title: Focused Crawling Through Reinforcement Learning

abstract: Focused crawling aims at collecting as many Web pages relevant to a target topic as possible while avoiding irrelevant pages, reflecting limited resources available to a Web crawler. We improve on the efficiency of focused crawling by proposing an approach based on reinforcement learning. Our algorithm evaluates hyperlinks most profitable to follow over the long run, and selects the most promising link based on this estimation. To properly model the crawling environment as a Markov decision process, we propose new representations of states and actions considering both content information and the link structure. The size of the state-action space is reduced by a generalization process. Based on this generalization, we use a linear-function approximation to update value functions. We investigate the trade-off between synchronous and asynchronous methods. In experiments, we compare the performance of a crawling task with and without learning; crawlers based on reinforcement learning show better performance for various target topics.


title: FISA-XP: an agile-based integration of security activities with extreme programming

abstract: The steep rise in security threats has forced organizations to adopt sound security practices right from the development stage of any software project. With the rising popularity of lightweight, agile methodologies, this becomes more complicated. This paper proposes a framework, FISA-XP, which can be adopted for the development of a secure software system. The proposed framework integrates security activities with the core activities of Extreme Programming based on their degree of agility. In order to calculate agility degree, some agility features are selected using a threshold value. The compatibility of the agile activities with security activities is subsequently assessed by introducing an integration matrix that describes whether integration of an agile activity with each security activity is possible or not. This framework assists in integrating security activities with agile activities, keeping the combined agility degree within acceptable limits. Thus, our approach introduces an Acceptable Agility Reduction Factor, which gives a threshold value for an acceptable reduction in agility degree. If reduction in combined agility degree is below the threshold value then that security activity is not accepted for integration. TISA-XP, an automated tool, has been designed to enable developers to use FISA-XP practically. This tool has been used by a software-developing company on an experimental basis and the feedback reflects its practical feasibility.


title: Single image deblurring using motion density functions

abstract: We present a novel single image deblurring method to estimate spatially non-uniform blur that results from camera shake. We use existing spatially invariant deconvolution methods in a local and robust way to compute initial estimates of the latent image. The camera motion is represented as a Motion Density Function (MDF) which records the fraction of time spent in each discretized portion of the space of all possible camera poses. Spatially varying blur kernels are derived directly from the MDF. We show that 6D camera motion is well approximated by 3 degrees of motion (in-plane translation and rotation) and analyze the scope of this approximation. We present results on both synthetic and captured data. Our system out-performs current approaches which make the assumption of spatially invariant blur.


title: Review of electron beam therapy physics.

abstract: For over 50 years, electron beams have been an important modality for providing an accurate dose of radiation to superficial cancers and disease and for limiting the dose to underlying normal tissues and structures. This review looks at many of the important contributions of physics and dosimetry to the development and utilization of electron beam therapy, including electron treatment machines, dose specification and calibration, dose measurement, electron transport calculations, treatment and treatment-planning tools, and clinical utilization, including special procedures. Also, future changes in the practice of electron therapy resulting from challenges to its utilization and from potential future technology are discussed.


title: ICT and Tourism: Challenges and Opportunities

abstract: The revolution in ICTs has profound implications for economic and social development. It has pervaded every aspect of human life whether it is health, education, economics, governance, entertainment etc. Dissemination, propagation and accessibility of these technologies are viewed to be integral to a country’s development strategy. The most important benefit associated with the access to the new technologies is the increase in the supply of information. Information is shared and disseminated to larger audience. Secondly it reduces the cost of production. Knowledge is produced, transmitted, accessed and shared at the minimum cost. With the reduction in the transactional costs, there is also a reduction in the degree of inefficiencies and uncertainty. Thirdly it has overcome the constraints of distance and geography. ICTs have cut across the geographic boundaries of the nation states. Buyers and sellers are able to share information, specifications, production process etc across the national borders. It enables all to know the comparative advantage in the market economy. It leads to the larger markets and increased access to global supply chains. Fourthly it has led to more transparency. Networking and information sharing definitely leads to demands for greater openness and transparency. Whether you want to know the status of the central banks’ foreign exchange agency or the cost prize of potatoes in the local market, ICTs empowers the individual with the information access, which is transparent. Efforts are under way to integrate ICTs to all sectors and developmental activity. Tourism is one such potential area. Tourism and economy are closely interconnected. Discussion on Tourism involves the discussion on economic enterprise also.


title: Improving the Neighborhood Selection Strategy in Simulated Annealing using the Optimal Stopping Problem

abstract: The search method utilized by metaheuristic algorithms is in fact a local search in the solution space. Investigations of these methods show that their efficiency in solving differenttypes of problems significantly depends on the applied strategy in searching the solution space. Traditional neighborhood selection methods have disregarded the essential characteristics embodied in the chosen neighborhood. In this research, we enhance the efficiency of solving combinator ial optimization problems using simulated annealing method by taking the advantages embodied in neighborhood structures. This new algorithm improves simulated annealing in two different aspects: First, by employing multiple neighborhood structures, it performs a more powerful search and second, using optimal stopping problem, it finds the best time to change the temperature which is a critical issue in simulated annealing. The novel algorithm is illustrated on the traveling salesman problem.


title: Power Harvesting With PZT Ceramics

abstract: Piezoelectric materials have been proposed as embedded power source, which are capable of converting mechanical energy into electrical energy. However, power generated from a piezoelectric material usually comes with poor characteristics such as high voltage, low current and high impedance. In order to drive the embedded sensor circuit, piezoelectric power needs to be characterized and regulated. In this paper, the authors present an analysis on the power generation characteristics of the stiff lead zirconate titanate (PZT) ceramics and its equivalent circuit. It is then verified by simulation and experimental results. Since a single PZT element may not meet the needs of real-world embedded applications, we present experiments and analysis using four identical PZTs. Finally, the authors outline an application where PZT elements are used for power generation in a total knee replacement (TKR) implant.


title: BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies

abstract: We propose BlackOut, an approximation algorithm to efficiently train massive recurrent neural network language models (RNNLMs) with million word vocabularies. BlackOut is motivated by using a discriminative loss, and we describe a new sampling strategy which significantly reduces computation while improving stability, sample efficiency, and rate of convergence. One way to understand BlackOut is to view it as an extension of the DropOut strategy to the output layer, wherein we use a discriminative training loss and a weighted sampling scheme. We also establish close connections between BlackOut, importance sampling, and noise contrastive estimation (NCE). Our experiments, on the recently released one billion word language modeling benchmark, demonstrate scalability and accuracy of BlackOut; we outperform the state-of-the art, and achieve the lowest perplexity scores on this dataset. Moreover, unlike other established methods which typically require GPUs or CPU clusters, we show that a carefully implemented version of BlackOut requires only 1-10 days on a single machine to train a RNNLM with a million word vocabulary and billions of parameters on one billion words. Although we describe BlackOut in the context of RNNLM training, it can be used to any networks with large softmax output layers.


title: Implementing WEKA for medical data classification and early disease prediction

abstract: In recent years, the advent of latest web and data technologies has encouraged massive data growth in almost every sector. Businesses and leading industries are viewing these huge data repositories as a tool to design future strategies, prediction models by analyzing patterns and gaining knowledge from this unstructured data by applying different data mining techniques. Medical domain has now become richer in term of maintaining digital records of patients related to their diagnosis and treatment. These huge data repositories can range from patient personnel data, diagnosis, treatment histories, test diagnosis, images and various scans. This terabytes of medical data is quantity rich but weaker in information in terms of knowledge and robust tools to identify hidden patterns of knowledge specifically in medical sector. Data Mining as a field of research has already well proven capabilities of identifying hidden patterns, analysis and knowledge applied on different research domains, now gaining popularity day by day among researchers and scientist towards generating novel and deep insights of these large biomedical datasets also. Uncovering new biomedical and healthcare related knowledge in order to support clinical decision making, is another dimension of data mining. Through massive literature survey, it is found that early disease prediction is the most demanded area of research in health care sector. As health care domain is bit wider domain and having different disease characteristics, different techniques have their own prediction efficiencies, which can be enhanced and changed in order to get into most optimize way. In this research work, authors have comprehensively compared different data classification techniques and their prediction accuracy for chronic kidney disease. Authors have compared J48, Naive Bayes, Random Forest, SVM and k-NN classifiers using performance measures like ROC, kappa statistics, RMSE and MAE using WEKA tool. Authors have also compared these classifiers on various accuracy measures like TP rate, FP rate, precision, recall and f-measure by implementing on WEKA. Experimental result shows that random forest classifier has better classification accuracy over others for chronic kidney disease dataset.


title: Secure Access for Healthcare Data in the Cloud Using Ciphertext-Policy Attribute-Based Encryption

abstract: As more and more healthcare organizations adopt electronic health records (EHRs), the case for cloud data storage becomes compelling for deploying EHR systems: not only is it inexpensive but it also provides the flexible, wide-area mobile access increasingly needed in the modern world. However, before cloud-based EHR systems can become a reality, issues of data security, patient privacy, and overall performance must be addressed. As standard encryption (including symmetric key and public key) techniques for EHR encryption/decryption cause increased access control and performance overhead, this paper proposes the use of Ciphertext-Policy Attribute-Based Encryption (CPABE) to encrypt EHRs based on healthcare providers' attributes or credentials, to decrypt EHRs, they must possess the set of attributes needed for proper access. The design and usage of a cloud-based EHR system based on CP-ABE is motivated and presented, along with preliminary experiments to analyze the flexibility and scalability of the proposed approach.


