title: A mouse model of higher visual cortical function

abstract: During sensory experience, the retina transmits a diverse array of signals to the brain, which must be parsed to generate meaningful percepts that can guide decisions and actions. Decades of anatomical and physiological studies in primates and carnivores have revealed a complex parallel and hierarchical organization by which distinct visual features are distributed to, and processed by, different brain regions. However, these studies have been limited in their ability to dissect the circuit mechanisms involved in the transformation of sensory inputs into complex cortical representations and action patterns. Multiple groups have therefore pushed to explore the organization and function of higher visual areas in the mouse. Here we review the anatomical and physiological findings of these recent explorations in mouse visual cortex. These studies find that sensory input is processed in a diverse set of higher areas that are each interconnected with specific limbic and motor systems. This hierarchical and parallel organization is consistent with the multiple streams that have been found in the higher visual areas of primates. We therefore propose that the mouse visual system is a useful model to explore the circuits underlying the transformation of sensory inputs into goal-directed perceptions and actions.


title: Brain state dependent activity in the cortex and thalamus

abstract: Cortical and thalamocortical activity is highly state dependent, varying between patterns that are conducive to accurate sensory-motor processing, to states in which the brain is largely off-line and generating internal rhythms irrespective of the outside world. The generation of rhythmic activity occurs through the interaction of stereotyped patterns of connectivity together with intrinsic membrane and synaptic properties. One common theme in the generation of rhythms is the interaction of a positive feedback loop (e.g., recurrent excitation) with negative feedback control (e.g., inhibition, adaptation, or synaptic depression). The operation of these state-dependent activities has wide ranging effects from enhancing or blocking sensory-motor processing to the generation of pathological rhythms associated with psychiatric or neurological disorders.


Image processing for the positive identification of forensic ballistics specimens


title: Clothoid-Based Speed Profiler and Control for Autonomous Driving

abstract: This paper presents a method for optimal speed profile generation in specified clothoid-based paths with known semantic -- maximum speed and longitudinal and lateral acceleration -- and geometric information. A clothoid can be described using only its kink-points information, i.e. the points defining the start and end of a clothoid. Using the clothoid-based path representation, we formulate the speed profile generation as a convex optimization problem where the objective is to produce a smooth speed that is close to the maximum allowed speed. The vehicle and the road profile define the constraints of the problem. Furthermore, we develop a longitudinal controller by using the speed profiler in a receding-horizon fashion. Thus, we only consider a finite horizon when computing the optimal inputs every sampling time and, in addition, the longitudinal controller also takes into account the newest prediction available from measurements and from the lateral controller. We present simulations that demonstrate the ability of the method to generate safe and feasible speed profiles and the tracking of those by the longitudinal controller. We also study the influence of the clothoid-based path representation in the optimality of the speed profile obtained. We show that we can get a very good suboptimal speed profile approximation with few more points than the kink-points. In addition, we analyze the influence of an acceleration penalization factor in the smoothness of the speed profiler. The higher the acceleration penalization the smoother and the further from the maximum allowed speed is the speed profile.


title: Sharing Visual Features for Multiclass and Multiview Object Detection

abstract: We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity and the (training-time) sample complexity scale linearly with the number of classes to be detected. We present a multitask learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required and, therefore, the runtime cost of the classifier, is observed to scale approximately logarithmically with the number of classes. The features selected by joint training are generic edge-like features, whereas the features chosen by training each class separately tend to be more object-specific. The generic features generalize better and considerably reduce the computational cost of multiclass object detection


title: The Bitcoin Backbone Protocol: Analysis and Applications

abstract: Bitcoin is the first and most popular decentralized cryptocurrency to date. In this work, we extract and analyze the core of the Bitcoin protocol, which we term the Bitcoin backbone, and prove two of its fundamental properties which we call common prefix and chain quality in the static setting where the number of players remains fixed. Our proofs hinge on appropriate and novel assumptions on the “hashing power” of the adversary relative to network synchronicity; we show our results to be tight under high synchronization.


title: A Genetic Algorithm-Based Moving Object Detection for Real-time Traffic Surveillance

abstract: Recent developments in vision systems such as distributed smart cameras have encouraged researchers to develop advanced computer vision applications suitable to embedded platforms. In the embedded surveillance system, where memory and computing resources are limited, simple and efficient computer vision algorithms are required. In this letter, we present a moving object detection method for real-time traffic surveillance applications. The proposed method is a combination of a genetic dynamic saliency map (GDSM), which is an improved version of dynamic saliency map (DSM) and background subtraction. The experimental results show the effectiveness of the proposed method in detecting moving objects.


title: Modulation of MicroRNAs by Chemical Carcinogens and Anticancer Drugs in Human Cancer: Potential Inkling to Therapeutic Advantage

abstract: The disorder of microRNAs (miRNAs) often referred as ‘micromanagers of gene expression’ has been implicated with a vast array of neoplasmthe discovery establishes an important connection with the etiology, diagnosis and potential therapy of human cancer. Indeed, the wide range of profiling studies enabled to create miRNA signatures of solid tumors as well as cancers of blood origin. MiRNAs have been observed to play a significant role in the regulation of gene expression-a critical aspect of many biological processes, including cell development, differentiation, apoptosis and proliferation. The differential expression levels of miRNAs in tumors and their normal counterpart have enabled scientists to designate their roles as oncomir or tumor suppressor. Interestingly, the diminishment of oncogenic or enhanced levels of tumor suppressor miRNAs (antagomirs) have been reported to modulate the sensitivity of cancer cells to anticancer agents. To the other end, carcinogenic chemicals either possess the ability of silencing beneficial tumor suppressive miRNAs or maintain the augmented levels of their oncogenic counterpart. In this article we provide a comprehensive overview on the modulation of these “micromanaging oligos” by cancer causing as well as cancer preventing agents.


