title: An HMM and structural entropy based detector for Android malware

abstract: Smartphones are becoming more and more popular and, as a consequence, malware writers are increasingly engaged to develop new threats and propagate them through official and third-party markets. In addition to the propagation vectors, malware is also evolving quickly the techniques adopted for infecting victims and hiding their malicious nature to antimalware scanning. From SMS Trojans to legitimate applications repacked with malicious payload, from AES encrypted root exploits to the dynamic loading of a payload retrieved from a remote server: malicious code is becoming more and more hard to detect.In this paper we experimentally evaluate two techniques for detecting Android malware: the first one is based on Hidden Markov Model, while the second one exploits structural entropy. These two techniques have been successfully applied to detect PCs viruses in previous works, and only one work in literature analyzes the application of HMM to the detection of Android malware. We demonstrate that these methods, which reveal effective for PCs virus, are also successful for detecting and classifying mobile malware.Our results are promising: we obtain a precision of 0.96 to discriminate a malware application, and a precision of 0.978 to identify the malware family.


title: A domain knowledge-based approach for automatic correction of printed invoices

abstract: Although OCR technology is now commonplace, character recognition errors are still a problem, in particular, in automated systems for information extraction from printed documents. This paper proposes a method for the automatic detection and correction of OCR errors in an information extraction system. Our algorithm uses domain-knowledge about possible misrecognition of characters to propose corrections; then it exploits knowledge about the type of the extracted information to perform syntactic and semantic checks in order to validate the proposed corrections. We assess our proposal on a real-world, highly challenging dataset composed of nearly 800 values extracted from approximately 100 commercial invoices and we obtained very good results.


title: EEG Databases for Emotion Recognition

abstract: Emotion recognition from Electroencephalogram (EEG) rapidly gains interest from research community. Two affective EEG databases are presented in this paper. Two experiments are conducted to set up the databases. Audio and visual stimuli are used to evoke emotions during the experiments. The stimuli are selected from IADS and IAPS databases.14 subjects participated in each experiment. Emotiv EEG device is used for the data recording. The EEG data are rated by the participants with arousal, valence, and dominance levels. The correlation between powers of different EEG bands and the affective ratings is studied. The results agree with the literature findings and analyses of benchmark DEAP database that proves the reliability of the two databases. Similar brain patterns of emotions are obtained between the established databases and the benchmark database. A SVM-based emotion recognition algorithm is proposed and applied to both databases and the benchmark database. Use of a Fractal Dimension feature in combination with statistical and Higher Order Crossings (HOC) features gives us results with the best accuracy. Up to 8 emotions can be recognized. The accuracy is consistent between the established databases and the benchmark database.


title: Outer Product-based Neural Collaborative Filtering

abstract: In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model.


title: Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays

abstract: Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer and max-min pooling for classifying common thoracic diseases as well as localizing suspicious lesion regions on chest X-rays. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of proposed model and its better performance against the state-of-the-art pipelines.


title: Crowds by Example

abstract: We present an example-based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real-world examples, our autonomous agents display complex natural behaviors that are often missing in crowd simulations. Examples are created from tracked video segments of real pedestrian crowds. During a simulation, autonomous agents search for examples that closely match the situation that they are facing. Trajectories taken by real people in similar situations, are copied to the simulated agents, resulting in seemingly natural behaviors.


title: Being happy, healthy and whole watching movies that affect our emotions

abstract: This paper discusses the power of emotions in our health, happiness and wholeness, and the emotional impact of movies. It presents iFelt, an interactive video application to classify, access, explore and visualize movies based on their emotional properties and impact.


title: Course correction: using analytics to predict course success

abstract: Predictive analytics techniques applied to a broad swath of student data can aid in timely intervention strategies to help prevent students from failing a course. This paper discusses a predictive analytic model that was created for the University of Phoenix. The purpose of the model is to identify students who are in danger of failing the course in which they are currently enrolled. Within the model's architecture, data from the learning management system (LMS), financial aid system, and student system are combined to calculate a likelihood of any given student failing the current course. The output can be used to prioritize students for intervention and referral to additional resources. The paper includes a discussion of the predictor and statistical tests used, validation procedures, and plans for implementation.


title: Network Function Virtualization as a Service for multi-tenant software defined networks

abstract: In this paper, we assess the performance of a Network Function Virtualization as a Service (NFVaaS) platform when deployed using a unikernel minimal operating system and running on top of a network hypervisor for software defined networks (SDNs). Our NFVaaS infrastructure is built on top of the unikernel minimal operating system ClickOS and the OpenVirteX network hypervisor, which enforces network isolation in multi-tenant SDN networks. Our experimental results demonstrate the effectiveness of using ClickOS when deploying and moving individual virtual network functions (VNFs) in multi-tenant SDN networks. We also identified the main limitations in terms of number of active VNFs that can be deployed at the same time, as well as the maximum memory size that can be allocated to each VNF in respect to the underlying available hardware resources. Moreover, we performed a thorough analysis and assessment of the impact of VNFs' placement on network performance, especially when multiple VNFs are organized as a service chain to create more sophisticated network functionalities.


Detecting Untranslated Content for Neural Machine Translation


title: Halleux. Pex-white box test generation for.NET

abstract: Pex automatically produces a small test suite with high code coverage for a .NET program. To this end, Pex performs a systematic program analysis (using dynamic symbolic execution, similar to path-bounded model-checking) to determine test inputs for Parameterized Unit Tests. Pex learns the program behavior by monitoring execution traces. Pex uses a constraint solver to produce new test inputs which exercise different program behavior. The result is an automatically generated small test suite which often achieves high code coverage. In one case study, we applied Pex to a core component of the .NET runtime which had already been extensively tested over several years. Pex found errors, including a serious issue.


title: Learning a commonsense moral theory

abstract: We introduce a computational framework for understanding the structure and dynamics of moral learning, with a focus on how people learn to trade off the interests and welfare of different individuals in their social groups and the larger society. We posit a minimal set of cognitive capacities that together can solve this learning problem: (1) an abstract and recursive utility calculus to quantitatively represent welfare trade-offs; (2) hierarchical Bayesian inference to understand the actions and judgments of others; and (3) meta-values for learning by value alignment both externally to the values of others and internally to make moral theories consistent with one’s own attachments and feelings. Our model explains how children can build from sparse noisy observations of how a small set of individuals make moral decisions to a broad moral competence, able to support an infinite range of judgments and decisions that generalizes even to people they have never met and situations they have not been in or observed. It also provides insight into the causes and dynamics of moral change across time, including cases when moral change can be rapidly progressive, changing values significantly in just a few generations, and cases when it is likely to move more slowly.


title: Cri du chat syndrome: A critical review

abstract: A new syndrome was identified in 1963, when Lejeune et al. reported a genetic disease resulting from a partial or total deletion on the short arm of chromosome 5 (5p-) and named it the cri du chat syndrome (CdCS). This term makes reference to the main clinical feature of the syndrome, a high-pitched monochromatic cat-like crying, that usually disappears in the first years of life. CdCS is one of the most common chromosomal deletion syndromes in humans, with an incidence of 1:15.000-1:50.000 live-births. Our purpose was to review different aspects of this syndrome (concept, epidemiology, aetiology, clinical features, diagnostic methods and prognosis) emphasizing both: the breakthrough in this field introduced by new cytoge netic and molecular techniques, and the orofacial manifestations most frequently reported. The main orofacial anomalies registered were mandibular microretrognathia, high palate but rarely cleft, variable malocclusion (frequently anterior open-bite), enamel hypoplasia, poor oral hygiene, generalized chronic periodontitis, and retardation of tooth eruption, although there is not enough evidence to support any clear association between these pathologies and the CdCS.


title: Technological paradigms: past, present and future

abstract: The special issue is introduced and contextualised. “Technological paradigms” emerged as “science push” models of innovation were being displaced by “demand pull” models that justified a more international, market-focussed political economy. Technological paradigms help explain the strengths and weaknesses of both models and why the governance choice is not between either markets or governments, but an appropriate mixture of both. While “technological paradigms” have successfully shifted policy and management attention to building stocks of knowledge, they still have substantial underexploited analytical potential. Copyright 2008 , Oxford University Press.


title: The ductuli efferentes of the epididymal region of birds.

abstract: Ultrastructural studies were undertaken on the efferent ductules of the testis of the domestic fowl (Gallus domesticus), Japanese quail (Coturnix coturnix japonica) and guinea-fowl (Numida meleagris). Four cell types were identified and described: ciliated cells which were found in the epithelium of both the proximal and distal segments of the efferent ductules, non-ciliated Type I cell which, together with the ciliated cell, formed the epithelium of the proximal efferent ductule and the non-ciliated Type II cell which, together with the ciliated cell, formed the epithelium of the distal efferent ductule. Intraepithelial lymphocytes were the fourth cell type found in the epithelium of both segments of the efferent ductule of the fowl only.


