title: The Communicative Functions of Touch in Humans, Nonhuman Primates, and Rats: A Review and Synthesis of the Empirical Research

abstract: Although touch is one of the most neglected modalities of communication, several lines of research bear on the important communicative functions served by the modality. The authors highlighted the importance of touch by reviewing and synthesizing the literatures pertaining to the communicative functions served by touch among humans, nonhuman primates, and rats. In humans, the authors focused on the role that touch plays in emotional communication, attachment, bonding, compliance, power, intimacy, hedonics, and liking. In nonhuman primates, the authors examined the relations among touch and status, stress, reconciliation, sexual relations, and attachment. In rats, the authors focused on the role that touch plays in emotion, learning and memory, novelty seeking, stress, and attachment. The authors also highlighted the potential phylogenetic and ontogenetic continuities and discussed suggestions for future research.


title: Using mobile phone sensors to detect driving behavior

abstract: In India, an increasing number of vehicles on the roads, in recent past, have led to an increase in the number of road accidents. There have been alarming statistics regarding the number of accidents per day in India. At least 1,42,000 people died due to road accidents in India in the year 2011. Bad driving, lax traffic control, and poor road conditions are the main reason for this. In this work, we present a mobile phone application that uses combination of in-built sensors, GPS, micro-phone and accelerometer, to detect driving behavior along with road and traffic conditions. This application will prove helpful in detecting bad driving as well as road and traffic conditions in order to assist a willing individual to change his or her driving behavior. The law and enforcement agencies may also use this data in analyzing the ground realities for increasing number of road accidents.


title: Modeling term dependencies with quantum language models for IR

abstract: Traditional information retrieval (IR) models use bag-of-words as the basic representation and assume that some form of independence holds between terms. Representing term dependencies and defining a scoring function capable of integrating such additional evidence is theoretically and practically challenging. Recently, Quantum Theory (QT) has been proposed as a possible, more general framework for IR. However, only a limited number of investigations have been made and the potential of QT has not been fully explored and tested. We develop a new, generalized Language Modeling approach for IR by adopting the probabilistic framework of QT. In particular, quantum probability could account for both single and compound terms at once without having to extend the term space artificially as in previous studies. This naturally allows us to avoid the weight-normalization problem, which arises in the current practice by mixing scores from matching compound terms and from matching single terms. Our model is the first practical application of quantum probability to show significant improvements over a robust bag-of-words baseline and achieves better performance on a stronger non bag-of-words baseline.


title: Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood

abstract: We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (fCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that fCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.


title: Learning SO(3) Equivariant Representations with Spherical CNNs

abstract: We address the problem of 3D rotation equivariance in convolutional neural networks. 3D rotations have been a challenging nuisance in 3D classification tasks requiring higher capacity and extended data augmentation in order to tackle it. We model 3D data with multi-valued spherical functions and we propose a novel spherical convolutional network that implements exact convolutions on the sphere by realizing them in the spherical harmonic domain. Resulting filters have local symmetry and are localized by enforcing smooth spectra. We apply a novel pooling on the spectral domain and our operations are independent of the underlying spherical resolution throughout the network. We show that networks with much lower capacity and without requiring data augmentation can exhibit performance comparable to the state of the art in standard retrieval and classification benchmarks.


title: A Pose-Adaptive Constrained Local Model for Accurate Head Pose Tracking

abstract: Robust and precise face tracking under unconstrained imaging conditions is still a challenging task. Recently, the Constrained Local Model (CLM) framework has proven to be very powerful to track frontal and near frontal facial movements. In this paper, we introduce a Pose-Adaptive CLM which is able to accurately track large 3D head rotations. This model relies on two main parts: (1) an adaptive 3D Point Distribution Model that ensures consistency between a tracked point in the image and the corresponding point in the shape model and (2) an adaptive appearance model that deals with appearance variation of a point under different viewing angle. We present comparative experimental results highlighting the improvement in both robustness and accuracy of our method. We also introduce a new challenging dataset with accurate head pose annotation.


A PREDICTION MODEL FOR BROKEN RAILS AND AN ANALYSIS OF THEIR ECONOMIC IMPACT


title: A Graph-theoretic perspective on centrality

abstract: The concept of centrality is often invoked in social network analysis, and diverse indices have been proposed to measure it. This paper develops a unified framework for the measurement of centrality. All measures of centrality assess a node's involvement in the walk structure of a network. Measures vary along four key dimensions: type of nodal involvement assessed, type of walk considered, property of walk assessed, and choice of summary measure. If we cross-classify measures by type of nodal involvement (radial versus medial) and property of walk assessed (volume versus length), we obtain a four-fold polychotomization with one cell empty which mirrors Freeman's 1979 categorization. At a more substantive level, measures of centrality summarize a node's involvement in or contribution to the cohesiveness of the network. Radial measures in particular are reductions of pair-wise proximities/cohesion to attributes of nodes or actors. The usefulness and interpretability of radial measures depend on the fit of the cohesion matrix to the one-dimensional model. In network terms, a network that is fit by a one-dimensional model has a core-periphery structure in which all nodes revolve more or less closely around a single core. This in turn implies that the network does not contain distinct cohesive subgroups. Thus, centrality is shown to be intimately connected with the cohesive subgroup structure of a network.


title: Optimizing quantum optimization algorithms via faster quantum gradient computation

abstract: We consider a generic framework of optimization algorithms based on gradient descent. We develop a quantum algorithm that computes the gradient of a multi-variate real-valued function f : Rd → R by evaluating it at only a logarithmic number of times in superposition. Our algorithm is an improved version of Jordan's gradient computation algorithm [28], providing an approximation of the gradient ▽f with quadratically better dependence on the evaluation accuracy of f, for an important class of smooth functions. Furthermore, we show that objective functions arising from variational quantum circuits usually satisfy the necessary smoothness conditions, hence our algorithm provides a quadratic improvement in the complexity of computing their gradient. We also show that in a continuous phase-query model, our gradient computation algorithm has optimal query complexity up to poly-logarithmic factors, for a particular class of smooth functions. Moreover, we show that for low-degree multivariate polynomials our algorithm can provide exponential speedups compared to Jordan's algorithm in terms of the dimension d. ::: ::: One of the technical challenges in applying our gradient computation procedure for quantum optimization problems is the need to convert between a probability oracle (which is common in quantum optimization procedures) and a phase oracle (which is common in quantum algorithms) of the objective function f. We provide efficient subroutines to perform this delicate interconversion between the two types of oracles incurring only a logarithmic overhead, which might be of independent interest. Finally, using these tools we improve the runtime of prior approaches for training quantum auto-encoders, variational quantum eigensolvers (VQE), and quantum approximate optimization algorithms (QAOA).


title: What to Expect When the Unexpected Happens: Social Media Communications Across Crises

abstract: The use of social media to communicate timely information during crisis situations has become a common practice in recent years. In particular, the one-to-many nature of Twitter has created an opportunity for stakeholders to disseminate crisis-relevant messages, and to access vast amounts of information they may not otherwise have. Our goal is to understand what affected populations, response agencies and other stakeholders can expect-and not expect-from these data in various types of disaster situations. Anecdotal evidence suggests that different types of crises elicit different reactions from Twitter users, but we have yet to see whether this is in fact the case. In this paper, we investigate several crises-including natural hazards and human-induced disasters-in a systematic manner and with a consistent methodology. This leads to insights about the prevalence of different information types and sources across a variety of crisis situations.


title: Body Composition Analysis of Computed Tomography Scans in Clinical Populations: The Role of Deep Learning

abstract: Background: Body composition is increasingly being recognized as an important prognostic factor for health outcomes across cancer, liver cirrhosis, and critically ill patients. Computed tomography (CT) scans, when taken as part of routine care, provide an excellent opportunity to precisely measure the quantity and quality of skeletal muscle and adipose tissue. However, manual analysis of CT scans is costly and time-intensive, limiting the widespread adoption of CT-based measurements of body composition. Summary: Advances in deep learning have demonstrated excellent success in biomedical image analysis. Several recent publications have demonstrated excellent accuracy in comparison to human raters for the measurement of skeletal muscle, visceral adipose, and subcutaneous adipose tissue from the lumbar vertebrae region, indicating that analysis of body composition may be successfully automated using deep neural networks. Key Messages: The high accuracy and drastically improved speed of CT body composition analysis (<1 s/scan for neural networks vs. 15 min/scan for human analysis) suggest that neural networks may aid researchers and clinicians in better understanding the role of body composition in clinical populations by enabling cost-effective, large-scale research studies. As the role of body composition in clinical settings and the field of automated analysis advance, it will be critical to examine how clinicians interact with these systems and to evaluate whether these technologies are beneficial in improving treatment and health outcomes for patients.


title: A review of 0.18-/spl mu/m full adder performances for tree structured arithmetic circuits

abstract: The general objective of our work is to investigate the area and power-delay performances of low-voltage full adder cells in different CMOS logic styles for the predominating tree structured arithmetic circuits. A new hybrid style full adder circuit is also presented. The sum and carry generation circuits of the proposed full adder are designed with hybrid logic styles. To operate at ultra-low supply voltage, the pass logic circuit that cogenerates the intermediate XOR and XNOR outputs has been improved to overcome the switching delay problem. As full adders are frequently employed in a tree structured configuration for high-performance arithmetic circuits, a cascaded simulation structure is introduced to evaluate the full adders in a realistic application environment. A systematic and elegant procedure to scale the transistor for minimal power-delay product is proposed. The circuits being studied are optimized for energy efficiency at 0.18-/spl mu/m CMOS process technology. With the proposed simulation environment, it is shown that some survival cells in stand alone operation at low voltage may fail when cascaded in a larger circuit, either due to the lack of drivability or unsatisfactory speed of operation. The proposed hybrid full adder exhibits not only the full swing logic and balanced outputs but also strong output drivability. The increase in the transistor count of its complementary CMOS output stage is compensated by its area efficient layout. Therefore, it remains one of the best contenders for designing large tree structured arithmetic circuits with reduced energy consumption while keeping the increase in area to a minimum.