From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach


title: Surpassing Human-Level Face Verification Performance on LFW with GaussianFace

abstract: Face verification remains a challenging problem in very complex conditions with large variations such as pose, illumination, expression, and occlusions. This problem is exacerbated when we rely unrealistically on a single training data source, which is often insufficient to cover the intrinsically complex face variations. This paper proposes a principled multi-task learning approach based on Discriminative Gaussian Process Latent Variable Model (DGPLVM), named GaussianFace, for face verification. In contrast to relying unrealistically on a single training data source, our model exploits additional data from multiple source-domains to improve the generalization performance of face verification in an unknown target-domain. Importantly, our model can adapt automatically to complex data distributions, and therefore can well capture complex face variations inherent in multiple sources. To enhance discriminative power, we introduced a more efficient equivalent form of Kernel Fisher Discriminant Analysis to DGPLVM. To speed up the process of inference and prediction, we exploited the low rank approximation method. Extensive experiments demonstrated the effectiveness of the proposed model in learning from diverse data sources and generalizing to unseen domains. Specifically, the accuracy of our algorithm achieved an impressive accuracy rate of 98.52% on the well-known and challenging Labeled Faces in the Wild (LFW) benchmark. For the first time, the human-level performance in face verification (97.53%) on LFW is surpassed.


title: Detecting Insider Threats Using RADISH: A System for Real-Time Anomaly Detection in Heterogeneous Data Streams

abstract: We present a scalable system for high-throughput real-time analysis of heterogeneous data streams. Our architecture enables incremental development of models for predictive analytics and anomaly detection as data arrives into the system. In contrast with batch data-processing systems, such as Hadoop, that can have high latency, our architecture allows for ingest and analysis of data on the fly, thereby detecting and responding to anomalous behavior in near real time. This timeliness is important for applications such as insider threat, financial fraud, and network intrusions. We demonstrate an application of this system to the problem of detecting insider threats, namely, the misuse of an organization's resources by users of the system and present results of our experiments on a publicly available insider threat dataset.


title: Recognizing functions in binaries with neural networks

abstract: Binary analysis facilitates many important applications like malware detection and automatically fixing vulnerable software. In this paper, we propose to apply artificial neural networks to solve important yet difficult problems in binary analysis. Specifically, we tackle the problem of function identification, a crucial first step in many binary analysis techniques. Although neural networks have undergone a renaissance in the past few years, achieving breakthrough results in multiple application domains such as visual object recognition, language modeling, and speech recognition, no researchers have yet attempted to apply these techniques to problems in binary analysis. Using a dataset from prior work, we show that recurrent neural networks can identify functions in binaries with greater accuracy and efficiency than the state-of-the-art machine-learning-based method. We can train the model an order of magnitude faster and evaluate it on binaries hundreds of times faster. Furthermore, it halves the error rate on six out of eight benchmarks, and performs comparably on the remaining two.


Distance Metric Learning: A Comprehensive Survey


title: A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection

abstract: We present a multi-stream bi-directional recurrent neural network for fine-grained action detection. Recently, twostream convolutional neural networks (CNNs) trained on stacked optical flow and image frames have been successful for action recognition in videos. Our system uses a tracking algorithm to locate a bounding box around the person, which provides a frame of reference for appearance and motion and also suppresses background noise that is not within the bounding box. We train two additional streams on motion and appearance cropped to the tracked bounding box, along with full-frame streams. Our motion streams use pixel trajectories of a frame as raw features, in which the displacement values corresponding to a moving scene point are at the same spatial position across several frames. To model long-term temporal dynamics within and between actions, the multi-stream CNN is followed by a bi-directional Long Short-Term Memory (LSTM) layer. We show that our bi-directional LSTM network utilizes about 8 seconds of the video sequence to predict an action label. We test on two action detection datasets: the MPII Cooking 2 Dataset, and a new MERL Shopping Dataset that we introduce and make available to the community with this paper. The results demonstrate that our method significantly outperforms state-of-the-art action detection methods on both datasets.


title: Meta-analysis of modifiable risk factors for Alzheimer's disease

abstract: OBJECTIVE: ::: The aim of our present systematic review and meta-analysis was to roundly evaluate the association between AD and its modifiable risk factors. ::: BACKGROUND: ::: The prevalence of Alzheimer’s disease is increasing but that there is currently no cure solution for the disease. Recent studies showed that improved physical and cognitive functioning may contribute to decline of the dementia prevalence. These findings are suggestive of critical roles of adjusting modifiable risk factors in preventing AD incidence. ::: DESIGN/METHODS: ::: We systematically searched the PubMed, the Cochrane Database of Systematic Reviews from inception to July, 2014, and references of retrieved relevant articles. We included prospective cohort studies and retrospective case-control studies. ::: RESULTS: ::: 16906 articles were identified of which 323 with 93 factors met the inclusion criteria for meta-analysis. Among factors with relatively strong evidence (pooled population>5000) in our meta-analysis, we found grade I evidence for four medical exposures (estrogen, statin, anti-hypertensive medications and NSAIDs therapy) as well as four dietary exposures (folate, Vitamin E/C and coffee) as protective factors of AD. We found grade I evidence showing that one biochemical exposure (hyperhomocysteine) and one psychological condition (depression) significantly increase risk of developing AD. Grade I evidence also indicated complex roles of pre-existing disease (frailty, carotid atherosclerosis, hypertension, low DBP, DM-2 (Asian population) increasing risk whereas history of arthritis, heart disease, metabolic syndrome and cancer decreasing risk) and lifestyle (low education, high BMI in mid-life and low BMI increasing the risk whereas cognitive activity, current smoking (Western population), light-to-moderate drinking, stress, high BMI in late-life decreasing the risk) in influencing AD risk. We identified no evidence suggestive of significant association with occupational exposures. ::: CONCLUSIONS: ::: Effective interventions in diet, medications, biochemical exposures, psychological condition, pre-existing disease, and lifestyle may decrease new incidence of AD. Disclosure: Dr. Tan has nothing to disclose. Dr. Xu has nothing to disclose. Dr. Yu has nothing to disclose.


Projection-Based Augmented Reality Distributed Systems Seminar FS2013


title: Sentiment and Sarcasm Classification With Multitask Learning

abstract: Sentiment classification and sarcasm detection are both important natural language processing (NLP) tasks. Sentiment is always coupled with sarcasm where intensive emotion is expressed. Nevertheless, most literature considers them as two separate tasks. We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa. We show that these two tasks are correlated, and present a multi-task learning-based framework using a deep neural network that models this correlation to improve the performance of both tasks in a multi-task learning setting. Our method outperforms the state of the art by 3-4% in the benchmark dataset.


title: Quantum Bootstrap Aggregation

abstract: We set out a strategy for quantizing attribute bootstrap aggregation to enable variance-resilient quantum machine learning. To do so, we utilise the linear decomposability of decision boundary parameters in the Rebentrost et al. Support Vector Machine to guarantee that stochastic measurement of the output quantum state will give rise to an ensemble decision without destroying the superposition over projective feature subsets induced within the chosen SVM implementation. We achieve a linear performance advantage, O(d), in addition to the existing O(log(n)) advantages of quantization as applied to Support Vector Machines. The approach extends to any form of quantum learning giving rise to linear decision boundaries.


title: Predictive client-side profiles for personalized advertising

abstract: Personalization is ubiquitous in modern online applications as it provides significant improvements in user experience by adapting it to inferred user preferences. However, there are increasing concerns related to issues of privacy and control of the user data that is aggregated by online systems to power personalized experiences. These concerns are particularly significant for user profile aggregation in online advertising. This paper describes a practical, learning-driven client-side personalization approach for keyword advertising platforms, an emerging application previously not addressed in literature. Our approach relies on storing user-specific information entirely within the user's control (in a browser cookie or browser local storage), thus allowing the user to view, edit or purge it at any time (e.g., via a dedicated webpage). We develop a principled, utility-based formulation for the problem of iteratively updating user profiles stored client-side, which relies on calibrated prediction of future user activity. While optimal profile construction is NP-hard for pay-per-click advertising with bid increments, it can be efficiently solved via a greedy approximation algorithm guaranteed to provide a near-optimal solution due to the fact that keyword profile utility is submodular: it exhibits the property of diminishing returns with increasing profile size. We empirically evaluate client-side keyword profiles for keyword advertising on a large-scale dataset from a major search engine. Experiments demonstrate that predictive client-side personalization allows ad platforms to retain almost all of the revenue gains from personalization even if they give users the freedom to opt out of behavior tracking backed by server-side storage. Additionally, we show that advertisers can potentially increase their return on investment significantly by utilizing bid increments for keyword profiles in their ad campaigns.