title: SeaModem: A low-cost underwater acoustic modem for shallow water communication

abstract: SeaModem is a low-cost underwater acoustic modem developed by AppliCon, a spin-off of the University of Calabria, for shallow water communications. In its default configuration, it operates in the 25–35 KHz frequency range and offers a wide range of selectable features including selectable 2-4-8 FSK modulation tones, error detection and correction algorithms via CRC and Viterbi, selectable power transmission up to 40 W, one-way and two-way range calculation capabilities, network capabilities allowing communications among a maximum of 15 modems in the same area and a host interface via a configurable UART. The paper contains a detailed description of the system and the results of several tests are also provided.


title: Data is more than knowledge: implications of the reversed knowledge hierarchy for knowledge management and organizational memory

abstract: In knowledge management literature it is often pointed out that it is important to distinguish between data, information and knowledge. The generally accepted view sees data as simple facts that become information as data is combined into meaningful structures, which subsequently become knowledge as meaningful information is put into a context and when it can be used to make predictions. This view sees data as a prerequisite for information, and information as a prerequisite for knowledge. I explore the conceptual hierarchy of data, information and knowledge, showing that data emerges only after we have information, and that information emerges only after we already have knowledge. The reversed hierarchy of knowledge is shown to lead to a different approach in developing information systems that support knowledge management and organizational memory. It is also argued that this difference may have major implications for organizational flexibility and renewal.


title: Assessing vulnerability exploitability risk using software properties

abstract: Attacks on computer systems are now attracting increased attention. While the current trends in software vulnerability discovery indicate that the number of newly discovered vulnerabilities continues to be significant, the time between the public disclosure of vulnerabilities and the release of an automated exploit is shrinking. Thus, assessing the vulnerability exploitability risk is critical because this allows decision-makers to prioritize among vulnerabilities, allocate resources to patch and protect systems from these vulnerabilities, and choose between alternatives. Common vulnerability scoring system (CVSS) metrics have become the de facto standard for assessing the severity of vulnerabilities. However, the CVSS exploitability measures assign subjective values based on the views of experts. Two of the factors in CVSS, Access Vector and Authentication, are the same for almost all vulnerabilities. CVSS does not specify how the third factor, Access Complexity, is measured, and hence it is unknown whether it considers software properties as a factor. In this work, we introduce a novel measure, Structural Severity, which is based on software properties, namely attack entry points, vulnerability location, the presence of the dangerous system calls, and reachability analysis. These properties represent metrics that can be objectively derived from attack surface analysis, vulnerability analysis, and exploitation analysis. To illustrate the proposed approach, 25 reported vulnerabilities of Apache HTTP server and 86 reported vulnerabilities of Linux Kernel have been examined at the source code level. The results show that the proposed approach, which uses more detailed information, can objectively measure the risk of vulnerability exploitability and results can be different from the CVSS base scores.


title: Comparative Analysis of Discontinuous and Continuous PWM Techniques in VSI-Fed Five-Phase Induction Motor

abstract: This paper reports on flux harmonic distortion factor and current ripple analysis for various discontinuous pulse-width-modulation (PWM) techniques, which are applicable in conjunction with five-phase two-level inverter-fed induction motors. The analysis is based on the application of the complex space vector approach. The results are compared throughout with the corresponding continuous PWM techniques. Different space vector PWM and carrier-based PWM methods are encompassed by the analysis. This paper is supported by extensive simulation results. Verification of theoretical findings is provided through experimental measurements on a five-phase induction motor.


The implementation of functional languages using custom hardware


title: We don't need another hero.

abstract: Everybody loves the stories of heroes like Martin Luther King, Jr., Mother Teresa, and Gandhi. But the heroic model of moral leadership usually doesn't work in the corporate world. Modesty and restraint are largely responsible for the achievements of the most effective moral leaders in business. The author, a specialist in business ethics, says the quiet leaders he has studied follow four basic rules in meeting ethical challenges and making decisions. The rules constitute an important resource for executives who want to encourage the development of such leaders among their middle managers. The first rule is "Put things off till tomorrow." The passage of time allows turbulent waters to calm and lets leaders' moral instincts emerge. "Pick your battles" means that quiet leaders don't waste political capital on fights they can't win; they save it for occasions when they really want to fight. "Bend the rules, don't break them" sounds easier than it is--bending the rules in order to resolve a complicated situation requires imagination, discipline, restraint, flexibility, and entrepreneurship. The fourth rule, "Find a compromise," reflects the author's finding that quiet leaders try not to see situations as polarized tests of ethical principles. These individuals work hard to craft compromises that are "good enough"--responsible and workable enough--to satisfy themselves, their companies, and their customers. The vast majority of difficult problems are solved through the consistent striving of people working far from the limelight. Their quiet approach to leadership doesn't inspire, thrill, or provide story lines for uplifting TV shows. But the unglamorous efforts of quiet leaders make a tremendous difference every day in the corporate world.


title: Predicting The Semantic Orientation Of Adjectives

abstract: We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achieving 82% accuracy in this task when each conjunction is considered independently. Combining the constraints across many adjectives, a clustering algorithm separates the adjectives into groups of different orientations, and finally, adjectives are labeled positive or negative. Evaluations on real data and simulation experiments indicate high levels of performance: classification precision is more than 90% for adjectives that occur in a modest number of conjunctions in the corpus.


title: Predicting disease transmission from geo-tagged micro-blog data

abstract: Researchers have begun to mine social network data in order to predict a variety of social, economic, and health related phenomena. While previous work has focused on predicting aggregate properties, such as the prevalence of seasonal influenza in a given country, we consider the task of fine-grained prediction of the health of specific people from noisy and incomplete data. We construct a probabilistic model that can predict if and when an individual will fall ill with high precision and good recall on the basis of his social ties and co-locations with other people, as revealed by their Twitter posts. Our model is highly scalable and can be used to predict general dynamic properties of individuals in large realworld social networks. These results provide a foundation for research on fundamental questions of public health, including the identification of non-cooperative disease carriers ("Typhoid Marys"), adaptive vaccination policies, and our understanding of the emergence of global epidemics from day-today interpersonal interactions.


title: Joint Unsupervised Learning of Deep Representations and Image Clusters

abstract: In this paper, we propose a recurrent framework for Joint Unsupervised LEarning (JULE) of deep representations and image clusters. In our framework, successive operations in a clustering algorithm are expressed as steps in a recurrent process, stacked on top of representations output by a Convolutional Neural Network (CNN). During training, image clusters and representations are updated jointly: image clustering is conducted in the forward pass, while representation learning in the backward pass. Our key idea behind this framework is that good representations are beneficial to image clustering and clustering results provide supervisory signals to representation learning. By integrating two processes into a single model with a unified weighted triplet loss and optimizing it end-to-end, we can obtain not only more powerful representations, but also more precise image clusters. Extensive experiments show that our method outperforms the state-of-the-art on image clustering across a variety of image datasets. Moreover, the learned representations generalize well when transferred to other tasks.


title: Real-Time Automatic Traffic Accident Recognition Using HFG

abstract: Recently, the problem of automatic traffic accident recognition has appealed to the machine vision community due to its implications on the development of autonomous Intelligent Transportation Systems (ITS). In this paper, a new framework for real-time automated traffic accidents recognition using Histogram of Flow Gradient (HFG) is proposed. This framework performs two major steps. First, HFG-based features are extracted from video shots. Second, logistic regression is employed to develop a model for the probability of occurrence of an accident by fitting data to a logistic curve. In case of occurrence of an accident, the trajectory of vehicle by which the accident was occasioned is determined. Preliminary results on real video sequences confirm the effectiveness and the applicability of the proposed approach, and it can offer delay guarantees for real-time surveillance and monitoring scenarios.


title: Code Comparison System based on Abstract Syntax Tree

abstract: The code comparison technology plays a very important part in the work of plagiarism detection and software evaluation. Software plagiarism mainly appears as copy-and-paste or with a little modification after this, which will not change the function of the code, such as replacing the name of methods or variables, reordering the sequence of the statements etc. This paper introduces a plagiarism detection tool named CCS (Code Comparison System) which is based on the Abstract Syntax Tree (AST). According to the syntax tree's characteristics, CCS calculates their hash values, transforms their storage forms, and then compares them node by node. As a result, the efficiency improves. Moreover, CCS preprocesses a large amount of source code in its database for potential use, which also accelerate the course of plagiarism detection. CCS also takes special measurement to reduce mistakes when calculating the hash values of the operations like subtraction and division. It performs well in the code comparison field, and is able to help with the copyright protecting of the source code.