title: Tomosar platform supports for Sentinel-1 tops persistent scatterers interferometry

abstract: Developing and improving methods to monitor both natural and non-natural environments such as forest and urban in space and time is a timely challenge. To overcome this challenge, we created a software platform — TomoSAR. The kernel of this platform supports the entire processing from SAR, Interferometry, Polarimetry, to Tomography (so called TomoSAR). The objective of this paper is to introduce this platform about its capability in Persistent Scatterers Interferometry (PSI) technique to estimate subsidence using TOPS Sentinel-1 data.


title: Determining feature extractors for unsupervised learning on satellite images

abstract: Advances in satellite imagery presents unprecedented opportunities for understanding natural and social phenomena at global and regional scales. Although the field of satellite remote sensing has evaluated imperative questions to human and environmental sustainability, scaling those techniques to very high spatial resolutions at regional scales remains a challenge. Satellite imagery is now more accessible with greater spatial, spectral and temporal resolution creating a data bottleneck in identifying the content of images. Because satellite images are unlabeled, unsupervised methods allow us to organize images into coherent groups or clusters. However, the performance of unsupervised methods, like all other machine learning methods, depends on features. Recent studies using features from pre-trained networks have shown promise for learning in new datasets. This suggests that features from pre-trained networks can be used for learning in temporally and spatially dynamic data sources such as satellite imagery. It is not clear, however, which features from which layer and network architecture should be used for learning new tasks. In this paper, we present an approach to evaluate the transferability of features from pre-trained Deep Convolutional Neural Networks for satellite imagery. We explore and evaluate different features and feature combinations extracted from various deep network architectures, and systematically evaluate over 2,000 network-layer combinations. In addition, we test the transferability of our engineered features and learned features from an unlabeled dataset to a different labeled dataset. Our feature engineering and learning are done on the unlabeled Draper Satellite Chronology dataset, and we test on the labeled UC Merced Land dataset to achieve near state-of-the-art classification results. These results suggest that even without any or minimal training, these networks can generalize well to other datasets. This method could be useful in the task of clustering unlabeled images and other unsupervised machine learning tasks.


title: Moving furniture with teams of autonomous robots

abstract: The authors wish to organize furniture in a room with a team of robots that can push objects. The authors show how coordinated pushing by robots can change the pose (position and orientation) of objects and then they ask whether planning, global control, and explicit communication are necessary for cooperatively changing the pose of objects. The authors answer in the negative and present, as witnesses, four cooperative manipulation protocols that use different amounts of state, sensing, and communication. The authors analyze these protocols in the information invariant framework. The authors formalize the notion of resource tradeoffs for robot protocols and give the tradeoffs for the specific protocols discussed here.


Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders.


title: Unmanned Aerial Vehicles for Power Line Inspection: A Cooperative Way in Platforms and Communications

abstract: The emerging technology of unmanned aerial vehicle (UAV) has become more affordable and practicable for power line inspections. In this paper, we propose a multi-platform UAV system and multi-model communication system for highly efficient power line inspection tasks in China. The different UAVs cooperatively serve as long-distance imaging, short dis- tance imaging and communication relay. The high quality im- age/video is transmitted in realtime to the on-site control station for UAV navigation and far end office for analysis. Our experi- ence shows that the cooperative inspection for multi-UAVs can achieve a much higher efficiency than traditional inspection methods.  Index Terms—UAV, hexrotor, fixed wing, tethered rotor, pow- er line inspection, communication relay, datalink


title: Reduced-Diameter Designs of Coax-Fed Microwave Ablation Antennas Equipped With Baluns

abstract: We present a technique for minimizing the impact of coaxial baluns on the invasiveness of coax-fed microwave ablation (MWA) antennas. The technique uses an air-filled coax section at the end of a Teflon-filled coaxial feedline. The air-filled coax section has a smaller outer-conductor diameter compared to the Teflon-filled coax section for the same inner conductor diameter and characteristic impedance. Therefore, the coaxial balun used in a typical MWA antenna can be implemented on the air-filled coax section without exceeding the outer diameter of the Teflon-filled coaxial feedline. The efficacy of this technique is demonstrated for two different MWA antennas, a choke dipole and a floating sleeve dipole. Prototypes of these antennas as well as prototypes of their unmodified versions were designed, fabricated, and used to perform ablation experiments in ex vivo porcine muscle at 7 GHz with a power level of 30 W for 5 min. Simulation and measurement results confirmed that the modified antennas had similar responses as the unmodified antennas while offering 30% smaller diameters compared to their conventional balun-equipped coax-fed MWA antennas. Specifically, the antennas exhibited good impedance matching and produced localized ablation zones with shapes and dimensions similar to those produced by the original designs.


title: Maintenance Workforce Scheduling Using Arena Simulation

abstract: This paper analyzed the problem faced by a maintenance department of an Information Technology company, which recorded a huge amount of penalty cost each month. The cost is incurred when a service breakdown is solved beyond the agreed-upon duration, as distinctively stated in the Service Level Agreement established with its customers. Therefore, the objective of the paper is to present a workforce schedule model that would minimize the average total time of service in the system and maximize the total number of completed jobs. A simulation modeling was used and two alternatives of workforce schedule are compared. In comparison to the existing schedule, switching to the first alternative of Grouped-Efficiency-based Staff Schedule is expected to reduce the average total time in system of the highest severity service by 69% and increase the total number of completed jobs by 15.7%.


title: Security-Aware Design Methodology and Optimization for Automotive Systems

abstract: In this article, we address both security and safety requirements and solve security-aware design problems for the controller area network (CAN) protocol and time division multiple access (TDMA)-based protocols. To provide insights and guidelines for other similar security problems with limited resources and strict timing constraints, we propose a general security-aware design methodology to address security with other design constraints in a holistic framework and optimize design objectives. The security-aware design methodology is further applied to solve a security-aware design problem for vehicle-to-vehicle (V2V) communications with dedicated short-range communication (DSRC) technology. Experimental results demonstrate the effectiveness of our approaches in system design without violating design constraints and indicate that it is necessary to consider security together with other metrics during design stages.


