title: Exchange rate forecasting using echo state networks for trading strategies

abstract: Because of the diversity of portfolios based on assets throughout international markets, exchange rate prediction plays an important role in risk management, asset allocation, and trading strategies. This paper aims to investigate the use of a recent paradigm of recurrent neural networks, echo state networks (ESNs), applied to forecasting and trading currency exchange rates. It does so by benchmarking the statistical and trading performance of ESNs against a naive strategy, an Autoregressive Moving Average (ARMA) model, and a multilayer perceptron neural network. One can interpret ESNs as a recurrent structure that provides both the simplicity of the resulting mathematical model and the ability to express a wide range of nonlinear and time-varying dynamics. As an application, this paper carries out computational experiments that include the Brazilian Real, the European Union Euro, the Japanese Yen, and British Pounds with American Dollar exchange rates from January 4, 2000, through December 31, 2012. The results reveal that the ESN and the ARMA model provide similar results, statistically outperforming the other models in terms of accuracy. However, when trading indicators are considered, the performance of the ESN is superior to that of the alternative approaches.


title: Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis

abstract: In hyperspectral image analysis, the principal components analysis (PCA) and the maximum noise fraction (MNF) are most commonly used techniques for dimensionality reduction (DR), referred to as PCA-DR and MNF-DR, respectively. The criteria used by the PCA-DR and the MNF-DR are data variance and signal-to-noise ratio (SNR) which are designed to measure data second-order statistics. This paper presents an independent component analysis (ICA) approach to DR, to be called ICA-DR which uses mutual information as a criterion to measure data statistical independency that exceeds second-order statistics. As a result, the ICA-DR can capture information that cannot be retained or preserved by second-order statistics-based DR techniques. In order for the ICA-DR to perform effectively, the virtual dimensionality (VD) is introduced to estimate number of dimensions needed to be retained as opposed to the energy percentage that has been used by the PCA-DR and MNF-DR to determine energies contributed by signal sources and noise. Since there is no prioritization among components generated by the ICA-DR due to the use of random initial projection vectors, we further develop criteria and algorithms to measure the significance of information contained in each of ICA-generated components for component prioritization. Finally, a comparative study and analysis is conducted among the three DR techniques, PCA-DR, MNF-DR, and ICA-DR in two applications, endmember extraction and data compression where the proposed ICA-DR has been shown to provide advantages over the PCA-DR and MNF-DR.


title: Microcephaly, retinal dysplasia, pedal edema, mental retardation, and short stature.

abstract: Microcephaly, retinal dysplasia, pedal edema syndrome is a rare syndrome and possibly under diagnosed. We could find less than 25 cases reported in the literature. Patients were initially categorized as having either microcephaly and lymphedema or microcephaly and chorioretinal dysplasia. The existence of the 3 criteria in the same patients is reported. Other features such as mental retardation and short stature were noticed in other patients. In the Pediatric Department of the Armed Forces Hospital Southern Region, Kingdom of Saudi Arabia, we report a case with all clinical manifestations described in the above-related syndromes. The girl has microcephaly, retinal dysplasia, pedal edema, short stature, mental retardation, and some other dysmorphic features. The parents are not relatives, but both have retinal dysplastic changes. This report documents the existence of all different features reported in the literature in one patient, suggesting that different clinical features of reported patients are possibly the variable expression of the same syndrome.


title: Dream It, Design It, Print It in 3-D: What Can 3-D Printing Do for You?

abstract: So maybe we are not all driving to work in a flying car or are being beamed up by a transporter yet, but we should be clearing out a space in the lab and perhaps in the office for a replicator. Sure, they may not work the same as the replicators on Star Trek that instantaneously pop out hot, full-course meals to suit any crew member's whim, but they can generate three-dimensional (3-D) objects to match your computer design.


title: Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

abstract: Model-free deep reinforcement learning methods have successfully learned complex behavioral strategies for a wide range of tasks, but typically require many samples to achieve good performance. Model-based algorithms, in principle, can provide for much more efficient learning, but have proven difficult to extend to expressive, high-capacity models such as deep neural networks. In this work, we demonstrate that medium-sized neural network models can in fact be combined with model predictive control to achieve excellent sample complexity in a model-based reinforcement learning algorithm, producing stable and plausible gaits to accomplish various complex locomotion tasks. We also propose using deep neural network dynamics models to initialize a model-free learner, in order to combine the sample efficiency of model-based approaches with the high task-specific performance of model-free methods. We perform this pre-initialization by using rollouts from the trained model-based controller as supervision to pre-train a policy, and then fine-tune the policy using a model-free method. We empirically demonstrate that this resulting hybrid algorithm can drastically accelerate model-free learning and outperform purely model-free learners on several MuJoCo locomotion benchmark tasks, achieving sample efficiency gains over a purely model-free learner of 330x on swimmer, 26x on hopper, 4x on half-cheetah, and 3x on ant. Videos can be found at https://sites.google.com/view/mbmf


title: Dynamic inductive power transfer lane design for e-bikes

abstract: This paper presents the concept and initial test results of an inductive lane design capable of dynamic and wirelessly transfer power to electric bicycles (e-bikes). On the lane side, a sequence of oblong primary coils embedded underneath ground surface, along the vehicle path, can be independently excited by high frequency alternating current. The oscillating magnetic field of each primary coil is individually enabled when a Radio Frequency Identification (RFID) tag on board of the e-bike is detected and authenticated by an auxiliary coil laying close to that primary coil. On the e-bike, energy for the powertrain is harvested from the lane by a secondary coil that is installed around its rear wheel. When the e-bike is moving over inter-coil gaps, or anywhere away from the inductive lane, on-board power is sustained with the excess energy stored during transits over energized coils. Preliminary results from a prototyped module demonstrate the feasibility of the system, which could also be used by similarly adapted lightweight electric vehicles, such as rickshaws, electric wheel chairs and other electric personal mobility devices, favoring a new, low cost, sustainable urban modal variant.


title: PhishDef: URL Names Say It All

abstract: Phishing is an increasingly sophisticated method to steal personal user information using sites that pretend to be legitimate. In this paper, we take the following steps to identify phishing URLs. First, we carefully select lexical features of the URLs that are resistant to obfuscation techniques used by attackers. Second, we evaluate the classification accuracy when using only lexical features, both automatically and hand-selected, vs. when using additional features. We show that lexical features are sufficient for all practical purposes. Third, we thoroughly compare several classification algorithms, and we propose to use an online method (AROW) that is able to overcome noisy training data. Based on the insights gained from our analysis, we propose PhishDef, a phishing detection system that uses only URL names and combines the above three elements. PhishDef is a highly accurate method (when compared to state-of-the-art approaches over real datasets), lightweight (thus appropriate for online and client-side deployment), proactive (based on online classification rather than blacklists), and resilient to training data inaccuracies (thus enabling the use of large noisy training data).


title: A lightweight web-based case tool for sequence diagrams

abstract: This paper presents a simple lightweight Case tool to support manipulation of UML sequence diagrams. The tool is intended to have high usability to facilitate communication and discussion in the early part of the design process, where sequence diagrams are frequently used to explore and explain object interaction. The tool is web-based to maximise accessibility and use by teams, and uses a simple yet effective image tiling technology to allow manipulation of the diagrams from any web browser.


title: Twisting key absolute space for stretchy polynomial regression

abstract: This paper proposes a novel solution for compressive polynomial regression learning. The solution comes in primal and dual closed-forms similar to that of ridge regression. Essentially, the proposed solution stretches the covariance computation by a power term thereby compresses or amplifies the estimation. Our experiments on both synthetic data and real-world data show effectiveness of the proposed method for compressive learning.


title: c ○ 1997 Kluwer Academic Publishers Rollout Algorithms for Combinatorial Optimization ∗

abstract: We consider the approximate solution of discrete optimization problems using procedures that are capable of magnifying the effectiveness of any given heuristic algorithm through sequential application. In particular, we embed the problem within a dynamic programming framework, and we introduce several types of rollout algorithms, which are related to notions of policy iteration. We provide conditions guaranteeing that the rollout algorithm improves the performance of the original heuristic algorithm. The method is illustrated in the context of a machine maintenance and repair problem.


title: Approximate nearest neighbor algorithm based on navigable small world graphs

abstract: Abstract We propose a novel approach to solving the approximate k-nearest neighbor search problem in metric spaces. The search structure is based on a navigable small world graph with vertices corresponding to the stored elements, edges to links between them, and a variation of greedy algorithm for searching. The navigable small world is created simply by keeping old Delaunay graph approximation links produced at the start of construction. The approach is very universal, defined in terms of arbitrary metric spaces and at the same time it is very simple. The algorithm handles insertions in the same way as queries: by finding approximate neighbors for the inserted element and connecting it to them. Both search and insertion can be done in parallel requiring only local information from the structure. The structure can be made distributed. The accuracy of the probabilistic k-nearest neighbor queries can be adjusted without rebuilding the structure. The performed simulation for data in the Euclidean spaces shows that the structure built using the proposed algorithm has small world navigation properties with log 2 ( n ) insertion and search complexity at fixed accuracy, and performs well at high dimensionality. Simulation on a CoPHiR dataset revealed its high efficiency in case of large datasets (more than an order of magnitude less metric computations at fixed recall) compared to permutation indexes. Only 0.03% of the 10 million 208-dimensional vector dataset is needed to be evaluated to achieve 0.999 recall (virtually exact search). For recall 0.93 processing speed 2800 queries/s can be achieved on a dual Intel X5675 Xenon server node with Java implementation.