title: Using STPA in Compliance with ISO 26262 for Developing a Safe Architecture for Fully Automated Vehicles

abstract: Safety has become of paramount importance in the development lifecycle of the modern automobile systems. However, the current automotive safety standard ISO 26262 does not specify clearly the methods for safety analysis. Different methods are recommended for this purpose. FTA (Fault Tree Analysis) and FMEA (Failure Mode and Effects Analysis) are used in the most recent ISO 26262 applications to identify component failures, errors and faults that lead to specific hazards (in the presence of faults). However, these methods are based on reliability theory, and they are not adequate to address new hazards caused by dysfunctional component interactions, software failure or human error. A holistic approach was developed called STPA (Systems-Theoretic Process Analysis) which addresses more types of hazards and treats safety as a dynamic control problem rather than an individual component failure. STPA also addresses types of hazardous causes in the absence of failure. Accordingly, there is a need for investigating hazard analysis techniques like STPA. In this paper, we present a concept on how to use STPA to extend the safety scope of ISO 26262 and support the Hazard Analysis and Risk Assessments (HARA) process. We applied the proposed concept to a current project of a fully automated vehicle at Continental. As a result, we identified 24 system- level accidents, 176 hazards, 27 unsafe control actions, and 129 unsafe scenarios. We conclude that STPA is an effective and efficient approach to derive detailed safety constraints. STPA can support the functional safety engineers to evaluate the architectural design of fully automated vehicles and build the functional safety concept.


title: A Case Study of Laser Wind Sensor Performance Validation by Comparison to an Existing Gage

abstract: A case study concerning validation of wind speed measurements made by a laser wind sensor mounted on a 190 square foot floating platform in Muskegon Lake through comparison with measurements made by pre-existing cup anemometers mounted on a met tower on the shore line is presented. The comparison strategy is to examine the difference in measurements over time using the paired-t statistical method to identify intervals when the measurements were equivalent and to provide explanatory information for the intervals when the measurements were not equivalent. The data was partitioned into three sets: not windy (average wind speed measured by the cup anemometers ≤ 6.7m/s) windy but no enhanced turbulence (average wind speed measured by the cup anemometers > 6.7m/s), and windy with enhanced turbulence associated with storm periods. For the not windy data set, the difference in the average wind speeds was equal in absolute value to the precision of the gages and not statistically significant. Similar results were obtained for the windy with no enhanced turbulence data set and the average difference was not statistically significant (a=0.01). The windy with enhanced turbulence data set showed significant differences between the buoy mounted laser wind sensor and the on-shore mast mounted cup anemometers. The sign of the average difference depended on the direction of the winds. Overall, validation evidence is obtained in the absence of enhanced turbulence. In addition, differences in wind speed during enhanced turbulence were isolated in time, studied and explained.


title: The relationship between Person-Organization Fit, burnout, and turnover Intention among CIC academic staff

abstract: The purpose of this study was to investigate the relationship between person organization fit (P-O fit), burnout ( as a whole and its three dimensions) , and turnover intention, between CIC (Canadian International College) academic members, or how can the misfit between the academic member and his/her organization causes burnout, which is in turn leads to a desire to leave work or turnover intention .The findings of the study confirmed the role of P-O misfit as an antecedent to burnout in general and to each one of its three dimensions (emotional exhaustion ,depersonalization and reduced personal accomplishments), meanwhile the existence of turnover intention as a consequence to burnout in general and to its three dimensions (emotional exhaustion ,depersonalization and reduced personal accomplishments), as well as the strong negative relationship between P-O fit and turnover intention .


title: NotiMind: Utilizing Responses to Smart Phone Notifications as Affective sensors

abstract: Today’s mobile phone users are faced with large numbers of notifications on social media, ranging from new followers on Twitter and emails to messages received from WhatsApp and Facebook. These digital alerts continuously disrupt activities through instant calls for attention. This paper examines closely the way everyday users interact with notifications and their impact on users’ emotion. Fifty users were recruited to download our application NotiMind and use it over a five-week period. Users’ phones collected thousands of social and system notifications along with affect data collected via self-reported Positive and Negative Affect Schedule tests three times a day. Results showed a noticeable correlation between positive affective measures and keyboard activities. When large numbers of post and remove notifications occur, a corresponding increase in negative affective measures is detected. Our predictive model has achieved a good accuracy level using three different ”in the wild” classifiers (F-measure 74%–78% within-subject model, 72%–76% global model). Our findings show that it is possible to automatically predict when people are experiencing positive, neutral, or negative affective states based on interactions with notifications. We also show how our findings open the door to a wide range of applications in relation to emotion awareness on social and mobile communication.


title: RDFProv: A Relational RDF Store for Querying and Managing Scientific Workflow Provenance

abstract: Provenance metadata has become increasingly important to support scientific discovery reproducibility, result interpretation, and problem diagnosis in scientific workflow environments. The provenance management problem concerns the efficiency and effectiveness of the modeling, recording, representation, integration, storage, and querying of provenance metadata. Our approach to provenance management seamlessly integrates the interoperability, extensibility, and inference advantages of Semantic Web technologies with the storage and querying power of an RDBMS to meet the emerging requirements of scientific workflow provenance management. In this paper, we elaborate on the design of a relational RDF store, called RDFProv, which is optimized for scientific workflow provenance querying and management. Specifically, we propose: i) two schema mapping algorithms to map an OWL provenance ontology to a relational database schema that is optimized for common provenance queries; ii) three efficient data mapping algorithms to map provenance RDF metadata to relational data according to the generated relational database schema, and iii) a schema-independent SPARQL-to-SQL translation algorithm that is optimized on-the-fly by using the type information of an instance available from the input provenance ontology and the statistics of the sizes of the tables in the database. Experimental results are presented to show that our algorithms are efficient and scalable. The comparison with two popular relational RDF stores, Jena and Sesame, and two commercial native RDF stores, AllegroGraph and BigOWLIM, showed that our optimizations result in improved performance and scalability for provenance metadata management. Finally, our case study for provenance management in a real-life biological simulation workflow showed the production quality and capability of the RDFProv system. Although presented in the context of scientific workflow provenance management, many of our proposed techniques apply to general RDF data management as well.


title: Computational thinking and tinkering: Exploration of an early childhood robotics curriculum

abstract: By engaging in construction-based robotics activities, children as young as four can play to learn a range of concepts. The TangibleK Robotics Program paired developmentally appropriate computer programming and robotics tools with a constructionist curriculum designed to engage kindergarten children in learning computational thinking, robotics, programming, and problem-solving. This paper documents three kindergarten classrooms' exposure to computer programming concepts and explores learning outcomes. Results point to strengths of the curriculum and areas where further redesign of the curriculum and technologies would be appropriate. Overall, the study demonstrates that kindergartners were both interested in and able to learn many aspects of robotics, programming, and computational thinking with the TangibleK curriculum design. We explore the efficacy of an early childhood robotics and programming curriculum.53 kindergarteners used developmentally appropriate robotics and programming tools.Children's concept mastery tended to be high but varied with concept difficulty.Results suggest effective curricular designs and areas warranting redesign.


title: LineUp: Visual Analysis of Multi-Attribute Rankings

abstract: Rankings are a popular and universal approach to structuring otherwise unorganized collections of items by computing a rank for each item based on the value of one or more of its attributes. This allows us, for example, to prioritize tasks or to evaluate the performance of products relative to each other. While the visualization of a ranking itself is straightforward, its interpretation is not, because the rank of an item represents only a summary of a potentially complicated relationship between its attributes and those of the other items. It is also common that alternative rankings exist which need to be compared and analyzed to gain insight into how multiple heterogeneous attributes affect the rankings. Advanced visual exploration tools are needed to make this process efficient. In this paper we present a comprehensive analysis of requirements for the visualization of multi-attribute rankings. Based on these considerations, we propose LineUp - a novel and scalable visualization technique that uses bar charts. This interactive technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. It enables users to interactively combine attributes and flexibly refine parameters to explore the effect of changes in the attribute combination. This process can be employed to derive actionable insights as to which attributes of an item need to be modified in order for its rank to change. Additionally, through integration of slope graphs, LineUp can also be used to compare multiple alternative rankings on the same set of items, for example, over time or across different attribute combinations. We evaluate the effectiveness of the proposed multi-attribute visualization technique in a qualitative study. The study shows that users are able to successfully solve complex ranking tasks in a short period of time.


title: Power System T using Fuzzy-P

abstract: A static synchronous compensator is one of the FACTS devices used to improve the transient stability of the power system. In this paper a two Fuzzy PI based controller is designed for STATCOM. One is designed for voltage regulator and other is designed for current regulator. For Voltage Regulator the inputs to the fuzzy based controllers are error i.e. difference between V meas and V ref and rate of change of error and the output is the ΔK p (change in proportional gain) and ΔK i (change in integral gain). The proposed controller is tested on two machine system using Matlab Simulink Environment. The Results are compared with conventional PI STATCOM Controller.