title: Discriminative Approach to Fill-in-the-Blank Quiz Generation for Language Learners

abstract: We propose discriminative methods to generate semantic distractors of fill-in-theblank quiz for language learners using a large-scale language learners’ corpus. Unlike previous studies, the proposed methods aim at satisfying both reliability and validity of generated distractors; distractors should be exclusive against answers to avoid multiple answers in one quiz, and distractors should discriminate learners’ proficiency. Detailed user evaluation with 3 native and 23 non-native speakers of English shows that our methods achieve better reliability and validity than previous methods.


title: Leveraging big data for grasp planning

abstract: We propose a new large-scale database containing grasps that are applied to a large set of objects from numerous categories. These grasps are generated in simulation and are annotated with different grasp stability metrics. We use a descriptive and efficient representation of the local object shape at which each grasp is applied. Given this data, we present a two-fold analysis: (i) We use crowdsourcing to analyze the correlation of the metrics with grasp success as predicted by humans. The results show that the metric based on physics simulation is a more consistent predictor for grasp success than the standard υ-metric. The results also support the hypothesis that human labels are not required for good ground truth grasp data. Instead the physics-metric can be used to generate datasets in simulation that may then be used to bootstrap learning in the real world. (ii) We apply a deep learning method and show that it can better leverage the large-scale database for prediction of grasp success compared to logistic regression. Furthermore, the results suggest that labels based on the physics-metric are less noisy than those from the υ-metric and therefore lead to a better classification performance.


title: Executive function predicts artificial language learning

abstract: Previous research suggests executive function (EF) advantages among bilinguals compared to monolingual peers, and these advantages are generally attributed to experience controlling two linguistic systems. However, the possibility that the relationship between bilingualism and EF might be bidirectional has not been widely considered; while experience with two languages might improve EF, better EF skills might also facilitate language learning. In the current studies, we tested whether adults’ and preschool children’s EF abilities predicted success in learning a novel artificial language. After controlling for working memory and English receptive vocabulary, adults’ artificial language performance was predicted by their inhibitory control ability (Study 1) and children’s performance was predicted by their attentional monitoring and shifting ability (Study 2). These findings provide preliminary evidence suggesting that EF processes may be employed during initial stages of language learning, particularly vocabulary acquisition, and support the possibility of a bidirectional relationship between EF and language acquisition.


title: Directional histogram model for three-dimensional shape similarity

abstract: In this paper, we propose a novel shape representation we call directional histogram model (DHM). It captures the shape variation of an object and is invariant to scaling and rigid transforms. The DHM is computed by first extracting a directional distribution of thickness histogram signatures, which are translation invariant. We show how the extraction of the thickness histogram distribution can be accelerated using conventional graphics hardware. Orientation invariance is achieved by computing the spherical harmonic transform of this distribution. Extensive experiments show that the DHM is capable of high discrimination power and is robust to noise.


title: Are Today's SDN Controllers Ready for Primetime?

abstract: SDN efficiency is driven by the ability of controllers to process small packets based on a global view of the network. The goal of such controllers is thus to treat new flows coming from hundreds of switches in a timely fashion. In this paper, we show this ideal remains impossible through the most extensive evaluation of SDN controllers. We evaluated five state-of-the-art SDN controllers and discovered that the most efficient one spends a fifth of his time in packet serialization. More dramatically, we show that this limitation is inherent to the object oriented design principle of these controllers. They all treat each single packet as an individual object, a limitation that induces an unaffordable per-packet overhead. To eliminate the responsibility of the hardware from our results, we ported these controllers on a network-efficient architecture, Tilera, and showed even worse performance. We thus argue for an in-depth rethinking of the design of the SDN controller into a lower level software that leverages both operating system optimizations and modern hardware features.


title: Decision Support Systems Using Intelligent Paradigms

abstract: Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing (SC) technologies that underlie the conception, design and utilization of intelligent systems. In this paper, we present different SC paradigms involving an artificial neural network trained using the scaled conjugate gradient algorithm, two different fuzzy inference methods optimised using neural network learning/evolutionary algorithms and regression trees for developing intelligent decision support systems. We demonstrate the efficiency of the different algorithms by developing a decision support system for a Tactical Air Combat Environment (TACE). Some empirical comparisons between the different algorithms are also provided.


title: Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction

abstract: Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., linear chains) in which search and parameter estimation can be performed exactly. Unfortunately, in many complex problems, it is rare that exact search or parameter estimation is tractable. Instead of learning exact models and searching via heuristic means, we embrace this difficulty and treat the structured output problem in terms of approximate search. We present a framework for learning as search optimization, and two parameter updates with convergence the-orems and bounds. Empirical evidence shows that our integrated approach to learning and decoding can outperform exact models at smaller computational cost.


title: Design and measurement of a kind of dual polarized Vivaldi antenna

abstract: This paper designed and fabricated a dual polarized ultra wide band (UWB) Vivaldi antenna in order to meet the requirement of the practical work. The discussed working frequency range of the designed antenna is 3.1∼10.6GHz, the designed dual polarized Vivaldi antenna is composed of two single polarized Vivaldi antennas which are orthogonally laid. For each single polarized Vivaldi antenna, a linear tapered microstrip feeding structure is designed to realize the impedance matching. These two feeding microstrip lines are separated to realize independent feeding of each polarized port. The structure and parameters of the dual polarized Vivaldi antenna is designed and simulated. The fabricated dual polarized Vivaldi antenna is measured in the anechoic chamber. The isolation degree is about 20dB and the gain o f each polarization port is larger than 1.37dBi. Some measured patterns and gain curves are given. Yet the performances of two ports are not symmetric and some characteristic parameters are not ideal compared with the simulation results which should be further improved through improving the fabrication level. The design and experimental results in this paper can provide a technical reference for the fabrication of other dual polarized UWB antennas.