title: Decentralized Inventory sharing With Asymmetric Information.

abstract: We study the information asymmetry issues in a decentralized inventory sharing system consisting of a manufacturer and two independent retailers, who privately hold demand information, non-cooperatively place their orders, but cooperatively share inventories with each other. We find that while the manufacturer needs retailers’ mean demand and standard deviation for her wholesale price decision, each retailer only needs to know the other retailer’s demand standard deviation for his order quantity decision. However, an incentive compatibility analysis shows that retailers have incentives to share their demand information untruthfully. Although a truth-inducing scheme can be developed for a system with symmetric retailers who share information between themselves, no such scheme can be developed to ensure truth-telling to the manufacturer. Further, we develop a coordination mechanism (CIS) for the decentralized inventory sharing system, considering information asymmetry. We show that CIS coordinates the manufacturer-retailers system and leads to an all-win situation under complete information. More importantly, CIS minimizes the value of information such that each party can obtain expected profits very close to their first-best profits even under asymmetric information, hence indirectly solves the information asymmetry problem. To our knowledge, this work is the first to study decentralized inventory sharing and its coordination considering asymmetric information.


title: Synthesizing Microstrip Branch-Line Couplers With Predetermined Compact Size and Bandwidth

abstract: A new method for designing the microstrip branch- line couplers with predetermined compact size and bandwidth is proposed in this paper. With the proposed approach, the size of the quarter-wavelength transmission line in the branch-line coupler can be reduced greatly. In addition, the proposed couplers can be easily fabricated on the printed circuit board without any lumped element. A chart concludes the relationship between bandwidth and size reduction rate. It shows that open stubs with low impedance perform better than those with high impedance; moreover, the more open stubs with low impedance utilized, the broader the bandwidth will be. Furthermore, the measured frequency responses show good agreement with the theoretical results.


title: Submodular functions, matroids, and certain polyhedra

abstract: The viewpoint of the subject of matroids, and related areas of lattice theory, has always been, in one way or another, abstraction of algebraic dependence or, equivalently, abstraction of the incidence relations in geometric representations of algebra. Often one of the main derived facts is that all bases have the same cardinality. (See Van der Waerden, Section 33.)


title: (Short Paper) Developing a Smart Storage Container for a Blockchain-Based Supply Chain Application

abstract: Blockchain and Internet of Things are supposed to have big impact on logistics. This study presents a prototypical smart contract using smart storage containers in order to investigate the potential and the maturity of Blockchain and Internet of Things for logistical processes. A smart storage container is developed and connected to an Ethereum-based smart contract. The smart contract is based on a multi signature wallet of three parties to process the payment and arbitrate disagreements. Further research implications for the development of smart contracts for supply chains are derived from the experiences of this prototype study.


title: Overview of radiofrequency simulation for automotive antennas at Renault

abstract: Performance of embedded radiofrequency (RF) devices used in the automotive industry such as Remote Keyless Entry (RKE), Hand Free Systems (HFS), RADARs (Radio Detection and Ranging) for ADAS (Advanced Driver Assistance Systems) functions, TPMS (Tire Pressure Monitoring systems), FM (Frequency Modulation) and DAB (Digital Audio Broadcasting) antennas, and TCUs (Telematic Control Unit) is unavoidably modified from that awaited under ideal conditions. The most difficult part in designing and implementing those antennas is that the final specification is described according to vehicle mounting position. Nevertheless, during the development phase of a new antenna, the vehicle is often unavailable, or practically does not exist yet. Furthermore, the acceleration in the development of vehicles allows less to wait for the first vehicle prototype in order to tune the product to its environment. Therefore, it becomes mandatory to apply numerical techniques covering entire project phases. This paper shows how 3D simulation can be applied to predict the installed performance of RF antennas in their operational environment.


title: Signal and noise modeling in confocal laser scanning fluorescence microscopy

abstract: Fluorescence confocal laser scanning microscopy (CLSM) has revolutionized imaging of subcellular structures in biomedical research by enabling the acquisition of 3D time-series of fluorescently-tagged proteins in living cells, hence forming the basis for an automated quantification of their morphological and dynamic characteristics. Due to the inherently weak fluorescence, CLSM images exhibit a low SNR. We present a novel model for the transfer of signal and noise in CLSM that is both theoretically sound as well as corroborated by a rigorous analysis of the pixel intensity statistics via measurement of the 3D noise power spectra, signal-dependence and distribution. Our model provides a better fit to the data than previously proposed models. Further, it forms the basis for (i) the simulation of the CLSM imaging process indispensable for the quantitative evaluation of CLSM image analysis algorithms, (ii) the application of Poisson denoising algorithms and (iii) the reconstruction of the fluorescence signal.


title: Unsupervised Deep Learning: A Short Review

abstract: Abstract Deep neural networks with several layers have recently become a highly successful and popular research topic in machine learning due to their excellent performance in many benchmark problems and applications. A key idea in deep learning is to learn not only the nonlinear mapping between the inputs and outputs but also the underlying structure of the data (input) vectors. In this chapter, we first consider problems with training deep networks using backpropagation-type algorithms. After this, we consider various structures used in deep learning, including restricted Boltzmann machines, deep belief networks, deep Boltzmann machines, and nonlinear autoencoders. In the latter part of this chapter, we discuss in more detail the recently developed neural autoregressive distribution estimator and its variants.


title: Illuminating Pedestrians via Simultaneous Detection and Segmentation

abstract: Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion network to enable joint supervision on semantic segmentation and pedestrian detection. When placed properly, the additional supervision helps guide features in shared layers to become more sophisticated and helpful for the downstream pedestrian detector. Using this approach, we find weakly annotated boxes to be sufficient for considerable performance gains. We provide an in-depth analysis to demonstrate how shared layers are shaped by the segmentation supervision. In doing so, we show that the resulting feature maps become more semantically meaningful and robust to shape and occlusion. Overall, our simultaneous detection and segmentation framework achieves a considerable gain over the state-of-the-art on the Caltech pedestrian dataset, competitive performance on KITTI, and executes 2x faster than competitive methods.


Wheeler's atlas of tooth form


title: A New Framework for Cognitive Mobility of Visually Impaired Users in Using Tactile Device

abstract: This paper proposes a novel framework for a better understanding of human cognitive locomotion and its interaction with the new tactile technologies that assist mobility and the acquisition of spatial knowledge in the absence of sight [case of visually impaired people (VIP), seniors, etc.]. Unlike the existing mobility models, the proposed framework encompasses four elements: 1) walking with obstacle avoidance, 2) orientation, 3) spatial awareness, and 4) actual physical displacement, and is based on the concept of the tactile gist, a refreshable (dynamic) egocentric tactile representation of the perceived scene. The relevance of the tactile gist concept for achieving the assistance of human mobility has been evaluated with three basic original spatial experiments (homing with obstacle avoidance, localization of a passage and its size estimation, and navigation in an apartment) using a purposely designed perception-movement platform and a touch stimulating tangible wearable refreshable device. The collected results show that the tactile gist might be considered as a scene representation and should be integrated into an assistive device for sightless people. The proposed approach may impact the design of mobility aids for VIP as it reinforces their natural mobility skills and may lead to new mobility strategies of VIP in (un-)known environment.


title: On the relationship between math anxiety and math achievement in early elementary school: The role of problem solving strategies

abstract: Abstract Even at young ages, children self-report experiencing math anxiety, which negatively relates to their math achievement. Leveraging a large dataset of first and second grade students’ math achievement scores, math problem solving strategies, and math attitudes, we explored the possibility that children’s math anxiety (i.e., a fear or apprehension about math) negatively relates to their use of more advanced problem solving strategies, which in turn relates to their math achievement. Our results confirm our hypothesis and, moreover, demonstrate that the relation between math anxiety and math problem solving strategies is strongest in children with the highest working memory capacity. Ironically, children who have the highest cognitive capacity avoid using advanced problem solving strategies when they are high in math anxiety and, as a result, underperform in math compared with their lower working memory peers.