A Review on FPGA Implementation of Edge Detection Algorithms


title: Towards Unified Human Parsing and Pose Estimation

abstract: We study the problem of human body configuration analysis, more specifically, human parsing and human pose estimation. These two tasks, ie identifying the semantic regions and body joints respectively over the human body image, are intrinsically highly correlated. However, previous works generally solve these two problems separately or iteratively. In this work, we propose a unified framework for simultaneous human parsing and pose estimation based on semantic parts. By utilizing Parselets and Mixture of Joint-Group Templates as the representations for these semantic parts, we seamlessly formulate the human parsing and pose estimation problem jointly within a unified framework via a tailored and-or graph. A novel Grid Layout Feature is then designed to effectively capture the spatial co-occurrence/occlusion information between/within the Parselets and MJGTs. Thus the mutually complementary nature of these two tasks can be harnessed to boost the performance of each other. The resultant unified model can be solved using the structure learning framework in a principled way. Comprehensive evaluations on two benchmark datasets for both tasks demonstrate the effectiveness of the proposed framework when compared with the state-of-the-art methods.


THE BITCOIN ECOSYSTEM : DISRUPTION BEYOND FINANCIAL SERVICES ?


title: Performance Comparison of Five Exact Graph Matching Algorithms on Biological Databases

abstract: Graphs are a powerful data structure that can be applied to several problems in bioinformatics. Graph matching, in its diverse forms, is an important operation on graphs, involved when there is the need to compare two graphs or to find substructures into larger structures. Many graph matching algorithms exist, and their relative efficiency depends on the kinds of graphs they are applied to. In this paper we will consider some popular and freely available matching algorithms, and will experimentally compare them on graphs derived from bioinformatics applications, in order to help the researchers in this field to choose the right tool for the problem at hand.


title: Security and Privacy Implications of NFC-enabled Contactless Payment Systems

abstract: Nowadays, contactless payments are becoming increasingly common as new smartphones, tablets, point-of-sale (POS) terminals and payment cards (often termed "tap-and-pay" cards) are designed to support Near Field Communication (NFC) technology. However, as NFC technology becomes pervasive, there have been concerns about how well NFC-enabled contactless payment systems protect individuals and organizations from emerging security and privacy threats. In this paper, we examine the security of contactless payment systems by considering the privacy threats and the different adversarial attacks that these systems must defend against. We focus our analysis on the underlying trust assumptions, security measures and technologies that form the basis on which contactless payment cards and NFC-enabled mobile wallets exchange sensitive transaction data with contactless POS terminals. We also explore the EMV and ISO standards for contactless payments and disclose their shortcomings with regards to enforcing security and privacy in contactless payment transactions. Our findings shed light on the discrepancies between the EMV and ISO standards, as well as how card issuing banks and mobile wallet providers configure their contactless payment cards and NFC-enabled mobile wallets based on these standards, respectively. These inconsistencies are disconcerting as they can be exploited by an adversary to compromise the integrity of contactless payment transactions.


title: Geocoding Billions of Addresses: Toward a Spatial Record Linkage System with Big Data

abstract: Address is one of the most commonly used spatial data in everyday life. Comparing two addresses (e.g., if they are referring to the same location) is a fundamental problem for address-related record linkage. In this paper, a fast, reliable, expandable address parser/standardizer/geocoder has been developed as an initial step towards spatial record linkage. First, a CASS-based geocoding test set was created and performance of on-line geocoding API providers (Google, Yahoo, Bing) was evaluated. Considering high time consumption and geocoding precision flaws, we developed an in-house TIGER/Line based hierarchical geocoder, Intelius Address Parser (IAP) that provides on-par geocoding precision compared to on-line geocoding APIs. Given over one billion addresses, on a 25-node Hadoop cluster setup on with Amazon AWS, the time consumption and cost are reported and compared with commercial solutions. Strategies for using geocoded addresses for record linkage is presented and plans on expanding the use of geocoded result are discussed.


title: Basal ganglia function, stuttering, sequencing, and repair in adult songbirds

abstract: Apallial-basal-ganglia-thalamic-pallialloopinsongbirdsisinvolvedinvocalmotorlearning.Damagetoitsbasal ganglia part, Area X, in adult zebra finches has been noted to have no strong effects on song and itsfunction is unclear. Here we report that neurotoxic damage to adult Area X induced changes in singingtempo and global syllable sequencing in all animals, and considerably increased syllable repetition in birdswhose song motifs ended with minor repetitions before lesioning. This stuttering-like behavior started atone month, and improved over six months. Unexpectedly, the lesioned region showed considerablerecovery,includingimmigrationofnewlygeneratedorrepairedneuronsthatbecameactiveduringsinging.The timingofthe recoveryand stutteringsuggestthat immaturerecovering activity of the circuitmight beassociated with stuttering. These findings indicate that even after juvenile learning is complete, the adultstriatum plays a role in higher level organization of learned vocalizations.


title: Performance of CsI photocathodes in liquid Xe, Kr, and Ar

abstract: Abstract The successful detection of the UV scintillation light in liquid Xe, Kr and Ar by a reflective CsI photocathode with liquid-phase electron extraction is reported. High quantum efficiencies of 31% in liquid Xe, 17% in liquid Kr, and 10% in liquid Ar have been achieved. Excellent intrinsic energy resolutions of 2.8% FWHM for liquid Xe, 5.2% FWHM for liquid Kr, and 8.4% FWHM for liquid Ar have been measured for 5.5 MMeV alpha particles. The enhancement of the quantum efficiency due to the applied electric field and the favorable effect of the negative electron affinity of liquid rare gases on electron extraction from the photocathode are discussed.


title: SPARQL-DL: SPARQL Query for OWL-DL

abstract: There are many query languages (QLs) that can be used to query RDF and OWL ontologies but neither type is satisfactory for querying OWL-DL ontologies. RDF-based QLs (RDQL, SeRQL, SPARQL) are harder to give a semantics w.r.t. OWL-DL and are more powerful than what OWL-DL reasoners can provide. DL-based QLs (DIG ask queries, nRQL) have clear semantics but are not powerful enough in the general case. In this paper we describe SPARQL-DL, a substantial subset of SPARQL for which we provide a clear OWL-DL based semantics. SPARQL-DL is significantly more expressive than existing DL QLs (by allowing mixed TBox/RBox/ABox queries) and can still be implemented without too much effort on top of existing OWL-DL reasoners. We discuss design decisions and practical issues that arise for defining SPARQL-DL and report about our preliminary prototype implemented on top of OWL-DL reasoner Pellet.