title: MVC: A Dataset for View-Invariant Clothing Retrieval and Attribute Prediction

abstract: Clothing retrieval and clothing style recognition are important and practical problems. They have drawn a lot of attention in recent years. However, the clothing photos collected in existing datasets are mostly of front- or near-front view. There are no datasets designed to study the influences of different viewing angles on clothing retrieval performance. To address view-invariant clothing retrieval problem properly, we construct a challenge clothing dataset, called Multi-View Clothing dataset. This dataset not only has four different views for each clothing item, but also provides 264 attributes for describing clothing appearance. We adopt a state-of-the-art deep learning method to present baseline results for the attribute prediction and clothing retrieval performance. We also evaluate the method on a more difficult setting, cross-view exact clothing item retrieval. Our dataset will be made publicly available for further studies towards view-invariant clothing retrieval.


title: Simple Random Sampling from Relational Databases*

abstract: Sampling as a fundamental operation for the auditing and statistical analysis of large databases is discussed. Methods of obtaining samples from the results of relational queries without first performing the query are presented. Specifically, simple random sampling from selections, projections, joins, unions, and intersections is examined. Also discussed are data structures and algorithms for sampling, and their performance. It is shown how samples of relational queries can often be computed for a small fraction of the effort of computing the entire relational query. (DWL)


Non-sentential Question Resolution using Sequence to Sequence Learning


title: Probabilistic near-duplicate detection using simhash

abstract: This paper offers a novel look at using a dimensionality-reduction technique called simhash to detect similar document pairs in large-scale collections. We show that this algorithm produces interesting intermediate data, which is normally discarded, that can be used to predict which of the bits in the final hash are more susceptible to being flipped in similar documents. This paves the way for a probabilistic search technique in the Hamming space of simhashes that can be significantly faster and more space-efficient than the existing simhash approaches. We show that with 95% recall compared to deterministic search of prior work, our method exhibits 4-14 times faster lookup and requires 2-10 times less RAM on our collection of 70M web pages.


title: A feature selection-based framework for human activity recognition using wearable multimodal sensors

abstract: Human activity recognition is important for many applications. This paper describes a human activity recognition framework based on feature selection techniques. The objective is to identify the most important features to recognize human activities. We first design a set of new features (called physical features) based on the physical parameters of human motion to augment the commonly used statistical features. To systematically analyze the impact of the physical features on the performance of the recognition system, a single-layer feature selection framework is developed. Experimental results indicate that physical features are always among the top features selected by different feature selection methods and the recognition accuracy is generally improved to 90%, or 8% better than when only statistical features are used. Moreover, we show that the performance is further improved by 3.8% by extending the single-layer framework to a multi-layer framework which takes advantage of the inherent structure of human activities and performs feature selection and classification in a hierarchical manner.


DEEP RESIDUAL LEARNING FOR TOMATO PLANT LEAF DISEASE IDENTIFICATION


Intelligent Bayesian Asset Allocation via Market Sentiment Views


Just entertainment: effects of TV series about intrigue on young adults


title: High resolution maps from wide angle sonar

abstract: We describe the use of multiple wide-angle sonar range measurements to map the surroundings of an autonomous mobile robot. A sonar range reading provides information concerning empty and occupied volumes in a cone (subtending 30 degrees in our case) in front of the sensor. The reading is modelled as probability profiles projected onto a rasterized map, where somewhere occupied and everywhere empty areas are represented. Range measurements from multiple points of view (taken from multiple sensors on the robot, and from the same sensors after robot moves) are systematically integrated in the map. Overlapping empty volumes re-inforce each other, and serve to condense the range of occupied volumes. The map definition improves as more readings are added. The final map shows regions probably occupied, probably unoccupied, and unknown areas. The method deals effectively with clutter, and can be used for motion planning and for extended landmark recognition. This system has been tested on the Neptune mobile robot at CMU.


title: Feature Selection Using a Neural Framework With Controlled Redundancy

abstract: We first present a feature selection method based on a multilayer perceptron (MLP) neural network, called feature selection MLP (FSMLP). We explain how FSMLP can select essential features and discard derogatory and indifferent features. Such a method may pick up some useful but dependent (say correlated) features, all of which may not be needed. We then propose a general scheme for dealing with feature selection with “controlled redundancy” (CoR). The proposed scheme, named as FSMLP-CoR, can select features with a controlled redundancy both for classification and function approximation/prediction type problems. We have also proposed a new more effective training scheme named mFSMLP-CoR. The idea is general in nature and can be used with other learning schemes also. We demonstrate the effectiveness of the algorithms using several data sets including a synthetic data set. We also show that the selected features are adequate to solve the problem at hand. Here, we have considered a measure of linear dependency to control the redundancy. The use of nonlinear measures of dependency, such as mutual information, is straightforward. Here, there are some advantages of the proposed schemes. They do not require explicit evaluation of the feature subsets. Here, feature selection is integrated into designing of the decision-making system. Hence, it can look at all features together and pick up whatever is necessary. Our methods can account for possible nonlinear subtle interactions between features, as well as that between features, tools, and the problem being solved. They can also control the level of redundancy in the selected features. Of the two learning schemes, mFSMLP-CoR, not only improves the performance of the system, but also significantly reduces the dependency of the network's behavior on the initialization of connection weights.


title: KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor

abstract: Motivation: KEGG PATHWAY is a service of Kyoto Encyclopedia of Genes and Genomes (KEGG), constructing manually curated pathway maps that represent current knowledge on biological networks in graph models. While valuable graph tools have been implemented in R/Bioconductor, to our knowledge there is currently no software package to parse and analyze KEGG pathways with graph theory. ::: ::: Results: We introduce the software package KEGGgraph in R and Bioconductor, an interface between KEGG pathways and graph models as well as a collection of tools for these graphs. Superior to existing approaches, KEGGgraph captures the pathway topology and allows further analysis or dissection of pathway graphs. We demonstrate the use of the package by the case study of analyzing human pancreatic cancer pathway. ::: ::: Availability:KEGGgraph is freely available at the Bioconductor web site (http://www.bioconductor.org). KGML files can be downloaded from KEGG FTP site (ftp://ftp.genome.jp/pub/kegg/xml). ::: ::: Contact: ed.grebledieh-zfkd@gnahz.j ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: Diode reverse recovery analysis of Cockcroft-Walton voltage multiplier for high voltage generation

abstract: Cockcroft-Walton voltage multiplier circuit is widely used for high voltage generation circuit. The diode reverse recovery effect of Cockcroft-Walton voltage multiplier circuit is investigated by analysis and circuit simulation. According to the analysis and circuit simulation study, it can be concluded that the multiplier diode reverse recovery problem is mainly caused by the diodes in the first stage voltage multiplier. The most effective and economic way to alleviate the diode reverse recovery problem is employing diodes with good reverse recovery performance such as silicon carbide Schottky diodes only in the first stage for good system performance. The experimental results of 3 stages Cockcroft-Walton voltage multiplier circuit hardware prototype operating at 300kHz switching frequency validate the concept based on analysis and simulation study. The silicon carbide diode without reverse recovery needs to be used only in the first stage of voltage multiplier circuit to effectively mitigate the reverse recovery problems in high frequency operations with good circuit performance.


Grasp Planning for a Multifingered Hand with a Humanoid Robot


title: Probabilistic inference for determining options in reinforcement learning

abstract: Tasks that require many sequential decisions or complex solutions are hard to solve using conventional reinforcement learning algorithms. Based on the semi Markov decision process setting (SMDP) and the option framework, we propose a model which aims to alleviate these concerns. Instead of learning a single monolithic policy, the agent learns a set of simpler sub-policies as well as the initiation and termination probabilities for each of those sub-policies. While existing option learning algorithms frequently require manual specification of components such as the sub-policies, we present an algorithm which infers all relevant components of the option framework from data. Furthermore, the proposed approach is based on parametric option representations and works well in combination with current policy search methods, which are particularly well suited for continuous real-world tasks. We present results on SMDPs with discrete as well as continuous state-action spaces. The results show that the presented algorithm can combine simple sub-policies to solve complex tasks and can improve learning performance on simpler tasks.


title: DESIGN AND SYNTHESIS OF SYNCHRONIZATION SKELETONS USING BRANCHING TIME TEMPORAL LOGIC

abstract: We Propose a method of constructing concurrent programs in which the synchronization skeletonof the program is automatically synthesized from a high-level (branching time) Temporal Logic specification. The synchronization skeleton is an abstraction of the actual program where detail irrelevant to synchronization is suppressed. For example, in the synchronization skeleton for a solution to the critical section problem each process's critical section may be viewed as a single node since the internal structure of the critical section is unimportant. Most solutions to synchronization problems in the literature are in fact given as synchronization skeletons. Because synchronization skeletons are in general finite state, the propositional version of Temporal Logic can be used to specify their properties.


title: Data reorganization in memory using 3D-stacked DRAM

abstract: In this paper we focus on common data reorganization operations such as shuffle, pack/unpack, swap, transpose, and layout transformations. Although these operations simply relocate the data in the memory, they are costly on conventional systems mainly due to inefficient access patterns, limited data reuse and roundtrip data traversal throughout the memory hierarchy. This paper presents a two pronged approach for efficient data reorganization, which combines (i) a proposed DRAM-aware reshape accelerator integrated within 3D-stacked DRAM, and (ii) a mathematical framework that is used to represent and optimize the reorganization operations. We evaluate our proposed system through two major use cases. First, we demonstrate the reshape accelerator in performing a physical address remapping via data layout transform to utilize the internal parallelism/locality of the 3D-stacked DRAM structure more efficiently for general purpose workloads. Then, we focus on offloading and accelerating commonly used data reorganization routines selected from the Intel Math Kernel Library package. We evaluate the energy and performance benefits of our approach by comparing it against existing optimized implementations on state-of-the-art GPUs and CPUs. For the various test cases, in-memory data reorganization provides orders of magnitude performance and energy efficiency improvements via low overhead hardware.