title: Stochastic-based Optimal Daily Energy Management of Microgrids in Distribution Systems

abstract: Microgrid (MG) with different technologies in distributed generations (DG) and different control facilities require proper management and scheduling strategies. In these strategies, in order to reach the optimal management, the stochastic nature of some decision variables should be considered. Therefore, we proposes a new stochastic-based method for optimal daily energy management (SDEM) of MGs considering economic and reliability aspects. The optimization aim is to minimize overall operating cost, power losses cost, pollutants emission cost and cost of energy not supply (ENS). The network is assumed to be supplied by renewable and dispatch able generators and energy storage systems (ESS). The system uncertainties are considered using a set of scenarios and a scenario reduction method is applied to enhance a trade-off between the accuracy of the solution and the computational burden. Cuckoo optimization algorithm (COA) is applied to minimize the objective function as an optimization algorithm. The effectiveness and efficiency of the proposed method are validated through extensive numerical tests on PG&E 69-bus test distribution system. The results show that the proposed framework can be considered as an efficient tool in optimal daily energy management of smart distribution networks.


title: Melanoma of the Glans Penis and Urethra

abstract: Primary melanoma of the glans or male urethra is a rare malignant tumor with high mortality. We searched PubMed and found 129 articles reporting on 220 patients. All articles were case reports or case series. Median patient age was 65 years. Median survival was 28 months, with 5-year survival in approximately 10%. All patients who survived over 5 years had a localized disease (stage I/A) with invasion depth


How Much Would You Pay for Clean Air? The Role of Cost/Benefit Analysis in Setting NAAQS


title: Relative contributions of infraspinatus and deltoid during external rotation in healthy shoulders

abstract: Balanced forces around the shoulder are important for normal function; however, rehabilitation guidelines are not well defined because the muscle contributions and optimal exercise technique to recruit them are poorly understood. This study aimed to determine (1) the conditions of resisted isometric external rotation that optimized the contribution of infraspinatus and (2) the load of external rotation at which the adduction strategy was most effective at reducing deltoid contributions. Eighteen subjects with healthy shoulders (n = 36) performed resisted isometric external rotation at 3 increasing loads—10%, 40%, and 70% of their maximal resisted external rotation voluntary isometric contraction—with and without adduction. Surface electromyographic activity of the infraspinatus, posterior and middle deltoid, and pectoralis major was recorded and normalized against the average activity of all 4 muscles, representing each muscle’s relative contribution to the task. To optimize the relative contribution of the infraspinatus with the least deltoid involvement during isometric external rotation, a load between 10% and 40% maximal voluntary isometric contraction is appropriate. At low loads, use of the adduction strategy during external rotation reduces middle deltoid involvement. In contrast, the posterior deltoid is activated in parallel with the infraspinatus at low loads and may even act as an adductor with the arm by the side. This study provides a useful guide to optimize rehabilitative exercises for rotator cuff dysfunction; in particular, highlighting that activation of the deltoid could be counterproductive to infraspinatus retraining.


title: The Cortical Topography of Tonal Structures Underlying Western Music

abstract: Western tonal music relies on a formal geometric structure that determines distance relationships within a harmonic or tonal space. In functional magnetic resonance imaging experiments, we identified an area in the rostromedial prefrontal cortex that tracks activation in tonal space. Different voxels in this area exhibited selectivity for different keys. Within the same set of consistently activated voxels, the topography of tonality selectivity rearranged itself across scanning sessions. The tonality structure was thus maintained as a dynamic topography in cortical areas known to be at a nexus of cognitive, affective, and mnemonic processing.


title: Visual entity linking

abstract: Entity linking is the task of identifying entities like people and places in textual data and linking them to corresponding entities in a knowledge base. In this paper we solve a visual equivalent of this task called visual entity linking. The goal is to link regions of images to corresponding entities in knowledge bases. Visual entity linking will enable computers to better understand visual content and thus can be used in tasks like image retrieval and visual question answering. More specifically, we propose a novel approach for linking image regions to entities in Dbpedia and Freebase. First, we select candidate entities using an automatic image description generation algorithm. We then extract image regions using object detection methods and compare them to depictions of entities in a knowledge base. We evaluate our approach on the Flickr8k dataset through surveys on Amazon Mechanical Turk, and present an extensive analysis to identify the sources of errors in our system.


title: How to get mature global virtual teams: a framework to improve team process management in distributed software teams

abstract: Managing global software development teams is not an easy task because of the additional problems and complexities that have to be taken into account. This paper defines VTManager, a methodology that provides a set of efficient practices for global virtual team management in software development projects. These practices integrate software development techniques in global environments with others such as explicit practices for global virtual team management, definition of skills and abilities needed to work in these teams, availability of collaborative work environments and shared knowledge management practices. The results obtained and the lessons learned from implementing VTManager in a pilot project to develop software tools for collaborative work in rural environments are also presented. This project was carried out by geographically distributed teams involving people from seven countries with a high level of virtualness.


title: Abstract Automatic surface modelling of a ship hull

abstract: When defining a ship hull surface, the main objective is to obtain a faired surface or surfaces that contain some specific points of the hull, that have been selected in the design process and give the ship its hydrodynamic, stability and other properties. So, the hull surface should be a compromise between fairness and precision, and this is not and easy task. In this paper, authors present a thorough procedure for automatic modelling with a fair NURBS surface, having lists of points on the stations of the vessel as initial data. The construction of spline curves, and their application in the definition of ship lines is reviewed. Approximation of spline curves fitting the data on the stations is made, with special emphasis on the choice of parametrization, which is relevant to increase the accuracy of the splines. NURBS surfaces modelling the hull of the vessel are constructed and the fairing process adapted to maintain certain ship characteristics is described.


title: Investigating service quality dimensions in South African motor vehicle servicing

abstract: The motor industry plays a critical role in the economy of South Africa, with respect to both GDP and employment. Service quality is a fundamental aspect of service provision, and this is especially the case with motor vehicles, where substantial profits are generated in the servicing of vehicles. The study was conducted using a convenience sample of 761 respondents who were owners of various brands of motor vehicles. The questionnaire was a self-completion questionnaire which consisted of three sections. Factor analysis of the research indicated that five service quality factors could be identified. Apart from the tangible factor, the factors identified differ from the dimensions of service quality proposed by Parasuraman et al. (1988). The dimensions identified also were differed from other studies conducted into service quality dimensions in this context, highlighting the variable nature of service quality in this context. The implications of this study are that the customer perceives the way in which the service is delivered as critical in evaluating service quality.


title: Big data analytics in supply chain management: A state-of-the-art literature review

abstract: The rapid growing interest from both academics and practitioners towards the application of Big Data Analytics (BDA) in Supply Chain Management (SCM) has urged the need of review up-to-date research development in order to develop new agenda. This review responds to this call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structured based on the content analysis method of Mayring (2008), addressing four research questions on (1) what areas of SCM that BDA is being applied, (2) what level of analytics is BDA used in these application areas, (3) what types of BDA models are used, and finally (4) what BDA techniques are employed to develop these models. The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.


Health as Liberation: Medicine, Theology, and the Quest for Justice


title: Significant Permission Identification for Machine-Learning-Based Android Malware Detection

abstract: The alarming growth rate of malicious apps has become a serious issue that sets back the prosperous mobile ecosystem. A recent report indicates that a new malicious app for Android is introduced every 10 s. To combat this serious malware campaign, we need a scalable malware detection approach that can effectively and efficiently identify malware apps. Numerous malware detection tools have been developed, including system-level and network-level approaches. However, scaling the detection for a large bundle of apps remains a challenging task. In this paper, we introduce Significant Permission IDentification (SigPID) , a malware detection system based on permission usage analysis to cope with the rapid increase in the number of Android malware. Instead of extracting and analyzing all Android permissions, we develop three levels of pruning by mining the permission data to identify the most significant permissions that can be effective in distinguishing between benign and malicious apps. SigPID then utilizes machine-learning-based classification methods to classify different families of malware and benign apps. Our evaluation finds that only 22 permissions are significant. We then compare the performance of our approach, using only 22 permissions, against a baseline approach that analyzes all permissions. The results indicate that when a support vector machine is used as the classifier, we can achieve over 90% of precision, recall, accuracy, and F-measure, which are about the same as those produced by the baseline approach while incurring the analysis times that are 4–32 times less than those of using all permissions. Compared against other state-of-the-art approaches, SigPID is more effective by detecting 93.62% of malware in the dataset and 91.4% unknown/new malware samples.