A Tweets Classifier based on Cosine Similarity


title: Biodegradation of Polyethylene by Bacillus sp. Indigenous to the Niger Delta Mangrove Swamp

abstract: The ability of Bacillus mycoides and Bacillus subtilis ( Bacillus species indigenous to the Niger Delta mangrove soil) to biodegrade polyethylene was studied. Low-density polyethylene (LDPE) and high-density polyethylene (HDPE) films were exposed outdoor for 24 weeks. The two isolates were able to grow on polyethylene (PE), forming visible biofilms. The plasmid pattern of the Bacillus species showed a similar pattern among the two Bacillus species, with one plasmid number and molecular weight around 25 kb, indicating a mega plasmid. The mean heterotrophic bacterial counts in the mangrove soil ranged between 2.81 x 10 5 – 3.20 x 10 8 CFU/g. The rate of degradation was determined by measurement of the residual weight of the PE films. Biodegradation in Erlenmeyer flasks by the bacteria after 60 days of incubation ranged between 8.41%-23.15%. Biodegradation was confirmed by Fourier-Transform Infrared (FTIR) spectroscopy, which showed introduction of carbonyl groups after natural weathering, which decreased after microbial treatment. Decrease in carbonyl index ranged between 10.5%-13.7%. The result showed that certain Bacillus sp. indigenous to the Niger Delta mangrove soil are capable of growing on PE films and biodegrade them, after an initial abiotic degradation. Key words: Bacillus mycoides, Bacillus subtilis , biodegradation, carbonyl index, Natural weathering, Niger Delta mangrove.


title: On Learning Mixed Community-specific Similarity Metrics for Cold-start Link Prediction

abstract: We study the cold-start link prediction problem where edges between vertices is unavailable by learning vertex-based similarity metrics. Existing metric learning methods for link prediction fail to consider communities which can be observed in many real-world social networks. Because different communities usually exhibit different intra-community homogeneities, learning a global similarity metric is not appropriate. In this paper, we thus propose to learn community-specific similarity metrics via joint community detection. Experiments on three real-world networks show that the intra-community homogeneities can be well preserved, and the mixed community-specific metrics perform better than a global similarity metric in terms of prediction accuracy.


title: Intelligent Control of Heat Exchangers

abstract: This work deals with the design and application of a neuro-fuzzy controller for a heat exchanger. To deal with the problem of parameter adjustment, efficient neuro-fuzzy scheme known as the ANFIS (Adaptive Network-based Fuzzy Inference System) can be used. The ANFIS is a cross between an artificial neural network and a fuzzy inference system (FIS) and represents Takagi-Sugeno fuzzy model as generalized feedforward neural network, and trains it with plant I/O data, thereby adjusting the parameters of the antecedent membership functions as well as those of the functional consequents. The neuro-fuzzy control of the heat exchanger is compared with classical PID control. The simulation results confirm that fuzzy is one of the possibilities for successful control of heat exchangers. The advantage of this approach is that it is not a linear-model-based strategy. Comparison of the simulation results obtained using fuzzy and those obtained using classical PID control demonstrates the effectiveness and superiority of the proposed approach because of the smaller consumption of the heating medium.


title: Latent Contextual Bandits and their Application to Personalized Recommendations for New Users

abstract: Personalized recommendations for new users, also known as the cold-start problem, can be formulated as a contextual bandit problem. Existing contextual bandit algorithms generally rely on features alone to capture user variability. Such methods are inefficient in learning new users' interests. In this paper we propose Latent Contextual Bandits. We consider both the benefit of leveraging a set of learned latent user classes for new users, and how we can learn such latent classes from prior users. We show that our approach achieves a better regret bound than existing algorithms. We also demonstrate the benefit of our approach using a large real world dataset and a preliminary user study.


title: Large area sensor integration in textiles

abstract: In recent years, the integration of electronics in technical textiles has gained increasing attention especially in Europe. Many industry driven projects have already been started in this area on national as well as on European level. An example is the European project PASTA. In order to make the step towards industrial manufacturing of wearable electronics as well as smart large area technical textiles it is necessary to develop modular concepts as well as integration processes suitable for high volume production. Within PASTA new electronic packaging technologies and the development of new textile structures are investigated. By introducing new concepts for electronic packaging and module interconnects, a seamless, more comfortable and more robust integration of electronics in textiles will be possible. The main technological developments concentrate on different levels of integration: a new concept for bare die integration into a yarn, a new interconnect technology based on mechanical crimping, and the development of a stretchable interposer serving as a stress relief interface between the rigid component and the elastic fabric. The technologies will also be analyzed and evaluated regarding functionality and reliability. The proposed solutions for integration of electronics in textiles will cover a whole range of components, from ultra-small LEDs to complex multichip modules for sensor data processing. Moreover, a system design task will address the power distribution and system partitioning aspects to provide a complete solution for integration of a distributed sensor/actuator system in fabric. Different approaches are possible for large area sensors in textiles. The scope of this paper is to show which technologies enable the use of conductive yarns in a fabric for pressure and damage detection and in-situ monitoring of accumulated stress in composites to predict the residual lifetime and to indicate damage of industrial components.


title: Cleaning up systematic error in eye-tracking data by using required fixation locations

abstract: In the course of running an eye-tracking experiment, one computer system or subsystem typically presents the stimuli to the participant and records manual responses, and another collects the eye movement data, with little interaction between the two during the course of the experiment. This article demonstrates how the two systems can interact with each other to facilitate a richer set of experimental designs and applications and to produce more accurate eye tracking data. In an eye-tracking study, a participant is periodically instructed to look at specific screen locations, orexplicit required fixation locations (RFLs), in order to calibrate the eye tracker to the participant. The design of an experimental procedure will also often produce a number ofimplicit RFLs—screen locations that the participant must look at within a certain window of time or at a certain moment in order to successfully and correctly accomplish a task, but without explicit instructions to fixate those locations. In these windows of time or at these moments, the disparity between the fixations recorded by the eye tracker and the screen locations corresponding to implicit RFLs can be examined, and the results of the comparison can be used for a variety of purposes. This article shows how the disparity can be used to monitor the deterioration in the accuracy of the eye tracker calibration and to automatically invoke a re-calibration procedure when necessary. This article also demonstrates how the disparity will vary across screen regions and participants and how each participant’s uniqueerror signature can be used to reduce the systematic error in the eye movement data collected for that participant.


Semantic Visualization for Short Texts with Word Embeddings


title: Security evaluation of the OAuth 2.0 framework

abstract: Purpose – The interoperability of cloud data between web applications and mobile devices has vastly improved over recent years. The popularity of social media, smartphones and cloud-based web services have contributed to the level of integration that can be achieved between applications. This paper investigates the potential security issues of OAuth, an authorisation framework for granting third-party applications revocable access to user data. OAuth has rapidly become an interim de facto standard for protecting access to web API data. Vendors have implemented OAuth before the open standard was officially published. To evaluate whether the OAuth 2.0 specification is truly ready for industry application, an entire OAuth client server environment was developed and validated against the speciation threat model. The research also included the analysis of the security features of several popular OAuth integrated websites and comparing those to the threat model. High-impacting exploits leading to account hijack...


title: Cloud-assisted industrial cyber-physical systems

abstract: The development of industrialization and information communication technology (ICT) has deeply changed our way of life. In particular, with the emerging theory of "Industry 4.0", the integration of cloud technologies and industrial cyber-physical systems (ICPS) becomes increasingly important, as this will greatly improve the manufacturing chain and business services. In this paper, we first describe the development and character of ICPS. ICPS will inevitably play an important role in manufacturing, sales, and logistics. With the support of the cloud, ICPS development will impact value creation, business models, downstream services, and work organization. Then, we present a service-oriented ICPS model. With the support of the cloud, infrastructure platform and service application, ICPS will promote the manufacturing efficiency, increase quality of production, enable a sustainable industrial system and more environmentally friendly businesses. Thirdly, we focus on some key enabling technologies, which are critical in supporting smart factories. These key enabling technologies will also help companies to realize high quality, high output, and low cost. Finally, we talk about some challenges of ICPS implementation and the future work.


title: Integration of renewable energy into the transport and electricity sectors through V2G

abstract: Large-scale sustainable energy systems will be necessary for substantial reduction of CO2. However, large-scale implementation faces two major problems: (1) we must replace oil in the transportation sector, and (2) since today's inexpensive and abundant renewable energy resources have fluctuating output, to increase the fraction of electricity from them, we must learn to maintain a balance between demand and supply. Plug-in electric vehicles (EVs) could reduce or eliminate oil for the light vehicle fleet. Adding "vehicle-to-grid" (V2G) technology to EVs can provide storage, matching the time of generation to time of load. Two national energy systems are modelled, one for Denmark, including combined heat and power (CHP) and the other a similarly sized country without CHP (the latter being more typical of other industrialized countries). The model (EnergyPLAN) integrates energy for electricity, transport and heat, includes hourly fluctuations in human needs and the environment (wind resource and weather-driven need for heat). Four types of vehicle fleets are modelled, under levels of wind penetration varying from 0% to 100%. EVs were assumed to have high power (10Â kW) connections, which provide important flexibility in time and duration of charging. We find that adding EVs and V2G to these national energy systems allows integration of much higher levels of wind electricity without excess electric production, and also greatly reduces national CO2 emissions.


title: Challenges in network forensics

abstract: Network forensics is a branch of digital forensics that focuses on monitoring, capturing, recording, and analysis of network traffic. More accurately, it is the use of scientifically proved techniques to collect and analyse network packets and events for investigative purposes. Network forensics is an extension of the network security model which traditionally emphasizes prevention and detection of network attacks. Current network forensics approaches are costly and time consuming. However, unlike other areas of digital forensics, network forensics deals with volatile and dynamic data. It helps organizations to investigate attacks that originated from outside and inside of the company. It's also important for law enforcement agencies when solving crimes. Paper presents different challenges that are facing investigators due to the rapid growth of network and attacker's skill, and possible framework solutions that would help to solve or minimize problems.