CACTI 6.0: A Tool to Understand Large Caches


title: AES-128 ECB encryption on GPUs and effects of input plaintext patterns on performance

abstract: In the recent years, the Graphics Processing Units (GPUs) have gained popularity for general purpose applications, immensely outperforming traditional optimized CPU based implementations. A class of such applications implemented on GPUs to achieve faster execution than CPUs include cryptographic techniques like the Advanced Encryption Standard (AES) which is a widely deployed symmetric encryption/decryption scheme in various electronic communication domains. With the drastic advancements in electronic communication technology, and growth in the user space, the size of data exchanged electronically has increased substantially. So, such cryptographic techniques become a bottleneck to fast transfers of information. In this work, we implement the AES-128 ECB Encryption on two of the recent and advanced GPUs (NVIDIA Quadro FX 7000 and Tesla K20c) with different memory usage schemes and varying input plaintext sizes and patterns. We obtained a speedup of up to 87x against an advanced CPU (Intel Xeon X5690) based implementation. Moreover, our experiments reveal that the different degrees of pattern repetitions in input plaintext affect the encryption performance on GPU.


title: Single Image 3D Interpreter Network

abstract: Understanding 3D object structure from a single image is an important but difficult task in computer vision, mostly due to the lack of 3D object annotations in real images. Previous work tackles this problem by either solving an optimization task given 2D keypoint positions, or training on synthetic data with ground truth 3D information.


title: Academic Self-Efficacy, Coping, and Academic Performance in College

abstract: This study serves as a pilot study for a possible future study including the same variables. The purpose of the pilot study was to find a relationship in the college academic setting between academic self-efficacy, stress coping skills, and academic performance. Sixty-six undergraduate students, 17 male and 49 female, from a university in northwestern United States participated in the study. Stress was measured using the COPE Inventory (Carver, Scheier, & Weintraub, 1989). Self-efficacy was measured using the Academic Self-Efficacy Scale (Chemers, Hu, & Garcia, 2001). Academic performance was measured using the participants’ college GPA. Academic Self-Efficacy and the Planning subscale of the COPE Inventory were positively correlated with GPA (r = .49, p < .01 and r = .32, p < .05). Academic self-efficacy was positively correlated with the COPE Inventory subscales Positive Reinterpretation and Growth (r = .36, p r = .35, p < .01), Acceptance (r = .46, p < .01), and Planning (r = .25, p < .05). Academic self-efficacy was negatively correlated with the COPE Inventory subscale Substance Use (r = -.32 at p < 0.1).


title: Object category understanding via eye fixations on freehand sketches

abstract: The study of eye gaze fixations on photographic images is an active research area. In contrast, the image sub-category of freehand sketches has not received as much attention for such studies. In this paper, we analyze the results of a free-viewing gaze fixation study conducted on 3904 freehand sketches distributed across 160 object categories. Our analysis shows that fixation sequences exhibit marked consistency within a sketch, across sketches of a category and even across suitably grouped sets of categories. This multi-level consistency is remarkable given the variability in depiction and extreme image content sparsity that characterizes hand-drawn object sketches. In this paper, we show that the multi-level consistency in the fixation data can be exploited to 1) predict a test sketch’s category given only its fixation sequence and 2) build a computational model which predicts part-labels underlying fixations on objects. We hope that our findings motivate the community to deem sketch-like representations worthy of gaze-based studies vis-a-vis photographic images.


title: A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining

abstract: We present the Trip-MAML dataset, a Multi-Lingual dataset of hotel reviews that have been manually annotated at the sentence-level with Multi-Aspect sentiment labels. This dataset has been built as an extension of an existent English-only dataset, adding documents written in Italian and Spanish. We detail the dataset construction process, covering the data gathering, selection, and annotation. We present inter-annotator agreement figures and baseline experimental results, comparing the three languages. Trip-MAML is a multi-lingual dataset for aspect-oriented opinion mining that enables researchers (i) to face the problem on languages other than English and (ii) to the experiment the application of cross-lingual learning methods to the task.


title: A Vision of Swarmlets

abstract: Swarmlets are applications and services that leverage networked sensors and actuators with cloud services and mobile devices. This article offers a way to construct swarmlets by composing "accessors,"' which are wrappers for sensors, actuators, and services, that export an actor interface. Actor semantics provides ways to compose accessors with disciplined and understandable concurrency models, while hiding from the swarmlet the details of the mechanisms by which the accessor provides sensor data, controls an actuator, or accesses a service. This architecture can leverage the enormous variety of mechanisms that have emerged for such interactions, including HTTP, Websockets, CoAP, and MQTT. Recognizing that these standards have emerged because of huge variability of requirements for bandwidth, latency, and security, accessors embrace heterogeneity instead of attempting to homogenize.


title: The logic in language: How all quantifiers are alike, but each quantifier is different

abstract: Abstract Quantifier words like each , every , all and three are among the most abstract words in language. Unlike nouns, verbs and adjectives, the meanings of quantifiers are not related to a referent out in the world. Rather, quantifiers specify what relationships hold between the sets of entities, events and properties denoted by other words. When two quantifiers are in the same clause, they create a systematic ambiguity. “ Every kid climbed a tree ” could mean that there was only one tree, climbed by all, or many different trees, one per climbing kid. In the present study, participants chose a picture to indicate their preferred reading of different ambiguous sentences – those containing every , as well as the other three quantifiers. In Experiment 1, we found large systematic differences in preference, depending on the quantifier word. In Experiment 2, we then manipulated the choice of a particular reading of one sentence, and tested how this affected participants’ reading preference on a subsequent target sentence. We found a priming effect for all quantifiers, but only when the prime and target sentences contained the same quantifier. For example, all-a sentences prime other all-a sentences, while each-a primes each-a , but sentences with each do not prime sentences with all or vice versa. In Experiment 3, we ask whether the lack of priming across quantifiers could be due to the two sentences sharing one fewer word. We find that changing the verb between the prime and target sentence does not reduce the priming effect. In Experiment 4, we discover one case where there is priming across quantifiers – when one number (e.g. three ) is in the prime, and a different one (e.g. four ) is in the target. We discuss how these findings relate to linguistic theories of quantifier meaning and what they tell us about the division of labor between conceptual content and combinatorial semantics, as well as the mental representations of quantification and of the abstract logical structure of language.


title: RankClus: integrating clustering with ranking for heterogeneous information network analysis

abstract: As information networks become ubiquitous, extracting knowledge from information networks has become an important task. Both ranking and clustering can provide overall views on information network data, and each has been a hot topic by itself. However, ranking objects globally without considering which clusters they belong to often leads to dumb results, e.g., ranking database and computer architecture conferences together may not make much sense. Similarly, clustering a huge number of objects (e.g., thousands of authors) in one huge cluster without distinction is dull as well. In this paper, we address the problem of generating clusters for a specified type of objects, as well as ranking information for all types of objects based on these clusters in a multi-typed (i.e., heterogeneous) information network. A novel clustering framework called RankClus is proposed that directly generates clusters integrated with ranking. Based on initial K clusters, ranking is applied separately, which serves as a good measure for each cluster. Then, we use a mixture model to decompose each object into a K-dimensional vector, where each dimension is a component coefficient with respect to a cluster, which is measured by rank distribution. Objects then are reassigned to the nearest cluster under the new measure space to improve clustering. As a result, quality of clustering and ranking are mutually enhanced, which means that the clusters are getting more accurate and the ranking is getting more meaningful. Such a progressive refinement process iterates until little change can be made. Our experiment results show that RankClus can generate more accurate clusters and in a more efficient way than the state-of-the-art link-based clustering methods. Moreover, the clustering results with ranks can provide more informative views of data compared with traditional clustering.


title: Macroeconomic Risk and Debt Overhang

abstract: – Since corporate debt tends to be riskier in recessions, transfers from equity holders to debt holders that accompany corporate decisions also tend to concentrate in recessions. Such systematic risk exposures of debt overhang have important implications for corporate investment and financing decisions, and for the ex ante costs of debt overhang. Using a calibrated dynamic capital structure model, we show that the costs of debt overhang become higher in the presence of macroeconomic risk. We also provide several new predictions on how the cyclicality of a firm’s assets in place and growth options affect its investment and capital structure decisions.