A Position-Biased PageRank Algorithm for Keyphrase Extraction.


title: Isolation and characterization of a cellulolytic Geobacillus thermoleovorans T4 strain from sugar refinery wastewater

abstract: A novel, cellulolytic, bacterial thermophilic strain, T4, was isolated from sugar refinery wastewater in southern Taiwan. This isolate, a Gram-negative, motile, aerobically growing sporulating rod, can secrete thermostable endocellulase (endo-1,4-β-D-glucanase, EC 3.2.1.4) and hydrolyze carboxymethylcellulose (CMC), phosphoric acid-swollen cellulose, Avicel, filter paper, and salicin. When strain T4 was grown in CMC medium, the cellulolytic enzyme activity in culture supernatants was stable up to 70°C. More than 10% of the original activity was still detectable after heating to 100°C with a pH 7.0 for 1 h. Based on 16S rDNA sequence analysis, DNA base composition, phenotypic and physiological characteristics, as well as DNA–DNA hybridization, strain T4 was classified as Geobacillus thermoleovorans T4 (DSM 14791 = CCRC 17200). We also demonstrated that the type species G. stearothermophilus (DSM 22 = ATCC 12980) could hydrolyze amorphous and crystalline (filter paper) celluloses at a rate of 13 and 14%, respectively, in comparison with strain T4.


title: A fuzzy approach to trust based access control in internet of things

abstract: In the Internet of thing (IoT), the activities of daily life are supported by a multitude of heterogeneous, loosely coupled ubiquitous devices. Traditional access control model are not suitable to the nomadic, decentralized and dynamic scenarios in the IoT where identities are not known in advance. This makes the trust management in IoT more promising to address the access control issues .This paper present a Fuzzy approach to the Trust Based Access Control (FBAC) with the notion of trust levels for identity management. The presented fuzzy approach for trust calculations deals with the linguistic information of devices to address access control in the IoT. The simulation result shows that the fuzzy approach for trust based access control guarantees scalability ad it is energy efficient. This paper also proposes FTBAC framework or trust based dynamic access control in distributed IoT. FTBAC framework i a flexible and scalable a increasing number o devices do not affect the functioning and performance.


title: Performance comparison of IEEE 802.11p and ARIB STD-T109

abstract: Vehicular networking is moving from pure research to first deployments around the world. This shifts the focus of research and development to aspects like higher layer performance; yet different regions (Japan, Europe and the U.S.) employ vastly different lower layer protocols for medium access and transmission. Without means to compare their performance it remains unclear to what degree simulation results obtained for one region (that is, one set of lower layer protocols) can be transferred to the other. Our paper fills this gap by conducting an extensive simulation study comparing the performance of IEEE 802.11p and ARIB T109 taking into account both their differences on the physical layer (5.9 GHz vs. 700 MHz band) as well as in medium access (pure CSMA/CA vs. a combination with TDMA). We base this study on the first Open Source implementation of the ARIB T109 standard we developed for the vehicular network simulation framework Veins. This also encompasses parameters for a computationally inexpensive shadow fading model for urban environments. We briefly report on the results of an extensive measurement campaign that underlies these parameters.


title: ADS-B, Friend or Foe: ADS-B Message Authentication for NextGen Aircraft

abstract: Legacy air traffic control systems have become outdated and cannot keep up with the trending increase in airliner traffic. With the goal of modernizing the system from one of manual air traffic positioning through radar and radio communication to a network of e-enabled aircraft, the Federal Aviation Administration (FAA) is shifting to the use of Automatic Dependent Surveillance-Broadcast (ADS-B) as a surveillance technology with higher accuracy and less man-in-the-loop dependence. However, ADS-B in its current implementation lacks the security measures needed to provide authenticity and integrity, making it an easy target for spoofing attacks. This paper presents a message authentication scheme that (1) uses a Public Key Infrastructure (PKI) to verify all ADS-B signals are from FAA registered aircraft, (2) uses asymmetric cryptography to exchange a symmetric session key, and (3) uses this symmetric session key to validate data authenticity and integrity. The scheme description is followed by a discussion of its scalability and feasibility in context of the full aviation realm that it applies to, as well as the changes that would be needed in existing technologies.


title: Modeling the global freight transportation system: A multi-level modeling perspective

abstract: The interconnectedness of different actors in the global freight transportation industry has rendered such a system as a large complex system where different sub-systems are interrelated. On such a system, policy-related- exploratory analyses which have predictive capacity are difficult to perform. Although there are many global simulation models for various large complex systems, there is unfortunately very little research aimed to develop a global freight transportation model. In this paper, we present a multi-level framework to develop an integrated model of the global freight transportation system. We employ a system view to incorporate different relevant sub-systems and categorize them in different levels. The fourstep model of freight transport is used as the basic foundation of the framework proposed. In addition to that, we also present the computational framework which adheres to the high level modeling framework to provide a conceptualization of the discrete-event simulation model which will be developed.


title: I/O-complexity of graph algorithms

abstract: We show lower bounds of Q( $ Sort(V)) for the I/Ocomplexity of graph theoretic problems like connected components, biconnected components, and minimum spanning trees, where E and V are the number of edges and vertices in the input graph, respectively. We also present a deterministic O($ Sort(V) ’ max(l,loglog 9)) algorithm for the problem of graph connectivity, where B and D denote respectively the block size and number of disks. Our algorithm includes a breadth first search; this maybe of independent


title: Core design for better misalignment tolerance and higher range of wireless charging for HEV

abstract: An optimized core design for wireless power transfer system for hybrid vehicle application is proposed in this paper. One of the main challenge of wireless power in hybrid vehicle charging application is the limited distance range and significant efficiency drop under small misalignments between the transmitter (Tx) and the receiver (Rx) coil. Appropriately designed transmitter and receiver coil with core can make the system more tolerant against misalignment. For a planar spiral coil, an optimum core is designed here, using minimum amount of core and higher range of wireless power is achieved with better misalignment tolerance. The proposed model is simulated in Finite Element Analysis (FEA) and verified with experimental data of an implemented system of 3kW power for different gap and misalignment. Implementing the proposed optimized core for the system, the maximum efficiency is achieved much higher compared to the system without core.


title: Bandgap Engineering of Strained Monolayer and Bilayer MoS2

abstract: We report the influence of uniaxial tensile mechanical strain in the range 0–2.2% on the phonon spectra and bandstructures of monolayer and bilayer molybdenum disulfide (MoS2) two-dimensional crystals. First, we employ Raman spectroscopy to observe phonon softening with increased strain, breaking the degeneracy in the E′ Raman mode of MoS2, and extract a Gruneisen parameter of ∼1.06. Second, using photoluminescence spectroscopy we measure a decrease in the optical band gap of MoS2 that is approximately linear with strain, ∼45 meV/% strain for monolayer MoS2 and ∼120 meV/% strain for bilayer MoS2. Third, we observe a pronounced strain-induced decrease in the photoluminescence intensity of monolayer MoS2 that is indicative of the direct-to-indirect transition of the character of the optical band gap of this material at applied strain of ∼1%. These observations constitute a demonstration of strain engineering the band structure in the emergent class of two-dimensional crystals, transition-metal dichalcogenides.


title: A Fresh Look at iPS Cells

abstract: The potential of induced pluripotent stem (iPS) cells is enormous, but many obstacles remain before their medical and pharmaceutical applications can be fully realized.


title: The Feasibility of Using Augmented Auditory Feedback From a Pressure Detecting Insole To Reduce the Knee Adduction Moment: a Proof of Concept Study

abstract: The objective of this work was to conduct a proof of concept study utilizing auditory feedback from a pressure-detecting shoe insole to shift plantar pressure medially in order to reduce the knee adduction moment (KAM). When compared with normal walking, 32 healthy subjects significantly reduced their peak KAM using feedback (p < 0.001). When compared with medial thrust gait, an established gait modification, walking with pressure-based feedback was equally effective at reducing the peak KAM, yet it successfully mitigated other potentially detrimental gait measures such as the peak knee flexion moment (KFM), knee internal rotation moment (KIrM), and a reduction in speed.


title: More than fun and money. Worker Motivation in Crowdsourcing – A Study on Mechanical Turk

abstract: The payment in paid crowdsourcing markets like Amazon Mechanical Turk is ::: very low, and still collected demographic data shows that the participants ::: are a very diverse group including highly skilled full time workers. Many ::: existing studies on their motivation are rudimental and not grounded on ::: established motivation theory. Therefore, we adapt different models from ::: classic motivation theory, work motivation theory and Open Source Software ::: Development to crowdsourcing markets. The model is tested with a survey of ::: 431 workers on Mechanical Turk. We find that the extrinsic motivational ::: categories (imme-diate payoffs, delayed payoffs, social motivation) have a ::: strong effect on the time spent on the platform. For many workers, however, ::: intrinsic motivation aspects are more important, especially the different ::: facets of enjoyment based motivation like “task autonomy” and “skill ::: variety”. Our contribution is a preliminary model based on established ::: theory intended for the comparison of different crowdsourcing platforms.


title: Multi-layer Representation Learning for Medical Concepts

abstract: Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits from Electronic Health Records (EHR) has broad applications in healthcare analytics. Patient EHR data consists of a sequence of visits over time, where each visit includes multiple medical concepts, e.g., diagnosis, procedure, and medication codes. This hierarchical structure provides two types of relational information, namely sequential order of visits and co-occurrence of the codes within a visit. In this work, we propose Med2Vec, which not only learns the representations for both medical codes and visits from large EHR datasets with over million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts. In the experiments, Med2Vec shows significant improvement in prediction accuracy in clinical applications compared to baselines such as Skip-gram, GloVe, and stacked autoencoder, while providing clinically meaningful interpretation.