title: seL4: formal verification of an OS kernel

abstract: Complete formal verification is the only known way to guarantee that a system is free of programming errors. We present our experience in performing the formal, machine-checked verification of the seL4 microkernel from an abstract specification down to its C implementation. We assume correctness of compiler, assembly code, and hardware, and we used a unique design approach that fuses formal and operating systems techniques. To our knowledge, this is the first formal proof of functional correctness of a complete, general-purpose operating-system kernel. Functional correctness means here that the implementation always strictly follows our high-level abstract specification of kernel behaviour. This encompasses traditional design and implementation safety properties such as the kernel will never crash, and it will never perform an unsafe operation. It also proves much more: we can predict precisely how the kernel will behave in every possible situation. seL4, a third-generation microkernel of L4 provenance, comprises 8,700 lines of C code and 600 lines of assembler. Its performance is comparable to other high-performance L4 kernels.


title: Word, graph and manifold embedding from Markov processes

abstract: Continuous vector representations of words and objects appear to carry surprisingly rich semantic content. In this paper, we advance both the conceptual and theoretical understanding of word embeddings in three ways. First, we ground embeddings in semantic spaces studied in cognitive-psychometric literature and introduce new evaluation tasks. Second, in contrast to prior work, we take metric recovery as the key object of study, unify existing algorithms as consistent metric recovery methods based on co-occurrence counts from simple Markov random walks, and propose a new recovery algorithm. Third, we generalize metric recovery to graphs and manifolds, relating co-occurence counts on random walks in graphs and random processes on manifolds to the underlying metric to be recovered, thereby reconciling manifold estimation and embedding algorithms. We compare embedding algorithms across a range of tasks, from nonlinear dimensionality reduction to three semantic language tasks, including analogies, sequence completion, and classification.


title: Speeding-up the discrete wavelet transform computation with multicore and GPU-based algorithms.

abstract: In this work we propose several parallel algorithms to compute the twodimensional discrete wavelet transform (2D-DWT), exploiting the available hardware resources. In particular, we will explore OpenMP optimized versions of 2DDWT over a multicore platform and we will also develop CUDA-based 2D-DWT algorithms which are able to run on GPUs (Graphics Processing Unit). The proposed algorithms are based on several 2D-DWT computation approaches as (1) filter-bank convolution, (2) lifting transform and (3) matrix convolution, so we can determine which of them better adapts to our parallel versions. All proposed algorithms are based on the Daubechies 9/7 filter which is widely used in image/video compression.


title: A polynomial-time nash equilibrium algorithm for repeated games

abstract: With the increasing reliance on game theory as a foundation for auctions and electronic commerce, efficient algorithms for computing equilibria in multiplayer general-sum games are of great theoretical and practical interest. The computational complexity of finding a Nash equilibrium for a one-shot bimatrix game is a well known open problem. This paper treats a closely related problem, that of finding a Nash equilibrium for an average-payoff phrepeated bimatrix game, and presents a polynomial-time algorithm. Our approach draws on the "folk theorem" from game theory and shows how finite-state equilibrium strategies can be found efficiently and expressed succinctly.


title: Dynamics of the temperature-humidity index in the Mediterranean basin

abstract: The study was aimed at describing the temperature humidity index (THI) dynamics over the Mediterranean basin for the period 1951-2007. The THI combines temperature and humidity into a single value, and may help to predict the effects of environmental warmth in farm animals. In particular, on the basis of THI values, numerous studies have been performed to establish thresholds for heat stress in dairy cows. The THI was calculated by using monthly mean values of temperature and humidity obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research reanalysis project. The analysis demonstrated a high degree of heterogeneity of THI patterns over the Mediterranean basin, a strong north-south gradient, and an overall warming during the study period, which was particularly marked during summer seasons. Results indicated that several areas of the basin present summer THI values which were unfavorable to cow welfare and productivity, and that risk of heat stress for cows is generally greater in the countries of the south coast of the basin. Furthermore, THI data from the summer 2003 revealed that severe positive anomalies may impact areas normally characterized by a favorable climate for animal production. In conclusion, THI dynamics should be taken into careful consideration by farmers and policy makers operating in Mediterranean countries when planning investments in the sector of animal production. The investments should at least partially be directed towards implementation of adaptation measures, which may help to alleviate the impact of hot on farm animals welfare, performance and health.


title: A 0.003 mm$^{2}$ 10 b 240 MS/s 0.7 mW SAR ADC in 28 nm CMOS With Digital Error Correction and Correlated-Reversed Switching

abstract: This paper describes a single-channel, calibration-free Successive-Approximation-Register (SAR) ADC with a resolution of 10 bits at 240 MS/s. A DAC switching technique and an addition-only digital error correction technique based on the non-binary search are proposed to tackle the static and dynamic non-idealities attributed to capacitor mismatch and insufficient DAC settling. The conversion speed is enhanced, and the power and area of the DAC are also reduced by 40% as a result. In addition, a switching scheme lifting the input common mode of the comparator is proposed to further enhance the speed. Moreover, the comparator employs multiple feedback paths for an enhanced regeneration strength to alleviate the metastable problem. Occupying an active area of 0.003 mm $^{2}$ and dissipating 0.68 mW from 1 V supply at 240 MS/s in 28 nm CMOS, the proposed design achieves an SNDR of 57 dB with low-frequency inputs and 53 dB at the Nyquist input. This corresponds to a conversion efficiency of 4.8 fJ/c.-s. and 7.8 fJ/c.-s. respectively. The DAC switching technique improves the INL and DNL from +1.15/-1.01 LSB and +0.92/-0.28 LSB to within +0.55/-0.45 LSB and +0.45/-0.23 LSB, respectively. This ADC is at least 80% smaller and 32% more power efficient than reported state-of-the-art ADCs of similar resolutions and Nyquist bandwidths larger than 75 MHz.


title: Symbiosis in the microbial world: from ecology to genome evolution

abstract: ABSTRACT The concept of symbiosis – defined in 1879 by de Bary as ‘the living together of unlike organisms’ – has a rich and convoluted history in biology. In part, because it questioned the concept of the individual, symbiosis fell largely outside mainstream science and has traditionally received less attention than other research disciplines. This is gradually changing. In nature organisms do not live in isolation but rather interact with, and are impacted by, diverse beings throughout their life histories. Symbiosis is now recognized as a central driver of evolution across the entire tree of life, including, for example, bacterial endosymbionts that provide insects with vital nutrients and the mitochondria that power our own cells. Symbioses between microbes and their multicellular hosts also underpin the ecological success of some of the most productive ecosystems on the planet, including hydrothermal vents and coral reefs. In November 2017, scientists working in fields spanning the life sciences came together at a Company of Biologists’ workshop to discuss the origin, maintenance, and long-term implications of symbiosis from the complementary perspectives of cell biology, ecology, evolution and genomics, taking into account both model and non-model organisms. Here, we provide a brief synthesis of the fruitful discussions that transpired.


Deep Learning for Detecting Processing History of Images


title: Zika virus targets blood monocytes

abstract: Two studies identify circulating monocytes as the primary cellular target of Zika virus infection in human blood. Monocytes are an ideal target as they have the potential to be used as a Trojan horse to infiltrate immune-sheltered tissues, including placenta, testes and the brain, to spread Zika virus.


title: Circularly polarized square patch antenna with improved axial ratio bandwidth

abstract: This paper proposes and analyses a circularly polarized square patch antenna with an improved axial ratio bandwidth covering a frequency of 1.665GHz to 1.845GHz. The antenna attains circular polarization using the perturbations — truncated corners and well-shaped slots embedded on the patch. The results indicate that the antenna attains desirable axial ratio (below 3dB) for a wide elevation range of −134° to 47°. When compared to conventional truncated corner circularly polarized square patch antenna, the axial ratio bandwidth of the proposed antenna is increased by 30MHz. The antenna model is fabricated, tested and the measured results show a high correlation with the predicted ones.


title: Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding

abstract: Voice interfaces are becoming accepted widely as input methods for a diverse set of devices. This development is driven by rapid improvements in automatic speech recognition (ASR), which now performs on par with human listening in many tasks. These improvements base on an ongoing evolution of DNNs as the computational core of ASR. However, recent research results show that DNNs are vulnerable to adversarial perturbations, which allow attackers to force the transcription into a malicious output. ::: In this paper, we introduce a new type of adversarial examples based on psychoacoustic hiding. Our attack exploits the characteristics of DNN-based ASR systems, where we extend the original analysis procedure by an additional backpropagation step. We use this backpropagation to learn the degrees of freedom for the adversarial perturbation of the input signal, i.e., we apply a psychoacoustic model and manipulate the acoustic signal below the thresholds of human perception. To further minimize the perceptibility of the perturbations, we use forced alignment to find the best fitting temporal alignment between the original audio sample and the malicious target transcription. These extensions allow us to embed an arbitrary audio input with a malicious voice command that is then transcribed by the ASR system, with the audio signal remaining barely distinguishable from the original signal. In an experimental evaluation, we attack the state-of-the-art speech recognition system Kaldi and determine the best performing parameter and analysis setup for different types of input. Our results show that we are successful in up to 98% of cases with a computational effort of fewer than two minutes for a ten-second audio file. Based on user studies, we found that none of our target transcriptions were audible to human listeners, who still understand the original speech content with unchanged accuracy.


