title: Real-World Uplift Modelling with Significance-Based Uplift Trees

abstract: This paper seeks to document the current state of the art in ‘uplift modelling’—the practice of modelling the change in behaviour that results directly from a specified treatment such as a marketing intervention. We include details of the SignificanceBased Uplift Trees that have formed the core of the only packaged uplift modelling software currently available. The paper includes a summary of some of the results that have been delivered using uplift modelling in practice, with examples drawn from demand-stimulation and customer-retention applications. It also surveys and discusses approaches to each of the major stages involved in uplift modelling—variable selection, model construction, quality measures and postcampaign evaluation—all of which require different approaches from traditional response modelling.


title: Applying Ant Colony Optimization in software testing to generate prioritized optimal path and test data

abstract: Software testing is one of the most important parts of software development lifecycle. Among various types of software testing approaches structural testing is widely used. Structural testing can be improved largely by traversing all possible code paths of the software. Genetic algorithm is the most used search technique to automate path testing and test case generation. Recently, different novel search based optimization techniques such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Artificial Immune System (AIS), Particle Swarm Optimization (PSO) have been applied to generate optimal path to complete software coverage. In this paper, ant colony optimization (ACO) based algorithm has been proposed which will generate set of optimal paths and prioritize the paths. Additionally, the approach generates test data sequence within the domain to use as inputs of the generated paths. Proposed approach guarantees full software coverage with minimum redundancy. This paper also demonstrates the proposed approach applying it in a program module.


title: Contention Resolution Diversity Slotted ALOHA (CRDSA): An Enhanced Random Access Schemefor Satellite Access Packet Networks

abstract: In this paper a new multiple access scheme dubbed contention resolution diversity slotted ALOHA (CRDSA) is introduced and its performance and implementation are thoroughly analyzed. The scheme combines diversity transmission of data bursts with efficient interference cancellation techniques. It is shown that CRDSA largely outperforms the classical lotted ALOHA (SA) technique in terms of throughput under equal packet loss ratio conditions (e.g. 17-fold improvement at packet loss ratio = 2 middot 10-2). CRDSA allows to boost the performance of random access (RA) channels in the return link of interactive satellite networks, making RA very efficient and providing low latency for the transmission of small size sparse packets. Implementation-wise it is shown that the CRDSA technique can be easily integrated in systems equipped with digital burst demodulators.


title: Ontology View Extraction: An Approach Based on Ontological Meta-properties

abstract: Ontologies have been applied in Computer Science to ensure the semantic interoperability among multiple systems. With the increasing of ontologies availability, many approaches for promoting the share and reuse of ontologies have been investigated in recent years, like ontology module extraction (modularization) and ontology view extraction. Approaches for ontology module extraction are used for extracting modules in large ontologies. On the other hand, ontology views have been used for providing to the user only the parts of the ontology that are useful for a given task. Thus, both ontology views and ontology modules encapsulate a subset of the original ontology, but they have different purposes. The literature has explored the ontological meta-properties (such as identity and rigidity) for guiding the modeling decisions that are made during the ontology engineering process. Some of these meta-properties were formalized in foundational ontologies like UFO (Unified foundational ontology). In this paper, we explore the use of ontological meta-properties for extracting ontology views. We propose a characterization of the notion of well-founded ontology view, considering the ontological meta-properties of the concepts. Besides that, we also propose a language-independent algorithm for sub-ontology extraction that is guided by ontological meta-properties. Finally, we present a case study for illustrating the application of our approach.


title: X Goes First: Teaching a Simple Game through Multimodal Interaction

abstract: What would it take to teach a computer to play a game entirely through language and sketching? In this paper, we present an implemented program through which an instructor can teach the rules of simple board games using such input. We describe the architecture, information flow, and vocabulary of instructional events and walk through an annotated example. In our approach, the instructional and communication events guide abductive reasoning for language interpretation and help to integrate information from sketching and language. Having a general target representation enables the learning process to be viewed more as translation and problem solving than as induction. Lastly, learning by demonstration complements and extends instruction, resulting in concrete, operational rules.


title: Kernel and symbol criteria for Schatten classes and $r$-nuclearity on compact manifolds

abstract: Abstract In this Note, we present criteria on both symbols and integral kernels ensuring that the corresponding operators on compact manifolds belong to Schatten classes. A specific test for nuclearity is established as well as the corresponding trace formulae. In the special case of compact Lie groups, kernel criteria in terms of (locally and globally) hypoelliptic operators are also given. A notion of invariant operator and its full symbol associated with an elliptic operator are introduced. Some applications to the study of r -nuclearity on L p spaces are also obtained.


title: Ranking chemical structures for drug discovery: a new machine learning approach.

abstract: With chemical libraries increasingly containing millions of compounds or more, there is a fast-growing need for computational methods that can rank or prioritize compounds for screening. Machine learning methods have shown considerable promise for this task; indeed, classification methods such as support vector machines (SVMs), together with their variants, have been used in virtual screening to distinguish active compounds from inactive ones, while regression methods such as partial least-squares (PLS) and support vector regression (SVR) have been used in quantitative structure−activity relationship (QSAR) analysis for predicting biological activities of compounds. Recently, a new class of machine learning methods − namely, ranking methods, which are designed to directly optimize ranking performance − have been developed for ranking tasks such as web search that arise in information retrieval (IR) and other applications. Here we report the application of these new ranking methods in machine learning to t...


title: Empty LTCC Integrated Waveguide with Compact Transitions for Ultra-low Loss Millimeter-wave Applications

abstract: An Empty LTCC (Low Temperature Cofired Ceramics) Integrated Waveguide (E-LIW) for the 57-64 GHz band is proposed. In addition to being air-filled, this SIW-like structure uses long rectangular side-wall vias to reduce conductor and radiation losses. In simulation, the proposed structure shows an insertion loss of 0.084 dB/cm at 60 GHz compared to 0.325 dB/cm, when the same structure is dielectric filled, and 0.48 dB/cm when it is dielectric filled but with conventional cylindrical vias. A very compact transition between E-LIW and CPW is also proposed. A back-to-back configuration including the proposed transition and E-LIW has been fabricated. Measurement results show a maximum total insertion loss of 0.5± 0.2 dB with better than 20 dB return loss for the entire structure and over the entire frequency band of interest. These results are in good agreement with those obtained by 3D field simulation.


title: Analyzing writing styles with Coh-Metrix

abstract: Computer scientists, linguists, stylometricians, and cognitive scientists have successfully divided corpora into modes, domains, genres, registers, and authors. The limitations for these successes, however, often result from insufficient indices with which their corpora are analyzed. In this paper, we use Coh-Metrix, a computational tool that analyzes text on over 200 indices of cohesion and difficulty. We demonstrate how, with the benefit of statistical analysis, texts can be analyzed for subtle, yet meaningful differences. In this paper, we report evidence that authors within the same register can be computationally distinguished despite evidence that stylistic markers can also shift significantly over time.


title: Computer-aided music composition with LSTM neural network and chaotic inspiration

abstract: In this paper a new neural network system for composition of melodies is proposed. The Long Short-Term Memory (LSTM) neural network is adopted as the neural network model. We include an independent melody as an additional input in order to provide an inspiration source to the network. This melody is given by a chaotic composition algorithm and works as an inspiration to the network enhancing the subjective measure of the composed melodies. As the chaotic system we use the Henon map with two variables, which are mapped to pitch and rhythm. We adopt a measure to conduct the degree of melodiousness (Euler's gradus suavitatis) of the output melody, which is compared with a reference value. Varying a specific parameter of the chaotic system, we can control the complexity of the chaotic melody. The system runs until the degree of melodiousness falls within a predetermined range.


title: Blind Deconvolution with Non-local Sparsity Reweighting

abstract: Blind deconvolution has made significant progress in the past decade. Most successful algorithms are classified either as Variational or Maximum a-Posteriori ($MAP$). In spite of the superior theoretical justification of variational techniques, carefully constructed $MAP$ algorithms have proven equally effective in practice. In this paper, we show that all successful $MAP$ and variational algorithms share a common framework, relying on the following key principles: sparsity promotion in the gradient domain, $l_2$ regularization for kernel estimation, and the use of convex (often quadratic) cost functions. Our observations lead to a unified understanding of the principles required for successful blind deconvolution. We incorporate these principles into a novel algorithm that improves significantly upon the state of the art.


title: An Electric Fence Energizer Design Method

abstract: This paper introduces fundamental concepts of electric fence technology, presents a new design method for a livestock electric fence energizer circuit and impulse transformer as well a mathematic analyze of the circuit. A new expression for design single impulse transformers for this kind of application is presented who has different project criteria from conventional transformer applications. The Energizer equipment is rounded about many concepts, safety standards and data performance that are discussed. An electric circuit prototype of Electric Fence Energizer Equipment for livestock use was implemented and the results are showed. This work is based on a study developed in a Master Thesis [1].