title: Fine-grained vehicle classificationusing deep residual networks with multiscale attention windows

abstract: Fine-grained vehicle classiflcation is a challenging task due to the subtle differences between vehicle classes. Several successful approaches to fine-grained image classification rely on part-based models, where the image is classified according to discriminative object parts. Such approaches require however that parts in the training images be manually annotated, a laborintensive process. We propose a convolutional architecture realizing a transform network capable of discovering the most discriminative parts of a vehicle at multiple scales. We experimentally show that our architecture outperforms a baseline reference if trained on class labels only, and performs closely to a reference based on a part-model if trained on loose vehicle localization bounding boxes.


title: Query reformulation using WordNet and genetic algorithm

abstract: Search on the web is a delay process and it can be hard task especially for beginners when they attempt to use a keyword query language. Beginner (inexpert) searchers commonly attempt to find information with ambiguous queries. These ambiguous queries make the search engine returns irrelevant results. This work aims to get more relevant pages to query through query reformulation and expanding search space. The proposed system has three basic parts WordNet, Google search engine and Genetic Algorithm. Every part has a special task. The system uses WordNet to remove ambiguity from queries by displaying the meaning of every keyword in user query and selecting the proper meaning for keywords. The system obtains synonym for every keyword from WordNet and generates query list. Genetic algorithm is used to create generation for every query in query list. Every query in system is navigated using Google search engine to obtain results from group of documents on the Web. The system has been tested on number of ambiguous queries and it has obtained more relevant URL to user query especially when the query has one keyword. The results are promising and therefore open further research directions.


title: Effect of Heavy Metals on Some Biochemical Parameters of Sal (Shorea robusta) Seedling at Nursery Level, Doon Valley, India

abstract: The effects of cadmium, arsenic and lead (1, 5 and 10ppm) on the content of chlorophyll, amino acid, proline, and ascorbic acid were investigated in ten month old sal seedlings ( Shorea robusta) grown in nursery with various treatment of different concentration of Cadmium, Pb, and As. Control and heavy metal-treated plants were grown for a period of six months in natural condition. Total chlorophyll, amino acid, proline and ascorbic acid content were measured in the leaves of the six month old seedling with the help of UV Spectrophotometer. The result showed that the total chlorophyll and amino acid content decreased with increased concentrations of heavy metals. A significant increase of proline and ascorbic acid content was observed in leaves after a four month exposure to heavy metals. The strongest effect of cadmium, followed by Pb++ and As++ was observed on total chlorophyll, amino acid, proline and ascorbic acid content of plants.


title: Industrial Control System Cyber Attacks

abstract: This paper presents a set of attacks against SCADA control systems. The attacks are grouped into 4 classes; reconnaissance, response and measurement injection, command injection and denial of service. The 4 classes are defined and each attack is described in detail. The response and measurement injection and command injection classes are subdivided into sub-classes based on attack complexity. Each attack described in this paper has been exercised against industrial control systems in a laboratory setting.


title: Leveraging Statistical Multi-Agent Online Planning with Emergent Value Function Approximation

abstract: Making decisions is a great challenge in distributed autonomous environments due to enormous state spaces and uncertainty. Many online planning algorithms rely on statistical sampling to avoid searching the whole state space, while still being able to make acceptable decisions. However, planning often has to be performed under strict computational constraints making online planning in multi-agent systems highly limited, which could lead to poor system performance, especially in stochastic domains. In this paper, we propose Emergent Value function Approximation for Distributed Environments (EVADE), an approach to integrate global experience into multi-agent online planning in stochastic domains to consider global effects during local planning. For this purpose, a value function is approximated online based on the emergent system behaviour by using methods of reinforcement learning. We empirically evaluated EVADE with two statistical multi-agent online planning algorithms in a highly complex and stochastic smart factory environment, where multiple agents need to process various items at a shared set of machines. Our experiments show that EVADE can effectively improve the performance of multi-agent online planning while offering efficiency w.r.t. the breadth and depth of the planning process.


title: A Generalized Surface Appearance Representation for Computer Graphics

abstract: For image synthesis in computer graphics, two major approaches for representing a surface's appearance are texture mapping, which provides spatial detail, such as wallpaper, or wood grain; and the 4D bi-directional reflectance distribution function (BRDF) which provides angular detail, telling how light reflects off surfaces. I combine these two modes of variation to form the 6D spatial bi-directional reflectance distribution function (SBRDF). My compact SBRDF representation simply stores BRDF coefficients at each pixel of a map. I propose SBRDFs as a surface appearance representation for computer graphics and present a complete system for their use. ::: I acquire SBRDFs of real surfaces using a device that simultaneously measures the BRDF of every point on a material. The system has the novel ability to measure anisotropy (direction of threads, scratches, or grain) uniquely at each surface point. I fit BRDF parameters using an efficient nonlinear optimization approach specific to BRDFs. ::: SBRDFs can be rendered using graphics hardware. My approach yields significantly more detailed, general surface appearance than existing techniques for a competitive rendering cost. I also propose an SBRDF rendering method for global illumination using prefiltered environment maps. This improves on existing prefiltered environment map techniques by decoupling the BRDF from the environment maps, so a single set of maps may be used to illuminate the unique BRDFs at each surface point. ::: I demonstrate my results using measured surfaces including gilded wallpaper, plant leaves, upholstery fabrics, wrinkled gift-wrapping paper and glossy book covers.


title: Poisson Coordinates

abstract: Harmonic functions are the critical points of a Dirichlet energy functional, the linear projections of conformal maps. They play an important role in computer graphics, particularly for gradient-domain image processing and shape-preserving geometric computation. We propose Poisson coordinates, a novel transfinite interpolation scheme based on the Poisson integral formula, as a rapid way to estimate a harmonic function on a certain domain with desired boundary values. Poisson coordinates are an extension of the Mean Value coordinates (MVCs) which inherit their linear precision, smoothness, and kernel positivity. We give explicit formulas for Poisson coordinates in both continuous and 2D discrete forms. Superior to MVCs, Poisson coordinates are proved to be pseudoharmonic (i.e., they reproduce harmonic functions on n-dimensional balls). Our experimental results show that Poisson coordinates have lower Dirichlet energies than MVCs on a number of typical 2D domains (particularly convex domains). As well as presenting a formula, our approach provides useful insights for further studies on coordinates-based interpolation and fast estimation of harmonic functions.


title: What caused what? An irreducible account of actual causation

abstract: Actual causation is concerned with the question "what caused what?". Consider a transition between two subsequent observations within a system of elements. Even under perfect knowledge of the system, a straightforward answer to this question may not be available. Counterfactual accounts of actual causation based on graphical models, paired with system interventions, have demonstrated initial success in addressing specific problem cases. We present a formal account of actual causation, applicable to discrete dynamical systems of interacting elements, that considers all counterfactual states of a state transition from t-1 to t. Within such a transition, causal links are considered from two complementary points of view: we can ask if any occurrence at time t has an actual cause at t-1, but also if any occurrence at time t-1 has an actual effect at t. We address the problem of identifying such actual causes and actual effects in a principled manner by starting from a set of basic requirements for causation (existence, composition, information, integration, and exclusion). We present a formal framework to implement these requirements based on system manipulations and partitions. This framework is used to provide a complete causal account of the transition by identifying and quantifying the strength of all actual causes and effects linking two occurrences. Finally, we examine several exemplary cases and paradoxes of causation and show that they can be illuminated by the proposed framework for quantifying actual causation.


title: Techniques in carotid artery surgery

abstract: ✓The major objective in carotid endarterectomy is to achieve safe and complete removal of intimal plaque and provide lasting, nonstenotic closure. Controversy exists as to which technical variation best achieves this. In this paper, the authors review the operative nuances and outcomes with conventional and eversion endarterectomy, with a focus on the latter. The views expressed reflect specific neurosurgical and vascular perspectives in the context of a multi-disciplinary stroke unit, where carotid stenosis is managed with all available open and endovascular means. The neurosurgical approach was almost entirely conventional endarterectomy with primary repair, while the vascular surgeons used the eversion method with few exceptions.


title: eSports: collaborative and synchronous video annotation system in grid computing environment

abstract: We designed eSports - a collaborative and synchronous video annotation platform, which is to be used in Internet scale cross-platform grid computing environment to facilitate computer supported cooperative work (CSCW) in education settings such as distance sport coaching, distance classroom etc. Different from traditional multimedia annotation systems, eSports provides the capabilities to collaboratively and synchronously play and archive real time live video, to take snapshots, to annotate video snapshots using whiteboard and to play back the video annotations synchronized with original video streams. eSports is designed based on the grid based collaboration paradigm $the shared event model using NaradaBrokering, which is a publish/subscribe based distributed message passing and event notification system. In addition to elaborate the design and implementation of eSports, we analyze the potential use cases of eSports under different education settings. We believed that eSports is very useful to improve the online collaborative coaching and education.