title: Comprehensive Dobutamine Stress CMR Versus Echocardiography in LBBB and Suspected Coronary Artery Disease

abstract: OBJECTIVES ::: This study aimed to compare dobutamine stress cardiac magnetic resonance (DSCMR) with dobutamine stress echocardiography (DSE) in patients with left bundle branch block (LBBB) and suspected coronary artery disease (CAD). ::: ::: ::: BACKGROUND ::: Noninvasive diagnosis of CAD in patients with pre-existent LBBB is difficult because single-photon emission computed tomography and stress echocardiography both have limitations. We hypothesized that a comprehensive DSCMR examination including cine, perfusion, and late gadolinium enhancement imaging would be more accurate than DSE, thus potentially reducing the number of unnecessary invasive coronary angiograms. ::: ::: ::: METHODS ::: We prospectively evaluated 82 consecutive patients with LBBB referred to our cardiology clinic for investigation of suspected CAD. All 82 patients underwent DSE, DSCMR, and invasive quantitative coronary angiography within 14 days. We compared the diagnostic accuracy of DSE, CMR cine imaging, the additive value of first-pass perfusion, and late gadolinium enhancement. In the comprehensive examination, a positive result was adjudged as the presence of either subendocardial or transmural late gadolinium enhancement with or without inducible peri-infarct ischemia or an inducible perfusion defect corresponding to an inducible regional wall motion abnormality. ::: ::: ::: RESULTS ::: CMR cine imaging (regional wall motion abnormalities) had higher specificity, negative predictive value, and overall diagnostic accuracy than did DSE (87.5% vs. 72.9%; 80.8% vs. 67.3%; and 80.4% vs. 72.0%, respectively), although sensitivity was the same (72.0%). The addition of first-pass stress perfusion and late gadolinium enhancement (scar) further improved diagnostic confidence (sensitivity 82.4%, specificity 95.8%, positive predictive value 93.3%, negative predictive value 88.5%, and diagnostic accuracy 90.2%). ::: ::: ::: CONCLUSIONS ::: DSCMR is a safe procedure and has greater diagnostic accuracy than does DSE in assessing patients with suspected CAD and LBBB. A comprehensive examination with the addition of perfusion and late gadolinium enhancement to CMR cine imaging significantly boosted specificity and sensitivity, making DSCMR a reliable alternative to invasive quantitative coronary angiography in this group of patients.


title: RGS13 controls g protein-coupled receptor-evoked responses of human mast cells.

abstract: IgE-mediated mast cell degranulation and release of vasoactive mediators induced by allergens elicits allergic responses. Although G protein-coupled receptor (GPCR)-induced signals may amplify IgE-dependent degranulation, how GPCR signaling in mast cells is regulated remains incompletely defined. We investigated the role of regulator of G protein signaling (RGS) proteins in the modulation of these pathways in human mast cells. Several RGS proteins were expressed in mast cells including RGS13, which we previously showed inhibited IgE-mediated mast cell degranulation and anaphylaxis in mice. To characterize how RGS13 affects GPCR-mediated functions of human mast cells, we analyzed human mast cell lines (HMC-1 and LAD2) depleted of RGS13 by specific small interfering RNA or short hairpin RNA and HMC-1 cells overexpressing RGS13. Transient RGS13 knockdown in LAD2 cells lead to increased degranulation to sphingosine-1-phosphate but not to IgE-Ag or C3a. Relative to control cells, HMC-1 cells stably expressing RGS13-targeted short hairpin RNA had greater Ca(2+) mobilization in response to several natural GPCR ligands such as adenosine, C5a, sphingosine-1-phosphate, and CXCL12 than wild-type cells. Akt phosphorylation, chemotaxis, and cytokine (IL-8) secretion induced by CXCL12 were also greater in short hairpin RGS13-HMC-1 cells compared with control. RGS13 overexpression inhibited CXCL12-evoked Ca(2+) mobilization, Akt phosphorylation and chemotaxis. These results suggest that RGS13 restricts certain GPCR-mediated biological responses of human mast cells.


title: Active Detection and Prevention of Sophisticated ARP- Poisoning Man-in-the-Middle Attacks on Switched Ethernet LANs

abstract: In this paper we describe two novel methods for active detection and prevention of ARPpoisoning-based Man-in-the-Middle (MitM) attacks on switched Ethernet LANs. As a stateless and inherently insecure protocol, ARP has been used as a relatively simple means to launch Denial-of-Service (DoS) and MitM attacks on local networks and multiple solutions have been proposed to detect and prevent these types of attacks. MitM attacks are particularly dangerous, because they allow an attacker to monitor network traffic and break the integrity of data being sent over the network. We introduce backward compatible techniques to prevent ARP poisoning and deal with sophisticated stealth MitM programs.


title: RMSProp and equilibrated adaptive learning rates for non-convex optimization

abstract: Parameter-specific adaptive learning rate methods are computationally efficient ways to reduce the ill-conditioning problems encountered when training large deep networks. Following recent work that strongly suggests that most of the critical points encountered when training such networks are saddle points, we find how considering the presence of negative eigenvalues of the Hessian could help us design better suited adaptive learning rate schemes. We show that the popular Jacobi preconditioner has undesirable behavior in the presence of both positive and negative curvature, and present theoretical and empirical evidence that the so-called equilibration preconditioner is comparatively better suited to non-convex problems. We introduce a novel adaptive learning rate scheme, called ESGD, based on the equilibration preconditioner. Our experiments show that ESGD performs as well or better than RMSProp in terms of convergence speed, always clearly improving over plain stochastic gradient descent.


title: Temperamental basis of sense of humor: Validating the state-trait-cheerfulness-inventory in Mainland China

abstract: Abstract This paper examined the reliability and validity of the State-Trait-Cheerfulness-Inventory (STCI) in Mainland China. The Chinese translation of the STCI-T<106i>, STCI-S<45i> and the Temporal Satisfaction with Life Scale (TSWLS) were administered to 476 university students (313 females, 157 males, 6 missing; 20.40 ± 1.35 years of age). Results showed that the STCI-T<106> and the STCI-S<45> had high Cronbach alphas ranging from 0.60 to 0.92 in Mainland China, and the factorial structures of the instruments were supported. The STCI-T<60> and STCI-S<30> were developed utilizing three criteria for the selection of items. Cronbach alphas were satisfactory ranging from 0.67 to 0.94. The factor structure of the items appeared to be highly generalizable in Mainland China. Joint factor analyses of the state and trait items yielded three factors (cheerfulness, seriousness and bad mood) both as traits and states with the homologous concepts positively correlated. As expected, cheerfulness correlated negatively with bad mood, and cheerfulness correlated negatively with state seriousness. Meanwhile, seriousness correlated positively with bad mood. Moreover, life satisfaction was related positively with cheerfulness and negatively with bad mood, and could be predicted by high cheerfulness and low bad mood. Applications of the STCI-T<60> and STCI-S<30> in the Chinese context and future research are discussed.


title: Camera Pose Estimation from Lines using Pl\"ucker Coordinates

abstract: Correspondences between 3D lines and their 2D images captured by a camera are often used to determine position and orientation of the camera in space. In this work, we propose a novel algebraic algorithm to estimate the camera pose. We parameterize 3D lines using Pl\"ucker coordinates that allow linear projection of the lines into the image. A line projection matrix is estimated using Linear Least Squares and the camera pose is then extracted from the matrix. An algebraic approach to handle mismatched line correspondences is also included. The proposed algorithm is an order of magnitude faster yet comparably accurate and robust to the state-of-the-art, it does not require initialization, and it yields only one solution. The described method requires at least 9 lines and is particularly suitable for scenarios with 25 and more lines, as also shown in the results.


title: Paving the Way to Smart Micro Energy Internet: Concepts, Design Principles, and Engineering Practices

abstract: Energy internet is one of the most promising future energy infrastructures which could enhance energy efficiency and improve operating flexibility from the energy point of view. In analog to the micro-grid, micro energy internet puts more emphasis on the distribution level and demand side. The concept and design principles of the smart micro energy internet are proposed to accommodate micro-grids, distributed poly-generation systems, energy storage facilities, and related energy distribution infrastructures. The dispatch and control system of the smart micro energy internet should be responsible for external disturbance and able to approach a satisfactory operating point which compromises multiple criteria, such as safety, economy, and environment protection. To realize the vision of the smart micro energy internet, engineering game theory based energy management system with self-approaching-optimum capability are investigated. Based on the proposed concepts and design principles, and energy management system, a prototype of the first domestic conceptual solar-based smart micro energy internet is initially established in Qinghai University.


title: Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data

abstract: Urbanization is a substantial contributor to anthropogenic environmental change, and often occurs at a rapid pace that demands frequent and accurate monitoring. Time series of satellite imagery collected at fine spatial resolution using stable spectral bands over decades are most desirable for this purpose. In practice, however, temporal spectral variance arising from variations in atmospheric conditions, sensor calibration, cloud cover, and other factors complicates extraction of consistent information on changes in urban land cover. Moreover, the construction and application of effective training samples is time-consuming, especially at continental and global scales. Here, we propose a new framework for satellite-based mapping of urban areas based on transfer learning and deep learning techniques. We apply this method to Landsat observations collected during 1984–2016 and extract annual records of urban areas in four cities in the temperate zone (Beijing, New York, Melbourne, and Munich). The method is trained using observations of Beijing collected in 1999, and then used to map urban areas in all target cities for the entire 1984–2016 period. The method addresses two central challenges in long term detection of urban change: temporal spectral variance and a scarcity of training samples. First, we use a recurrent neural network to minimize seasonal urban spectral variance. Second, we introduce an automated transfer strategy to maximize information gain from limited training samples when applied to new target cities in similar climate zones. Compared with other state-of-the-art methods, our method achieved comparable or even better accuracy: the average change detection accuracy during 1984–2016 is 89% for Beijing, 94% for New York, 93% for Melbourne, and 89% for Munich, and the overall accuracy of single-year urban maps is approximately 96 ± 3% among the four target cities. The results demonstrate the practical potential and suitability of the proposed framework. The method is a promising tool for detecting urban change in massive remote sensing data sets with limited training data.