title: Shared Embedding Based Neural Networks for Knowledge Graph Completion

abstract: Knowledge Graphs (KGs) have facilitated many real-world applications (e.g., vertical search and intelligent question answering). However, they are usually incomplete, which affects the performance of such KG based applications. To alleviate this problem, a number of Knowledge Graph Completion (KGC) methods have been developed to predict those implicit triples. Tensor/matrix based methods and translation based methods have attracted great attention for a long time. Recently, neural network has been introduced into KGC due to its extensive superiority in many fields (e.g., natural language processing and computer vision), and achieves promising results. In this paper, we propose a Shared Embedding based Neural Network (SENN) model for KGC. It integrates the prediction tasks of head entities, relations and tail entities into a neural network based framework with shared embeddings of entities and relations, while explicitly considering the differences among these prediction tasks. Moreover, we propose an adaptively weighted loss mechanism, which dynamically adjusts the weights of losses according to the mapping properties of relations, and the prediction tasks. Since relation prediction usually performs better than head and tail entity predictions, we further extend SENN to SENN+ by employing it to assist head and tail entity predictions. Experiments on benchmark datasets validate the effectiveness and merits of the proposed SENN and SENN+ methods. The shared embeddings and the adaptively weighted loss mechanism are also testified to be effective.


title: Erotic Image Recognition Method of Bagging Integrated Convolutional Neural Network

abstract: With1 the development of current Internet era, the exchange of data information is becoming more frequent and the spread of erotic images is becoming easier. Under these circumstances, it becomes even more important to identify and classify the images. Deep learning has been widely used in the field of image recognition because of its great advantage in automatically extracting features. However, in the case of small amount of data, it is easy to cause over-fitting of training data. Based on current situation, we propose a new erotic image recognition model. This model adopts Bagging Integrated Convolutional Neural Network and combines traditional Color Features-Histogram of Color based on the depth features. While improving the recognition accuracy, it also increases the sensitivity of the model to the color of the picture. Result of the experiment shows that, when identifying and classifying images in the NPDI data sets, the accuracy of the proposed model reaches 99.31%, which is 2.67% higher than that of the Convolutional Neural Network model, and it has a favorable classification recognition effect.


title: numpywren: serverless linear algebra

abstract: Linear algebra operations are widely used in scientific computing and machine learning applications. However, it is challenging for scientists and data analysts to run linear algebra at scales beyond a single machine. Traditional approaches either require access to supercomputing clusters, or impose configuration and cluster management challenges. In this paper we show how the disaggregation of storage and compute resources in so-called"serverless"environments, combined with compute-intensive workload characteristics, can be exploited to achieve elastic scalability and ease of management. We present numpywren, a system for linear algebra built on a serverless architecture. We also introduce LAmbdaPACK, a domain-specific language designed to implement highly parallel linear algebra algorithms in a serverless setting. We show that, for certain linear algebra algorithms such as matrix multiply, singular value decomposition, and Cholesky decomposition, numpywren's performance (completion time) is within 33% of ScaLAPACK, and its compute efficiency (total CPU-hours) is up to 240% better due to elasticity, while providing an easier to use interface and better fault tolerance. At the same time, we show that the inability of serverless runtimes to exploit locality across the cores in a machine fundamentally limits their network efficiency, which limits performance on other algorithms such as QR factorization. This highlights how cloud providers could better support these types of computations through small changes in their infrastructure.


title: Lessons learnt from using DSLs for automated software testing

abstract: Domain Specific Languages (DSLs) provide a means of unambiguously expressing concepts in a particular domain. Although they may not refer to it as such, companies build and maintain DSLs for software testing on a day-to-day basis, especially when they define test suites using the Gherkin language. However, although the practice of specifying and automating test cases using the Gherkin language and related technologies such as Cucumber has become mainstream, the curation of such languages presents a number of challenges. In this paper we discuss lessons learnt from five case studies on industry systems, two involving the use of Gherkin-type syntax and another three case studies using more rigidly defined language grammars. Initial observations indicate that the likelihood of success of such efforts is increased if one manages to use an approach which separates the concerns of domain experts who curate the language, users who write scripts with the language, and engineers who wire the language into test automation technologies thus producing executable test code. We also provide some insights into desirable qualities of testing DSLs in different contexts.


title: Comparison of PID and fuzzy logic controller for DC servo motor in the development of lower extremity exoskeleton for rehabilitation

abstract: Impairment of motor function of the lower extremities is a common result of stroke or spinal cord injury due to the harm caused to the spinal column. Rehabilitation using a lower extremity exoskeleton for therapy purposes has been examined to improve motor function of the affected limb. PID controller is the most widely recognized types of feedback controller in control strategy. The simplicity and functionality of the PID controller made it a common controller in various applications. Many researchers have adopted the PID as the controller for their system and the exoskeleton manipulator for rehabilitation devices is a part of it. However, the conventional PID controller is not suitable when controlling a real-time system that needs fast response time with less overshoot due to the trial-error process. Thus, the Fuzzy Logic controller can be a solution to control the lower extremity exoskeleton in a real-time system. This paper shows the simulation of PID tuning (Ziegler Nichols and Cohen Coon) and Fuzzy Logic Control for DC Servo Motor by using Matlab/Simulink. A performance comparison between the PID tuned and Fuzzy Controller for DC servo motor was presented.


title: Learning Visual Predictive Models of Physics for Playing Billiards

abstract: The ability to plan and execute goal specific actions in varied, unexpected settings is a central requirement of intelligent agents. In this paper, we explore how an agent can be equipped with an internal model of the dynamics of the external world, and how it can use this model to plan novel actions by running multiple internal simulations ("visual imagination"). Our models directly process raw visual input, and use a novel object-centric prediction formulation based on visual glimpses centered on objects (fixations) to enforce translational invariance of the learned physical laws. The agent gathers training data through random interaction with a collection of different environments, and the resulting model can then be used to plan goal-directed actions in novel environments that the agent has not seen before. We demonstrate that our agent can accurately plan actions for playing a simulated billiards game, which requires pushing a ball into a target position or into collision with another ball.


title: Neuroethics: The Ethical, Legal, and Societal Impact of Neuroscience

abstract: Advances in cognitive, affective, and social neuroscience raise a host of new questions concerning the ways in which neuroscience can and should be used. These advances also challenge our intuitions about the nature of humans as moral and spiritual beings. Neuroethics is the new field that grapples with these issues. The present article surveys a number of applications of neuroscience to such diverse arenas as marketing, criminal justice, the military, and worker productivity. The ethical, legal, and societal effects of these applications are discussed. Less practical, but perhaps ultimately more consequential, is the impact of neuroscience on our worldview and our understanding of the human person.


title: Proximity-Fed Circularly Polarized Slotted Patch Antenna for RFID Handheld Reader

abstract: A novel, compact X-shaped slotted square-patch antenna is proposed for circularly polarized (CP) radiation. A cross-strip is embedded along the X-shaped slot for a novel proximity-fed technique to produce CP radiation in the UHF band. In the proposed design, two pairs of T-shaped slots are etched orthogonally on the square patch, connected to the center of the X-shaped slot for CP radiation and antenna size reduction. Proper adjustment of the length and coupling gap of the cross-strip will excite two orthogonal modes with 90 ° phase difference for good CP radiation. Simulated and measured results indicate that the proposed structure can achieve circular polarization. A measured impedance bandwidth (VSWR ≤ 2) of about 3.0% (909-937 MHz) and a 3-dB axial-ratio (AR) bandwidth of about 1.3% (917-929 MHz) were obtained.


title: From monkey-like action recognition to human language: an evolutionary framework for neurolinguistics

abstract: The article analyzes the neural and functional grounding of language skills as well as their emergence in hominid evolution, hypothesizing stages leading from abilities known to exist in monkeys and apes and presumed to exist in our hominid ancestors right through to modern spoken and signed languages. The starting point is the observation that both premotor area F5 in monkeys and Broca's area in humans contain a "mirror system" active for both execution and observation of manual actions, and that F5 and Broca's area are homologous brain regions. This grounded the mirror system hypothesis of Rizzolatti and Arbib (1998) which offers the mirror system for grasping as a key neural "missing link" between the abilities of our nonhuman ancestors of 20 million years ago and modern human language, with manual gestures rather than a system for vocal communication providing the initial seed for this evolutionary process. The present article, however, goes "beyond the mirror" to offer hypotheses on evolutionary changes within and outside the mirror sys- tems which may have occurred to equip Homo sapiens with a language-ready brain. Crucial to the early stages of this progression is the mirror system for grasping and its extension to permit imitation. Imitation is seen as evolving via a so-called simple system such as that found in chimpanzees (which allows imitation of complex "object-oriented" sequences but only as the result of extensive practice) to a so-called complex system found in humans (which allows rapid imitation even of complex sequences, under appropriate conditions) which supports pantomime. This is hypothesized to have provided the substrate for the development of protosign, a combinatorially open reper- toire of manual gestures, which then provides the scaffolding for the emergence of protospeech (which thus owes little to nonhuman vo- calizations), with protosign and protospeech then developing in an expanding spiral. It is argued that these stages involve biological evo- lution of both brain and body. By contrast, it is argued that the progression from protosign and protospeech to languages with full-blown syntax and compositional semantics was a historical phenomenon in the development of Homo sapiens, involving few if any further bio- logical changes.