title: A secure IoT architecture for Smart Cities

abstract: We present a secure Internet of Things (IoT) architecture for Smart Cities. The large-scale deployment of IoT technologies within a city promises to make city operations efficient while improving quality of life for city inhabitants. Mission-critical Smart City data, captured from and carried over IoT networks, must be secured to prevent cyber attacks that might cripple city functions, steal personal data and inflict catastrophic harm. We present an architecture containing four basic IoT architectural blocks for secure Smart Cities: Black Network, Trusted SDN Controller, Unified Registry and Key Management System. Together, these basic IoT-centric blocks enable a secure Smart City that mitigates cyber attacks beginning at the IoT nodes themselves.


title: Fielded Sequential Dependence Model for Ad-Hoc Entity Retrieval in the Web of Data

abstract: Previously proposed approaches to ad-hoc entity retrieval in the Web of Data (ERWD) used multi-fielded representation of entities and relied on standard unigram bag-of-words retrieval models. Although retrieval models incorporating term dependencies have been shown to be significantly more effective than the unigram bag-of-words ones for ad hoc document retrieval, it is not known whether accounting for term dependencies can improve retrieval from the Web of Data. In this work, we propose a novel retrieval model that incorporates term dependencies into structured document retrieval and apply it to the task of ERWD. In the proposed model, the document field weights and the relative importance of unigrams and bigrams are optimized with respect to the target retrieval metric using a learning-to-rank method. Experiments on a publicly available benchmark indicate significant improvement of the accuracy of retrieval results by the proposed model over state-of-the-art retrieval models for ERWD.


title: Neurocognitive bases of emotion regulation development in adolescence

abstract: Emotion regulation is the ability to recruit processes to influence emotion generation. In recent years there has been mounting interest in how emotions are regulated at behavioural and neural levels, as well as in the relevance of emotional dysregulation to psychopathology. During adolescence, brain regions involved in affect generation and regulation, including the limbic system and prefrontal cortex, undergo protracted structural and functional development. Adolescence is also a time of increasing vulnerability to internalising and externalising psychopathologies associated with poor emotion regulation, including depression, anxiety and antisocial behaviour. It is therefore of particular interest to understand how emotion regulation develops over this time, and how this relates to ongoing brain development. However, to date relatively little research has addressed these questions directly. This review will discuss existing research in these areas in both typical adolescence and in adolescent psychopathology, and will highlight opportunities for future research. In particular, it is important to consider the social context in which adolescent emotion regulation develops. It is possible that while adolescence may be a time of vulnerability to emotional dysregulation, scaffolding the development of emotion regulation during this time may be a fruitful preventative target for psychopathology.


title: Computer vision based road traffic accident and anomaly detection in the context of Bangladesh

abstract: Road traffic accident is one of the major causes of death in Bangladesh. In this article, we propose a method that uses road side video data to learn the traffic pattern and uses vision based techniques to track and determine various kinetic features of vehicles. Finally, the proposed method detects anomaly (possibility of accident) and accidents on the road. The proposed method shows approximately 85% accuracy level in detecting special situations.


title: PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors

abstract: We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry. Based on the folding-based auto-encoding of well known point pair features, PPF-FoldNet offers many desirable properties: it necessitates neither supervision, nor a sensitive local reference frame, benefits from point-set sparsity, is end-to-end, fast, and can extract powerful rotation invariant descriptors. Thanks to a novel feature visualization, its evolution can be monitored to provide interpretable insights. Our extensive experiments demonstrate that despite having six degree-of-freedom invariance and lack of training labels, our network achieves state of the art results in standard benchmark datasets and outperforms its competitors when rotations and varying point densities are present. PPF-FoldNet achieves $9\%$ higher recall on standard benchmarks, $23\%$ higher recall when rotations are introduced into the same datasets and finally, a margin of $>35\%$ is attained when point density is significantly decreased.


title: Shift invariance and the neocognitron

abstract: Abstract We investigate the ability of the neocognitron to perform shift-invariant pattern recognition. Both an intuitive analysis and a more formal investigation show that the performance of the neocognitron is not intrinsically shift invariant, and that certain model parameters must be chosen appropriately to obtain approximate shift invariance. It is shown how these parameters should be chosen to reach a compromise between invariance and classification sensitivity.


title: Clinical Outcomes of Impending Nasal Skin Necrosis Related to Nose and Nasolabial Fold Augmentation with Hyaluronic Acid Fillers.

abstract: Background:Although there are several case reports of facial skin ischemia/necrosis caused by hyaluronic acid filler injections, no systematic study of the clinical outcomes of a series of cases with this complication has been reported.Methods:The authors report a study of 20 consecutive patients wh


title: DDOS attack in WSN: A survey

abstract: Wireless Sensor Networks (WSNs) is collection of large number of sensor nodes which are of limited capabilities, to collect sensitive information. With the advancement of this technology, one of the major concerns these days is of security. There are so many attacks possible on WSN, in Distributed-Denial of Service (DDOS) attacks, malicious nodes adapts many attacks such as flooding attack, black hole attack and warm hole attack to halt the overall functioning of network The risks are even more when we talk about military and industrial applications. Furthermore in WSN there are so many constrains like limited battery power, low capabilities of nodes etc. To present a security model that will consider these constraints and provide security is a major challenge these days. There are many mechanisms which are proposed by researchers to detect or defend from this DDOS attack In this paper we will review these approaches on basis of various parameters.


title: Optimizing area under the ROC curve via extreme learning machines

abstract: Abstract Recently, Extreme learning machine (ELM), an efficient training algorithm for single-hidden-layer feedforward neural networks (SLFN), has gained increasing popularity in machine learning communities. In this paper the ELM based Area Under the ROC Curve (AUC) optimization algorithms are studied so as to further improve the performance of ELM for imbalanced datasets. For binary class problems, a novel ELM algorithm is proposed based on an efficient least square method. For multi-class problems, the following works are done in this paper: First of all, theoretical comparison analysis is proposed for the potential multi-class extensions of AUC; Secondly, a unified objective function for multi-class AUC optimization is proposed following the theoretical analysis; Subsequently, two ELM based multi-class AUC optimization algorithms called E L M M A U C and E L M m a c r o A U C respectively are proposed followed with complexity analyses; Finally, the generalization analysis is established for E L M M A U C in search of theoretical supports. Empirical study on a variety of real-world datasets show the effectiveness of our proposed algorithms.


