device which also can reduce user metabolic input during ambulation.


title: SPIN with Cluster for Data Centric Wireless Sensor Networks

abstract: Routing Algorithms are driving the growth of the data transmission in wireless sensor networks. Contextually, many algorithms considered the data gathering and data aggregation. This paper uses the scenario of clustering and its impact over the SPIN protocol and also finds out the effect over the energy consumption in SPIN after uses of clustering. The proposed scheme is implemented using TCL/C++ programming language and evaluated using Ns2.34 simulator and compare with LEACH. Simulation shows proposed protocol exhibits significant performance gains over the LEACH for lifetime of network and guaranteed data transmission.


title: Bands Sensitive Convolutional Network for Hyperspectral Image Classification

abstract: Hyperspectral image (HSI) classification deals with the problem of pixel-wise spectrum labelling. Traditional HSI classification algorithms focus on two major stages: feature extraction and classifier design. Though studied for decades, HSI classification hasn't been perfectly solved. One of the main reasons relies on the fact that features extracted by embedding methods can hardly match an ad hoc classifier. Recently, deep learning methods achieve an end-to-end mechanism and can learn features suitable for classification from the raw data. Inspired by the newly proposed work on deep learning for HSI classification, in this paper, we propose to build a deep convolutional network based on the analysis of spectral band discriminative characteristics. More specifically, we first split the spectrum bands into groups based on their correlation relationships. Then we build a band variant CNN submodel, where each group is modelled by one of those submodels. Meanwhile, a conventional CNN model is also learned globally on the spatial-spectral space, to maintain robustness of submodel changes. Lastly, we concatenate the global CNN model and band-specific CNN submodels to one unique model. In this way, global robustness and band variance are mixed together. Experiments on publicly available datasets demonstrate the great performance of the proposed method.


title: Cyber Physical Systems Security: A Brief Survey

abstract: Security challenges in Cyber Physical Systems necessitate solutions that are robust to threats posed especially when used in applications to monitor and secure critical infrastructures. In this work, we provide a decent bibliographic review of the existing literature on security of Cyber Physical Systems, identify key research challenges and discuss future directions on open research issues.


title: Extracting Opinion Expressions with semi-Markov Conditional Random Fields

abstract: Extracting opinion expressions from text is usually formulated as a token-level sequence labeling task tackled using Conditional Random Fields (CRFs). CRFs, however, do not readily model potentially useful segment-level information like syntactic constituent structure. Thus, we propose a semi-CRF-based approach to the task that can perform sequence labeling at the segment level. We extend the original semi-CRF model (Sarawagi and Cohen, 2004) to allow the modeling of arbitrarily long expressions while accounting for their likely syntactic structure when modeling segment boundaries. We evaluate performance on two opinion extraction tasks, and, in contrast to previous sequence labeling approaches to the task, explore the usefulness of segmentlevel syntactic parse features. Experimental results demonstrate that our approach outperforms state-of-the-art methods for both opinion expression tasks.


title: Mediating the expression of emotion in educational collaborative virtual environments: an experimental study

abstract: The use of avatars with emotionally expressive faces is potentially highly beneficial to communication in collaborative virtual environments (CVEs), especially when used in a distance learning context. However, little is known about how, or indeed whether, emotions can effectively be transmitted through the medium of a CVE. Given this, an avatar head model with limited but human-like expressive abilities was built, designed to enrich CVE communication. Based on the facial action coding system (FACS), the head was designed to express, in a readily recognisable manner, the six universal emotions. An experiment was conducted to investigate the efficacy of the model. Results indicate that the approach of applying the FACS model to virtual face representations is not guaranteed to work for all expressions of a particular emotion category. However, given appropriate use of the model, emotions can effectively be visualised with a limited number of facial features. A set of exemplar facial expressions is presented.