Siamese Convolutional Networks for Cognate Identification


title: Yin and Yang: Balancing and Answering Binary Visual Questions

abstract: The complex compositional structure of language makes problems at the intersection of vision and language challenging. But language also provides a strong prior that can result in good superficial performance, without the underlying models truly understanding the visual content. This can hinder progress in pushing state of art in the computer vision aspects of multi-modal AI. In this paper, we address binary Visual Question Answering (VQA) on abstract scenes. We formulate this problem as visual verification of concepts inquired in the questions. Specifically, we convert the question to a tuple that concisely summarizes the visual concept to be detected in the image. If the concept can be found in the image, the answer to the question is "yes", and otherwise "no". Abstract scenes play two roles (1) They allow us to focus on the high-level semantics of the VQA task as opposed to the low-level recognition problems, and perhaps more importantly, (2) They provide us the modality to balance the dataset such that language priors are controlled, and the role of vision is essential. In particular, we collect fine-grained pairs of scenes for every question, such that the answer to the question is "yes" for one scene, and "no" for the other for the exact same question. Indeed, language priors alone do not perform better than chance on our balanced dataset. Moreover, our proposed approach matches the performance of a state-of-the-art VQA approach on the unbalanced dataset, and outperforms it on the balanced dataset.


title: Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations

abstract: Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. Here we introduce a novel approach that makes use of the sequence of transitions observed in tree-structured conversation threads in Twitter. The conversation threads are formed by harvesting users' replies to one another, which results in a nested tree-like structure. Previous work addressing the stance classification task has treated each tweet as a separate unit. Here we analyse tweets by virtue of their position in a sequence and test two sequential classifiers, Linear-Chain CRF and Tree CRF, each of which makes different assumptions about the conversational structure. We experiment with eight Twitter datasets, collected during breaking news, and show that exploiting the sequential structure of Twitter conversations achieves significant improvements over the non-sequential methods. Our work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations.


A weight pushing algorithm for large vocabulary speech recognition


title: Molecular de-novo design through deep reinforcement learning

abstract: This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.


title: A millimeter wave wideband differential line to waveguide transition using short ended slot line

abstract: This paper presents a millimeter wave wideband differential line to waveguide transition using a short ended slot line. The slot line connected in parallel to the rectangular waveguide can effectively compensate the frequency dependence of the susceptance in the waveguide. Thus it is suitable to achieve a wideband characteristic together with a simpler structure. It is experimentally demonstrated that the proposed transitions have the relative bandwidth of 20.2 % with respect to -10 dB reflection, which is a significant wideband characteristic compared with the conventional transition's bandwidth of 11%.


title: Automatic malware categorization using cluster ensemble

abstract: In this paper, resting on the analysis of instruction frequency and function-based instruction sequences, we develop an Automatic Malware Categorization System (AMCS) for automatically grouping malware samples into families that share some common characteristics using a cluster ensemble by aggregating the clustering solutions generated by different base clustering algorithms. We propose a principled cluster ensemble framework for combining individual clustering solutions based on the consensus partition. The domain knowledge in the form of sample-level constraints can be naturally incorporated in the ensemble framework. In addition, to account for the characteristics of feature representations, we propose a hybrid hierarchical clustering algorithm which combines the merits of hierarchical clustering and k-medoids algorithms and a weighted subspace K-medoids algorithm to generate base clusterings. The categorization results of our AMCS system can be used to generate signatures for malware families that are useful for malware detection. The case studies on large and real daily malware collection from Kingsoft Anti-Virus Lab demonstrate the effectiveness and efficiency of our AMCS system.


title: High-Frequency-Link Soft-Switching PWM DC–DC Converter for EV On-Board Battery Chargers

abstract: This paper proposes a high-frequency-link soft-switching pulse-width modulated dc-dc converter for electric vehicle (EV) on-board battery chargers. Zero-voltage switching of power switches is achieved from light load to full load. Reverse-recovery losses can be reduced in the secondary side output diodes without using any additional circuit components. Zero-current switching of output diodes is achieved by using the series-resonant circuit in the secondary side. The circulating current in the primary side full-bridge circuit can be changed by the operation modes of the output diode current. As a result, a high efficiency can be achieved for EV on-board battery chargers. The performance of the proposed converter is evaluated throughout experimental results for a 2.0-kW prototype circuit.


title: Teaching and Programing for Robots. On-line Learning of Robot Arm Impedance Using Neural Networks.

abstract: Impedance control is one of the most effective control methods for a manipulator in contact with its environments. The characteristics of force and motion control, however, are determined by the impedance parameters of the end-effector of the manipulator which must be designed according to the given task. In this paper, we propose a method to regulate impedance parameters of the manipulator's end-effector while identifying the characteristics of the environments using neural networks through on-line learning. Four kinds of neural networks are used: three for the position, velocity and force control of the end-effector, and one for the identification of environments. First, the neural networks for the position and velocity control are trained during free movements. Then, the neural networks for the force control and the identification of environments are trained during contact movements. Computer simulations show that the method can regulate stiffness, viscosity and inertia parameters of the end-effector and identify the unknown property of the environments through on-line learning.


title: An Empirical Study of Community Overlap: Ground-truth, Algorithmic Solutions, and Implications

abstract: In real-world social networks, communities tend to be overlapped with each other because a vertex can belong to multiple communities. To identify these overlapping communities, a number of overlapping community detection methods have been proposed over the recent years. However, there have been very few studies on the characteristics and the implications of the community overlap. In this paper, we investigate the properties of the nodes and the edges placed within the overlapped regions between the communities using the ground-truth communities as well as algorithmic communities derived from the state-of-the-art overlapping community detection methods. We find that the overlapped nodes and the overlapped edges play different roles from the ones that are not in the overlapped regions. Using real-world data, we empirically show that the highly overlapped nodes are involved in structure holes of a network. Also, we show that the overlapped nodes and edges play an important role in forming new links in evolving networks and diffusing information through a network.


Offline Arabic Text Recognition – An Overview


title: Web Personalization Systems and Web Usage Mining: A Review

abstract: Nowadays, the field of web personalization is growing exponentially. From e-mail, e-trading, internet forum to social networking based websites, directly or indirectly utilize web personalization and recommendation system for providing customized services to their loyal users. Personalization is achieved through web mining, i.e. extracting knowledge from the collected data. Knowledge is then filtered and processed to model user behavior that forms the basis of a personalized system. This paper presents a brief review of recent research efforts in web personalization and recommendation by means of web usage mining, for the benefit of research in this area. It also elaborates the role of web usage mining in personalization, and presents the open challenges that are yet to be met.


Mini-Bot: a Patrol Robot with Multi Mechanical Joint and Swap Algorithm


title: The Nuts and Bolts of Micropayments: A Survey

abstract: In this paper, we undertake a comprehensive survey of key trends and innovations in the development of research-based and commercial micropayment systems. Based on our study, we argue that past solutions have largely failed because research has focused heavily on cryptographic and engineering innovation, whereas fundamental issues pertaining to usability, psychology, and economics have been neglected. We contextualize the range of existing challenges for micropayments systems, discuss potential deployment strategies, and identify critical stumbling blocks, some of which we believe researchers and developers have yet to fully recognize. We hope this effort will motivate and guide the development of micropayments systems.


title: Reliable communication in the presence of failures

abstract: The design and correctness of a communication facility for a distributed computer system are reported on. The facility provides support for fault-tolerant process groups in the form of a family of reliable multicast protocols that can be used in both local- and wide-area networks. These protocols attain high levels of concurrency, while respecting application-specific delivery ordering constraints, and have varying cost and performance that depend on the degree of ordering desired. In particular, a protocol that enforces causal delivery orderings is introduced and shown to be a valuable alternative to conventional asynchronous communication protocols. The facility also ensures that the processes belonging to a fault-tolerant process group will observe consistent orderings of events affecting the group as a whole, including process failures, recoveries, migration, and dynamic changes to group properties like member rankings. A review of several uses for the protocols in the ISIS system, which supports fault-tolerant resilient objects and bulletin boards, illustrates the significant simplification of higher level algorithms made possible by our approach.