title: DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning

abstract: Learning physics-based locomotion skills is a difficult problem, leading to solutions that typically exploit prior knowledge of various forms. In this paper we aim to learn a variety of environment-aware locomotion skills with a limited amount of prior knowledge. We adopt a two-level hierarchical control framework. First, low-level controllers are learned that operate at a fine timescale and which achieve robust walking gaits that satisfy stepping-target and style objectives. Second, high-level controllers are then learned which plan at the timescale of steps by invoking desired step targets for the low-level controller. The high-level controller makes decisions directly based on high-dimensional inputs, including terrain maps or other suitable representations of the surroundings. Both levels of the control policy are trained using deep reinforcement learning. Results are demonstrated on a simulated 3D biped. Low-level controllers are learned for a variety of motion styles and demonstrate robustness with respect to force-based disturbances, terrain variations, and style interpolation. High-level controllers are demonstrated that are capable of following trails through terrains, dribbling a soccer ball towards a target location, and navigating through static or dynamic obstacles.


title: Power MOSFET temperature measurements

abstract: Three temperature-sensitive electrical parameters are compared as thermometers for power MOSFET devices. The parameters are the forward drain-body diode voltage, the source-gate voltage, and the on-resistance. The results are also compared with temperatures measured with an infrared microradiometer. The procedure, apparatus, and circuits required to use each of the parameters as a thermometer are described. Some general considerations for measuring the temperature of power semiconductor devices are also discussed. Each parameter is found to be satisfactory for measuring the temperature of power MOSFETs. The sourcegate voltage measures a temperature nearest to the peak device temperature, and the drain-body diode voltage shows the least variation in calbiration from device to device.


title: Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo

abstract: Recently there have been exciting developments in Monte Carlo methods, with the development of new MCMC and sequential Monte Carlo (SMC) algorithms which are based on continuous-time, rather than discrete-time, Markov processes. This has led to some fundamentally new Monte Carlo algorithms which can be used to sample from, say, a posterior distribution. Interestingly, continuous-time algorithms seem particularly well suited to Bayesian analysis in big-data settings as they need only access a small sub-set of data points at each iteration, and yet are still guaranteed to target the true posterior distribution. Whilst continuous-time MCMC and SMC methods have been developed independently we show here that they are related by the fact that both involve simulating a piecewise deterministic Markov process. Furthermore we show that the methods developed to date are just specific cases of a potentially much wider class of continuous-time Monte Carlo algorithms. We give an informal introduction to piecewise deterministic Markov processes, covering the aspects relevant to these new Monte Carlo algorithms, with a view to making the development of new continuous-time Monte Carlo more accessible. We focus on how and why sub-sampling ideas can be used with these algorithms, and aim to give insight into how these new algorithms can be implemented, and what are some of the issues that affect their efficiency.


title: A Unified Model for Near and Remote Sensing

abstract: We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead and ground-level images in an end-to-end trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map. The output of this network is a dense estimate of the geospatial function in the form of a pixel-level labeling of the overhead image. To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age. We find that our approach is more accurate for all tasks, in some cases dramatically so.


title: Small-signal analysis of closed-loop PWM boost converter in CCM with complex impedance load

abstract: The following closed-loop transfer functions of the boost converter operating in continuous-conduction mode (CCM) supplying a complex impedance load are derived and analyzed: input-to-output voltage Mvcl and reference-to-output Tcl. The load of the boost dc-dc converter is composed of a series-connected resistance and inductance. The dynamic characteristics of the closed-loop boost converter with a third-order double-lead integral compensator are evaluated for different load inductances. The theoretically predicted results are validated through switching-circuit simulations using a suitable converter design example.


title: A Multiresolution 3D Morphable Face Model and Fitting Framework

abstract: 3D Morphable Face Models are a powerful tool in computer vision. They consist of a PCA model of face shape and colour information and allow to reconstruct a 3D face from a single 2D image. 3D Morphable Face Models are used for 3D head pose estimation, face analysis, face recognition, and, more recently, facial landmark detection and tracking. However, they are not as widely used as 2D methods - the process of building and using a 3D model is much more involved. In this paper, we present the Surrey Face Model, a multi-resolution 3D Morphable Model that we make available to the public for non-commercial purposes. The model contains different mesh resolution levels and landmark point annotations as well as metadata for texture remapping. Accompanying the model is a lightweight open-source C++ library designed with simplicity and ease of integration as its foremost goals. In addition to basic functionality, it contains pose estimation and face frontalisation algorithms. With the tools presented in this paper, we aim to close two gaps. First, by offering different model resolution levels and fast fitting functionality, we enable the use of a 3D Morphable Model in time-critical applications like tracking. Second, the software library makes it easy for the community to adopt the 3D Morphable Face Model in their research, and it offers a public place for collaboration.


title: Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition

abstract: We present a simple and effective architecture for fine-grained recognition called Bilinear Convolutional Neural Networks (B-CNNs) . These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs are related to orderless texture representations built on deep features but can be trained in an end-to-end manner. Our most accurate model obtains 84.1, 79.4, 84.5 and 91.3 percent per-image accuracy on the Caltech-UCSD birds [1] , NABirds [2] , FGVC aircraft [3] , and Stanford cars [4] dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at http://vis-www.cs.umass.edu/bcnn .


EFFECT OF MOBILE-BASED LENDING PROCESS ON NON-PERFORMING LOANS IN COMMERCIAL BANKS IN NAKURU TOWN, KENYA


title: Emerging security mechanisms for medical cyber physical systems

abstract: The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These Medical Cyber Physical Systems (MCPS) will be capable of transmitting the acquired data to a private or public cloud for storage and processing. Machine learning algorithms running in the cloud and processing this data can provide decision support to healthcare professionals. There is no doubt that the security and privacy of the medical data is one of the most important concerns in designing an MCPS. In this paper, we depict the general architecture of an MCPS consisting of four layers: data acquisition, data aggregation, cloud processing, and action. Due to the differences in hardware and communication capabilities of each layer, different encryption schemes must be used to guarantee data privacy within that layer. We survey conventional and emerging encryption schemes based on their ability to provide secure storage, data sharing, and secure computation. Our detailed experimental evaluation of each scheme shows that while the emerging encryption schemes enable exciting new features such as secure sharing and secure computation, they introduce several orders-of-magnitude computational and storage overhead. We conclude our paper by outlining future research directions to improve the usability of the emerging encryption schemes in an MCPS.


title: Interaction, narrative, and drama creating an adaptive interactive narrative using performance arts theories. Interaction Studies 8(2

abstract: Interactive narratives have been used in a variety of applications, including video games, educational games, and training simulations. Maintaining engagement within such environments is an important problem, because it affects entertainment, motivation, and presence. Performance arts theorists have discussed and formalized many techniques that increase engagement and enhance dramatic content of art productions. While constructing a narrative manually, using these techniques, is acceptable for linear media, using this approach for interactive environments results in inflexible experiences due to the unpredictability of users’ actions. Few researchers attempted to develop adaptive interactive narrative experiences. However, developing a quality interactive experience is largely an art process, and many of these adaptive techniques do not encode artistic principles. This paper presents a new interactive narrative architecture designed using a set of dramatic techniques that have been formulated based on several years of training in film and theatre.


title: Morphological Annotation of Quranic Arabic

abstract: The Quranic Arabic Corpus (http://corpus.quran.com) is an annotated linguistic resource with multiple layers of annotation including morphological segmentation, part-of-speech tagging, and syntactic analysis using dependency grammar. The motivation behind this work is to produce a resource that enables further analysis of the Quran, the 1,400 year old central religious text of Islam. This paper describes a new approach to morphological annotation of Quranic Arabic, a genre difficult to compare with other forms of Arabic. Processing Quranic Arabic is a unique challenge from a computational point of view, since the vocabulary and spelling differ from Modern Standard Arabic. The Quranic Arabic Corpus differs from other Arabic computational resources in adopting a tagset that closely follows traditional Arabic grammar. We made this decision in order to leverage a large body of existing historical grammatical analysis, and to encourage online collaborative annotation. In this paper, we discuss how the unique challenge of morphological annotation of Quranic Arabic is solved using a multi-stage approach. The different stages include automatic morphological tagging using diacritic edit-distance, two-pass manual verification, and online collaborative annotation. This process is evaluated to validate the appropriateness of the chosen methodology.


title: Data mining strategies and techniques for CRM systems

abstract: Whenever millions of data is being stored in database regularly, data mining is responsible to discover the hidden knowledge, rules and patterns from it. Data mining is going to be involved in every organization for extracting extra information which are not visible for everyone. Organizations always planning to get useful information from it. Though, study on customer relationship management (CRM) is reaching more practical and attractive factor for the growth of every organization in the same way, discovery the hidden gold is also supporting to achieve the goal and for the success of organization. The main critical success factor for any (CRM) includes, Marketing Management, Customer Support Management, Sales Management and Facilities Management, etc. In this paper we proposed, analyzed and validated that data mining is also a major success factor in the success of CRM. We first presented the CRM model and then explained the main role of each feature, then we add data mining feature in the CRM model. Further more, we applied data mining strategies and techniques for the generation of new rules and patterns. We talk about that within the boundaries of CRM strategies the data mining tool also play an affective and valuable role for the establishment and growth of the organization.