title: A Review of the Research on Internet Addiction

abstract: Research indicates that maladaptive patterns of Internet use constitute behavioral addiction. This article explores the research on the social effects of Internet addiction. There are four major sections. The Introduction section overviews the field and introduces definitions, terminology, and assessments. The second section reviews research findings and focuses on several key factors related to Internet addiction, including Internet use and time, identifiable problems, gender differences, psychosocial variables, and computer attitudes. The third section considers the addictive potential of the Internet in terms of the Internet, its users, and the interaction of the two. The fourth section addresses current and projected treatments of Internet addiction, suggests future research agendas, and provides implications for educational psychologists.


title: Augmented reality working planes: a foundation for action and construction at a distance

abstract: This paper introduces the concept of augmented reality working planes to action and construction at a distance for mobile outdoor augmented reality systems. We have based our new AR working planes technique on CAD working planes, but by using coordinate systems relative to the body they can be specified and used much more intuitively than on a desktop system. We demonstrate in this paper how AR working planes can be used for the display of information, the manipulation of existing 3D objects, and the creation of new geometry. AR working planes are particularly well suited to supporting 3D modelling in mobile outdoor AR systems. This modelling task is a particularly difficult problem, because the environment is typically at a scale much larger than the user, and direct manipulation techniques can not be used.


title: Comparative analysis of antimicrobial activity of leaf extracts from fresh green tea, commercial green tea and black tea on pathogens

abstract: Medicinal plants have been a major source of therapeutic agents for alleviation and cure diseases. In the present investigation comparative analysis of antimicrobial activity of green tea Camellia sinensis fresh leaves, commercial green tea leaves and dust tea against enteropathogens and specific fungi were carried out. The antimicrobial activity of the extracts of Camellia sinensis was analyzed by using well diffusion method paper disk diffusion method and minimum inhibitory concentration. Synergistic activity of green tea and commercial antibiotic chlorophenicol was analyzed. The allopathic antibacterial drugs are said to be costlier and have more side effects. Moreover multiple drug resistant strains are on the raise in this era and thus complicating treatment. On the other hand herbal preparations are comparatively cheaper and have lesser side effects. So, herbal preparations can supplement other systems of medicine for the treatment of diseases caused by bacteria and fungi.


title: Osteonecrosis Secondary to Paget's Disease: Radiologic and Pathologic Features

abstract: Paget's disease (PD) is a chronic progressive disease of the bone characterized by abnormal bone metabolism affecting either a single bone (monostotic) or many bones (polyostotic) with uncertain etiology. We report a case of PD in a 70-year-old male, which was initially identified as osteonecrosis of the maxilla. Non-drug induced osteonecrosis in PD is rare and very few cases have been reported in the literature.


title: From Social Media to Social Product Development: The Impact of Social Media on Co-Creation of Innovation

abstract: The objective of this paper is to discuss the impact of social media on customer co-creation in the innovation process. Customer co-creation denotes an active, creative and social collaboration process between producers and customers (users), facilitated by a company, in the context of new product or service development. We propose a typology of co-creation activities in order to develop conceptual arguments how social media can impact the relationships among customers involved in co-creation as well as the relationship between customers and the hosting firm. Social media can make economic-exchange relations more collaborative and social, but interestingly may also turn relations formerly based on social-exchange into "money markets" with strong competition among actors. As a result, we develop a set of questions that can lead future research in these regards.


title: A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems

abstract: Considerable scientific and technological efforts have been devoted to develop neuroprostheses and hybrid bionic systems that link the human nervous system with electronic or robotic prostheses, with the main aim of restoring motor and sensory functions in disabled patients. A number of neuroprostheses use interfaces with peripheral nerves or muscles for neuromuscular stimulation and signal recording. Herein, we provide a critical overview of the peripheral interfaces available and trace their use from research to clinical application in controlling artificial and robotic prostheses. The first section reviews the different types of non-invasive and invasive electrodes, which include surface and muscular electrodes that can record EMG signals from and stimulate the underlying or implanted muscles. Extraneural electrodes, such as cuff and epineurial electrodes, provide simultaneous interface with many axons in the nerve, whereas intrafascicular, penetrating, and regenerative electrodes may contact small groups of axons within a nerve fascicle. Biological, technological, and material science issues are also reviewed relative to the problems of electrode design and tissue injury. The last section reviews different strate- gies for the use of information recorded from peripheral interfaces and the current state of control neuroprostheses and hybrid bionic systems.


Bayesian decision methods


Surpassing Humans in Boundary Detection using Deep Learning


title: RED: Reinforced Encoder-Decoder Networks for Action Anticipation

abstract: Action anticipation aims to detect an action before it happens. Many real world applications in robotics and surveillance are related to this predictive capability. Current methods address this problem by first anticipating visual representations of future frames and then categorizing the anticipated representations to actions. However, anticipation is based on a single past frame's representation, which ignores the history trend. Besides, it can only anticipate a fixed future time. We propose a Reinforced Encoder-Decoder (RED) network for action anticipation. RED takes multiple history representations as input and learns to anticipate a sequence of future representations. One salient aspect of RED is that a reinforcement module is adopted to provide sequence-level supervision; the reward function is designed to encourage the system to make correct predictions as early as possible. We test RED on TVSeries, THUMOS-14 and TV-Human-Interaction datasets for action anticipation and achieve state-of-the-art performance on all datasets.


Literature Survey : Study of Neural Machine Translation


title: A New Replica Placement Policy for Hadoop Distributed File System

abstract: Today, Hadoop Distributed File System (HDFS) is widely used to provide scalable and fault-tolerant storage of large volumes of data. One of the key issues that affect the performance of HDFS is the placement of data replicas. Although the current HDFS replica placement policy can achieve both fault tolerance and read/write efficiency, the policy cannot evenly distribute replicas across cluster nodes, and has to rely on load balancing utility to balance replica distributions. In this paper, we present a new replica placement policy for HDFS, which can generate replica distributions that are not only perfectly even but also meet all HDFS replica placement requirements.


title: The relative importance of task, citizenship, and counterproductive performance to global ratings of job performance: A policy-capturing approach.

abstract: A review of research on job performance suggests 3 broad components: task, citizenship, and counterproductive performance. This study examined the relative importance of each component to ratings of overall performance by using an experimental policy-capturing design. Managers in 5 jobs read hypothetical profiles describing employees' task, citizenship, and counterproductive performance and provided global ratings of performance. Within-subjects regression analyses indicated that the weights given to the 3 performance components varied across raters. Hierarchical cluster analyses indicated that raters' policies could be grouped into 3 homogeneous clusters: (a) task performance weighted highest, (b) counterproductive performance weighted highest, and (c) equal and large weights given to task and counterproductive performance. Hierarchical linear modeling indicated that demographic variables were not related to raters' weights.


title: Exploring Trust In Cloud Computing: A Multi-Method Approach

abstract: The cloud computing paradigm promises to provide flexible IT services delivered via the internet. The cost efficiency of cloud computing services is mainly enabled by a high degree of automation realized by SLAs and minor direct human interaction. In this paper, we analyze eight expert interviews to identify and discuss the different levels of trust from an end-user perspective (trust in mediator and provider) that are prevalent in cloud computing and the main drivers influencing trust. Then, we use the findings to postulate hypotheses that we subsequently verify by means of a serious game. We discover risk aversion, information, disposition of trust, affectedness, reputation and costs to be important factors influencing trust in cloud computing mediators or providers, respectively. Furthermore, by analyzing the descriptive statistics gathered through the serious game, we formulate further hypotheses.


title: Space-efficient online computation of quantile summaries

abstract: An ∈-approximate quantile summary of a sequence of N elements is a data structure that can answer quantile queries about the sequence to within a precision of ∈N. We present a new online algorithm for computing∈-approximate quantile summaries of very large data sequences. The algorithm has a worst-case space requirement of O(1÷∈ log(∈N)). This improves upon the previous best result of O(1÷∈ log2(∈N)). Moreover, in contrast to earlier deterministic algorithms, our algorithm does not require a priori knowledge of the length of the input sequence. Finally, the actual space bounds obtained on experimental data are significantly better than the worst case guarantees of our algorithm as well as the observed space requirements of earlier algorithms.


title: Alternating refinement relations

abstract: Alternating transition systems are a general model for composite systems which allow the study of collaborative as well as adversarial relationships between individual system components. Unlike in labeled transition systems, where each transition corresponds to a possible step of the system (which may involve some or all components), in alternating transition systems, each transition corresponds to a possible move in a game between the components. In this paper, we study refinement relations between alternating transition systems, such as Does the implementation refine the set A of specification components without constraining the components not in A? In particular, we generalize the definitions of the simulation and trace containment preorders from labeled transition systems to alternating transition systems. The generalizations are called alternating simulation and alternating trace containment. Unlike existing refinement relations, they allow the refinement of individual components within the context of a composite system description. We show that, like ordinary simulation, alternating simulation can be checked in polynomial time using a fixpoint computation algorithm. While ordinary trace containment is PSPACE-complete, we establish altemating trace containment to be EXPTIME-complete. Finally, we present logical characterizations for the two preorders in terms of ATL, a temporal logic capable of referring to games between system components.