title: A SP9T cellular antenna switch in 2.5 V CMOS thin-film SOI

abstract: This paper presents the results of a compact wire-bond SP9T antenna switch that was designed as technology demonstrator for a 2.5 V CMOS, 250 fs Ron-Coff thin-film SOI process. Through “layout-driven” circuit design, a small die size of 1.52 mm2 was achieved for a fully integrated switch die containing RF-section, I/O pads, ESD, decoder, level shifters and dual frequency charge pump to generate negative vss. The dual frequency charge pump was a requirement to achieve a fast start-up time of 10 μs and switch rise times of 3 μs. A low insertion loss of 0.42 dB for cellular low-band at 915 MHz and 0.55 dB for cellular high-band at 1910 MHz, harmonic powers better -76 dBc over battery and Band I/V IMD3 of -110 dBm were achieved. All ports show a high ESD tolerance of 2 kV HBM.


title: Black ice! Using Information Centric Networks for timely vehicular safety information dissemination

abstract: Vehicles are increasingly equipped with sensors for safety applications. Sharing information among vehicles can further improve the safety of the overall transportation environment. Enabling each vehicle to get the “right information at the right time” can be valuable to avoid dangerous situations. Information-Centric Networks (ICN) that use the notion of “named-objects” enable information dissemination regardless of location of the publisher or consumer. ICNs, especially supporting publish/subscribe capabilities, can provide timely delivery of the safety information. Our V-ICE architecture utilizes Roadside Units (RSUs) as infrastructure-based aggregators to communicate with vehicles generating notifications. RSUs disseminate information to vehicles that subscribe to the RSUs on their route. To evaluate the benefit of V-ICE, we demonstrate its use in propagating “black ice” warnings to vehicles that will likely be affected on their routes. The critical need is to deliver the information in a timely manner, providing other vehicles sufficient time to react. We build V-ICE's namespace and architecture using the roadways of Luxembourg as an example, and evaluate our approach with a trace-driven simulation using a 4-hour trace generated by SUMO. We show that V-ICE performs better than a server-based approach or even V2V broadcast, in terms of timeliness, relevance, and reduced network traffic.


title: Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

abstract: Drug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance. Since many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances. The experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https://github.com/zw9977129/drug-drug-interaction/ ::: .


Assessing Cyberloafing Behaviour among University Students : A Validation of the Cyberloafing Scale


title: Text-to-Clip Video Retrieval with Early Fusion and Re-Captioning.

abstract: We propose a novel method capable of retrieving clips from untrimmed videos based on natural language queries. This cross-modal retrieval task plays a key role in visual-semantic understanding, and requires localizing clips in time and computing their similarity to the query sentence. Current methods generate sentence and video embeddings and then compare them using a late fusion approach, but this ignores the word order in queries and prevents more fine-grained comparisons. Motivated by the need for fine-grained multi-modal feature fusion, we propose a novel early fusion embedding approach that combines video and language information at the word level. Furthermore, we use the inverse task of dense video captioning as a side-task to improve the learned embedding. Our full model combines these components with an efficient proposal pipeline that performs accurate localization of potential video clips. We present a comprehensive experimental validation on two large-scale text-to-clip datasets (Charades-STA and DiDeMo) and attain state-of-the-art retrieval results with our model.


title: IEEE standard review — Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems

abstract: In September 2009, the IEEE Board of Directors approved the new IEEE tagline — Advancing Technology for Humanity — as recommended by the IEEE Public Visibility Committee. Aligned with the IEEE tagline, IEEE Standards Association takes the initiative to address ethics in engineering design under “Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems” focusing on Artificial Intelligence and Autonomous Systems (AI/AS). The intention is to cover, as much as possible, the ethical concerns on AI/AS through a rigorous regard to the problem from different perspectives. The ultimate objective of this ongoing initiative is to provide guidelines/procedures/standards to prioritize human wellbeing in the forthcoming evolutions on artificial intelligence and autonomous systems. This article reviews different aspects addressed in Version 1 of this initiative.


title: Deep Semantic Feature Matching

abstract: Estimating dense visual correspondences between objects with intra-class variation, deformations and background clutter remains a challenging problem. Thanks to the breakthrough of CNNs there are new powerful features available. Despite their easy accessibility and great success, existing semantic flow methods could not significantly benefit from these without extensive additional training. We introduce a novel method for semantic matching with pre-trained CNN features which is based on convolutional feature pyramids and activation guided feature selection. For the final matching we propose a sparse graph matching framework where each salient feature selects among a small subset of nearest neighbors in the target image. To improve our method in the unconstrained setting without bounding box annotations we introduce novel object proposal based matching constraints. Furthermore, we show that the sparse matching can be transformed into a dense correspondence field. Extensive experimental evaluations on benchmark datasets show that our method significantly outperforms existing semantic matching methods.


title: Word Salad: Relating Food Prices and Descriptions

abstract: We investigate the use of language in food writing, specifically on restaurant menus and in customer reviews. Our approach is to build predictive models of concrete external variables, such as restaurant menu prices. We make use of a dataset of menus and customer reviews for thousands of restaurants in several U.S. cities. By focusing on prediction tasks and doing our analysis at scale, our methodology allows quantitative, objective measurements of the words and phrases used to describe food in restaurants. We also explore interactions in language use between menu prices and sentiment as expressed in user reviews.


title: Interactive Machine Learning for End-User Innovation

abstract: User interaction with intelligent systems need not be limited to interaction where pre-trained software has intelligence “baked in.” End-user training, including interactive machine learning (IML) approaches, can enable users to create and customise systems themselves. We propose that the user experience of these users is worth considering. Furthermore, the user experience of system developers—people who may train and configure both learning algorithms and their user interfaces—also deserves attention. We additionally propose that IML can improve user experiences by supporting user-centred design processes, and that there is a further role for user-centred design in improving interactive and classical machine learning systems. We are developing this approach and embodying it through the design of a new User Innovation Toolkit, in the context of the European Commission-funded project RAPID-MIX.


title: The chronic fatigue syndrome: a comprehensive approach to its definition and study. International Chronic Fatigue Syndrome Study Group.

abstract: The complexities of the chronic fatigue syndrome and the methodologic problems associated with its study indicate the need for a comprehensive, systematic, and integrated approach to the evaluation, classification, and study of persons with this condition and other fatiguing illnesses. We propose a conceptual framework and a set of guidelines that provide such an approach. Our guidelines include recommendations for the clinical evaluation of fatigued persons, a revised case definition of the chronic fatigue syndrome, and a strategy for subgrouping fatigued persons in formal investigations.


title: A First-Estimates Jacobian EKF for Improving SLAM Consistency

abstract: In this work, we study the inconsistency of EKF-based SLAM from the perspective of observability. We analytically prove that when the Jacobians of the system and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system employed in the EKF has observable subspace of dimension higher than that of the actual, nonlinear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in directions of the state space where no information is available, which is a primary cause of the inconsistency. Furthermore, a new “First-Estimates Jacobian” (FEJ) EKF is proposed to improve the estimator’s consistency during SLAM. The proposed algorithm performs better in terms of consistency, because when the filter Jacobians are calculated using the first-ever available estimates for each state variable, the error-state system model has an observable subspace of the same dimension as the underlying nonlinear SLAM system. The theoretical analysis is validated through both simulations and experiments.


title: Mechanical Translation of Set Theoretic Problem Specifications Into Efficient RAM Code - A Case Study

abstract: There have been various proposals for program development methodologies in which a computer system is used to aid in the formulation of precise problem specifications, in the synthesis of efficient and correct programs, and in the analysis of performance. A few systems have actually been implemented. Although some of these systems are fully general in expressive power, they all lack a sufficient degree of automation to make them viable alternatives to conventional ad hoc approaches.


title: Adaptive Workflow Scheduling on Cloud Computing Platforms with IterativeOrdinal Optimization

abstract: The scheduling of multitask jobs on clouds is an NP-hard problem. The problem becomes even worse when complex workflows are executed on elastic clouds, such as Amazon EC2 or IBM RC2. The main difficulty lies in the large search space and high overhead of generating optimal schedules, especially for real-time applications with dynamic workloads. In this work, a new iterative ordinal optimization (IOO) method is proposed. The ordinal optimization method is applied in each iteration to achieve sub-optimal schedules. IOO aims at generating more efficient schedules from a global perspective over a long period. We prove through overhead analysis the advantages in time and space efficiency in using the IOO method. The IOO method is designed to adapt to system dynamism to yield suboptimal performance. In cloud experiments on IBM RC2 cloud, we execute 20,000 tasks in LIGO ( Laser Interferometer Gravitational-wave Observatory ) verification workflow on 128 virtual machines. The IOO schedule is generated in less than 1,000 seconds, while using the Monte Carlo simulation takes 27.6 hours, 100 times longer to yield an optimal schedule. The IOO-optimized schedule results in a throughput of 1,100 tasks/sec with 7 GB memory demand, compared with 60 percent decrease in throughput and 70 percent increase in memory demand in using the Monte Carlo method. Our LIGO experimental results clearly demonstrate the advantage of using the IOO-based workflow scheduling over the traditional blind-pick, ordinal optimization, or Monte Carlo methods. These numerical results are also validated by the theoretical complexity and overhead analysis provided.