title: Secure IoT Platform for Industrial Control Systems

abstract: Supervisory control and data acquisition (SCADA) systems, are part of industrial control system (ICS), have been playing crucial roles in real-time industrial automation and controls. Through the evolution of 3rd generation, or networks based system, SCADA systems are connected to almost types of networks such as wired, wireless, and cellular and satellite communication, but security is still a big challenge for SCADA system while communicating within. Internet of things (IoT) is a ubiquitous platform, a new advance enhancement, for efficient SCADA system, where billions of network devices, with smart sensing capabilities, are networked over the Internet access. Deployment of smart IoT platform, SCADA system will significantly increase system efficiency, scalability, and reduce cost. Security is still a major issue for both-, as they were initially designed without any priority and requirements of security. This study modeled IoT-SCADA system and deployed a security mechanism, employing of cryptography based algorithm, which provided a secure transmission channel while each time communication occurred, between the field devices in the SCADA system. Proposed security implementation, and computed measurements analyzed as potential security building block against authentication and confidentiality attacks.


title: A Primary Care Clinician's and Consultant's Guide to Medicating for Pain and Anxiety Associated with Outpatient Procedures.

abstract: This article offers clinical suggestions for the design of a patient-specific outpatient treatment plan for managing and reducing short-term pain associated with outpatient procedures. These procedures may actually produce pain, or they may be perceived as causing pain and anxiety. The article will also focus on the design of a patient-specific outpatient treatment plan for managing and reducing short-term procedural-induced anxiety, as perceived pain and anxiety or pain and anxiety themselves may be a reason for which patients are noncompliant with their office visits. The treatment plan will focus on pre-procedure, peri-procedure, and post-procedure interventions using nonsteroidal anti-inflammatory drugs and anxiolytics (benzodiazepines, opiate agonists, and sedative hypnotics). The plan also includes adjuvant therapy to decrease the patient's perception of pain, anxiety, fear of procedure, and fear of outcomes in order to facilitate a higher degree of compliance with office procedures. This article also accounts for the patient's physiology, pathophysiology, pathology, and iatrogenic effects from drug treatment plans.


title: A Large-Scale, Long-Term Analysis of Mobile Device Usage Characteristics

abstract: Today, mobile devices like smartphones and tablets have become an indispensable part of people's lives, posing many new questions e.g., in terms of interaction methods, but also security. In this paper, we conduct a large scale, long term analysis of mobile device usage characteristics like session length, interaction frequency, and daily usage in locked and unlocked state with respect to location context and diurnal pattern. Based on detailed logs from 29,279 mobile phones and tablets representing a total of 5,811 years of usage time, we identify and analyze 52.2 million usage sessions with some participants providing data for more than four years. Our results show that context has a highly significant effect on both frequency and extent of mobile device usage, with mobile phones being used twice as much at home compared to in the office. Interestingly, devices are unlocked for only 46 % of the interactions. We found that with an average of 60 interactions per day, smartphones are used almost thrice as often as tablet devices (23), while usage sessions on tablets are three times longer, hence are used almost for an equal amount of time throughout the day. We conclude that usage session characteristics differ considerably between tablets and smartphones. These results inform future approaches to mobile interaction as well as security.


title: Data structure forsoft objects

abstract: We introduce the concept ofsoft objects whose shape changes in response to their surroundings. Established geometric modelling techniques exist to handle most engineering components, including ‘free form’ shapes such as car bodies and telephones. More recently, there has been a lot of interest in modelling natural pheomena such as smoke, clouds, mountains and coastlines where the shapes are described stochastically, or as fractals. None of these techniques lends itself to the description ofsoft objects. This class of objects includes fabrics, cushions, living forms, mud and water. In this paper, we describe a method of modelling such objects and discuss its uses in animation. Our method is to represent asoft object, or collection of objects, as a surface of constant value in a scalar field over three dimensions. The main technical problem is to avoid calculating the field value at too many points. We do this with a combination of data structures at some cost in internal memory usage.


title: A pattern recognition system for malicious PDF files detection

abstract: Malicious PDF files have been used to harm computer security during the past two-three years, and modern antivirus are proving to be not completely effective against this kind of threat. In this paper an innovative technique, which combines a feature extractor module strongly related to the structure of PDF files and an effective classifier, is presented. This system has proven to be more effective than other state-of-the-art research tools for malicious PDF detection, as well as than most of antivirus in commerce. Moreover, its flexibility allows adopting it either as a stand-alone tool or as plug-in to improve the performance of an already installed antivirus.


Evolutionary Computation and Convergence to a Pareto Front