title: Secure hash-and-sign signatures without the random oracle

abstract: We present a new signature scheme which is existentially unforgeable under chosen message attacks, assuming some variant of the RSA conjecture. This scheme is not based on "signature trees", and instead it uses the so called "hash-and-sign" paradigm. It is unique in that the assumptions made on the cryptographic hash function in use are well defined and reasonable (although non-standard). In particular, we do not model this function as a random oracle. ::: ::: We construct our proof of security in steps. First we describe and prove a construction which operates in the random oracle model. Then we show that the random oracle in this construction can be replaced by a hash function which satisfies some strong (but well defined!) computational assumptions. Finally, we demonstrate that these assumptions are reasonable, by proving that a function satisfying them exists under standard intractability assumptions.


title: Grid Method Classification of Islamic Geometric Patterns

abstract: This chapter proposes a rational classification of Islamic Geometric Patterns (IGP) based on the Minimum Number of Grids (MNG) and Lowest Geometric Shape (LGS) used in the construction of the symmetric elements. The existing classification of repeating patterns by their symmetric groups is in many cases not appropriate or prudent [13]. The symmetry group theories do not relate to the way of thinking of the artisans involved, and completely has ignored the attributes of the unit pattern and has focused exclusively on arrangement formats. The chapter considers the current symmetric group theories only as arrangement patterns and not as classifications of IGP since they have a "global approach" and have failed to explore the possibilities in the construction elements of IGP. We describe and demonstrate procedures for constructing Star/Rosette unit patterns based on our proposed classification in a grid formation dictated by the final design of the unit pattern.


title: Introducing STRATOS: A Cloud Broker Service

abstract: This paper introduces a cloud broker service (STRATOS) which facilitates the deployment and runtime management of cloud application topologies using cloud elements/services sourced on the fly from multiple providers, based on requirements specified in higher level objectives. Its implementation and use is evaluated in a set of experiments.


title: Integrated Class-D Amplifier With Active Current Sensing Suitable for Alternating Current Switches

abstract: An integrated class-D amplifier with active current sensing suitable for AC switches is presented in this paper. The proposed current-sensing technique is used to sense and control the inductor current at the output stage and allows the current-sensing element to be integrated into class-D amplifiers. To verify the performance of the proposed circuits, a class-D amplifier was designed and fabricated with 3.3-V 0.35- mum double-polyquadruple-metal CMOS technology on a chip area of 1.79 times 1.45 mm. The maximum power efficiency of the class-D amplifier can be up to 94% for an 8-Omega load. These active current-sensing circuits can be applied to other power converters with AC switches.


title: Learning Using Privileged Information: SVM+ and Weighted SVM

abstract: Abstract Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information available only at training time—a framework implemented by SVM+. We relate the privileged information to importance weighting and show that the prior knowledge expressible with privileged features can also be encoded by weights associated with every training example. We show that a weighted SVM can always replicate an SVM+ solution, while the converse is not true and we construct a counterexample highlighting the limitations of SVM+. Finally, we touch on the problem of choosing weights for weighted SVMs when privileged features are not available.


title: Towards data assurance and resilience in IoT using blockchain

abstract: Data assurance and resilience are crucial security issues in cloud-based IoT applications. With the widespread adoption of drones in IoT scenarios such as warfare, agriculture and delivery, effective solutions to protect data integrity and communications between drones and the control system have been in urgent demand to prevent potential vulnerabilities that may cause heavy losses. To secure drone communication during data collection and transmission, as well as preserve the integrity of collected data, we propose a distributed solution by utilizing blockchain technology along with the traditional cloud server. Instead of registering the drone itself to the blockchain, we anchor the hashed data records collected from drones to the blockchain network and generate a blockchain receipt for each data record stored in the cloud, reducing the burden of moving drones with the limit of battery and process capability while gaining enhanced security guarantee of the data. This paper presents the idea of securing drone data collection and communication in combination with a public blockchain for provisioning data integrity and cloud auditing. The evaluation shows that our system is a reliable and distributed system for drone data assurance and resilience with acceptable overhead and scalability for a large number of drones.


Business intelligence and analytics: From big data to big impact


Replacing Paper Contracts With Ethereum Smart Contracts


title: Molecular role of dopamine in anhedonia linked to reward deficiency syndrome RDS and anti- reward systems

abstract: Anhedonia is a condition that leads to the loss of feelings pleasure in response to natural reinforcers like food, sex, exercise, and social activities. This disorder occurs in addiction, and an array of related neuropsychiatric syndromes, including schizophrenia, depression, and Post Traumatic Stress Disorder (PTSD). Anhedonia may by due to derangements in mesolimbic dopaminergic pathways and their terminal fields (e.g., striatum, amygdala, and prefrontal cortex) that persist long after the traces of the causative drugs are eliminated (pharmacokinetically). Here we postulate that anhedonia is not a distinct entity but is rather an epiphenomenon of hypodopaminergic states and traits arising from the interaction of genetic traits and epigenetic neurobiological alterations in response to environmental influences. Moreover, dopaminergic activity is rather complex, and so it may give rise to differential pathophysiological processes such as incentive sensitization, aberrant learning and stress-like "anti-reward" phenomena. These processes may have additive, synergistic or antagonistic interactions with the concurrent reward deficiency states leading in some instances to more severe and long-lasting symptoms. Operant understanding of the neurogenetic antecedents to reward deficiency syndrome (RDS) and the elucidation of reward gene polymorphisms may provide a map for accessing an individual's genetic risk for developing Anhedonia. Prevention techniques that can restore homeostatic balance via physiological activation of dopaminergic receptors (D2/D3) may be instrumental for targeting not only anhedonia per se but also drug craving and relapse.


title: As-rigid-as-possible deformation transfer for facial animation

abstract: We present a novel tool for transferring facial motion capture performance between significantly different characters. This work combines the shape deformation techniques of Deformation Transfer (DT) and As-Rigid-As-Possible (ARAP) into a single formulation by defining rigidity over triangle edge sets as opposed to spokes. Further, it improves them by adding non-uniform rigidity using paintable weights. We examine three important use cases for the tool: adding physically plausible behavior where animation is not available; fixing animation transfer artifacts; and providing plausible deformation around manipulated vertices. By combining Deformation Transfer and As-Rigid-As-Possible, this tool allows users to control the balance between animation fidelity and physically plausible deformation.


title: Duration and Interval Hidden Markov Model for sequential data analysis

abstract: Analysis of sequential event data has been recognized as one of the essential tools in data modeling and analysis field. In this paper, after the examination of its technical requirements and issues to model complex but practical situation, we propose a new sequential data model, dubbed Duration and Interval Hidden Markov Model (DI-HMM), that efficiently represents “state duration” and “state interval” of data events. This has significant implications to play an important role in representing practical time-series sequential data. This eventually provides an efficient and flexible sequential data retrieval. Numerical experiments on synthetic and real data demonstrate the efficiency and accuracy of the proposed DI-HMM.


title: Computational Modeling, Formal Analysis, and Tools for Systems Biology

abstract: As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.


Surface Plasmon Resonance Sensors for Detection of Chemical and Biological Species


title: A hand exoskeleton device for robot assisted sensory-motor training after stroke

abstract: Robot assisted rehabilitation devices are becoming popular for stroke rehabilitation, but they are mainly highlighted in training and recovery of motor function. This paper describes the development of a hand exoskeleton rehabilitation device which focuses not only on rehabilitation of motor function, but also sensory training and stimulation. The device was designed to enable stroke patients to train hand movements with less aid of a therapist, and allows objective assessment of hand function. The mechanical design allows to control the movement of five fingers individually. Force feedback is provided via a leverage mechanism, which is driven by DC motors. Additionally, tactile feedback is presented through vibration motors, attached to each fingertip. We present three different operating modes, active assisted, passive assisted and active non-assisted haptic interaction mode. The performance of the device shows that it is possible to provide feedback forces up to about 14 N for each finger, a maximum joint angle of 62°, 88° for MCP and PIP joint respectively and a settling time of 0.37 s in the passive assisted mode. Based on the characterization of the proposed device, it has a potential to be utilized in hand rehabilitation in terms of regaining both sensory and motor function.


title: Chain of Reasoning for Visual Question Answering

abstract: Reasoning plays an essential role in Visual Question Answering (VQA). Multi-step and dynamic reasoning is often necessary for answering complex questions. For example, a question "What is placed next to the bus on the right of the picture?" talks about a compound object "bus on the right," which is generated by the relation . Furthermore, a new relation including this compound object is then required to infer the answer. However, previous methods support either one-step or static reasoning, without updating relations or generating compound objects. This paper proposes a novel reasoning model for addressing these problems. A chain of reasoning (CoR) is constructed for supporting multi-step and dynamic reasoning on changed relations and objects. In detail, iteratively, the relational reasoning operations form new relations between objects, and the object refining operations generate new compound objects from relations. We achieve new state-of-the-art results on four publicly available datasets. The visualization of the chain of reasoning illustrates the progress that the CoR generates new compound objects that lead to the answer of the question step by step.