title: AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models

abstract: Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition structure and to extend these compiled data structures to general weighted graphical models (e.g., probabilistic models). We present the AND/OR Multi-Valued Decision Diagram (AOMDD) which compiles a graphical model into a canonical form that supports polynomial (e.g., solution counting, belief updating) or constant time (e.g. equivalence of graphical models) queries. We provide two algorithms for compiling the AOMDD of a graphical model. The first is search-based, and works by applying reduction rules to the trace of the memory intensive AND/OR search algorithm. The second is inference-based and uses a Bucket Elimination schedule to combine the AOMDDs of the input functions via the the APPLY operator. For both algorithms, the compilation time and the size of the AOMDD are, in the worst case, exponential in the treewidth of the graphical model, rather than pathwidth as is known for ordered binary decision diagrams (OBDDs). We introduce the concept of semantic treewidth, which helps explain why the size of a decision diagram is often much smaller than the worst case bound. We provide an experimental evaluation that demonstrates the potential of AOMDDs.


title: Compact Equal and Unequal Dual-Frequency Power Dividers Based on Composite Right-/Left-Handed Transmission Lines

abstract: A novel approach using fully integrated composite right-/left-handed (CRLH) transmission lines (TLs) is proposed to design equal and unequal dual-frequency Wilkinson power dividers (WPDs) with controllable frequency ratio. Theoretical analysis indicates that the frequency ratio and the power-splitting ratio k2 of the divider can be controlled by changing the element values and the number of the constructing cells of the CRLH TLs. Design formulas are presented to calculate the element values of the cells for a given frequency ratio. Dispersion relation, transmission/reflection coefficient, and Bloch impedance studies show that the Π-shaped unit cells have smaller size than the T-shaped counterparts but the same performance. For demonstration, one equal and one unequal dual-frequency WPDs operating at 2.45 and 5.2 GHz, respectively, are implemented. Measurements show that both the equal and unequal dividers have 17.5% and 26% relative bandwidths at 2.45 and 5.2 GHz, respectively.


title: Electron-diffraction study of the molecular structure of dimethyldisulphide, (CH3)2S2

abstract: The molecular structure of (CH3)2S2 has been studied by gas-phase electron diffraction. The results confirm a published microwave study, but are superior in precision. The orientation of the methyl groups was refined and it is established that the C—H bonds are staggered (or nearly so) with respect to the S—S bond. The principal parameters are C—H = 1.090±0.007 A, S—C = 1.806±0.002 A, S—S = 2.022±0.003 A, ∠SSC = 104.1±0.3°; the dihedral angle of the CSSC skeleton is 83.9±0.9°. The S—S length is significantly shorter than found in H2S2. The work differs from a standard structure analysis in that two sets of data were used, based on different treatments of the background. Slight differences in some of the less well determined parameters were observed.


title: Learning Social Image Embedding with Deep Multimodal Attention Networks

abstract: Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search.


Decoupling Encoder and Decoder Networks for Abstractive Document Summarization


title: Semi-automatic abdominal aortic aneurysms geometry assessment based on 3D ultrasound

abstract: This paper presents a new approach for improving the surveillance of the size of abdominal aortic aneurysms (AAA). Use of 3D ultrasound imaging combined with semi-automatic quantification provides automatic selection of the optimal plane for diameter measurement. Quantification parameters are defined to characterize the aneurysm with more accuracy. Volume imaging also provides 3D visualization of the AAA geometry and CT-like multi-planar reconstructions. Multiple volume registrations are proposed to overcome limited field of view issues. Quantification results show good correlation with 2D reference measurements and obtained Pearson correlation coefficients are significant for 30 patients.


title: Supervised and Traditional Term Weighting Methods for Automatic Text Categorization

abstract: In vector space model (VSM), text representation is the task of transforming the content of a textual document into a vector in the term space so that the document could be recognized and classified by a computer or a classifier. Different terms (i.e. words, phrases, or any other indexing units used to identify the contents of a text) have different importance in a text. The term weighting methods assign appropriate weights to the terms to improve the performance of text categorization. In this study, we investigate several widely-used unsupervised (traditional) and supervised term weighting methods on benchmark data collections in combination with SVM and kNN algorithms. In consideration of the distribution of relevant documents in the collection, we propose a new simple supervised term weighting method, i.e. tf.rf, to improve the terms' discriminating power for text categorization task. From the controlled experimental results, these supervised term weighting methods have mixed performance. Specifically, our proposed supervised term weighting method, tf.rf, has a consistently better performance than other term weighting methods while other supervised term weighting methods based on information theory or statistical metric perform the worst in all experiments. On the other hand, the popularly used tf.idf method has not shown a uniformly good performance in terms of different data sets.


title: A novel redis security extension for NoSQL database using authentication and encryption

abstract: Redis is a new generation NoSQL database. Redis in its simplest form is a key value pair based data system. It supports all the data structures like variables, Linked list, arrays, strings, and queues. However unlike the conventional databases, Redis does not provide enough security for the data. Anyone can get the value if the key is known because the data is stored in the form of key value pair. Therefore such a database is unsuitable for enterprise and most practical application data. In this paper, the work is carried out to add immense security to a Redis system using following: a) Authentication Service b) Encryption Services c) Security to persistent data d) Security to blob data (multimedia data for images).


Positional information and the spatial pattern of cellular differentiation


title: The effect of training set size for the performance of neural networks of classification

abstract: Even though multilayer perceptrons and radial basis function networks belong to the class of artificial neural networks and they are used for similar tasks, they have very different structures and training mechanisms. So, some researchers showed better performance with radial basis function networks, while others showed some different results with multilayer perceptrons. This paper compares the classification accuracy of the two neural networks with respect to training data set size, and shows the performance of the two neural networks can be differently dependent on training data set size. Experiments show the tendency that multilayer perceptrons have better performance in relatively larger training data sets for some data sets, even though radial basis function networks have better performance in relatively smaller training set size for the same data sets. The experiment was done with four real world data sets.


Real-time Implementation of Robust Setpoint Weighted PID Controller for Magnetic Levitation System


title: Development of a New Robot Controller Architecture with FPGA-Based IC Design for Improved High-Speed Performance

abstract: In this paper, a new robot controller architecture is proposed to implement various complex control algorithms for improved high-speed performance. The main thrust of the research is to remove the servo control loop from the digital signal processor (DSP) and implement the high-speed servo loop in a field programmable gate array (FPGA). The main objective of this architecture is to utilize the ultra-high-speed hardwired logic of the FPGA to enhance the overall computational capability and relieve the computational load of the DSP for other tasks. The control algorithm is partitioned into a linear portion and a nonlinear portion. The linear portion with position/velocity feedback represents the major control loop and is implemented in the FPGA. The nonlinear portion acts as dynamic compensation to the linear portion to calculate model-related control gains/parameters, and it is implemented in the DSP. In tandem, with the newly developed control hardware architecture, an FPGA-based motion control integrated circuit (IC) is designed. Experiments are conducted on an industrial robot manipulator to compare the closed-loop performance with this new control architecture and the traditional one, when the same control algorithm is used. The experimental results demonstrate that the proposed new control architecture exhibits much improved motion performance indeed, especially in high-speed motions.


title: Wine, Beer, Alcohol and Polyphenols on Cardiovascular Disease and Cancer

abstract: Since ancient times, people have attributed a variety of health benefits to moderate consumption of fermented beverages such as wine and beer, often without any scientific basis. There is evidence that excessive or binge alcohol consumption is associated with increased morbidity and mortality, as well as with work related and traffic accidents. On the contrary, at the moment, several epidemiological studies have suggested that moderate consumption of alcohol reduces overall mortality, mainly from coronary diseases. However, there are discrepancies regarding the specific effects of different types of beverages (wine, beer and spirits) on the cardiovascular system and cancer, and also whether the possible protective effects of alcoholic beverages are due to their alcoholic content (ethanol) or to their non-alcoholic components (mainly polyphenols). Epidemiological and clinical studies have pointed out that regular and moderate wine consumption (one to two glasses a day) is associated with decreased incidence of cardiovascular disease (CVD), hypertension, diabetes, and certain types of cancer, including colon, basal cell, ovarian, and prostate carcinoma. Moderate beer consumption has also been associated with these effects, but to a lesser degree, probably because of beer’s lower phenolic content. These health benefits have mainly been attributed to an increase in antioxidant capacity, changes in lipid profiles, and the anti-inflammatory effects produced by these alcoholic beverages. This review summarizes the main protective effects on the cardiovascular system and cancer resulting from moderate wine and beer intake due mainly to their common components, alcohol and polyphenols.