title: Body Part Detection for Human Pose Estimation and Tracking

abstract: Accurate 3-D human body pose tracking from a monocular video stream is important for a number of applications. We describe a novel hierarchical approach for tracking human pose that uses edge-based features during the coarse stage and later other features for global optimization. At first, humans are detected by motion and tracked by fitting an ellipse in the image. Then, body components are found using edge features and used to estimate the 2D positions of the body joints accurately. This helps to bootstrap the estimation of 3D pose using a sampling-based search method in the last stage. We present experiment results with sequences of different realistic scenes to illustrate the performance of the method.


title: Central Pattern Generator Inspired Control for Adaptive Walking of Biped Robots

abstract: Inspired by the biological concept of central pattern generators (CPGs), this paper deals with adaptive walking control of biped robots. Using CPGs, a trajectory generator is designed consisting of a center-of-gravity (CoG) trajectory generator and a workspace trajectory modulation process. Entraining with feedback information, the CoG generator can generate adaptive CoG trajectories online and workspace trajectories can be modulated in real time based on the generated adaptive CoG trajectories. A motion engine maps trajectories from workspace to joint space. The proposed control strategy is able to generate adaptive joint control signals online to realize biped adaptive walking. The experimental results using a biped platform NAO confirm the effectiveness of the proposed control strategy.


title: Beam Division Multiple Access (BDMA) and modulation formats for 5G: Heir of OFDM?

abstract: The paper throws light on Beam Division Multiple Access (BDMA) technique and various possible heirs of Orthogonal Frequency Division Multiplexing (OFDM) as modulation schemes anticipated to be used in futuristic 5G communications. This paper emphasizes on the concept of BDMA, a multiple access technique to overcome the limitations of existing frequency and time resource based access techniques for multiple access of same channel. The possible modulation schemes such as Filter Bank Multicarrier (FBMC), Faster Than Nyquist (FTN) signaling and Single Carrier Modulations (SCMs) that can be heir of OFDM are studied, simulated and a comparison of these techniques is done based on the simulation results. Possible application scenarios of OFDM heirs with respect to 5G implementation requirements are also proposed.


title: A Critical Examination of Feedback in Early Reading Games

abstract: Learning games now play a role in both formal and informal learning, including foundational skills such as literacy. While feedback is recognised as a key pedagogical dimension of these games, particularly in early learning, there has been no research on how commercial games available to schools and parents reify learning theory into feedback. Using a systematic content analysis, we examine how evidence-based feedback principles manifest in five widely-used learning games designed to foster young children's reading skills. Our findings highlight strengths in how games deliver feedback when players succeed. Many of the games, however, were inconsistent and not proactive when providing error feedback, often promoting trial and error strategies. Furthermore, there was a lack of support for learning the game mechanics and a preference for task-oriented rewards less deeply embedded in the gameplay. Our research provides a design and research agenda for the inclusion of feedback in early learning games.


title: Sweetening the Till: The Use of Candy to Increase Restaurant Tipping1

abstract: A common practice among servers in restaurants is to give their dining parties an unexpected gift in the form of candy when delivering the check. Two experiments were conducted to evaluate the impact of this gesture on the tip percentages received by servers. Experiment 1 found that customers who received a small piece of chocolate along with the check tipped more than did customers who received no candy. Experiment 2 found that tips varied with the amount of the candy given to the customers as well as with the manner in which it was offered. It is argued that reciprocity is a stronger explanation for these findings than either impression management or the good mood effect.


title: Structural Deep Embedding for Hyper-Networks

abstract: Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.


TAMIL NADU,


title: Minimal Narrative Annotation Schemes and Their Applications

abstract: The increased use of large corpora in narrative research has created new opportunities for empirical research and intelligent narrative technologies. To best exploit the value of these corpora, several research groups are eschewing complex discourse analysis techniques in favor of high-level minimalist narrative annotation schemes that can be quickly applied, achieve high inter-rater agreement, and are amenable to automation using machine-learning techniques. In this paper we compare different annotation schemes that have been employed by two groups of researchers to annotate large corpora of narrative text. Using a dual-annotation methodology, we investigate the correlation between narrative clauses distinguished by their structural role (orientation, action, evaluation), their subjectivity, and their narrative level within the discourse. We find that each simple narrative annotation scheme captures a structurally distinct characteristic of real-world narratives, and each combination of labels is evident in a corpus of 19 weblog narratives (951 narrative clauses). We discuss several potential applications of minimalist narrative annotation schemes, noting the combination of label across these two annotation schemes that best support each task.


Tabu Search for the Flexible-Routing Job Shop Problem


title: Numerical study on the performance of improved masonry-to-timber connections in traditional masonry buildings

abstract: Abstract This paper deals with a numerical study on the structural performance of masonry-to-timber connections in ancient buildings. The work is supported on an experimental campaign carried out at University of Minho, which aims at characterising a strengthening solution based on the use of injected anchors for the improvement of the connection between masonry and timber frame walls. The numerical study resorts to a detailed 3D finite element model, which reproduces the experimental test setup and procedure. The modelling approach adopted allows an accurate characterisation of the behaviour of all structural elements, in terms of stress field and displacement distribution. The 3D model was validated against the available experimental results, which was then used to perform parametric analyses in order to evaluate the influence of key parameters. Finally, simplified analytical approaches to estimate the strength capacity of injected anchors on masonry are presented and discussed.


title: A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images

abstract: We address the problem of prostate lesion detection, localization, and segmentation in T2W magnetic resonance (MR) images. We train a deep convolutional encoder-decoder architecture to simultaneously segment the prostate, its anatomical structure, and the malignant lesions. To incorporate the 3D contextual spatial information provided by the MRI series, we propose a novel 3D sliding window approach, which preserves the 2D domain complexity while exploiting 3D information. Experiments on data from 19 patients provided for the public by the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) show that our approach outperforms traditional pattern recognition and machine learning approaches by a significant margin. Particularly, for the task of cancer detection and localization, the system achieves an average AUC of 0.995, an accuracy of 0.894, and a recall of 0.928. The proposed mono-modal deep learning-based system performs comparably to other multi-modal MR-based systems. It could improve the performance of a radiologist in prostate cancer diagnosis and treatment planning.


title: Learning to bid in bridge

abstract: Bridge bidding is considered to be one of the most difficult problems for game-playing programs. It involves four agents rather than two, including a cooperative agent. In addition, the partial observability of the game makes it impossible to predict the outcome of each action. In this paper we present a new decision-making algorithm that is capable of overcoming these problems. The algorithm allows models to be used for both opponent agents and partners, while utilizing a novel model-based Monte Carlo sampling method to overcome the problem of hidden information. The paper also presents a learning framework that uses the above decision-making algorithm for co-training of partners. The agents refine their selection strategies during training and continuously exchange their refined strategies. The refinement is based on inductive learning applied to examples accumulated for classes of states with conflicting actions. The algorithm was empirically evaluated on a set of bridge deals. The pair of agents that co-trained significantly improved their bidding performance to a level surpassing that of the current state-of-the-art bidding algorithm.


title: Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis

abstract: The rapid increase in the number of Android malware poses great challenges to anti-malware systems, because the sheer number of malware samples overwhelms malware analysis systems. The classification of malware samples into families, such that the common features shared by malware samples in the same family can be exploited in malware detection and inspection, is a promising approach for accelerating malware analysis. Furthermore, the selection of representative malware samples in each family can drastically decrease the number of malware to be analyzed. However, the existing classification solutions are limited because of the following reasons. First, the legitimate part of the malware may misguide the classification algorithms because the majority of Android malware are constructed by inserting malicious components into popular apps. Second, the polymorphic variants of Android malware can evade detection by employing transformation attacks. In this paper, we propose a novel approach that constructs frequent subgraphs ( fregraphs ) to represent the common behaviors of malware samples that belong to the same family. Moreover, we propose and develop FalDroid, a novel system that automatically classifies Android malware and selects representative malware samples in accordance with fregraphs . We apply it to 8407 malware samples from 36 families. Experimental results show that FalDroid can correctly classify 94.2% of malware samples into their families using approximately 4.6 sec per app. FalDroid can also dramatically reduce the cost of malware investigation by selecting only 8.5% to 22% representative samples that exhibit the most common malicious behavior among all samples.


title: Low Cross-Polarization Vivaldi Arrays

abstract: This paper introduces a Vivaldi-type ultra-wideband antenna array with low cross-polarization, termed the Sliced Notch Antenna (SNA) array. High cross-polarization when scanning in the non-principal planes has long been a problem in Vivaldi arrays without a universal solution. In this paper, we shed light on the root cause of this long-standing issue, clarifying that a high ratio of vertical-to-horizontal current potentials along the upper radiator fin is primarily responsible for elevated cross-polarization (and not strictly the element profile). This finding therein motivates the main technical innovation in the present work—a simple reconfiguration of the upper Vivaldi fin into a series of coupled conductor segments to effectively control the aforementioned current ratio for significant reductions in cross-polarization, without reducing element profile or considerably hindering the excellent match and radiation efficiency of the original Vivaldi. Theory and design methodology for the SNA array are formulated, and followed by a concise set of design guidelines that are applied to reconfigure a representative 10:1 Vivaldi element into an SNA for practical reference, exhibiting a 20 dB reduction in peak cross-polarization ratio. This paper considers the case of single-polarized arrays as a first introduction to the SNA, though the method has been verified to work equally well (if not better) in dual-polarized arrays.