A Comparative study on Internet Of Things (IoT) and its applications in Smart Agriculture


New distribution records of Acanthaceae in Guatemala


title: Recognition of handwritten Malayalam characters using vertical & horizontal line positional analyzer algorithm

abstract: This paper proposes an algorithm for the recognition of handwritten characters in Malayalam, a South Indian language. It introduces the salient features of Malayalam script and lists the approaches used for character recognition. Malayalam scripts are rich in patterns because of their complex curved form, larger number of basic elements and the presence of conjuncts. The combinations of such patterns make the recognition of characters much complex and these patterns should be exploited to arrive at the solution. Here an image of handwritten Malayalam characters is given as the input and an editable document of Malayalam characters in a predefined format is produced as output. In this paper, initially the overall structure of OCR system is presented. Then, the OCR process is presented in three modules: Pre-processing, Skeletonization and Recognition. In Pre-processing, we scan the input image and separate each character from it. In Skeletoniz ation, we obtain one pixel thick skeleton of the character. In Recognition, we classify the characters based on their features. The features of the characters are extracted based on the analysis of position and count of the horizontal and vertical lines.


title: Are Very Deep Neural Networks Feasible on Mobile Devices

abstract: In the recent years, the computing power of mobile devices has increased tremendously, a trend that is expected to continue in the future. With high-quality onboard cameras, these devices are capable of collecting large volumes of visual information. Motivated by the observation that processing this video on the mobile device can enable many new applications, we explore the feasibility of running very deep Convolutional Neural Networks (CNNs) for video processing tasks on an emerging class of mobile platforms with embedded GPUs. We find that the memory available in these mobile GPUs is significantly less than necessary to execute very deep CNNs. We then quantify the performance of several deep CNNspecific memory management techniques, some of which leverage the observation that these CNNs have a small number of layers that require most of the memory. We find that a particularly novel approach that offloads these bottleneck layers to the mobile device’s CPU and pipelines frame processing is a promising approach that does not impact the accuracy of these tasks. We conclude by arguing that such techniques will likely be necessary for the foreseeable future despite technological improvements.


title: SQLGraph: An Efficient Relational-Based Property Graph Store

abstract: We show that existing mature, relational optimizers can be exploited with a novel schema to give better performance for property graph storage and retrieval than popular noSQL graph stores. The schema combines relational storage for adjacency information with JSON storage for vertex and edge attributes. We demonstrate that this particular schema design has benefits compared to a purely relational or purely JSON solution. The query translation mechanism translates Gremlin queries with no side effects into SQL queries so that one can leverage relational query optimizers. We also conduct an empirical evaluation of our schema design and query translation mechanism with two existing popular property graph stores. We show that our system is 2-8 times better on query performance, and 10-30 times better in throughput on 4.3 billion edge graphs compared to existing stores.


title: Techniques of upper eyelid reconstruction.

abstract: Reconstruction of the upper eyelid is one of the greatest challenges facing the orbitofacial surgeon. This comprehensive review outlines the principles of reconstruction and the range of techniques available. Methods of assessing upper eyelid defects are discussed, and an algorithm for reconstruction based on defect size and lamellar involvement is given. The review contains numerous detailed examples of reconstructive techniques, including secondary intention healing, local flaps, distal flaps, simple and composite grafts, occlusive and non-occlusive methods, and canthal fixation. Eyebrow and eyelash reconstruction is also covered.


title: Characterizing Urban Landscapes Using Geolocated Tweets

abstract: The pervasiveness of cell phones and mobile social media applications is generating vast amounts of geolocalized user-generated content. Since the addition of geotagging information, Twitter has become a valuable source for the study of human dynamics. Its analysis is shedding new light not only on understanding human behavior but also on modeling the way people live and interact in their urban environments. In this paper, we evaluate the use of geolocated tweets as a complementary source of information for urban planning applications. Our contributions are focussed in two urban planing areas: (1) a technique to automatically determine land uses in a specific urban area based on tweeting patterns, and (2) a technique to automatically identify urban points of interest as places with high activity of tweets. We apply our techniques in Manhattan (NYC) using 49 days of geolocated tweets and validate them using land use and landmark information provided by various NYC departments. Our results indicate that geolocated tweets are a powerful and dynamic data source to characterize urban environments.


title: Management of Bartholin's duct cyst and gland abscess.

abstract: Bartholin's duct cysts and gland abscesses are common problems in women of reproductive age. Bartholin's glands are located bilaterally at the posterior introitus and drain through ducts that empty into the vestibule at approximately the 4 o'clock and 8 o'clock positions. These normally pea-sized glands are palpable only if the duct becomes cystic or a gland abscess develops. The differential diagnosis includes cystic and solid lesions of the vulva, such as epidermal inclusion cyst, Skene's duct cyst, hidradenoma papilliferum, and lipoma. The goal of management is to preserve the gland and its function if possible. Office-based procedures include insertion of a Word catheter for a duct cyst or gland abscess, and marsupialization of a cyst; marsupialization should not be used to treat a gland abscess. Broad-spectrum antibiotic therapy is warranted only when cellulitis is present. Excisional biopsy is reserved for use in ruling out adenocarcinoma in menopausal or perimenopausal women with an irregular, nodular Bartholin's gland mass.


title: Toward a Learning Health-care System – Knowledge Delivery at the Point of Care Empowered by Big Data and NLP

abstract: The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.


title: High-density low-power-operating DRAM device adopting 6F/sup 2/ cell scheme with novel S-RCAT structure on 80nm feature size and beyond

abstract: For the first time, the DRAM device composed of 6F/sup 2/ open-bit-line memory cell with 80nm feature size is developed. Adopting 6F/sup 2/ scheme instead of customary 8F/sup 2/ scheme made it possible to reduce chip size by up to nearly 20%. However, converting the cell scheme to 6F/sup 2/ accompanies some difficulties such as decrease of the cell capacitance, and more compact core layout. To overcome this strict obstacles which are originally stemming from the conversion of cell scheme to 6F/sup 2/, TIT structure with AHO (AfO/AlO/AfO) is adopted for higher cell capacitance, and bar-type contact is adopted for adjusting to compact core layout. Moreover, to lower cell V/sub th/ so far as suitable for characteristic of low power operation, the novel concept, S-RCAT (sphere-shaped-recess-channel-array transistor) is introduced. It is the improved scheme of RCAT used in 8F/sup 2/ scheme. By adopting S-RCAT, V/sub th/ can be lowered, SW, DIBL are improved. Additionally, data retention time characteristic can be improved.


title: Dynamics of Human Head and Eye Rotations Under Donders' Constraint

abstract: The rotation of human head and the eye are modeled as a perfect sphere with the rotation actuated by external torques. For the head movement, the axis of rotation is constrained by a law proposed in the 19th century by Donders. For the saccadic eye movement, Donders' Law is restricted to a law that goes by the name of Listing's Law. In this paper, head movement and saccadic eye movement are modeled using principles from classical mechanics and the associated Euler Lagrange's equations (EL) are analyzed. Geodesic curves are obtained in the space of allowed orientations for the head and the eye and projections of these curves on the space S2 of pointing directions of the eye/head are shown. A potential function and a damping term has been added to the geodesic dynamics from EL and the resulting head and eye trajectories settle down smoothly towards the unique point of minimum potential. The minimum point can be altered to regulate the end point of the trajectories (potential control). Throughout the paper, the restricted dynamics of the eye and the head movement have been compared with the unrestricted rotational dynamics on SO(3) and the corresponding EL equations have been analyzed. A version of the Donders' Theorem, on the possible head orientations for a specific head direction, has been stated and proved in Appendix I. In the case of eye movement, Donders' Theorem restricts to the well known Listing's Theorem. In Appendix II, a constraint on the angular velocity and the angular acceleration vectors is derived for the head movement satisfying Donders' constraint. A statement of this constraint that goes by the name “half angle rule,” has been derived.


title: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

abstract: Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study [30] revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call “fooling images” (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.


title: Computationally efficient winding loss calculation with multiple windings, arbitrary waveforms, and two-dimensional or three-dimensional field geometry

abstract: The squared-field-derivative method for calculating eddy-current (proximity-effect) losses in round-wire or litz-wire transformer and inductor windings is derived. The method is capable of analyzing losses due to two-dimensional and three-dimensional field effects in multiple windings with arbitrary waveforms in each winding. It uses a simple set of numerical magnetostatic field calculations, which require orders of magnitude less computation time than numerical eddy-current solutions, to derive a frequency-independent matrix describing the transformer or inductor. This is combined with a second, independently calculated matrix, based on derivatives of winding currents, to compute total AC loss. Experiments confirm the accuracy of the method.