abstract: Fabricating neural models for a wide range of mobile devices demands for a specific design of networks due to highly constrained resources. Both evolution algorithms (EA) and reinforced learning methods (RL) have been dedicated to solve neural architecture search problems. However, these combinations usually concentrate on a single objective such as the error rate of image classification. They also fail to harness the very benefits from both sides. In this paper, we present a new multi-objective oriented algorithm called MoreMNAS (Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search) by leveraging good virtues from both EA and RL. In particular, we incorporate a variant of multi-objective genetic algorithm NSGA-II, in which the search space is composed of various cells so that crossovers and mutations can be performed at the cell level. Moreover, reinforced control is mixed with a natural mutating process to regulate arbitrary mutation, maintaining a delicate balance between exploration and exploitation. Therefore, not only does our method prevent the searched models from degrading during the evolution process, but it also makes better use of learned knowledge. Our experiments conducted in Super-resolution domain (SR) deliver rivalling models compared to some state-of-the-art methods with fewer FLOPS.


title: Affordances and Limitations of Immersive Participatory Augmented Reality Simulations for Teaching and Learning

abstract: The purpose of this study was to document how teachers and students describe and comprehend the ways in which participating in an augmented reality (AR) simulation aids or hinders teaching and learning. Like the multi-user virtual environment (MUVE) interface that underlies Internet games, AR is a good medium for immersive collaborative simulation, but has different strengths and limitations than MUVEs. Within a design-based research project, the researchers conducted multiple qualitative case studies across two middle schools (6th and 7th grade) and one high school (10th grade) in the northeastern United States to document the affordances and limitations of AR simulations from the student and teacher perspective. The researchers collected data through formal and informal interviews, direct observations, web site posts, and site documents. Teachers and students reported that the technology-mediated narrative and the interactive, situated, collaborative problem solving affordances of the AR simulation were highly engaging, especially among students who had previously presented behavioral and academic challenges for the teachers. However, while the AR simulation provided potentially transformative added value, it simultaneously presented unique technological, managerial, and cognitive challenges to teaching and learning.


Business Intelligence


title: How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

abstract: We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available. Recent works in response generation have adopted metrics from machine translation to compare a model's generated response to a single target response. We show that these metrics correlate very weakly with human judgements in the non-technical Twitter domain, and not at all in the technical Ubuntu domain. We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future development of better automatic evaluation metrics for dialogue systems.


title: Collocated Intergenerational Console Gaming

abstract: We present results from a multi-generational study of collocated group console gaming. We examine the intergenerational gaming practices of four generations of gamers, from ages 3 to 83 and, in particular, the roles that gamers of different generations take on when playing together in groups. Our findings highlight the extent to which existing gaming technologies are amenable to interactions within collocated intergenerational groups and the broader set of roles that have emerged in these computer-mediated interactions than have previously been documented by studies of more traditional collocated, intergenerational interactions. We articulate attributes of the games that encourage intergenerational interaction.


title: Ensuring Security and Privacy Preservation for Cloud Data Services

abstract: With the rapid development of cloud computing, more and more enterprises/individuals are starting to outsource local data to the cloud servers. However, under open networks and not fully trusted cloud environments, they face enormous security and privacy risks (e.g., data leakage or disclosure, data corruption or loss, and user privacy breach) when outsourcing their data to a public cloud or using their outsourced data. Recently, several studies were conducted to address these risks, and a series of solutions were proposed to enable data and privacy protection in untrusted cloud environments. To fully understand the advances and discover the research trends of this area, this survey summarizes and analyzes the state-of-the-art protection technologies. We first present security threats and requirements of an outsourcing data service to a cloud, and follow that with a high-level overview of the corresponding security technologies. We then dwell on existing protection solutions to achieve secure, dependable, and privacy-assured cloud data services including data search, data computation, data sharing, data storage, and data access. Finally, we propose open challenges and potential research directions in each category of solutions.


title: Ocean-atmosphere interaction in the lifecycle of ENSO: The coupled wave oscillator

abstract: To explain the oscillatory nature of El Nino/Southern Oscillation (ENSO), many ENSO theories emphasize the free oceanic equatorial waves propagating/reflecting within the Pacific Ocean, or the discharge/recharge of Pacific-basin-averaged ocean heat content. ENSO signals in the Indian and Atlantic oceans are often considered as remote response to the Pacific SST anomaly through atmospheric teleconnections. This study investigates the ENSO life cycle near the equator using long-term observational datasets. Space-time spectral analysis is used to identify and isolate the dominant interannual oceanic and atmospheric wave modes associated with ENSO. Nino3 SST anomaly is utilized as the ENSO index, and lag-correlation/regression are used to construct the composite ENSO life cycle. The propagation, structure and feedback mechanisms of the dominant wave modes are studied in detail. The results show that the dominant oceanic equatorial wave modes associated with ENSO are not free waves, but are two ocean-atmosphere coupled waves including a coupled Kelvin wave and a coupled equatorial Rossby (ER) wave. These waves are not confined only to the Pacific Ocean, but are of planetary scale with zonal wavenumbers 1–2, and propagate all the way around the equator in more than three years, leading to the longer than 3-year period of ENSO. When passing the continents, they become uncoupled atmospheric waves. The coupled Kelvin wave has larger variance than the coupled ER wave, making the total signals dominated by eastward propagation. Surface zonal wind stress (x) acts to slow down the waves. The two coupled waves interact with each other through boundary reflection and superposition, and they also interact with an off-equatorial Rossby wave in north Pacific along 15N through boundary reflection and wind stress forcing. The precipitation anomalies of the two coupled waves meet in the eastern Pacific shortly after the SST maximum of ENSO and excite a dry atmospheric Kelvin wave which quickly circles the whole equator and leads to a zonally symmetric signal of troposphere temperature. ENSO signals in the Indian and Atlantic oceans are associated with the two coupled waves as well as the fast atmospheric Kelvin wave. The discharge/recharge of Pacific-basin-averaged ocean heat content is also contributed by the two coupled waves. The above results suggest the presence of an alternative coupled wave oscillator mechanism for the oscillatory nature of ENSO.


title: Instructional quality of Massive Open Online Courses (MOOCs)

abstract: We present an analysis of instructional design quality of 76 randomly selected Massive Open Online Courses (MOOCs). The quality of MOOCs was determined from first principles of instruction, using a course survey instrument. Two types of MOOCs (xMOOCs and cMOOCs) were analysed and their instructional design quality was assessed and compared. We found that the majority of MOOCs scored poorly on most instructional design principles. However, most MOOCs scored highly on organisation and presentation of course material. The results indicate that although most MOOCs are well-packaged, their instructional design quality is low. We outline implications for practice and ideas for future research. Instructional design quality of 76 randomly selected MOOCs was assessed.Quality was determined from first principles, using a Course Scan instrument.The majority of MOOCs scored poorly on most instructional design principles.Most MOOCs scored highly on organisation and presentation of course material.Although most MOOCs are well-packaged, their instructional design quality is low.


title: Practical Recommendations on Crawling Online Social Networks

abstract: Our goal in this paper is to develop a practical framework for obtaining a uniform sample of users in an online social network (OSN) by crawling its social graph. Such a sample allows to estimate any user property and some topological properties as well. To this end, first, we consider and compare several candidate crawling techniques. Two approaches that can produce approximately uniform samples are the Metropolis-Hasting random walk (MHRW) and a re-weighted random walk (RWRW). Both have pros and cons, which we demonstrate through a comparison to each other as well as to the "ground truth." In contrast, using Breadth-First-Search (BFS) or an unadjusted Random Walk (RW) leads to substantially biased results. Second, and in addition to offline performance assessment, we introduce online formal convergence diagnostics to assess sample quality during the data collection process. We show how these diagnostics can be used to effectively determine when a random walk sample is of adequate size and quality. Third, as a case study, we apply the above methods to Facebook and we collect the first, to the best of our knowledge, representative sample of Facebook users. We make it publicly available and employ it to characterize several key properties of Facebook.


title: Distributed data management using MapReduce

abstract: MapReduce is a framework for processing and managing large-scale datasets in a distributed cluster, which has been used for applications such as generating search indexes, document clustering, access log analysis, and various other forms of data analytics. MapReduce adopts a flexible computation model with a simple interface consisting of map and reduce functions whose implementations can be customized by application developers. Since its introduction, a substantial amount of research effort has been directed toward making it more usable and efficient for supporting database-centric operations. In this article, we aim to provide a comprehensive review of a wide range of proposals and systems that focusing fundamentally on the support of distributed data management and processing using the MapReduce framework.