title: Discriminative Least Squares Regression for Multiclass Classification and Feature Selection

abstract: This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called e-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the e-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L2,1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.


title: The Impact of HCI Design on Health Behavior: The Case for Visual, Interactive, Personalized-content (VIP) Feedback

abstract: There is overwhelming evidence of patients’ low compliance with taking medication and keeping health related diets. In this work, we explore Visual, Interactive, and Personalized-content (VIP) feedback, as a novel method for increasing a patient's compliance with health care prescriptions. We hypothesize that VIP feedback positively affects intentions to change health behavior by affecting the users' sense of involvement, self-efficacy and comprehension, and, thereby, affecting their intentions to change behavior. We then test the mediation model through a longitudinal experiment in which the subjects used a nutritional information system that supplied them with personal medical information (n=155). The results support the mediation model and present interesting implications for design. VIP feedback offers an opportunity to develop longterm intervention effects on users’ behavior.


title: Hierarchical Density Order Embeddings

abstract: By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty (Vilnis & McCallum, 2014; Athiwaratkun & Wilson, 2017). The uncertainty information can be particularly meaningful in capturing entailment relationships – whereby general words such as “entity” correspond to broad distributions that encompass more specific words such as “animal” or “instrument”. We introduce methodology to learn such representations effectively from labelled data. In particular, we propose simple yet effective loss functions and distance metrics, as well as graph-based schemes to select negative samples to better learn hierarchical probabilistic representations. Our approach provides state-of-the-art performance on the WordNet hypernym relationship prediction task and the challenging HyperLex lexical entailment dataset – while retaining a rich and interpretable probabilistic representation.


Application of x-MR control chart on monitoring displacement for prediction of abnormal ground behaviour in tunnelling


title: The Computational Impact of Partial Votes on Strategic Voting

abstract: In many real world elections, agents are not required to rank all candidates. We study three of the most common methods used to modify voting rules to deal with such partial votes. These methods modify scoring rules (like the Borda count), elimination style rules (like single transferable vote) and rules based on the tournament graph (like Copeland) respectively. We argue that with an elimination style voting rule like single transferable vote, partial voting does not change the situations where strategic voting is possible. However, with scoring rules and rules based on the tournament graph, partial voting can increase the situations where strategic voting is possible. As a consequence, the computational complexity of computing a strategic vote can change. For example, with Borda count, the complexity of computing a strategic vote can decrease or stay the same depending on how we score partial votes.


title: Using Argumentation to Structure E-Participation in Policy Making

abstract: Tools for e-participation are becoming increasingly important. In this paper we argue that existing tools exhibit a number of limitations, and that these can be addressed by basing tools on developments in the field of computational argumentation. After discussing the limitations, we present an argumentation scheme which can be used to justify policy proposals, and a way of modelling the domain so that arguments using this scheme and attacks upon them can be automatically generated. We then present two prototype tools: one to present justifications and receive criticism, and the other to elicit justifications of user-proposed policies and critique them. We use a running example of a genuine policy debate to illustrate the various aspects.


title: Endogenous Stratification in Randomized Experiments

abstract: Researchers and policy makers are often interested in estimating how treatments or policy interventions affect the outcomes of those most in need of help. This concern has motivated the increasingly common practice of disaggregating experimental data by groups constructed on the basis of an index of baseline characteristics that predicts the values that individual outcomes would take on in the absence of the treatment. This article shows that substantial biases may arise in practice if the index is estimated, as is often the case, by regressing the outcome variable on baseline characteristics for the full sample of experimental controls. We analyze the behavior of leave-one-out and repeated split sample estimators and show they behave well in realistic scenarios, correcting the large bias problem of the full sample estimator. We use data from the National JTPA Study and the Tennessee STAR experiment to demonstrate the performance of alternative estimators and the magnitude of their biases.


title: Functional Impairments in Attention Deficit Hyperactivity Disorder: The Mediating Role of Neuropsychological Functioning

abstract: Attention deficit hyperactivity disorder (ADHD) is associated with multiple neuropsychological deficits and the present study aimed to investigate to what extent these deficits are related to the functional impairments associated with the disorder. The results showed that all executive functioning deficits and reaction time variability acted as mediators in the relation between ADHD and academic achievement. However, only the effect of working memory for language skills, and the effects of reaction time variability and working memory for mathematics, remained significant when studying independent effects. Regulation of anger was a significant mediator for peer problems. Gender or symptoms of oppositional defiant disorder (ODD) or conduct disorder (CD) did not moderate these findings.


title: Analysing fundamental properties of marker-based vision system designs

abstract: This paper investigates fundamental properties of Marker-based Vision (MBV) systems. We present a theoretical analysis of the performance of basic tag designs which is extended through simulation to investigate the effects of different processing algorithms. Real-world data are processed and related to the simulated results. Image processing is performed using Cantag, an open source software toolkit for building Marker-based Vision (MBV) systems that can identify and accurately locate printed markers in t hree dimensions. Cantag supports multiple fiducial shapes, payload types, data sizes an d image processing algorithms in one framework. This paper explores the design space of tags within the Cantag system, and describes the design parameters and performance characteristics which an application writer can use to select the best tag system for any given scenario.


title: Intelligent Neural Sliding Control for Planetary Gear Type Inverted Pendulum Mechanism

abstract: An intelligent neural sliding controller is developed for planetary train type inverted pendulum mechanism. The control methodology is based on the sliding mode control. The switching function in the normal control law is replaced with a bipolar sigmoid function. A fuzzy neural network is used to identify the pendulum dynamics. Adaptive tuning law is derived. The bipolar sigmoid function is thus adjusted according to the result of the identification process.


title: Meta-learning with backpropagation

abstract: Introduces gradient descent methods applied to meta-learning (learning how to learn) in neural networks. Meta-learning has been of interest in the machine learning field for decades because of its appealing applications to intelligent agents, non-stationary time series, autonomous robots, and improved learning algorithms. Many previous neural network-based approaches toward meta-learning have been based on evolutionary methods. We show how to use gradient descent for meta-learning in recurrent neural networks. Based on previous work on fixed-weight learning neural networks, we hypothesize that any recurrent network topology and its corresponding learning algorithm(s) is a potential meta-learning system. We tested several recurrent neural network topologies and their corresponding forms of backpropagation for their ability to meta-learn. One of our systems, based on the long short-term memory neural network developed a learning algorithm that could learn any two-dimensional quadratic function (from a set of such functions) after only 30 training examples.


title: Scale ratio ICP for 3D point clouds with different scales

abstract: In this paper we propose a method for matching the scales of 3D point clouds. 3D point sets of the same scene obtained by 3D reconstruction techniques usually differ in scale. To match scales, we estimate the ratio of scales of two given 3D point clouds. By performing PCA of spin images over different scales of two point clouds, two sets of cumulative contribution rate curves are generated. Such sets of curves can be considered to characterize the scale of the given 3D point clouds. To find the scale ratio of two point clouds, we register the two sets of curves by using a variant of ICP that estimates the ratio of scales. Simulations with the Stanford bunny and experimental results with 3D reconstructions of artificial and real scenes demonstrate that the ratio of any 3D point clouds can be effectively used for scale matching.


Seasonal habitat use by female Rio Grande wild turkey in south Texas


Development of japonica Photo-Sensitive Genic Male Sterile Rice Lines by Editing Carbon Starved Anther Using CRISPR/Cas9


title: Secure Access Control Scheme of RFID System Application

abstract: Radio Frequency Identification (RFID) is a contactless technology, it considered the way to replace the barcode, since the barcode is data read with line of sight and limits the utility for item-level of logistic and supply chain application in the future. RFID is intimate linking real and virtual also creates considerable security and privacy risk in RFID adoption. Until now, many researches on the RFID’s security and/or privacy were proposed. In this paper, we surveys the literature of hash-based access control scheme and propose an effective scheme to enhance the security and privacy about the passive RFID tag.


title: Twitter gender classification using user unstructured information

abstract: This paper describes an approach to automatically detect the gender of Twitter users, based only on clues provided by their profile information in an unstructured form. A number of features that capture phenomena specific of Twitter users is proposed and evaluated on a dataset of about 242K English language users. Different supervised and unsupervised approaches are used to assess the performance of the proposed features, including Naive Bayes variants, Logistic Regression, Support Vector Machines, Fuzzy c-Means clustering, and K-means. An unsupervised approach based on Fuzzy c-Means proved to be very suitable for this task, returning the correct gender for about 96% of the users.