title: The RowHammer problem and other issues we may face as memory becomes denser

abstract: As memory scales down to smaller technology nodes, new failure mechanisms emerge that threaten its correct operation. If such failure mechanisms are not anticipated and corrected, they can not only degrade system reliability and availability but also, perhaps even more importantly, open up security vulnerabilities: a malicious attacker can exploit the exposed failure mechanism to take over the entire system. As such, new failure mechanisms in memory can become practical and significant threats to system security. In this work, we discuss the RowHammer problem in DRAM, which is a prime (and perhaps the first) example of how a circuit-level failure mechanism in DRAM can cause a practical and widespread system security vulnerability. RowHammer, as it is popularly referred to, is the phenomenon that repeatedly accessing a row in a modern DRAM chip causes bit flips in physically-adjacent rows at consistently predictable bit locations. It is caused by a hardware failure mechanism called DRAM disturbance errors, which is a manifestation of circuit-level cell-to-cell interference in a scaled memory technology. Researchers from Google Project Zero recently demonstrated that this hardware failure mechanism can be effectively exploited by user-level programs to gain kernel privileges on real systems. Several other recent works demonstrated other practical attacks exploiting RowHammer. These include remote takeover of a server vulnerable to RowHammer, takeover of a victim virtual machine by another virtual machine running on the same system, and takeover of a mobile device by a malicious user-level application that requires no permissions. We analyze the root causes of the RowHammer problem and examine various solutions. We also discuss what other vulnerabilities may be lurking in DRAM and other types of memories, e.g., NAND flash memory or Phase Change Memory, that can potentially threaten the foundations of secure systems, as the memory technologies scale to higher densities. We conclude by describing and advocating a principled approach to memory reliability and security research that can enable us to better anticipate and prevent such vulnerabilities.


The Best-Kept Secret in At-Risk Education


title: Attributed scattering centers for SAR ATR

abstract: High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behaviour predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides high-resolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a least-squares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter.


title: Detecting Curve Text in the Wild: New Dataset and New Solution

abstract: Scene text detection has been made great progress in recent years. The detection manners are evolving from axis-aligned rectangle to rotated rectangle and further to quadrangle. However, current datasets contain very little curve text, which can be widely observed in scene images such as signboard, product name and so on. To raise the concerns of reading curve text in the wild, in this paper, we construct a curve text dataset named CTW1500, which includes over 10k text annotations in 1,500 images (1000 for training and 500 for testing). Based on this dataset, we pioneering propose a polygon based curve text detector (CTD) which can directly detect curve text without empirical combination. Moreover, by seamlessly integrating the recurrent transverse and longitudinal offset connection (TLOC), the proposed method can be end-to-end trainable to learn the inherent connection among the position offsets. This allows the CTD to explore context information instead of predicting points independently, resulting in more smooth and accurate detection. We also propose two simple but effective post-processing methods named non-polygon suppress (NPS) and polygonal non-maximum suppression (PNMS) to further improve the detection accuracy. Furthermore, the proposed approach in this paper is designed in an universal manner, which can also be trained with rectangular or quadrilateral bounding boxes without extra efforts. Experimental results on CTW-1500 demonstrate our method with only a light backbone can outperform state-of-the-art methods with a large margin. By evaluating only in the curve or non-curve subset, the CTD + TLOC can still achieve the best results. Code is available at https://github.com/Yuliang-Liu/Curve-Text-Detector.


title: Control of Demagnetizing Field and Magnetostatic Coupling in FeCoV Wires for Zero-Speed Sensor

abstract: In this paper, a measurement of the effect of the demagnetizing field and a method of reducing these disadvantages by etching both ends of the wire are studied. A magnetization reversal in magnetic wires with bistable magnetization states induces pulse voltage in a pickup coil. Amplitude of the voltage does not depend on frequency of an applied magnetic field down to zero. The magnetization switching is accompanied with a large Barkhausen jump. The twisted Vicalloy wire reveals a uniaxial magnetic anisotropy along the wire direction. A magnetization of the hard core is not normally switched in sensor applications. The symmetric output and higher voltages in Vp and Vap obtained from the etched wire are preferable for the sensor applications, which cannot be achieved from the as-prepared wire. It is suggested that edge effects of a demagnetizing field and a magnetostatic coupling can be reduced by etching the soft layer at both ends of the wire.


Framework of Urdu Nastalique Optical Character Recognition System


Data Splitting


title: Approaches to Cross-Domain Sentiment Analysis: A Systematic Literature Review

abstract: A sentiment analysis has received a lot of attention from researchers working in the fields of natural language processing and text mining. However, there is a lack of annotated data sets that can be used to train a model for all domains, which is hampering the accuracy of sentiment analysis. Many research studies have attempted to tackle this issue and to improve cross-domain sentiment classification. In this paper, we present the results of a comprehensive systematic literature review of the methods and techniques employed in a cross-domain sentiment analysis. We focus on studies published during the period of 2010–2016. From our analysis of those works, it is clear that there is no perfect solution. Hence, one of the aims of this review is to create a resource in the form of an overview of the techniques, methods, and approaches that have been used to attempt to solve the problem of cross-domain sentiment analysis in order to assist researchers in developing new and more accurate techniques in the future.