title: A CMOS sensor optimized for laser spot-position detection

abstract: An optical sensor architecture optimized for flying-spot, triangulation-based, three-dimensional (3-D) laser scanners will be presented. The architecture implements a spot-position detection algorithm based on a two-step procedure that allows for improved dynamic range and readout speed. The sensor, which contains two linear arrays of pixels, analog readout channels, and digital signal preprocessing circuitry, has been fabricated in 0.6-/spl mu/m CMOS double-poly triple-metal technology and measures 8.17/spl times/5.67 mm/sup 2/. Pixel size and shape have been selected for reducing the effect of laser speckle and for the possibility of measuring color in a multiwavelength 3-D scanner. Electrooptical test results confirm the sensor behavior as expected from simulations on a dynamic range of 80 dB and exhibits a maximum speed of 50-k voxel/s.


title: Risk of Encountering Dorsal Scapular and Long Thoracic Nerves during Ultrasound-guided Interscalene Brachial Plexus Block with Nerve Stimulator

abstract: Background: Recently, ultrasound has been commonly used. Ultrasound-guided interscalene brachial plexus block (IBPB) by posterior approach is more commonly used because anterior approach has been reported to have the risk of phrenic nerve injury. However, posterior approach also has the risk of causing nerve injury because there are risks of encountering dorsal scapular nerve (DSN) and long thoracic nerve (LTN). Therefore, the aim of this study was to evaluate the risk of encountering DSN and LTN during ultrasound-guided IBPB by posterior approach. Methods: A total of 70 patients who were scheduled for shoulder surgery were enrolled in this study. After deciding insertion site with ultrasound, awake ultrasound-guided IBPB with nerve stimulator by posterior approach was performed. Incidence of muscle twitches (rhomboids, levator scapulae, and serratus anterior muscles) and current intensity immediately before muscle twitches disappeared were recorded. Results: Of the total 70 cases, DSN was encountered in 44 cases (62.8%) and LTN was encountered in 15 cases (21.4%). Both nerves were encountered in 10 cases (14.3%). Neither was encountered in 21 cases (30.4%). The average current measured immediately before the disappearance of muscle twitches was 0.44 mA and 0.50 mA at DSN and LTN, respectively. Conclusions: Physicians should be cautious on the risk of injury related to the anatomical structures of nerves, including DSN and LTN, during ultrasound-guided IBPB by posterior approach. Nerve stimulator could be another option for a safer intervention. Moreover, if there is a motor response, it is recommended to select another way to secure better safety. (Korean J Pain 2016; 29: 179-84)


title: Detecting Malicious Web Links and Identifying Their Attack Types

abstract: Malicious URLs have been widely used to mount various cyber attacks including spamming, phishing and malware. Detection of malicious URLs and identification of threat types are critical to thwart these attacks. Knowing the type of a threat enables estimation of severity of the attack and helps adopt an effective countermeasure. Existing methods typically detect malicious URLs of a single attack type. In this paper, we propose method using machine learning to detect malicious URLs of all the popular attack types and identify the nature of attack a malicious URL attempts to launch. Our method uses a variety of discriminative features including textual properties, link structures, webpage contents, DNS information, and network traffic. Many of these features are novel and highly effective. Our experimental studies with 40,000 benign URLs and 32,000 malicious URLs obtained from real-life Internet sources show that our method delivers a superior performance: the accuracy was over 98% in detecting malicious URLs and over 93% in identifying attack types. We also report our studies on the effectiveness of each group of discriminative features, and discuss their evadability.


title: Microsoft Kinect Sensor and Its Effect

abstract: Recent advances in 3D depth cameras such as Microsoft Kinect sensors (www.xbox.com/en-US/kinect) have created many opportunities for multimedia computing. The Kinect sensor lets the computer directly sense the third dimension (depth) of the players and the environment. It also understands when users talk, knows who they are when they walk up to it, and can interpret their movements and translate them into a format that developers can use to build new experiences. While the Kinect sensor incorporates several advanced sensing hardware, this article focuses on the vision aspect of the Kinect sensor and its impact beyond the gaming industry.


title: Distant Supervision for Topic Classification of Tweets in Curated Streams

abstract: We tackle the challenge of topic classification of tweets in the context of analyzing a large collection of curated streams by news outlets and other organizations to deliver relevant content to users. Our approach is novel in applying distant supervision based on semi-automatically identifying curated streams that are topically focused (for example, on politics, entertainment, or sports). These streams provide a source of labeled data to train topic classifiers that can then be applied to categorize tweets from more topically-diffuse streams. Experiments on both noisy labels and human ground-truth judgments demonstrate that our approach yields good topic classifiers essentially "for free", and that topic classifiers trained in this manner are able to dynamically adjust for topic drift as news on Twitter evolves.


title: OSPF Convergence Times

abstract: Following the merger of telecom and IP networks, there has been a sharp rise in the number and types of multimedia applications such as interactive real-time Voice/Video over IP. This has put a new service requirement on IP networks and thus has required the IP network solution providers and telecom operators to device new techniques and optimizations to meet the needs of these business critical applications. One way to address these demands is to implement fast and efficient routing mechanism as the data packets are exchanged end to end. Open Shortest Path First (OSPF) is one of the widely deployed routing protocols responsible for this. ::: Most of the important operations of OSPF that contribute to fast convergence such as fast failure detection, shortest path computation and flooding are controlled by timers. These timers, as specified in RFC2328 are fixed and too conservative for modern networks. Today, there has been an increasing effort to make these timers dynamic so that the values are determined based on the experienced network load and stability instead of a preset static value. ::: This thesis is in part a thorough assessment of the state of the art on OSPF timers and fast convergence techniques. The other major contribution of this work is the implementation of the Link State Advertisement (LSA) throttling algorithm as an adaptive technique to control unwanted LSA generations at times of network instabilities. We used two simulators; OPNET Modeler (Academic Version) because of its advanced graphical user interface (GUI) and result analysis tools, and the open source OMNET++ for its open OSPFv2 source code. The outcome of this thesis therefore the work done on literature review which embraces a set of recommended techniques to achieve sub-second convergence, a simulation supported analysis of the associated stability issues in terms of convergence time and CPU load and also the introduction of our own pseudo code and implementation of the LSA throttling algorithm, originally introduced in CISCO 12.0(25) S. The simulation work has proved the LSA throttling algorithm indeed improves a network’s convergence speed and can be deployed on any size OSPF network including large ISP networks consisting of thousands of routers.


title: Towards a Harmonic Complexity of Musical Pieces

abstract: Music information retrieval (MIR) is small, but fast grow- ing discipline. It aims at extraction of relevant information from music in order to improve the work with multimedia information systems. A popular approach in MIR is to apply signal processing techniques to ex- tract low-level features from a musical piece. However, in order to create helpful applications, high-level concepts, such as music harmony, need to be examined as well. In this paper, we present a new model for un- derstanding music harmony in computer systems. The proposed model takes the challenge of lling the gap between music theory and mathe- matical structures. Inspired by the computational complexity, we then derive a new term, harmonic complexity, that can be evaluated for a musical piece, and we test it on a genre classication problem.


title: Handheld devices for applications using dynamic multimedia data

abstract: Growing demand for ubiquitous and pervasive computing has triggered a sharp rise in handheld device usage. At the same time, dynamic multimedia data has become accepted as core material which many important applications depend on, despite intensive costs in computation and resources. This paper investigates the suitability and constraints of using handheld devices for such applications. We firstly analyse the capabilities and limitations of current models of handheld devices and advanced features offered by next generation models. We then categorise these applications and discuss the typical requirements of each class. Important issues to be considered include data organisation and management, communication, and input and user interfaces. Finally, we briefly discuss future outlook and identify remaining areas for research.


title: Simulation & performance study of wireless sensor network (WSN) using MATLAB

abstract: A wireless sensor network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. Different approaches have used for simulation and modeling of SN (Sensor Network) and WSN. Traditional approaches consist of various simulation tools based on different languages such as C, C++ and Java. In this paper, MATLAB (7.6) Simulink was used to build a complete WSN system. Simulation procedure includes building the hardware architecture of the transmitting nodes, modeling both the communication channel and the receiving master node architecture. Bluetooth was chosen to undertake the physical layer communication with respect to different channel parameters (i.e., Signal to Noise ratio, Attenuation and Interference). The simulation model was examined using different topologies under various conditions and numerous results were collected. This new simulation methodology proves the ability of the Simulink MATLAB to be a useful and flexible approach to study the effect of different physical layer parameters on the performance of wireless sensor networks.