title: A Fast-Locking Digital Phase-Locked Loop

abstract: A conventional digital phase-locked loop (DPLL) is designed using (Baker et al., 2003) to operate at 1GHz using 0.18 mum CMOS technology; its lock time is 4.19 mus. By adding a coarse/fine tuning control unit composed of a digital-to-analog converter (DAC) and a counter as well as switching the currents of the charge pump, a fast-locking DPLL results, with a lock time of 1.02 mus, i.e. an improvement by a factor of 4. Simulations for both DPLLs verified the performance improvement due to using a fast-locking technique


title: Human action recognition under log-euclidean riemannian metric

abstract: This paper presents a new action recognition approach based on local spatio-temporal features. The main contributions of our approach are twofold. First, a new local spatio-temporal feature is proposed to represent the cuboids detected in video sequences. Specifically, the descriptor utilizes the covariance matrix to capture the self-correlation information of the low-level features within each cuboid. Since covariance matrices do not lie on Euclidean space, the Log-Euclidean Riemannian metric is used for distance measure between covariance matrices. Second, the Earth Mover’s Distance (EMD) is used for matching any pair of video sequences. In contrast to the widely used Euclidean distance, EMD achieves more robust performances in matching histograms/distributions with different sizes. Experimental results on two datasets demonstrate the effectiveness of the proposed approach.


title: Removing rain and snow in a single image using guided filter

abstract: Dynamic weather conditions, such as rain and snow, degrade the performance of vision algorithms, which is served for video surveillance and analysis tasks. Many methods have been proposed to reduce the effects of raindrops and snow flakes that are regarded as noises in each frame of a certain video. However, the removing of rain-drop or snow-flake is quite challenging since each of them affects only on a very small region of an image, thus, leading us a rub to determine which region should be smoothed and which should not. In this paper, a guided filter, by which no pixel-based statistical information for detecting rain and snow are needed, is introduced in the removal stage. The results show that it may achieve better performance. In addition, the degradation-restoring algorithm is suitable for both single images and videos.


title: Ten-Year Research Update Review: Child Sexual Abuse

abstract: ABSTRACT Objective To provide clinicians with current information on prevalence, risk factors, outcomes, treatment, and prevention of child sexual abuse (CSA). To examine the best-documented examples of psychopathology attributable to CSA. Method Computer literature searches of Medline and PSYCInfo for key words. All English-language articles published after 1989 containing empirical data pertaining to CSA were reviewed. Results CSA constitutes approximately 10% of officially substantiated child maltreatment cases, numbering approximately 88,000 in 2000. Adjusted prevalence rates are 16.8% and 7.9% for adult women and men, respectively. Risk factors include gender, age, disabilities, and parental dysfunction. A range of symptoms and disorders has been associated with CSA, but depression in adults and sexualized behaviors in children are the best-documented outcomes. To date, cognitive-behavioral therapy (CBT) of the child and a nonoffending parent is the most effective treatment. Prevention efforts have focused on child education to increase awareness and home visitation to decrease risk factors. Conclusions CSA is a significant risk factor for psychopathology, especially depression and substance abuse. Preliminary research indicates that CBT is effective for some symptoms, but longitudinal follow-up and large-scale "effectiveness" studies are needed. Prevention programs have promise, but evaluations to date are limited.


title: A U-shaped layout for manual order-picking systems

abstract: In manual order-picking systems, order pickers walk or ride through a warehouse in order to collect items required by customers. When designing such a warehouse, two conflicting goals have to be considered, namely the maximization of space utilization and the minimization of the average length of the tours on which the requested items are collected. The achievement of these goals is significantly dependent on the layout of the warehouse. Whereas in a traditional warehouse layout, the picking aisles run in parallel to each other, the picking aisles in the warehouse layout presented here are arranged around a U-shaped central aisle. This type of layout, so-called U-shaped layout, provides a kind of compromise solution with respect to the above-mentioned goals. It has recently been implemented for spare-parts warehouses in practice, but has not been studied in the scientific literature so far. In this paper, we present a new routing strategy for the order pickers who operate in such a warehouse, and we derive an analytical expression for the expected tour length per picking order. By comparing this estimation with those of routing schemes for traditional warehouse layouts, it is demonstrated under which conditions such U-shaped layouts allow for operating warehouses more efficiently.


title: A 12.8 GS/s Time-Interleaved ADC With 25 GHz Effective Resolution Bandwidth and 4.6 ENOB

abstract: This paper presents a 12.8 GS/s 32-way hierarchically time-interleaved SAR ADC with 4.6 ENOB in 65 nm CMOS. The prototype utilizes hierarchical sampling and cascode sampler circuits to enable greater than 25 GHz 3 dB effective resolution bandwidth (ERBW). We further employ a pseudo-differential SAR ADC to save power and area. The core circuit occupies only 0.23 mm 2 and consumes a total of 162 mW from dual 1.2 V/1.1 V supplies. The design achieves a SNDR of 29.4 dB at low frequencies and 26.4 dB at 25 GHz, resulting in a figure-of-merit of 0.79 pJ/conversion-step. As will be further described in the paper, the circuit architecture used in this prototype enables expansion to 25.6 GS/s or 51.2 GS/s via additional interleaving without significantly impacting ERBW.


Sexual health promotion.


title: Association between Puberty and Delayed Phase Preference

abstract: Summary: Many teenagers go to bed and wake up significantly later than younger children, a developmental progression thought to reflect adolescent psychosocial processes. To determine whether biological processes may underlie a delay of phase preference in adolescents, 183 sixth-grade boys and 275 sixth-grade girls completed questionnaires for morningness/eveningness (M/E) and pubertal status. School environment and birth order were also evaluated. A significant relationship of pubertal status to M/E was found in girls, with a similar though nonsignificant trend in boys. No relationship between M/E and psychosocial factors was found. These data support involvement of a biological factor in the adolescent phase preference delay and indicate that our current under standing of adolescent sleep patterns may need revision. Key Words: Adolescent sleep patterns-Delayed phase preference- Puberty - Circadian rhythms.


title: Brand relationships and brand equity in franchising

abstract: Abstract Previous research suggests that building brand equity enhances the competitive advantage of retailers in B2B markets. However, limited attention has been paid to the concept of brand equity in B2B retailing contexts, particularly in franchise channels. This study seeks to understand how brand relationships can be leveraged to enhance brand citizenship behavior and ultimately brand equity in franchise channels. Accordingly, this study explores franchisees’ perceptions of their franchise brands, leading to a new conceptualisation of ‘franchisee-based brand equity’ . An interpretive research design is employed, comprising of semi-structured interviews with key informants. Findings suggest that franchisors play an important role in promoting brand citizenship behaviour of franchisees, which in turn enhances brand equity. The study provides insight on how to effectively manage brand relationships to enhance franchisees’ brand citizenship behaviour and brand equity. The concept of brand relationships has been discussed widely in consumer markets, but has received limited attention in B2B contexts. In response, this study provides new insight in B2B branding and specifically, in how brand relationships may enhance brand citizenship behaviour and brand equity in B2B markets.


title: Unsupervised Word Segmentation for Sesotho Using Adaptor Grammars

abstract: This paper describes a variety of non-parametric Bayesian models of word segmentation based on Adaptor Grammars that model different aspects of the input and incorporate different kinds of prior knowledge, and applies them to the Bantu language Sesotho. While we find overall word segmentation accuracies lower than these models achieve on English, we also find some interesting differences in which factors contribute to better word segmentation. Specifically, we found little improvement to word segmentation accuracy when we modeled contextual dependencies, while modeling morphological structure did improve segmentation accuracy.


title: Multi-objective Moth Flame Optimization

abstract: Mirjalili in 2015, proposed a new nature-inspired meta-heuristic Moth Flame Optimization (MFO). It is inspired by the characteristics of a moth in the dark night to either fly straight towards the moon or fly in a spiral path to arrive at a nearby artificial light source. It aims to reach a brighter destination which is treated as a global solution for an optimization problem. In this paper, the original MFO is suitably modified to handle multi-objective optimization problems termed as MOMFO. Typically concepts like the introduction of archive grid, coordinate based distance for sorting, non-dominance of solutions make the proposed approach different from the original single objective MFO. The performance of proposed MOMFO is demonstrated on six benchmark mathematical function optimization problems regarding superior accuracy and lower computational time achieved compared to Non-dominated sorting genetic algorithm-II (NSGA-II) and Multi-objective particle swarm optimization (MOPSO).


title: A utility framework for the automatic generation of audio-visual skims

abstract: In this paper, we present a novel algorithm for generating audio-visual skims from computable scenes. Skims are useful for browsing digital libraries, and for on-demand summaries in set-top boxes. A computable scene is a chunk of data that exhibits consistencies with respect to chromaticity, lighting and sound. There are three key aspects to our approach: (a) visual complexity and grammar, (b) robust audio segmentation and (c) an utility model for skim generation. We define a measure of visual complexity of a shot, and map complexity to the minimum time for comprehending the shot. Then, we analyze the underlying visual grammar, since it makes the shot sequence meaningful. We segment the audio data into four classes, and then detect significant phrases in the speech segments. The utility functions are defined in terms of complexity and duration of the segment. The target skim is created using a general constrained utility maximization procedure that maximizes the information content and the coherence of the resulting skim. The objective function is constrained due to multimedia synchronization constraints, visual syntax and by penalty functions on audio and video segments. The user study results indicate that the optimal skims show statistically significant differences with other skims with compression rates up to 90%.


title: LINGER – A SMART PERSONAL ASSISTANT FOR E-MAIL CLASSIFICATION

abstract: In this paper we present Linger a neural network based system for automated e-mail classification. Two scenarios are explored: filing e-mails into folders and spam e-mail filtering. Extensive experiments indicate that Linger compares favourably to other classification techniques. We study the effects of various feature selection, weighting and normalization methods, and also the portability of the anti-spam filter across different users.


title: Improvements on Cross-Validation: The 632+ Bootstrap Method

abstract: Abstract A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? This is an important question both for comparing models and for assessing a final selected model. The traditional answer to this question is given by cross-validation. The cross-validation estimate of prediction error is nearly unbiased but can be highly variable. Here we discuss bootstrap estimates of prediction error, which can be thought of as smoothed versions of cross-validation. We show that a particular bootstrap method, the .632+ rule, substantially outperforms cross-validation in a catalog of 24 simulation experiments. Besides providing point estimates, we also consider estimating the variability of an error rate estimate. All of the results here are nonparametric and apply to any possible prediction rule; however, we study only classification problems with 0–1 loss in detail. Our simulations include “smooth” prediction rules like Fisher's linear discriminant fun...


Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques


title: HPAT: high performance analytics with scripting ease-of-use

abstract: Big data analytics requires high programmer productivity and high performance simultaneously on large-scale clusters. However, current big data analytics frameworks (e.g. Apache Spark) have prohibitive runtime overheads since they are library-based. We introduce a novel auto-parallelizing compiler approach that exploits the characteristics of the data analytics domain such as the map/reduce parallel pattern and is robust, unlike previous auto-parallelization methods. Using this approach, we build High Performance Analytics Toolkit (HPAT), which parallelizes high-level scripting (Julia) programs automatically, generates efficient MPI/C++ code, and provides resiliency. Furthermore, it provides automatic optimizations for scripting programs, such as fusion of array operations. Thus, HPAT is 369X to 2033X faster than Spark on the Cori supercomputer and 20X to 256X times on Amazon AWS.


title: Real-time EEG-based emotion recognition and its applications

abstract: Since emotions play an important role in the daily life of human beings, the need and importance of automatic emotion recognition has grown with increasing role of human computer interface applications. Emotion recognition could be done from the text, speech, facial expression or gesture. In this paper, we concentrate on recognition of "inner" emotions from electroencephalogram (EEG) signals. We propose real-time fractal dimension based algorithm of quantification of basic emotions using Arousal-Valence emotion model. Two emotion induction experiments with music stimuli and sound stimuli from International Affective Digitized Sounds (IADS) database were proposed and implemented. Finally, the real-time algorithm was proposed, implemented and tested to recognize six emotions such as fear, frustrated, sad, happy, pleasant and satisfied. Real-time applications were proposed and implemented in 3D virtual environments. The user emotions are recognized and visualized in real time on his/her avatar adding one more so-called "emotion dimension" to human computer interfaces. An EEG-enabled music therapy site was proposed and implemented. The music played to the patients helps them deal with problems such as pain and depression. An EEG-based web-enable music player which can display the music according to the user's current emotion states was designed and implemented.


title: Longest common subsequence problem for unoriented and

abstract: Given a finite set of strings X, the Longest Common Subsequence problem (LCS) consists in finding a subsequence common to all strings in X that is of maximal length. LCS is a central problem in stringology and finds broad applications in text compression, conception of error-detecting codes, or biological sequence comparison. However, in numerous contexts, words represent cyclic or unoriented sequences of symbols and LCS must be generalized to consider both orientations and/or all cyclic shifts of the strings involved. This occurs especially in computational biology when genetic material is sequenced from circular DNA or RNA molecules. In this work, we define three variants of LCS when the input words are unoriented and/or cyclic. We show that these problems are NP-hard, and W[1]-hard if parameterized in the number of input strings. These results still hold even if the three LCS variants are restricted to input languages over a binary alphabet. We also settle the parameterized complexity of our problems for most relevant parameters. Moreover, we study the approximability of these problems: we discuss the existence of approximation bounds depending on the cardinality of the alphabet, on the length of the shortest sequence, and on the number of input sequences. For this we prove that Maximum Independent Set in r-uniform hypergraphs is W[1]-hard if parameterized in the cardinality of the sought independent set and at least as hard to approximate as Maximum Independent Set in graphs.


Relationship of cysteine and tyrosine residues to adenosine triphosphate hydrolysis by mitochondrial adenine triphosphatase.


title: Fraud detection in health insurance using data mining techniques

abstract: Fraud is widespread and very costly to the healthcare insurance system. Fraud involves intentional deception or misrepresentation intended to result in an unauthorized benefit. It is shocking because the incidence of health insurance fraud keeps increasing every year. In order to detect and avoid the fraud, data mining techniques are applied. This includes some preliminary knowledge of health care system and its fraudulent behaviors, analysis of the characteristics of health care insurance data. Data mining which is divided into two learning techniques viz., supervised and unsupervised is employed to detect fraudulent claims. But, since each of the above techniques has its own set of advantages and disadvantages, by combining the advantages of both the techniques, a novel hybrid approach for detecting fraudulent claims in health insurance industry is proposed.


title: Learning User Attributes via Mobile Social Multimedia Analytics

abstract: Learning user attributes from mobile social media is a fundamental basis for many applications, such as personalized and targeting services. A large and growing body of literature has investigated the user attributes learning problem. However, far too little attention has been paid to jointly consider the dual heterogeneities of user attributes learning by harvesting multiple social media sources. In particular, user attributes are complementarily and comprehensively characterized by multiple social media sources, including footprints from Foursqare, daily updates from Twitter, professional careers from Linkedin, and photo posts from Instagram. On the other hand, attributes are inter-correlated in a complex way rather than independent to each other, and highly related attributes may share similar feature sets. Towards this end, we proposed a unified model to jointly regularize the source consistency and graph-constrained relatedness among tasks. As a byproduct, it is able to learn the attribute-specific and attribute-sharing features via graph-guided fused lasso penalty. Besides, we have theoretically demonstrated its optimization. Extensive evaluations on a real-world dataset thoroughly demonstrated the effectiveness of our proposed model.


title: An evaluation of Heart Rate and ElectroDermal Activity as an objective QoE evaluation method for immersive virtual reality environments

abstract: Recently, we have seen an emergence of affordable Head Mounted Displays (HMD) such as the Oculus Rift, HTC Vive, and the PS4 Project Morpheus which allow users to experience 3D virtual reality (VR). These types of hardware aim to facilitate new and novel experiences for users above and beyond what is possible with traditional audiovisual displays. However, a very limited number of studies exist in the literature to determine the influence of these technologies on user Quality of Experience (QoE). In order to evaluate QoE as users consume VR content, this paper proposes the use of affordable consumer electronics to capture objective physiological metrics: Heart Rate (HR) and ElectroDermal Activity (EDA). Our findings indicate different HR and EDA dependent on VR and non-VR environments. Additionally, we examine the relationship between these objective metrics and user QoE captured via a post-test questionnaire. To the best of the authors knowledge, this is the first work which demonstrates a tangible relationship between the EDA/HR combination and user QoE of immersive VR environments.


title: Finding the Linchpins of the Dark Web: a Study on Topologically Dedicated Hosts on Malicious Web Infrastructures

abstract: Malicious Web activities continue to be a major threat to the safety of online Web users. Despite the plethora forms of attacks and the diversity of their delivery channels, in the back end, they are all orchestrated through malicious Web infrastructures, which enable miscreants to do business with each other and utilize others' resources. Identifying the linchpins of the dark infrastructures and distinguishing those valuable to the adversaries from those disposable are critical for gaining an upper hand in the battle against them. In this paper, using nearly 4 million malicious URL paths crawled from different attack channels, we perform a large-scale study on the topological relations among hosts in the malicious Web infrastructure. Our study reveals the existence of a set of topologically dedicated malicious hosts that play orchestrating roles in malicious activities. They are well connected to other malicious hosts and do not receive traffic from legitimate sites. Motivated by their distinctive features in topology, we develop a graph-based approach that relies on a small set of known malicious hosts as seeds to detect dedicate malicious hosts in a large scale. Our method is general across the use of different types of seed data, and results in an expansion rate of over 12 times in detection with a low false detection rate of 2%. Many of the detected hosts operate as redirectors, in particular Traffic Distribution Systems (TDSes) that are long-lived and receive traffic from new attack campaigns over time. These TDSes play critical roles in managing malicious traffic flows. Detecting and taking down these dedicated malicious hosts can therefore have more impact on the malicious Web infrastructures than aiming at short-lived doorways or exploit sites.


title: Wavelet Based Authentication/Secret Transmission Through Image Resizing(WASTIR)

abstract: The paper is aimed for a wavelet based steganographic or watermarking technique in frequency domain termed as WASTIR for secret message or image transmission or image authentication. Number system conversion of the secret image by changing radix form decimal to quaternary is the pre-processing of the technique. Cover image scaling through inverse discrete wavelet transformation with false Horizontal and vertical coefficients are embedded with quaternary digits through hash function and a secret key. Experimental results are computed and compared with the existing steganographic techniques like WTSIC, Yuancheng Lis Method and Region-Based in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Image Fidelity (IF) which show better performances in WASTIR.