title: A 4.86 mW 15-bit 22.5 MS/s pipelined ADC with 74 dB SNDR in 90 nm CMOS using averaging correlated level shifting technique

abstract: This paper proposes an averaging correlated level shifting (Averaging-CLS) technique as a switched-capacitor amplification technique for reducing finite opamp gain error. The Averaging-CLS inverts the finite opamp gain error in the second amplifying phase and then averages it with that in the first amplifying phase. With Averaging-CLS, a high-resolution pipelined ADC can employ medium-accuracy high-efficiency ring amplifiers, which lowers the power consumption and cost of the ADC. Compared with conventional CLS, Averaging-CLS halves the sampling capacitance under the same SNR requirement. A 15-bit pipelined ADC, which operates from a 1.2 V power supply and utilizes an input range of 2.2 V peak-to-peak differential, was realized in a 90 nm CMOS technology. The ADC achieves a 74 dB peak SNDR at 22.5 MS/s conversion rate and consumes 4.86 mW, resulting in the Walden and Schreier Figure-of-Merits of 52.7fJ/conv.-step and 167.6dB, respectively.


title: A Fractional Slot Concentrated Winding (FSCW) configuration for outer rotor squirrel cage induction motors

abstract: Fractional Slot Concentrated Windings (FSCW) or tooth windings have been proved to be very beneficial for synchronous and permanent magnet (PM) motors. However, the abundance of space harmonics in the air gap flux have rendered FSCW configurations unsuitable for induction motors. This paper proposes the use of a multi-layer FSCW wound around two layers of stator slots that eliminates sub (a fraction of fundamental) and higher order space harmonics and provides a non-overlapping winding arrangement. This winding configuration has several advantages like high torque density, shorter end-winding length and easier manufacturability for outer rotor squirrel cage induction motors for pumps, fans and in-wheel hub drives. A design comparison is performed between the proposed winding configuration and a conventional distributed winding.


title: Towards a Lexicographic Approach to Lexical Transfer in Machine Translation (Illustrated by the German–Russian Language Pair)

abstract: The translation of lexical items is still a formidableobstacle in the field of Machine Translation. The present articleaddresses this problem from the perspective of modernlexicography, putting forth detailed monolingual lexica whichcontain highly elaborated descriptions of lexical items. Leavingaside the resolution of homonymy on the source side and theselection of the appropriate synonym on the target side, weconcentrate on the case of ``multiple lexical correspondences'’,i.e., mismatches between monosemic lexical items. We proposeto use the lexical transfer methodology. Our proposal exploits afine-grained semantic decomposition of the meaning of both sourceand target lexical items as well as information on ``actantialvalency'' (semantic restrictions) and on collocations. The lexicaldescriptions are formalized so as to allow for automatic matchingthereof. We present a rule-based procedure that selects the mostappropriate translation equivalent of a source lexical item byusing matching. The proposal is illustrated drawing on threeGerman and three Russian monosemic speech-act nouns; their lexicalentries are given in an appendix. Two examples of lexical transferbetween these lexemes are analyzed in detail.


title: The hygiene hypothesis: current perspectives and future therapies

abstract: Developed countries have experienced a steady increase in atopic disease and disorders of immune dysregulation since the 1980s. This increase parallels a decrease in infectious diseases within the same time period, while developing countries seem to exhibit the opposite effect, with less immune dysregulation and a higher prevalence of infectious disease. The "hygiene hypothesis", proposed by Strachan in 1989, aimed to explain this peculiar generational rise in immune dysregulation. However, research over the past 10 years provides evidence connecting the commensal and symbiotic microbes (intestinal microbiota) and parasitic helminths with immune development, expanding the hygiene hypothesis into the "microflora" and "old friends" hypotheses, respectively. There is evidence that parasitic helminths and commensal microbial organisms co-evolved with the human immune system and that these organisms are vital in promoting normal immune development. Current research supports the potential for manipulation of the bacterial intestinal microbiota to treat and even prevent immune dysregulation in the form of atopic disease and other immune-mediated disorders (namely inflammatory bowel disease and type 1 diabetes). Both human and animal model research are crucial in understanding the mechanistic links between these intestinal microbes and helminth parasites, and the human immune system. Pro-, pre-, and synbiotic, as well as treatment with live helminth and excretory/secretory helminth product therapies, are all potential therapeutic options for the treatment and prevention of these diseases. In the future, therapeutics aimed at decreasing the prevalence of inflammatory bowel disease, type 1 diabetes, and atopic disorders will likely involve personalized microbiota and/or helminth treatments used early in life.


title: Aware technologies for aging in place: understanding user needs and attitudes

abstract: Although computing technology has made inroads into home environments, it has yet to instigate a major shift in the design of homes or home activities. The convergence of television and the Internet is lagging behind expectations, and the combination of desktop computers, entertainment consoles, televisions, and cell phones has yet to form a cohesive whole. One possible reason for this lag in progress is that these technologies don't address a coherent need -they merely augment current entertainment and communication practices. We base our research on the premise that the next revolution of technology in the home will arise from devices that help older adults maintain their independence. A coherent suite of technologies will eventually let older adults be proactive about their own healthcare, will aid them in daily activities and help them learn new skills, will create new avenues for social communication, and will help ensure their safety and well being. The Aware Home Research initiative hopes to help older adults "age in place" by creating devices that can assist with daily tasks, offer memory support, and monitor daily activities. However, understanding user needs and attitudes is essential to deploying this technology.


title: Project management topic coverage in journals

abstract: Abstract The paper presents an analysis of the relative frequency of attention to `Body of Knowledge' topics of project management in the papers published in the International Journal of Project Management from 1984 to 1998. The results show that some topics have had little or no attention. The number of papers on some topics has increased, and some decreased. Data is also presented on how attention to the topics differs between the International Journal of Project Management and the U.S. Project Management Institute's Project Management Journal over recent years. Possible reasons for these results are discussed and some recommendations offered to editors and authors.