title: Exposure to excess glucocorticoids alters dendritic morphology of adult hippocampal pyramidal neurons

abstract: We have used Golgi-impregnated tissue to demonstrate that exposure to excess glucocorticoids alters dendritic morphology in a specific population of neurons in the adult rat hippocampus. Daily injection of 10 mg of corticosterone for 21 days resulted in decreased numbers of apical dendritic branch points and decreased total apical dendritic length measured in a 100-microns-thick section in CA3 pyramidal cells compared to sham-injected and non-injected controls. In contrast, no changes were observed in CA3 pyramidal cell basal dendritic morphology. Furthermore, no changes were observed in the dendritic morphology of CA1 pyramidal cells or granule cells of the dentate gyrus. Cross-sectional cell body area of any of the 3 cell types examined in this study was unaffected by corticosterone treatment. Finally, qualitative analysis of Nissl-stained tissue from the same brains revealed increased numbers of darkly staining, apparently shrunken CA3 pyramidal cells in corticosterone treated compared to control brains. The changes in dendritic morphology we have observed may be indicative of neurons in the early stages of degeneration, as prolonged exposure to high levels of corticosterone has been shown by others to result in a loss of CA3 pyramidal cells. Additionally, these results suggest possible structural alterations which may occur under physiological conditions in which corticosterone levels are chronically elevated such as in aged animals.


title: A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning

abstract: This paper provides a functional analysis perspective of information-theoretic learning (ITL) by defining bottom-up a reproducing kernel Hilbert space (RKHS) uniquely determined by the symmetric nonnegative definite kernel function known as the cross-information potential (CIP). The CIP as an integral of the product of two probability density functions characterizes similarity between two stochastic functions. We prove the existence of a one-to-one congruence mapping between the ITL RKHS and the Hilbert space spanned by square integrable probability density functions. Therefore, all the statistical descriptors in the original information-theoretic learning formulation can be rewritten as algebraic computations on deterministic functional vectors in the ITL RKHS, instead of limiting the functional view to the estimators as is commonly done in kernel methods. A connection between the ITL RKHS and kernel approaches interested in quantifying the statistics of the projected data is also established.


title: Output recurrent wavelet neural network-based adaptive backstepping controller for a class of MIMO nonlinear non-affine uncertain systems

abstract: In this paper, an adaptive backstepping control problem is proposed for a class of multiple-input-multiple-output nonlinear non-affine uncertain systems. An output recurrent wavelet neural network (ORWNN) is used to approximate the unknown nonlinear functions to develop the proposed adaptive backstepping controller. The proposed ORWNN combines the advantages of wavelet-based neural network, fuzzy neural network, and output feedback layer to achieve higher approximation accuracy and faster convergence. According to the estimation of ORWNN, the control scheme is designed by backstepping approach such that the system outputs follow the desired trajectories. Based on the Lyapunov approach, our approach guarantees that the system outputs converge to a small neighborhood of the references signals, that is, all signals of the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results including double pendulums system and two inverted pendulums on carts system are shown to demonstrate the performance and effectiveness of our approach.


title: The regular divisions of the spermatocytes as related to a meiotic lysine-rich protein fraction

abstract: Lepidopteran primary spermatocytes are bipotential leading first to regular (eupyrene) and later to irregular (apyrene) meiotic divisions. The kinetics of the lysine-rich proteins during this dichotomous meiosis was studied using the fluorescent dye sulfoflavine. Throughout the spermatogonial divisions, the chromatin fluoresces while the cytoplasm remains unstained. Reversely, during the meiotic prophase, the cytoplasm fluoresces strongly while the nuclei show only a few weakly fluorescing structures. From premetaphase to telophase the meiotic chromosomes fluoresce strongly again. But during this period, only in the eupyrene cells the cytoplasm remains strongly fluorescent; the fluorescence vanishes in the cytoplasm of the apyrene spermatocytes. Thus, the regular (eupyrene) meiotic divisions and the presence of a lysine-rich protein fraction in the cytoplasm of the dividing spermatocytes of Lepidoptera, are probably related.


title: Automatic derivation of utility functions for monitoring software requirements

abstract: Utility functions can be used to monitor requirements of a dynamically adaptive system (DAS). More specifically, a utility function maps monitoring information to a scalar value proportional to how well a requirement is satisfied. Utility functions may be manually elicited by requirements engineers, or indirectly inferred through statistical regression techniques. This paper presents a goal-based requirements modeldriven approach for automatically deriving state-, metric-, and fuzzy logic-based utility functions for RELAXed goal models. State- and fuzzy logic-based utility functions are responsible for detecting requirements violations, and metric-based utility functions are used to detect conditions conducive to a requirements violation. We demonstrate the proposed approach by applying it to the goal model of an intelligent vehicle system (IVS) and use the derived utility functions to monitor the IVS under different environmental conditions at run time.


title: Calibration of Three-Axis Magnetometer Using Stretching Particle Swarm Optimization Algorithm

abstract: In this paper, a stochastic optimization algorithm is proposed to calibrate the three-axis magnetometer onboard. The sensor errors, namely, hard iron, soft iron, nonorthogonality, scale factors, and bias, are taken into account. Particle swarm optimization (PSO) strategy is used to do the calibration, enhanced by the stretching technique to improve the accuracy and robustness. The performance of this algorithm is evaluated with a series of laboratory experiments and a field experiment of autonomous underwater vehicle. Comparisons with other analytical calibration methods are made. The results demonstrate that both the PSO and the stretched PSO algorithm can significantly compensate the magnetometer readings, and the latter algorithm has higher accuracy and more robustness.


title: Measuring and Applying Invalid SSL Certificates: The Silent Majority

abstract: SSL and TLS are used to secure the most commonly used Internet protocols. As a result, the ecosystem of SSL certificates has been thoroughly studied, leading to a broad understanding of the strengths and weaknesses of the certificates accepted by most web browsers. Prior work has naturally focused almost exclusively on "valid" certificates--those that standard browsers accept as well-formed and trusted--and has largely disregarded certificates that are otherwise "invalid." Surprisingly, however, this leaves the majority of certificates unexamined: we find that, on average, 65% of SSL certificates advertised in each IPv4 scan that we examine are actually invalid. In this paper, we demonstrate that despite their invalidity, much can be understood from these certificates. Specifically, we show why the web's SSL ecosystem is populated by so many invalid certificates, where they originate from, and how they impact security. Using a dataset of over 80M certificates, we determine that most invalid certificates originate from a few types of end-user devices, and possess dramatically different properties than their valid counterparts. We find that many of these devices periodically reissue their (invalid) certificates, and develop new techniques that allow us to track these reissues across scans. We present evidence that this technique allows us to uniquely track over 6.7M devices. Taken together, our results open up a heretofore largely-ignored portion of the SSL ecosystem to further study.


title: Joint Object Pose Estimation and Shape Reconstruction in Urban Street Scenes Using 3D Shape Priors

abstract: Estimating the pose and 3D shape of a large variety of instances within an object class from stereo images is a challenging problem, especially in realistic conditions such as urban street scenes. We propose a novel approach for using compact shape manifolds of the shape within an object class for object segmentation, pose and shape estimation. Our method first detects objects and estimates their pose coarsely in the stereo images using a state-of-the-art 3D object detection method. An energy minimization method then aligns shape and pose concurrently with the stereo reconstruction of the object. In experiments, we evaluate our approach for detection, pose and shape estimation of cars in real stereo images of urban street scenes. We demonstrate that our shape manifold alignment method yields improved results over the initial stereo reconstruction and object detection method in depth and pose accuracy.


title: Estimation of Human Body Shape and Posture Under Clothing

abstract: Estimating the body shape and posture of a dressed human subject in motion represented as a sequence of (possibly incomplete) 3D meshes is important for virtual change rooms and security. To solve this problem, statistical shape spaces encoding human body shape and posture variations are commonly used to constrain the search space for the shape estimate. In this work, we propose a novel method that uses a posture-invariant shape space to model body shape variation combined with a skeleton-based deformation to model posture variation. Our method can estimate the body shape and posture of both static scans and motion sequences of dressed human body scans. In case of motion sequences, our method takes advantage of motion cues to solve for a single body shape estimate along with a sequence of posture estimates. We apply our approach to both static scans and motion sequences and demonstrate that using our method, higher fitting accuracy is achieved than when using a variant of the popular SCAPE model as statistical model.


title: Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models

abstract: This paper reports on a data-driven motion planning approach for interaction-aware, socially-compliant robot navigation among human agents. Autonomous mobile robots navigating in workspaces shared with human agents require motion planning techniques providing seamless integration and smooth navigation in such. Smooth integration in mixed scenarios calls for two abilities of the robot: predicting actions of others and acting predictably for them. The former requirement requests trainable models of agent behaviors in order to accurately forecast their actions in the future, taking into account their reaction on the robot's decisions. A human-like navigation style of the robot facilitates other agents-most likely not aware of the underlying planning technique applied-to predict the robot motion vice versa, resulting in smoother joint navigation. The approach presented in this paper is based on a feature-based maximum entropy model and is able to guide a robot in an unstructured, real-world environment. The model is trained to predict joint behavior of heterogeneous groups of agents from onboard data of a mobile platform. We evaluate the benefit of interaction-aware motion planning in a realistic public setting with a total distance traveled of over 4 km. Interestingly the motion models learned from human-human interaction did not hold for robot-human interaction, due to the high attention and interest of pedestrians in testing basic braking functionality of the robot.