title: ShaResNet: reducing residual network parameter number by sharing weights

abstract: Deep Residual Networks have reached the state of the art in many image processing tasks such image classification. However, the cost for a gain in accuracy in terms of depth and memory is prohibitive as it requires a higher number of residual blocks, up to double the initial value. To tackle this problem, we propose in this paper a way to reduce the redundant information of the networks. We share the weights of convolutional layers between residual blocks operating at the same spatial scale. The signal flows multiple times in the same convolutional layer. The resulting architecture, called ShaResNet, contains block specific layers and shared layers. These ShaResNet are trained exactly in the same fashion as the commonly used residual networks. We show, on the one hand, that they are almost as efficient as their sequential counterparts while involving less parameters, and on the other hand that they are more efficient than a residual network with the same number of parameters. For example, a 152-layer-deep residual network can be reduced to 106 convolutional layers, i.e. a parameter gain of 39\%, while loosing less than 0.2\% accuracy on ImageNet.


title: Self-folding origami at any energy scale

abstract: Programmable stiff sheets with a single low-energy folding motion have been sought in fields ranging from the ancient art of origami to modern meta-materials research. Despite such attention, only two extreme classes of crease patterns are usually studied; special Miura-Ori-based zero-energy patterns, in which crease folding requires no sheet bending, and random patterns with high-energy folding, in which the sheet bends as much as creases fold. We present a physical approach that allows systematic exploration of the entire space of crease patterns as a function of the folding energy. Consequently, we uncover statistical results in origami, finding the entropy of crease patterns of given folding energy. Notably, we identify three classes of Mountain-Valley choices that have widely varying 'typical' folding energies. Our work opens up a wealth of experimentally relevant self-folding origami designs not reliant on Miura-Ori, the Kawasaki condition or any special symmetry in space.


title: A Survey on Multicarrier Communications: Prototype Filters, Lattice Structures, and Implementation Aspects

abstract: Due to their numerous advantages, communications over multicarrier schemes constitute an appealing approach for broadband wireless systems. Especially, the strong penetration of orthogonal frequency division multiplexing (OFDM) into the communications standards has triggered heavy investigation on multicarrier systems, leading to re-consideration of different approaches as an alternative to OFDM. The goal of the present survey is not only to provide a unified review of waveform design options for multicarrier schemes, but also to pave the way for the evolution of the multicarrier schemes from the current state of the art to future technologies. In particular, a generalized framework on multicarrier schemes is presented, based on what to transmit, i.e., symbols, how to transmit, i.e., filters, and where/when to transmit, i.e., lattice. Capitalizing on this framework, different variations of orthogonal, bi-orthogonal, and nonorthogonal multicarrier schemes are discussed. In addition, filter design for various multicarrier systems is reviewed considering four different design perspectives: energy concentration, rapid decay, spectrum nulling, and channel/hardware characteristics. Subsequently, evaluation tools which may be used to compare different filters in multicarrier schemes are studied. Finally, multicarrier schemes are evaluated from the view of the practical implementation issues, such as lattice adaptation, equalization, synchronization, multiple antennas, and hardware impairments.


title: An Uncertain Future: Forecasting from Static Images using Variational Autoencoders

abstract: In a given scene, humans can easily predict a set of immediate future events that might happen. However, pixel-level anticipation in computer vision is difficult because machine learning struggles with the ambiguity in predicting the future. In this paper, we focus on predicting the dense trajectory of pixels in a scene—what will move in the scene, where it will travel, and how it will deform over the course of one second. We propose a conditional variational autoencoder as a solution to this problem. In this framework, direct inference from the image shapes the distribution of possible trajectories while latent variables encode information that is not available in the image. We show that our method predicts events in a variety of scenes and can produce multiple different predictions for an ambiguous future. We also find that our method learns a representation that is applicable to semantic vision tasks.


title: Prediction of cytomegalovirus load and resistance patterns after antiviral chemotherapy

abstract: Cytomegalovirus (CMV) replicates rapidly in its human host with a doubling time of ≈ 1 day. Using simple mathematical models we show that the efficacy of the anti-CMV drug ganciclovir (GCV) against wild-type strains is 91.5% [95% confidence interval (CI) 89–94%] when given i.v. (5 mg/kg twice a day) but only 46.5% (95% CI 45–47.5%) when given orally (1 g three times a day) whereas the corresponding figures for a typical GCV-resistant virus are 62% (95% CI 57–66%) and 35% (95% CI 33–37%), respectively. During prolonged periods of GCV therapy we show that the apparent sudden appearance of GCV resistance is explained by the combination of two exponentially increasing populations (wild type and mutant) at doses of GCV that do not completely inhibit CMV replication. Cell culture methods to assess CMV drug resistance in vivo will underestimate its prevalence because of the fitness loss of resistant virus in the absence of therapy. The parameters determined from these models then were used to predict the likely viral load and resistance patterns in patients on prolonged therapy with GCV. The modeled and experimental data showed excellent agreement over extended time periods (up to 270 days of therapy) and provide a framework to predict the virologic course of patients at therapeutic initiation.


title: Ultrawide-Bandwidth Single-Channel 0.4-THz Wireless Link Combining Broadband Quasi-Optic Photomixer and Coherent Detection

abstract: Free space communications with huge data capacity have become a key point for the development of mobile access, services, and network technologies convergence. Wireless links using emerging terahertz technologies, also referred to as T-ray communications, have become an intensive research field within last years since T-rays at the millimeter/submillimeter frontier remain more robust in terms of scintillation, dielectric obstacles, and fog compared with near-infrared signals. Using a passive THz hot-spot which can be fed by fiber optic networks, we investigate up bit rates up to 46 Gbps for a THz wireless transmission system at 400-GHz carrier frequency. Using a THz photomixer integrated with a specific broadband antenna and heterodyne electronic detection, we investigate the eye diagrams using 1-μW received power, highlighting the high sensitivity and ultra-wideband behavior of the whole THz system.


SPH Fluids in Computer Graphics


title: Trading Activity and Expected Stock Returns

abstract: Abstract Given the evidence that the level of liquidity affects asset returns, a reasonable hypothesis is that the second moment of liquidity should be positively related to asset returns, provided agents care about the risk associated with fluctuations in liquidity. Motivated by this observation, we analyze the relation between expected equity returns and the level as well as the volatility of trading activity, a proxy for liquidity. We document a result contrary to our initial hypothesis, namely, a negative and surprisingly strong cross-sectional relationship between stock returns and the variability of dollar trading volume and share turnover, after controlling for size, book-to-market ratio, momentum, and the level of dollar volume or share turnover. This effect survives a number of robustness checks, and is statistically and economically significant. Our analysis demonstrates the importance of trading activity-related variables in the cross-section of expected stock returns.


title: Bag-of-Discriminative-Words (BoDW) Representation via Topic Modeling

abstract: Many of the words in a given document either deliver facts ( objective ) or express opinions ( subjective ), respectively, depending on the topics they are involved in. For example, given a bunch of documents, the word “bug” assigned to the topic “ order Hemiptera ” apparently remarks one object (i.e., one kind of insects), while the same word assigned to the topic “ software ” probably conveys a negative opinion. Motivated by the intuitive assumption that different words have varying degrees of discriminative power in delivering the objective sense or the subjective sense with respect to their assigned topics, a model named as d iscriminatively o bjective- s ubjective LDA (dosLDA) is proposed in this paper. The essential idea underlying the proposed dosLDA is that a pair of objective and subjective selection variables are explicitly employed to encode the interplay between topics and discriminative power for the words in documents in a supervised manner. As a result, each document is appropriately represented as “ bag-of-discriminative-words ” (BoDW). The experiments reported on documents and images demonstrate that dosLDA not only performs competitively over traditional approaches in terms of topic modeling and document classification, but also has the ability to discern the discriminative power of each word in terms of its objective or subjective sense with respect to its assigned topic.


title: LiFi: Line-Of-Sight identification with WiFi

abstract: Wireless LANs, especially WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing each scenario-tailored application is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to distinguish Line-Of-Sight (LOS) path from NLOS paths acts as a key enabler for adaptive communication, cognitive radios, robust localization, etc. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with mere MAC layer RSSI. In this work, we dive into the PHY layer and strive to eliminate irrelevant noise and NLOS paths with long delays from the multipath channel responses. To further break away from the intrinsic bandwidth limit of WiFi, we extend to the spatial domain and harness natural mobility to magnify the randomness of NLOS paths while retaining the deterministic nature of the LOS component. We prototype LiFi, a statistical LOS identification scheme for commodity WiFi infrastructure and evaluate it in typical indoor environments covering an area of 1500 m2. Experimental results demonstrate an overall LOS identification rate of 90.4% with a false alarm rate of 9.3%.