title: A Balanced Assignment Mechanism for Online Taxi Recommendation

abstract: Majority of taxi recommender systems mainly focused on satisfaction of passengers without considering fairness in assignment of taxi drivers. In this paper we propose a balanced assignment mechanism for online taxi recommendation (BAMOTR). BAMOTR provides a mechanism for fair assignment of drivers at some locations with specific routes to pick up passengers and ensures a short waiting time for passengers. Fair assignment is intended to minimize the differences in income among the taxi drivers. Analysis shows out that fair assignment of drivers and shortening the time the passenger wait before pick up is a trade-off problem. In this paper, we set a regulatory factor that can adjust the trade-off between fair assignment of drivers and shortening of waiting time of passengers. We also propose an efficient range refinement algorithm to solve online taxi recommendation problem in BAMOTR. It is theoretically and experimentally proved that range refinement algorithm ensures the same recommendation result as brute-force algorithm, however it greatly reduces the time overhead. We validate the performances of BAMOTR with extensive evaluations. Experimental results show that BAMOTR achieve better recommendation fairness than compared approaches and guarantee a short waiting time for passengers to be picked up.


title: Disentangling diffusion from jumps

abstract: Abstract Realistic models for financial asset prices used in portfolio choice, option pricing or risk management include both a continuous Brownian and a jump components. This paper studies our ability to distinguish one from the other. I find that, surprisingly, it is possible to perfectly disentangle Brownian noise from jumps. This is true even if, unlike the usual Poisson jumps, the jump process exhibits an infinite number of small jumps in any finite time interval, which ought to be harder to distinguish from Brownian noise, itself made up of many small moves.


title: Research based on OSI model

abstract: OSI is an open access system interconnection reference model, is a very good agreement defined protocol specifications. OSI model has seven layers; each layer can have several sub-layers. According to the comparison of OSI and TCP/IP, and research based on the OSI model implementation models and protocols of the Internet, and the OSI model of data transmission between the layers.


title: Web information extraction and user modeling: towards closing the gap

abstract: Web search engines have become the primary method of accessing information on the web. Billions of queries are submitted to major web search engines, reflecting a wide range of information needs. While significant progress has been made on improving the relevance of the results, web search process often remains a frustrating experience. At the same time, web information extraction has seen tremendous progress, such that knowledge bases of millions of facts extracted from the web are now a reality. Yet it is not clear how effectively these knowledge bases support common user information needs. We posit that a key for web information extraction to significantly impact the web search experience is to connect the extraction process with user modeling, particularly with automatic methods for inferring user information needs and anticipated interaction patterns. In this paper we overview some recent efforts for user modeling and inferring user preferences in the context of closing the gap between web information extraction and user modeling.


title: Adapting game mechanics with Micro-Machinations

abstract: In early game development phases game designers adjust game rules in a rapid, iterative and flexible way. In later phases, when software prototypes are available, play testing provides more detailed feedback about player experience. More often than not, the realized and the intended gameplay emerging from game software differ. Unfortunately, adjusting it is hard because designers lack a means for efficiently defining, fine-tuning and balancing game mechanics. The language Machinations provides a graphical notation for expressing the rules of game economies that fits with a designer’s understanding and vocabulary, but is limited to design itself. Micro-Machinations (MM) formalizes the meaning of core language elements of Machinations enabling reasoning about alternative behaviors and assessing quality, making it also suitable for software development. We propose an approach for designing, embedding and adapting game mechanics iteratively in game software, and demonstrate how the game mechanics and the gameplay of a tower defense game can be easily changed and promptly play tested. The approach shows that MM enables the adaptability needed to reduce design iteration times, consequently increasing opportunities for quality improvements and reuse.


title: A 77-GHz Phased-Array Transceiver With On-Chip Antennas in Silicon: Receiver and Antennas

abstract: In this paper, we present the receiver and the on-chip antenna sections of a fully integrated 77-GHz four-element phased-array transceiver with on-chip antennas in silicon. The receiver section of the chip includes the complete down-conversion path comprising low-noise amplifier (LNA), frequency synthesizer, phase rotators, combining amplifiers, and on-chip dipole antennas. The signal combining is performed using a novel distributed active combining amplifier at an IF of 26 GHz. In the LO path, the output of the 52-GHz VCO is routed to different elements and can be phase shifted locally by the phase rotators. A silicon lens on the backside is used to reduce the loss due to the surface-wave power of the silicon substrate. Our measurements show a single-element LNA gain of 23 dB and a noise figure of 6.0 dB. Each of the four receive paths has a gain of 37 dB and a noise figure of 8.0 dB. Each on-chip antenna has a gain of +2 dBi.


title: The focus group method: Insights from focus group interviews on sexual health with adolescents

abstract: This article concerns the manner in which group interaction during focus groups impacted upon the data generated in a study of adolescent sexual health. Twenty-nine group interviews were conducted with secondary school pupils in Ireland, and data were subjected to a qualitative analysis. In exploring the relationship between method and theory generation, we begin by focusing on the ethnographic potential within group interviews. We propose that at times during the interviews, episodes of acting-out, or presenting a particular image in the presence of others, can be highly revealing in attempting to understand the normative rules embedded in the culture from which participants are drawn. However, we highlight a specific problem with distinguishing which parts of the group interview are a valid representation of group processes and which parts accurately reflect individuals' retrospective experiences of reality. We also note that at various points in the interview, focus groups have the potential to reveal participants' vulnerabilities. In addition, group members themselves can challenge one another on how aspects of their sub-culture are represented within the focus group, in a way that is normally beyond reach within individual interviews. The formation and composition of focus groups, particularly through the clustering of like-minded individuals, can affect the dominant views being expressed within specific groups. While focus groups have been noted to have an educational and transformative potential, we caution that they may also be a source of inaccurate information, placing participants at risk. Finally, the opportunities that focus groups offer in enabling researchers to cross-check the trustworthiness of data using a post-interview questionnaire are considered. We conclude by arguing that although far from flawless, focus groups are a valuable method for gathering data about health issues.


title: Twitter Paraphrase Identification with Simple Overlap Features and SVMs

abstract: We present an approach to identifying Twitter paraphrases using simple lexical over-lap features. The work is part of ongoing re-search into the applicability of knowledge-lean techniques to paraphrase identification. We utilize features based on overlap of word and character n-grams and train support vector machine (SVM). Our results demonstrate that character and word level overlap features in combination can give performance comparable to methods employing more sophisticated NLP processing tools and external resources. We achieve the highest F-score for identifying paraphrases on the Twitter Paraphrase Corpus as part of the SemEval-2015 Task1.


title: PHOG: probabilistic model for code

abstract: We introduce a new generative model for code called probabilistic higher order grammar (PHOG). PHOG generalizes probabilistic context free grammars (PCFGs) by allowing conditioning of a production rule beyond the parent non-terminal, thus capturing rich contexts relevant to programs. Even though PHOG is more powerful than a PCFG, it can be learned from data just as efficiently. We trained a PHOG model on a large JavaScript code corpus and show that it is more precise than existing models, while similarly fast. As a result, PHOG can immediately benefit existing programming tools based on probabilistic models of code.