title: Video-Based Face Spoofing Detection through Visual Rhythm Analysis

abstract: Recent advances on biometrics, information forensics, and security have improved the accuracy of biometric systems, mainly those based on facial information. However, an ever-growing challenge is the vulnerability of such systems to impostor attacks, in which users without access privileges try to authenticate themselves as valid users. In this work, we present a solution to video-based face spoofing to biometric systems. Such type of attack is characterized by presenting a video of a real user to the biometric system. To the best of our knowledge, this is the first attempt of dealing with video-based face spoofing based in the analysis of global information that is invariant to video content. Our approach takes advantage of noise signatures generated by the recaptured video to distinguish between fake and valid access. To capture the noise and obtain a compact representation, we use the Fourier spectrum followed by the computation of the visual rhythm and extraction of the gray-level co-occurrence matrices, used as feature descriptors. Results show the effectiveness of the proposed approach to distinguish between valid and fake users for video-based spoofing with near-perfect classification results.


title: ROC Analysis of Classifiers in Machine Learning: A Survey

abstract: The use of ROC Receiver Operating Characteristics analysis as a tool for evaluating the performance of classification models in machine learning has been increasing in the last decade. Among the most notable advances in this area are the extension of two-class ROC analysis to the multi-class case as well as the employment of ROC analysis in cost-sensitive learning. Methods now exist which take instance-varying costs into account. The purpose of our paper is to present a survey of this field with the aim of gathering important achievements in one place. In the paper, we present application areas of the ROC analysis in machine learning, describe its problems and challenges and provide a summarized list of alternative approaches to ROC analysis. In addition to presented theory, we also provide a couple of examples intended to illustrate the described approaches.


title: Unconscious cerebral initiative and the role of conscious will in voluntary action

abstract: Voluntary acts are preceded by electrophysiological “ readiness potentials” (RPs). With spontaneous acts involving no preplanning, the main negative RP shift begins at about -550 ms. Such RPs were used to indicate the minimum onset times for the cerebral activity that precedes a fully endogenous voluntary act. The time of conscious intention to act was obtained from the subject’s recall of the spatial clock position of a revolving spot at the time of his initial awareness of intending or wanting to move (W). W occurred at about -200 ms. Control experiments, in which a skin stimulus was timed (S), helped evaluate each subject’s error in reporting the clock times for awareness of any perceived event.


title: A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data

abstract: The availability of abundant data posts a challenge to integrate static customer data and longitudinal behavioral data to improve performance in customer churn prediction. Usually, longitudinal behavioral data are transformed into static data before being included in a prediction model. In this study, a framework with ensemble techniques is presented for customer churn prediction directly using longitudinal behavioral data. A novel approach called the hierarchical multiple kernel support vector machine (H-MK-SVM) is formulated. A three phase training algorithm for the H-MK-SVM is developed, implemented and tested. The H-MK-SVM constructs a classification function by estimating the coefficients of both static and longitudinal behavioral variables in the training process without transformation of the longitudinal behavioral data. The training process of the H-MK-SVM is also a feature selection and time subsequence selection process because the sparse non-zero coefficients correspond to the variables selected. Computational experiments using three real-world databases were conducted. Computational results using multiple criteria measuring performance show that the H-MK-SVM directly using longitudinal behavioral data performs better than currently available classifiers.


title: Internet of things: new security and privacy challenges

abstract: The Internet of Things, an emerging global Internet-based technical architecture facilitating the exchange of goods and services in global supply chain networks has an impact on the security and privacy of the involved stakeholders. Measures ensuring the architecture's resilience to attacks, data authentication, access control and client privacy need to be established. An adequate legal framework must take the underlying technology into account and would best be established by an international legislator, which is supplemented by the private sector according to specific needs and thereby becomes easily adjustable. The contents of the respective legislation must encompass the right to information, provisions prohibiting or restricting the use of mechanisms of the Internet of Things, rules on IT-security-legislation, provisions supporting the use of mechanisms of the Internet of Things and the establishment of a task force doing research on the legal challenges of the IoT.


title: The science of networking: New trends in music production, distribution and management

abstract: The way people consume music in our days has totally changed as a result of the current developments in hardware and software, as well as the evolution of the Internet. People do not buy CDs or records anymore. Nowadays, people store music data on their disks and listen to music via portable devices (i-pods, mobile phones etc.). However, the advent of Web 2.0 foretokens colossal changes in the fields of music production, distribution and management. Cloud computing, social networks and web-based operating systems are some of the recent trends that affect these fields and introduce a new era beyond MP3.


Development of measures of online privacy concern and protection for use on the Internet


title: 2-GHz Si power MOSFET technology

abstract: A 2-GHz Si power MOSFET with 50% power-added efficiency and 1.0-W output power at a 3.6-V supply voltage has been developed for use as an RF high-power amplifier in wireless applications. This MOSFET achieves this performance by using a 0.4-/spl mu/m gate power device with an Al-shorted metal-silicide/Si gate structure and a reduced gate finger width pattern.


title: Understanding student learning trajectories using multimodal learning analytics within an embodied-interaction learning environment

abstract: The aim of this paper is to show how multimodal learning analytics (MMLA) can help understand how elementary students explore the concept of feedback loops while controlling an embodied simulation of a predator-prey ecosystem using hand movements as an interface with the computer simulation. We represent student motion patterns from fine-grained logs of hands and gaze data, and then map these observed motion patterns against levels of student performance to make inferences about how embodiment plays a role in the learning process. Results show five distinct motion sequences in students' embodied interactions, and these motion patterns are statistically associated with initial and post-tutorial levels of students' understanding of feedback loops. Analysis of student gaze also shows distinctive patterns as to how low- and high-performing students attended to information presented in the simulation. Using MMLA, we show how students' explanations of feedback loops look differently according to cluster membership, which provides evidence that embodiment interacts with conceptual understanding.


title: Cognitive Smart Healthcare for Pathology Detection and Monitoring

abstract: We propose a cognitive healthcare framework that adopts the Internet of Things (IoT)-cloud technologies. This framework uses smart sensors for communications and deep learning for intelligent decision-making within the smart city perspective. The cognitive and smart framework monitors patients' state in real time and provides accurate, timely, and high-quality healthcare services at low cost. To assess the feasibility of the proposed framework, we present the experimental results of an EEG pathology classification technique that uses deep learning. We employ a range of healthcare smart sensors, including an EEG smart sensor, to record and monitor multimodal healthcare data continuously. The EEG signals from patients are transmitted via smart IoT devices to the cloud, where they are processed and sent to a cognitive module. The system determines the state of the patient by monitoring sensor readings, such as facial expressions, speech, EEG, movements, and gestures. The real-time decision, based on which the future course of action is taken, is made by the cognitive module. When information is transmitted to the deep learning module, the EEG signals are classified as pathologic or normal. The patient state monitoring and the EEG processing results are shared with healthcare providers, who can then assess the patient's condition and provide emergency help if the patient is in a critical state. The proposed deep learning model achieves better accuracy than the state-of-the-art systems.


title: Optical burst-over-circuit switching for multi-granularity traffic in data centers

abstract: We propose a composite optical crossconnect by adding a fast wavelength routing module to a MEMS-based switch. This crossconnect efficiently addresses the requirements of burst and circuit switching in data center optical networks.


title: DaDianNao: A Machine-Learning Supercomputer

abstract: Many companies are deploying services, either for consumers or industry, which are largely based on machine-learning algorithms for sophisticated processing of large amounts of data. The state-of-the-art and most popular such machine-learning algorithms are Convolutional and Deep Neural Networks (CNNs and DNNs), which are known to be both computationally and memory intensive. A number of neural network accelerators have been recently proposed which can offer high computational capacity/area ratio, but which remain hampered by memory accesses. However, unlike the memory wall faced by processors on general-purpose workloads, the CNNs and DNNs memory footprint, while large, is not beyond the capability of the on-chip storage of a multi-chip system. This property, combined with the CNN/DNN algorithmic characteristics, can lead to high internal bandwidth and low external communications, which can in turn enable high-degree parallelism at a reasonable area cost. In this article, we introduce a custom multi-chip machine-learning architecture along those lines. We show that, on a subset of the largest known neural network layers, it is possible to achieve a speedup of 450.65x over a GPU, and reduce the energy by 150.31x on average for a 64-chip system. We implement the node down to the place and route at 28nm, containing a combination of custom storage and computational units, with industry-grade interconnects.


title: The Curse of Expertise: When More Knowledge Leads to Miscalibrated Explanatory Insight

abstract: Does expertise within a domain of knowledge predict accurate self-assessment of the ability to explain topics in that domain? We find that expertise increases confidence in the ability to explain a wide variety of phenomena. However, this confidence is unwarranted; after actually offering full explanations, people are surprised by the limitations in their understanding. For passive expertise (familiar topics), miscalibration is moderated by education; those with more education are accurate in their self-assessments (Experiment 1). But when those with more education consider topics related to their area of concentrated study (college major), they also display an illusion of understanding (Experiment 2). This “curse of expertise” is explained by a failure to recognize the amount of detailed information that had been forgotten (Experiment 3). While expertise can sometimes lead to accurate self-knowledge, it can also create illusions of competence.