title: Fundamental Mode Orthogonal Fluxgate Magnetometer Applicable for Measurements of DC and Low-Frequency Magnetic Fields

abstract: Compatibility of low noise characteristic and offset stability is essential for a magnetometer to be applicable for measurements of dc and low-frequency magnetic fields. Fundamental mode orthogonal fluxgate (FM-OFG) magnetometer has advantages in its compact design and low noise characteristic, whereas its disadvantage is the offset drift which is unacceptably large for such measurement. An effective technique called bias switching has been proposed to suppress and stabilize the offset of open-loop FM-OFG magnetometers, yet when it is adopted with a feedback configuration to obtain better linearity, it increases noises attributed to the convergence process. In this paper, we present a design of sensor driving electronics of FM-OFG magnetometers to solve this problem and achieve compatibility of low noise and stable offset characteristics with a feedback configuration. The efficacy of the design was verified through various performance evaluations.


title: Learning Graph-Level Representation for Drug Discovery

abstract: Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of small-molecule based drug discovery. Molecules can be represented as an undirected graph, and we can utilize graph convolution networks to predication molecular properties. However, graph convolutional networks and other graph neural networks all focus on learning node-level representation rather than graph-level representation. Previous works simply sum all feature vectors for all nodes in the graph to obtain the graph feature vector for drug predication. In this paper, we introduce a dummy super node that is connected with all nodes in the graph by a directed edge as the representation of the graph and modify the graph operation to help the dummy super node learn graph-level feature. Thus, we can handle graph-level classification and regression in the same way as node-level classification and regression. In addition, we apply focal loss to address class imbalance in drug datasets. The experiments on MoleculeNet show that our method can effectively improve the performance of molecular properties predication.


title: Automatic system testing of programs without test oracles

abstract: Metamorphic testing has been shown to be a simple yet effective technique in addressing the quality assurance of applications that do not have test oracles, i.e., for which it is difficult or impossible to know what the correct output should be for arbitrary input. In metamorphic testing, existing test case input is modified to produce new test cases in such a manner that, when given the new input, the application should produce an output that can easily be computed based on the original output. That is, if input x produces output f(x), then we create input x' such that we can predict f(x') based on f(x); if the application does not produce the expected output, then a defect must exist, and either f(x), or f(x') (or both) is wrong. In practice, however, metamorphic testing can be a manually intensive technique for all but the simplest cases. The transformation of input data can be laborious for large data sets, or practically impossible for input that is not in human-readable format. Similarly, comparing the outputs can be error-prone for large result sets, especially when slight variations in the results are not actually indicative of errors (i.e., are false positives), for instance when there is non-determinism in the application and multiple outputs can be considered correct. In this paper, we present an approach called Automated Metamorphic System Testing. This involves the automation of metamorphic testing at the system level by checking that the metamorphic properties of the entire application hold after its execution. The tester is able to easily set up and conduct metamorphic tests with little manual intervention, and testing can continue in the field with minimal impact on the user. Additionally, we present an approach called Heuristic Metamorphic Testing which seeks to reduce false positives and address some cases of non-determinism. We also describe an implementation framework called Amsterdam, and present the results of empirical studies in which we demonstrate the effectiveness of the technique on real-world programs without test oracles.


title: CNN Based Transfer Learning for Historical Chinese Character Recognition

abstract: Historical Chinese character recognition has been suffering from the problem of lacking sufficient labeled training samples. A transfer learning method based on Convolutional Neural Network (CNN) for historical Chinese character recognition is proposed in this paper. A CNN model L is trained by printed Chinese character samples in the source domain. The network structure and weights of model L are used to initialize another CNN model T, which is regarded as the feature extractor and classifier in the target domain. The model T is then fine-tuned by a few labeled historical or handwritten Chinese character samples, and used for final evaluation in the target domain. Several experiments regarding essential factors of the CNNbased transfer learning method are conducted, showing that the proposed method is effective.


Enhancing the Plausibility of Law Equation Discovery


title: Advanced user interfaces for intelligent wheelchair system

abstract: With the aging of the population and increasing number of disabled people who have mobility impairments, the interest of the research on technology for health and independence represent an importance paramount in present day. In this paper an intelligent wheelchair is developed to help these types of patients using advanced system to control the movement of wheelchair in different directions by monitoring voice commands and also the simple movement of the patient's face. Automatic obstacle detection and avoidance is done using ultrasonic and infrared sensors which helps the patient to apply a temporary brake in case any obstacle suddenly comes in the way of the wheelchair. Also wall tracking and target tracking algorithms are developed in our wheelchair. A Fuzzy Logic Control of the wheelchair is also designed in such a way that it can be controlled easily with minimum effort from the patient. We focus the study on non conventional control algorithm implemented using a PC interfaced with an embedded microcontroller platform. Experimental results are carried out on a wheelchair platform.


title: Cryptanalysis of a novel ultra-lightweight mutual authentication protocol for IoT devices using RFID tags

abstract: Internet of Things (IoT) is an evolving architecture which connects multiple devices to Internet for communication or receiving updates from a cloud or a server. In future, the number of these connected devices will increase immensely making them an indistinguishable part of our daily lives. Although these devices make our lives more comfortable, they also put our personal information at risk. Therefore, security of these devices is also a major concern today. In this paper, we propose an ultra-lightweight mutual authentication protocol which uses only bitwise operation and thus is very efficient in terms of storage and communication cost. In addition, the computation overhead is very low. We have also compared our proposed work with the existing ones which verifies the strength of our protocol, as obtained results are promising. A brief cryptanalysis of our protocol that ensures untraceability is also presented.


title: A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids

abstract: False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge amounts of data. In this paper, a novel data analytical method is proposed to detect FDIAs based on data-centric paradigm employing the margin setting algorithm (MSA). The performance of the proposed method is demonstrated through simulation using the six-bus power network in a wide area measurement system environment, as well as experimental data sets. Two FDIA scenarios, playback attack and time attack, are investigated. Experimental results are compared with the support vector machine (SVM) and artificial neural network (ANN). The results indicate that MSA yields better results in terms of detection accuracy than both the SVM and ANN when applied to FDIA detection.


title: A constrained hidden Markov model approach for non-explicit citation context extraction

abstract: In this paper we present a constrained hidden markov model based approach for extracting non-explicit citing sentences in research articles. Our method involves first independently training a separate HMM for each citation in the article being processed and then performing a constrained joint inference to label non-explicit citing sentences. Results on a standard test collection show that our method significantly outperforms the baselines and is comparable to the state of the art approaches.


title: A Survey of Color Mapping and its Applications

abstract: Color mapping or color transfer methods aim to recolor a given image or video by deriving a mapping between that image and another image serving as a reference. This class of methods has received considerable attention in recent years, both in academic literature and in industrial applications. Methods for recoloring images have often appeared under the labels of color correction, color transfer or color balancing, to name a few, but their goal is always the same: mapping the colors of one image to another. In this report, we present a comprehensive overview of these methods and offer a classification of current solutions depending not only on their algorithmic formulation but also their range of applications. We discuss the relative merit of each class of techniques through examples and show how color mapping solutions can and have been applied to a diverse range of problems.


title: Diagnostic Accuracy Measures

abstract: Background: An increasing number of diagnostic tests and biomarkers have been validated during the last decades, and this will still be a prominent field of research in the future because of the need for personalized medicine. Strict evaluation is needed whenever we aim at validating any potential diagnostic tool, and the first requirement a new testing procedure must fulfill is diagnostic accuracy. Summary: Diagnostic accuracy measures tell us about the ability of a test to discriminate between and/or predict disease and health. This discriminative and predictive potential can be quantified by measures of diagnostic accuracy such as sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operating characteristic curve, overall accuracy and diagnostic odds ratio. Some measures are useful for discriminative purposes, while others serve as a predictive tool. Measures of diagnostic accuracy vary in the way they depend on the prevalence, spectrum and definition of the disease. In general, measures of diagnostic accuracy are extremely sensitive to the design of the study. Studies not meeting strict methodological standards usually over- or underestimate the indicators of test performance and limit the applicability of the results of the study. Key Messages: The testing procedure should be verified on a reasonable population, including people with mild and severe disease, thus providing a comparable spectrum. Sensitivities and specificities are not predictive measures. Predictive values depend on disease prevalence, and their conclusions can be transposed to other settings only for studies which are based on a suitable population (e.g. screening studies). Likelihood ratios should be an optimal choice for reporting diagnostic accuracy. Diagnostic accuracy measures must be reported with their confidence intervals. We always have to report paired measures (sensitivity and specificity, predictive values or likelihood ratios) for clinically meaningful thresholds. How much discriminative or predictive power we need depends on the clinical diagnostic pathway and on misclassification (false positives/negatives) costs.