title: Oscillometric Blood Pressure Estimation Based on Maximum Amplitude Algorithm Employing Gaussian Mixture Regression

abstract: This paper introduces a novel approach to estimate the systolic and diastolic blood pressure ratios (SBPR and DBPR) based on the maximum amplitude algorithm (MAA) using a Gaussian mixture regression (GMR). The relevant features, which clearly discriminate the SBPR and DBPR according to the targeted groups, are selected in a feature vector. The selected feature vector is then represented by the Gaussian mixture model. The SBPR and DBPR are subsequently obtained with the help of the GMR and then mapped back to SBP and DBP values that are more accurate than those obtained with the conventional MAA method.


title: Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations

abstract: We investigate techniques for visualizing time series data and evaluate their effect in value comparison tasks. We compare line charts with horizon graphs - a space-efficient time series visualization technique - across a range of chart sizes, measuring the speed and accuracy of subjects' estimates of value differences between charts. We identify transition points at which reducing the chart height results in significantly differing drops in estimation accuracy across the compared chart types, and we find optimal positions in the speed-accuracy tradeoff curve at which viewers performed quickly without attendant drops in accuracy. Based on these results, we propose approaches for increasing data density that optimize graphical perception.


title: Identifying Mislabeled Training Data

abstract: This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30%. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data.


title: How we BLESSed distributional semantic evaluation

abstract: We introduce BLESS, a data set specifically designed for the evaluation of distributional semantic models. BLESS contains a set of tuples instantiating different, explicitly typed semantic relations, plus a number of controlled random tuples. It is thus possible to assess the ability of a model to detect truly related word pairs, as well as to perform in-depth analyses of the types of semantic relations that a model favors. We discuss the motivations for BLESS, describe its construction and structure, and present examples of its usage in the evaluation of distributional semantic models.


title: Normative Beliefs About Aggression as a Mediator of Narcissistic Exploitativeness and Cyberbullying

abstract: The current study examined normative beliefs about aggression as a mediator between narcissistic exploitativeness and cyberbullying using two Asian adolescent samples from Singapore and Malaysia. Narcissistic exploitativeness was significantly and positively associated with cyberbullying and normative beliefs about aggression and normative beliefs about aggression were significantly and positively associated with cyberbullying. Normative beliefs about aggression were a significant partial mediator in both samples; these beliefs about aggression served as one possible mechanism of action by which narcissistic exploitativeness could exert its influence on cyberbullying. Findings extended previous empirical research by showing that such beliefs can be the mechanism of action not only in offline but also in online contexts and across cultures. Cyberbullying prevention and intervention efforts should include modification of norms and beliefs supportive of the legitimacy and acceptability of cyberbullying.


title: Event-based 3D SLAM with a depth-augmented dynamic vision sensor

abstract: o We present the D-eDVSn a combined event-based 3D sensor n and a novel event-based full-3D simultaneous lo- calization and mapping algorithm which works exclusively with the sparse stream of visual data provided by the D-eDVS. The D-eDVS is a combination of the established PrimeSense RGB- D sensor and a biologically inspired embedded dynamic vision sensor. Dynamic vision sensors only react to dynamic contrast changes and output data in form of a sparse stream of events which represent individual pixel locations. We demonstrate how an event-based dynamic vision sensor can be fused with a classic frame-based RGB-D sensor to produce a sparse stream of depth-augmented 3D points. The advantages of a sparse, event-based stream are a much smaller amount of generated data, thus more efcient resource usage, and a continuous representation of motion allowing lag-free tracking. Our event- based SLAM algorithm is highly efcient and runs 20 times faster than realtime, provides localization updates at several hundred Hertz, and produces excellent results. We compare our method against ground truth from an external tracking system and two state-of-the-art algorithms on a new dataset which we release in combination with this paper.


title: On Scaling Decentralized Blockchains

abstract: The increasing popularity of blockchain-based cryptocurrencies has made scalability a primary and urgent concern. We analyze how fundamental and circumstantial bottlenecks in Bitcoin limit the ability of its current peer-to-peer overlay network to support substantially higher throughputs and lower latencies. Our results suggest that reparameterization of block size and intervals should be viewed only as a first increment toward achieving next-generation, high-load blockchain protocols, and major advances will additionally require a basic rethinking of technical approaches. We offer a structured perspective on the design space for such approaches. Within this perspective, we enumerate and briefly discuss a number of recently proposed protocol ideas and offer several new ideas and open challenges.


title: Induction vs. synchronous motors — Different perspectives end user/motor manufacturer

abstract: This paper will review the many factors involved in the successful selection and operation of medium voltage (2300V and above) induction and synchronous motors for use in the petrochemical industry. Among these are the power requirements of the driven load, start-up and normal operating conditions, and plant power restrictions. In addition, consideration must be given to maintenance requirements, necessary training and skills for site personnel, and the ability of the manufacturer to provide services to maximize the reliability of the machine. These areas will be discussed to give readers a better understanding of the subject, and field experiences from multiple petrochemical plants will be reviewed.


title: Learning physical parameters from dynamic scenes

abstract: Abstract Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum, Kemp, Griffiths, & Goodman, 2011), we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultaneously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space.


title: Feature Propagation on Graph: A New Perspective to Graph Representation Learning

abstract: We study feature propagation on graph, an inference process involved in graph representation learning tasks. It's to spread the features over the whole graph to the $t$-th orders, thus to expand the end's features. The process has been successfully adopted in graph embedding or graph neural networks, however few works studied the convergence of feature propagation. Without convergence guarantees, it may lead to unexpected numerical overflows and task failures. In this paper, we first define the concept of feature propagation on graph formally, and then study its convergence conditions to equilibrium states. We further link feature propagation to several established approaches such as node2vec and structure2vec. In the end of this paper, we extend existing approaches from represent nodes to edges (edge2vec) and demonstrate its applications on fraud transaction detection in real world scenario. Experiments show that it is quite competitive.


title: DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis

abstract: Deep generative modelling for robust human body analysis is an emerging problem with many interesting applications, since it enables analysis-by-synthesis and unsupervised learning. However, the latent space learned by such models is typically not human-interpretable, resulting in less flexible models. In this work, we adopt a structured semi-supervised variational auto-encoder approach and present a deep generative model for human body analysis where the pose and appearance are disentangled in the latent space, allowing for pose estimation. Such a disentanglement allows independent manipulation of pose and appearance and hence enables applications such as pose-transfer without being explicitly trained for such a task. In addition, the ability to train in a semi-supervised setting relaxes the need for labelled data. We demonstrate the merits of our generative model on the Human3.6M and ChictopiaPlus datasets.


title: Machine Learning for the Geosciences: Challenges and Opportunities

abstract: Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, machine learning (ML) -- that has been widely successful in commercial domains -- offers immense potential to contribute to problems in geosciences. However, problems in geosciences have several unique challenges that are seldom found in traditional applications, requiring novel problem formulations and methodologies in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by geoscience problems and the opportunities that exist for advancing both machine learning and geosciences. We first highlight typical sources of geoscience data and describe their properties that make it challenging to use traditional machine learning techniques. We then describe some of the common categories of geoscience problems where machine learning can play a role, and discuss some of the existing efforts and promising directions for methodological development in machine learning. We conclude by discussing some of the emerging research themes in machine learning that are applicable across all problems in the geosciences, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines.


title: Optical constants of amorphous silicon films near the main absorption edge

abstract: The spectral dependence of the optical constants of amorphous silicon films near the main absorption edge shows that the energy band structure of crystalline silicon is essentially conserved after amorphization, but the absorption coefficient corresponding to indirect transitions is much increased. This is attributed to the structural disorder reigning in amorphous silicon.


title: 3D beamforming for wireless data centers

abstract: Contrary to prior assumptions, recent measurements show that data center traffic is not constrained by network bisection bandwidth, but is instead prone to congestion loss caused by short traffic bursts. Compared to the cost and complexity of modifying data center architectures, a much more attractive option is to augment wired links with flexible wireless links in the 60 GHz band. Current proposals, however, are severely constrained by two factors. First, 60 GHz wireless links are limited by line-of-sight, and can be blocked by even small obstacles between the endpoints. Second, even beamforming links leak power, and potential interference will severely limit concurrent transmissions in dense data centers. In this paper, we explore the feasibility of a new wireless primitive for data centers, 3D beamforming. We explore the design space, and show how bouncing 60 GHz wireless links off reflective ceilings can address both link blockage and link interference, thus improving link range and number of current transmissions in the data center.