title: Video Summarization with Long Short-term Memory

abstract: We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the problem as a structured prediction problem on sequential data, our main idea is to use Long Short-Term Memory (LSTM), a special type of recurrent neural networks to model the variable-range dependencies entailed in the task of video summarization. Our learning models attain the state-of-the-art results on two benchmark video datasets. Detailed analysis justifies the design of the models. In particular, we show that it is crucial to take into consideration the sequential structures in videos and model them. Besides advances in modeling techniques, we introduce techniques to address the need of a large number of annotated data for training complex learning models. There, our main idea is to exploit the existence of auxiliary annotated video datasets, albeit heterogeneous in visual styles and contents. Specifically, we show domain adaptation techniques can improve summarization by reducing the discrepancies in statistical properties across those datasets.


title: A Brief Introduction to Boosting

abstract: Boosting is a general method for improving the accuracy of any given learning algorithm. This short paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting. Some examples of recent applications of boosting are also described.


title: Progress in camera-based document image analysis

abstract: The increasing availability of high performance, low priced, portable digital imaging devices has created a tremendous opportunity for supplementing traditional scanning for document image acquisition. Digital cameras attached to cellular phones, PDAs, or as standalone still or video devices are highly mobile and easy to use; they can capture images of any kind of document including very thick books, historical pages too fragile to touch, and text in scenes; and they are much more versatile than desktop scanners. Should robust solutions to the analysis of documents captured with such devices become available, there is clearly a demand from many domains. Traditional scanner-based document analysis techniques provide us with a good reference and starting point, but they cannot be used directly on camera-captured images. Camera captured images can suffer from low resolution, blur, and perspective distortion, as well as complex layout and interaction of the content and background. In this paper we present a survey of application domains, technical challenges and solutions for recognizing documents captured by digital cameras. We begin by describing typical imaging devices and the imaging process. We discuss document analysis from a single camera-captured image as well as multiple frames and highlight some sample applications under development and feasible ideas for future development.


title: A strong arbiter PUF using resistive RAM

abstract: Physically Unclonable Functions (PUF) are the cost effective and reliable security primitives widely used in authentication and in-place secret key generation applications. With growing research in the area of non-CMOS technologies for memories and circuits, it's important to understand their implications on the design of security primitives. Resistive Random Accessible Memory (RRAM) offers easy integration with CMOS due to minimal changes in the process technology. RRAM also demonstrates resistance variability characteristics due to inherent defects in the conducting filament formed inside the metal oxide layer. RRAM based PUF designs exploit either the probabilistic switching of RRAM or the resistance variability during forming, set and reset processes. Memory PUFs using RRAM are weak PUFs with fewer number of Challenge Response Pairs (CRPs). We propose strong arbiter PUF based on 1T-1R bitcell which is obtained from conventional RRAM memory array with minimally invasive changes. Conventional voltage sense amplifier is employed to generate the response. The PUF is simulated using 65nm predictive technology models for CMOS and verilog-A model for a hafnium oxide based RRAM. The proposed PUF architecture is evaluated for uniqueness, uniformity and reliability and by running NIST benchmarks. The proposed PUF demonstrates mean intra-die Hamming Distance (HD) of mean 0.13% and inter-die HD of mean 51.3%.


IT FLEXIBILITY AND COMPETITIVE PERFORMANCE: THE MEDIATING ROLE OF IT-ENABLED DYNAMIC CAPABILITIES


title: Elucidating user behavior of mobile learning - A perspective of the extended technology acceptance model

abstract: Purpose – The purpose of this paper is to propose and verify that the technology acceptance model (TAM) can be employed to explain and predict the acceptance of mobile learning (M‐learning); an activity in which users access learning material with their mobile devices. The study identifies two factors that account for individual differences, i.e. perceived enjoyment (PE) and perceived mobility value (PMV), to enhance the explanatory power of the model.Design/methodology/approach – An online survey was conducted to collect data. A total of 313 undergraduate and graduate students in two Taiwan universities answered the questionnaire. Most of the constructs in the model were measured using existing scales, while some measurement items were created specifically for this research. Structural equation modeling was employed to examine the fit of the data with the model by using the LISREL software.Findings – The results of the data analysis shows that the data fit the extended TAM model well. Consumers hold posi...


title: Pitfalls and Opportunities in Nonverbal and Verbal Lie Detection

abstract: The question of whether discernible differences exist between liars and truth tellers has interested professional lie detectors and laypersons for centuries. In this article we discuss whether people can detect lies when observing someone’s nonverbal behavior or analyzing someone’s speech. An article about detecting lies by observing nonverbal and verbal cues is overdue. Scientific journals regularly publish overviews of research articles regarding nonverbal and verbal cues to deception, but they offer no explicit guidance about what lie detectors should do and should avoid doing to catch liars. We will present such guidance in the present article.


title: Schatten--von Neumann properties for Fourier integral operators with non-smooth symbols II

abstract: We establish continuity and Schatten-von Neumann properties for Fourier integral operators with amplitudes in weighted modulation spaces, when acting on modulation spaces themselves. The phase func ...


title: Teacher perspectives on integrating ICT into subject teaching: commitment, constraints, caution, and change

abstract: This paper examines how secondary teachers of the core subjects of English, mathematics, and science have begun to integrate information and communication technology (ICT) into mainstream classroom practice in English schools. It draws on an analysis of 18 focus‐group interviews with subject departments in these fields. Evident commitment to incorporating ICT was tempered by a cautious, critical approach, and by the influence of external constraints. Teacher accounts emphasized both the use of ICT to enhance and extend existing classroom practice, and change in terms of emerging forms of activity which complemented or modified practice. A gradual process of pedagogical evolution was apparent; teachers were developing and trialling new strategies specifically for mediating ICT‐supported learning. In particular, these overcame the potentially obstructive role of some forms of ICT by focusing pupils’ attention onto underlying learning objectives.


title: Large-scale Identification of Birds in Audio Recordings

abstract: This paper presents a computer-based technique for bird species identification at large scale. It automatically identifies multiple species simultaneously in a large number of audio recordings and provides the basis for the best scoring submission to the LifeCLEF 2014 Bird Identification Task. The method achieves a Mean Average Precision of 51.1% on the test set and 53.9% on the training set with an Area Under the Curve of 91.5% during cross-validation. Besides a general description of the underlying classification approach a number of additional research questions are addressed regarding the choice of features, selection of classifier hyperparameters and method of classification.


title: Towards a Functional System Architecture for Automated Vehicles

abstract: This paper presents a functional system architecture for an automated vehicle. It provides an overall, generic structure that is independent of a specific implementation of a particular vehicle project. Yet, it has been inspired and cross-checked with a real world automated driving implementation in the Stadtpilot project at the Technische Universit\"at Braunschweig. The architecture entails aspects like environment and self perception, planning and control, localization, map provision, Vehicle-To-X-communication, and interaction with human operators.


title: PhishGuard: A browser plug-in for protection from phishing

abstract: Phishing is an act of identity theft aimed at acquiring sensitive information such as usernames, passwords, credit card detail etc., by masquerading as a trustworthy entity in an electronic communication. Phishers use a number of different social engineering mechanism such as spoofed e-mail to try to trick their victims. Data suggests that some of the phishing attacks have convinced up to 5% of their recipients to provide sensitive information to spoofed websites resulting in a direct loss of multi Billion Dollars across the countries. Though there are many existing anti-phishing solutions, Phishers continue to succeed to lure victims. In this paper, we have proposed a novel algorithm which aims at identifying a forged website by submitting random credentials before the actual credentials in a login process of a website. We have also proposed a mechanism for analysing the responses from the server against the submissions of all those credentials to determine if the website is original or phished one. Though our idea is generic and would work in any authentication technologies which are based on exchange of any credentials, our current prototype is developed for sites supporting HTTP Digest Authentication and accepting userid and password pair as credential. Our algorithm is developed within a browser plug-in for Mozilla FireFox v3.0. and can detect phishing attack conclusively.


Statistical Texture Measures Computed from Gray Level Coocurrence Matrices


title: Comparison of cross-platform mobile development tools

abstract: Mobiles are an integral part of daily life. With time, customers are expecting good and very versatile applications in less time. It is a big challenge to develop high performance mobile applications in this competitive market that would meet the expectation of customers. Mobile operating systems vendors are giving their best available resources for making applications in more convenient ways, although the development of new applications for each mobile operating system in short time is fairly a problem. Cross-platform mobile application development tools contribute in solving this problem largely. This paper presents a pragmatic comparison among four very popular cross platform tools, which are Rhodes, PhoneGap, DragonRad and MoSync. One of the main focuses of the comparison is to provide an overview on the availability of application programming interfaces, programming languages, supported mobile operating systems, licences, and integrated development environments. Furthermore, it also presents some critical points such as the factor of extensibility in tools and the effects that they may bring on market share. The comparison is aimed at supporting developers to make the right choice with respect to their needs/constraints.