title: Random sampling techniques for space efficient online computation of order statistics of large datasets

abstract: In a recent paper [MRL98], we had described a general framework for single pass approximate quantile finding algorithms. This framework included several known algorithms as special cases. We had identified a new algorithm, within the framework, which had a significantly smaller requirement for main memory than other known algorithms. In this paper, we address two issues left open in our earlier paper. First, all known and space efficient algorithms for approximate quantile finding require advance knowledge of the length of the input sequence. Many important database applications employing quantiles cannot provide this information. In this paper, we present a novel non-uniform random sampling scheme and an extension of our framework. Together, they form the basis of a new algorithm which computes approximate quantiles without knowing the input sequence length. Second, if the desired quantile is an extreme value (e.g., within the top 1% of the elements), the space requirements of currently known algorithms are overly pessimistic. We provide a simple algorithm which estimates extreme values using less space than required by the earlier more general technique for computing all quantiles. Our principal observation here is that random sampling is quantifiably better when estimating extreme values than is the case with the median.


title: Creativity: Generating Diverse Questions Using Variational Autoencoders

abstract: Generating diverse questions for given images is an important task for computational education, entertainment and AI assistants. Different from many conventional prediction techniques is the need for algorithms to generate a diverse set of plausible questions, which we refer to as"creativity". In this paper we propose a creative algorithm for visual question generation which combines the advantages of variational autoencoders with long short-term memory networks. We demonstrate that our framework is able to generate a large set of varying questions given a single input image.


title: Improving Separability of Structures with Similar Attributes in 2D Transfer Function Design

abstract: The 2D transfer function based on scalar value and gradient magnitude (SG-TF) is popularly used in volume rendering. However, it is plagued by the boundary-overlapping problem: different structures with similar attributes have the same region in SG-TF space, and their boundaries are usually connected. The SG-TF thus often fails in separating these structures (or their boundaries) and has limited ability to classify different objects in real-world 3D images. To overcome such a difficulty, we propose a novel method for boundary separation by integrating spatial connectivity computation of the boundaries and set operations on boundary voxels into the SG-TF. Specifically, spatial positions of boundaries and their regions in the SG-TF space are computed, from which boundaries can be well separated and volume rendered in different colors. In the method, the boundaries are divided into three classes and different boundary-separation techniques are applied to them, respectively. The complex task of separating various boundaries in 3D images is then simplified by breaking it into several small separation problems. The method shows good object classification ability in real-world 3D images while avoiding the complexity of high-dimensional transfer functions. Its effectiveness and validation is demonstrated by many experimental results to visualize boundaries of different structures in complex real-world 3D images.


title: Support vector machines under adversarial label contamination

abstract: Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties against deliberate attacks have not yet been widely understood. Intelligent and adaptive attackers may indeed exploit specific vulnerabilities exposed by machine learning techniques to violate system security. Being robust to adversarial data manipulation is thus an important, additional requirement for machine learning algorithms to successfully operate in adversarial settings. In this work, we evaluate the security of Support Vector Machines (SVMs) to well-crafted, adversarial label noise attacks. In particular, we consider an attacker that aims to maximize the SVM?s classification error by flipping a number of labels in the training data. We formalize a corresponding optimal attack strategy, and solve it by means of heuristic approaches to keep the computational complexity tractable. We report an extensive experimental analysis on the effectiveness of the considered attacks against linear and non-linear SVMs, both on synthetic and real-world datasets. We finally argue that our approach can also provide useful insights for developing more secure SVM learning algorithms, and also novel techniques in a number of related research areas, such as semi-supervised and active learning.


title: Toward Centimeter-Scale Human Activity Sensing with Wi-Fi Signals

abstract: By allowing centimeter-scale human activity sensing with Wi-Fi signals, the Fresnel zone model could revolutionize wireless sensing and Internet of Things applications. The web extra at www.youtube.com/watch?v=R_vR6O8706g demonstrates how the Fresnel zone model can be leveraged for device-free sensing of human activities such as respiration and walking.


title: A Fast Variational Approach for Learning Markov Random Field Language Models

abstract: Language modelling is a fundamental building block of natural language processing. However, in practice the size of the vocabulary limits the distributions applicable for this task: specifically, one has to either resort to local optimization methods, such as those used in neural language models, or work with heavily constrained distributions. In this work, we take a step towards overcoming these difficulties. We present a method for global-likelihood optimization of a Markov random field language model exploiting long-range contexts in time independent of the corpus size. We take a variational approach to optimizing the likelihood and exploit underlying symmetries to greatly simplify learning. We demonstrate the efficiency of this method both for language modelling and for part-of-speech tagging.


Explicating Law: A Comparative Perspective of Chinese and Western Legal Culture


title: Measuring Docker Performance: What a Mess!!!

abstract: Today, a new technology is going to change the way platforms for the internet of services are designed and managed. This technology is called container (e.g. Docker and LXC). The internet of service industry is adopting the container technology both for internal usage and as commercial offering. The use of container as base technology for large-scale systems opens many challenges in the area of resource management at run-time, for example: autoscaling, optimal deployment and monitoring. Specifically, monitoring of container based systems is at the ground of any resource management solution, and it is the focus of this work. This paper explores the tools available to measure the performance of Docker from the perspective of the host operating system and of the virtualization environment, and it provides a characterization of the CPU and disk I/O overhead introduced by containers.


title: The ridematching problem with time windows in dynamic ridesharing: A model and a genetic algorithm

abstract: The increasing ubiquity of mobile handheld devices paved the way for the dynamic ridesharing which could save travel cost and reduce the environmental pollution. The ridematching problem with time windows in dynamic ridesharing considers matching drivers and riders with similar routes (with drivers detour flexibility) and time schedules on short notice. This problem is hard to solve. In this work, we model the ridematching problem with time windows in dynamic ridesharing as an optimization problem and propose a genetic algorithm to solve it. We consider minimizing the total travel distance and time of the drivers (vehicles) and the total travel time of the riders and maximizing the number of the matches. In addition, we provide datasets for the ridematching problem, derived from a real world travel survey for northeastern Illinois, to test the proposed algorithm. Experimentation results indicate that the idea of dynamic ridesharing is feasible and the proposed algorithm is able to solve the ridematching problem with time windows in reasonable time.


title: A Better Comparison Summary of Credit Scoring Classification

abstract: The credit scoring aim is to classify the customer credit as defaulter or non-defaulter. The credit risk analysis is more effective with further boosting and smoothing of the parameters of models. The objective of this paper is to explore the credit score classification models with an imputation technique and without imputation technique. However, data availability is low in case of without imputation because of missing values depletion from the large dataset. On the other hand, imputation based dataset classification accuracy with linear method of ANN is better than other models. The comparison of models with boosting and smoothing shows that error rate is better metric than area under curve (AUC) ratio. It is concluded that artificial neural network (ANN) is better alternative than decision tree and logistic regression when data availability is high in dataset.


title: ICDAR 2003 robust reading competitions: entries, results, and future directions

abstract: This paper describes the robust reading competitions for ICDAR 2003. With the rapid growth in research over the last few years on recognizing text in natural scenes, there is an urgent need to establish some common benchmark datasets and gain a clear understanding of the current state of the art. We use the term `robust reading' to refer to text images that are beyond the capabilities of current commercial OCR packages. We chose to break down the robust reading problem into three subproblems and run competitions for each stage, and also a competition for the best overall system. The subproblems we chose were text locating, character recognition and word recognition. By breaking down the problem in this way, we hoped to gain a better understanding of the state of the art in each of the subproblems. Furthermore, our methodology involved storing detailed results of applying each algorithm to each image in the datasets, allowing researchers to study in depth the strengths and weaknesses of each algorithm. The text-locating contest was the only one to have any entries. We give a brief description of each entry and present the results of this contest, showing cases where the leading entries succeed and fail. We also describe an algorithm for combining the outputs of the individual text locators and show how the combination scheme improves on any of the individual systems.


title: Optimal Electric Network Design for a Large Offshore Wind Farm Based on a Modified Genetic Algorithm Approach

abstract: The increasing development of large-scale offshore wind farms around the world has caused many new technical and economic challenges to emerge. The capital cost of the electrical network that supports a large offshore wind farm constitutes a significant proportion of the total cost of the wind farm. Thus, finding the optimal design of this electrical network is an important task, a task that is addressed in this paper. A cost model has been developed that includes a more realistic treatment of the cost of transformers, transformer substations, and cables. These improvements make this cost model more detailed than others that are currently in use. A novel solution algorithm is used. This algorithm is based on an improved genetic algorithm and includes a specific algorithm that considers different cable cross sections when designing the radial arrays. The proposed approach is tested with a large offshore wind farm; this testing has shown that the proposed algorithm produces valid optimal electrical network designs.