title: Flying Eyes and Hidden Controllers: A Qualitative Study of People’s Privacy Perceptions of Civilian Drones in The US

abstract: Abstract Drones are unmanned aircraft controlled remotely or operated autonomously. While the extant literature suggests that drones can in principle invade people’s privacy, little is known about how people actually think about drones. Drawing from a series of in-depth interviews conducted in the United States, we provide a novel and rich account of people’s privacy perceptions of drones for civilian uses both in general and under specific usage scenarios. Our informants raised both physical and information privacy issues against government, organization and individual use of drones. Informants’ reasoning about the acceptance of drone use was in part based on whether the drone is operating in a public or private space. However, our informants differed significantly in their definitions of public and private spaces. While our informants’ privacy concerns such as surveillance, data collection and sharing have been raised for other tracking technologies such as camera phones and closed-circuit television (CCTV), our interviews highlight two heightened issues of drones: (1) powerful yet inconspicuous data collection, (2) hidden and inaccessible drone controllers. These two aspects of drones render some of people’s existing privacy practices futile (e.g., notice recording and ask controllers to stop or delete the recording). Some informants demanded notifications of drones near them and expected drone controllers asking for their explicit permissions before recording. We discuss implications for future privacy-enhancing drone designs.


title: Using support vector machines to detect medical fraud and abuse

abstract: This paper examines the architecture and efficacy of Quash, an automated medical bill processing system capable of bill routing and abuse detection. Quash is designed to be used in conjunction with human auditors and a standard bill review software platform to provide a complete cost containment solution for medical claims. The primary contribution of Quash is to provide a real world speed up for medical fraud detection experts in their work. There will be a discussion of implementation details and preliminary experimental results. In this paper we are entirely focused on medical data and billing patterns that occur within the United States, though these results should be applicable to any financial transaction environment in which structured coding data can be mined.


title: Real-time taxi dispatching using Global Positioning Systems

abstract: Hailing a taxi in Singapore now employs the latest positioning technology for matching passengers with the nearest available cabs, thus achieving greater productivity and customer satisfaction.


2nd European conference on computer vision


title: Slugbot: An Application of a Novel and Scalable Open Domain Socialbot Framework

abstract: In this paper we introduce a novel, open domain socialbot for the Amazon Alexa Prize competition, aimed at carrying on friendly conversations with users on a variety of topics. We present our modular system, highlighting our different data sources and how we use the human mind as a model for data management. Additionally we build and employ natural language understanding and information retrieval tools and APIs to expand our knowledge bases. We describe our semistructured, scalable framework for crafting topic-specific dialogue flows, and give details on our dialogue management schemes and scoring mechanisms. Finally we briefly evaluate the performance of our system and observe the challenges that an open domain socialbot faces.


title: An efficient FPGA implementation of Optical Character Recognition for License Plate Recognition

abstract: Optical Character Recognition system (OCR) can be used in intelligent transportation systems for license plate detection. However, most times the systems are unable to work with noisy and imperfect images. In this work, a robust FPGA-based OCR system has been designed and tested with imperfect and noisy license plate images. The OCR system is based on a feedforward neural networks, which uses an efficient and precise neuron. The neuron transfer function is based on an approximation of the Hyperbolic Tangent Activation Function. The neuron is utilized in a 189 − 160 − 36 feed forward neural network configuration. The network parameters were optimized and then tested with noisy images of license plates numbers. The network was able to maintain a 98.2% accuracy in recognizing the characters despite the image imperfections.


title: An integrated closed-loop polar transmitter with saturation prevention and low-IF receiver for quad-band GPRS/EDGE

abstract: This paper describes a 2.5G cellular transceiver with standard DigRF interface, implemented in a low-cost 0.13µm CMOS process. This is the first CMOS single-chip, polar closed-loop transmitter, excluding the power amplifier (PA). This transmitter achieves an efficiency of 26% in EDGE mode by linearizing a saturated PA in a closed-loop feedback system. This is much higher than the typical 15-to-18% efficiency of systems using a linear PA [1]. Typical high-band/ low-band performance of −62/−64dBc in 30kHz for 400kHz offset spectral mask and 1.4/1.7% EVM in EDGE mode are significantly better than those reported earlier [1–3]. Using a low-noise quadrature mixer topology, the receiver, including T/R switch and SAW filters, achieves a sensitivity of −110dBm. The RF solution, consisting of PAs in a multi-chip integrated antenna switch module, SAW filters with integrated matching components and the transceiver, shown in Fig. 6.1.1, is the smallest form factor available in the market today.


title: High-speed TCP flow record extraction using GPUs

abstract: Traffic analysis is an essential part of capacity planning, quality of service assurance, and reinforcement of security in current telecommunication networks. Traffic volume increases with network speed and the analysis of large traffic traces is computationally intensive. The paper presents, for the first time ever, a flow extraction software that allows to obtain complex TCP-aware flow records at 4.4 millions of packets per second in a single GPU. Such TCP flow records include number of retransmissions and duplicates per flow, whose calculation is very challenging to obtain at high-speed. Our software significantly increases the processing performance of the recently proposed high-speed sniffers based on commodity hardware and demonstrates the advantages of applying massively parallel processing devices for traffic analysis.


title: Cost-sensitive label embedding for multi-label classification

abstract: Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of interest. Current LE algorithms often aim to optimize one specific cost function, but they can suffer from bad performance with respect to other cost functions. In this paper, we resolve the performance issue by proposing a novel cost-sensitive LE algorithm that takes the cost function of interest into account. The proposed algorithm, cost-sensitive label embedding with multidimensional scaling (CLEMS), approximates the cost information with the distances of the embedded vectors by using the classic multidimensional scaling approach for manifold learning. CLEMS is able to deal with both symmetric and asymmetric cost functions, and effectively makes cost-sensitive decisions by nearest-neighbor decoding within the embedded vectors. We derive theoretical results that justify how CLEMS achieves the desired cost-sensitivity. Furthermore, extensive experimental results demonstrate that CLEMS is significantly better than a wide spectrum of existing LE algorithms and state-of-the-art cost-sensitive algorithms across different cost functions.


title: A Primer in Game Theory

abstract: Static games of complete information dynamic games of complete information static games of incomplete information dynamic games of incomplete information.


title: Who Spread That Rumor: Finding the Source of Information in Large Online Social Networks With Probabilistically Varying Internode Relationship Strengths

abstract: We address the problem of estimating the source of a rumor in large-scale social networks. Previous works studying this problem have mainly focused on graph models with deterministic and homogenous internode relationship strengths. However, internode relationship strengths in real social networks are random. We model this uncertainty by using random, nonhomogenous edge weights on the underlying social network graph. We propose a novel two-stage algorithm that uses the modularity of the social network to locate the source of the rumor with fewer sensor nodes than other existing algorithms. We also propose a novel method to select these sensor nodes. We evaluate our algorithm using a large data set from Twitter and Sina Weibo. Real-world time series data are used to model the uncertainty in social relationship strengths. Simulations show that the proposed algorithm can determine the actual source within two hops, 69%–80% of the time, when the diameter of the networks varies between 7 and 13. Our numerical results also show that it is easier to estimate the source of a rumor when the source has higher betweenness centrality. Finally, we demonstrate that our two-stage algorithm outperforms the alternative algorithm in terms of the accuracy of localizing the source.


title: Programming full-body movements for humanoid robots by observation

abstract: Abstract The formulation and optimization of joint trajectories for humanoid robots is quite different from this same task for standard robots because of the complexity of humanoid robots’ kinematics and dynamics. In this paper we exploit the similarity between human motion and humanoid robot motion to generate joint trajectories for humanoids. In particular, we show how to transform human motion information captured by an optical tracking device into a high dimensional trajectory for a humanoid robot. We propose an automatic approach to relate humanoid robot kinematic parameters to the kinematic parameters of a human performer. Based on this relationship we infer the desired trajectories in robot joint space. B-spline wavelets are utilized to efficiently represent the trajectories. The density of the basis functions on the time axis is selected automatically. Large-scale optimization techniques are employed to solve the underlying computational problems efficiently. We applied our method to the task of teaching a humanoid robot how to make various naturally looking movements.


title: Action language BC+

abstract: Action languages are formal models of parts of natural language that are designed to describe effects of actions. Many of these languages can be viewed as high level notations of answer set programs structured to represent transition systems. However, the form of answer set programs considered in the earlier work is quite limited in comparison with the modern Answer Set Programming (ASP) language, which allows several useful constructs for knowledge representation, such as choice rules, aggregates, and abstract constraint atoms. We propose a new action language called BC+, which closes the gap between action languages and the modern ASP language. The main idea is to define the semantics of BC+ in terms of general stable model semantics for propositional formulas, under which many modern ASP language constructs can be identified with shorthands for propositional formulas. Language BC+ turns out to be sufficiently expressive to encompass the best features of other action languages, such as languages B, C, C+, and BC. Computational methods available in ASP solvers are readily applicable to compute BC+, which led to an implementation of the language by extending system CPLUS2ASP.


Nested LSTM : Modeling Taxonomy and Temporal Dynamics in Location-Based Social Network