Dense Connectivity Based Two-Stream Deep Feature Fusion Framework for Aerial Scene Classification


title: Learning Invariant Deep Representation for NIR-VIS Face Recognition

abstract: Visual versus near infrared (VIS-NIR) face recognition is still a challenging heterogeneous task due to large appearance difference between VIS and NIR modalities. This paper presents a deep convolutional network approach that uses only one network to map both NIR and VIS images to a compact Euclidean space. The low-level layers of this network are trained only on large-scale VIS data. Each convolutional layer is implemented by the simplest case of maxout operator. The highlevel layer is divided into two orthogonal subspaces that contain modality-invariant identity information and modalityvariant spectrum information respectively. Our joint formulation leads to an alternating minimization approach for deep representation at the training time and an efficient computation for heterogeneous data at the testing time. Experimental evaluations show that our method achieves 94% verification rate at FAR=0.1% on the challenging CASIA NIR-VIS 2.0 face recognition dataset. Compared with state-of-the-art methods, it reduces the error rate by 58% only with a compact 64-D representation.


title: Network Learning with Local Propagation

abstract: This paper presents a locally decoupled network parameter learning with local propagation. Three elements are taken into account: (i) sets of nonlinear transforms that describe the representations at all nodes, (ii) a local objective at each node related to the corresponding local representation goal, and (iii) a local propagation model that relates the nonlinear error vectors at each node with the goal error vectors from the directly connected nodes. The modeling concepts (i), (ii) and (iii) offer several advantages, including (a) a unified learning principle for any network that is represented as a graph, (b) understanding and interpretation of the local and the global learning dynamics, (c) decoupled and parallel parameter learning, (d) a possibility for learning in infinitely long, multi-path and multi-goal networks. Numerical experiments validate the potential of the learning principle. The preliminary results show advantages in comparison to the state-of-the-art methods, w.r.t. the learning time and the network size while having comparable recognition accuracy.


title: A Validated Seven-Subtest Short Form for the WAIS-IV

abstract: This study presents a short form of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV; Wechsler, 2008) using the subtests (Block Design, Similarities, Digit Span, Arithmetic, Information, Coding, and Picture Completion) suggested by Ward (1990). These seven subtests were used to predict the full WAIS-IV Full-Scale IQ, as well as the Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed Index scores. Two different data sets were used: the first consisted of 70 subjects and the second consisted of 32 subjects. The first data set was used to create a linear regression and the second data set was used to validate the results and compare to the prorated score method from the WAIS-IV manual. The prorated estimated scores correlated significantly with their counterparts and proved to be a better method of estimating the Full-Scale IQ and most of the index scores, but the regression equation was better at predicting the Processing Speed Index. The current study is consistent...


title: Object grasping using a 1 DOF variable stiffness gripper actuated by a hybrid variable stiffness actuator

abstract: For tasks requiring robot-environment interaction, compliant motion is important to ensure stable contact and operational safety. The compliant actuators such as a series elastic actuator and a variable stiffness actuator (VSA) are expected to be one of the promising solutions to provide a compliant motion. In this paper, we propose a gripper actuated by the hybrid variable stiffness actuator developed in our previous work to show how the VSA can improve the ability of grasping. The gripper, which is equipped with two symmetric 4-bar linkages, can conduct the position and stiffness control simultaneously, and the grasping force can be calculated by the estimated torque of the HVSA. The HVSA-actuated gripper is able to grasp fragile objects with compliant motion and the relatively heavy object with stiff motion. Furthermore, the features of the variable stiffness provide operational safety by adjusting the stiffness according to the task. In this study, the relationships between the joint torque and the grasping force, and between the joint stiffness and the Cartesian stiffness are analyzed to control the grasping force. From a series of experiments, it is shown that the gripper can grasp fragile objects such as an egg and a wine glass, as well as relatively heavy object without any soft cover and force/torque sensor.


title: Squares: Supporting Interactive Performance Analysis for Multiclass Classifiers

abstract: Performance analysis is critical in applied machine learning because it influences the models practitioners produce. Current performance analysis tools suffer from issues including obscuring important characteristics of model behavior and dissociating performance from data. In this work, we present Squares, a performance visualization for multiclass classification problems. Squares supports estimating common performance metrics while displaying instance-level distribution information necessary for helping practitioners prioritize efforts and access data. Our controlled study shows that practitioners can assess performance significantly faster and more accurately with Squares than a confusion matrix, a common performance analysis tool in machine learning.


title: Turing Incomputable Computation

abstract: Recent cyberattacks have demonstrated that current approaches to the malware problem (e.g., detection) are inadequate. This is not surprising as virus detection is Turing undecidable. 1 Further, some recent malware implementations use NP problems to encrypt and hide the malware. 2 Two goals guide an alternative approach: (a) Program execution should hide computational steps in order to hinder “reverse engineering” efforts by malware hackers; (b) New computational models should be explored that make it more difficult to hijack the purpose of program execution. The methods explained here pertain to (a) implemented with a new computational model, called the active element machine (AEM). 3 An AEM is composed of computational primitives called active elements that simultaneously transmit and receive pulses to and from other active elements. Each pulse has an amplitude and a width, representing how long the pulse amplitude lasts as input to the active element receiving the pulse. If active element Ei simultaneously receives pulses with amplitudes summing to a value greater than Ei’s threshold and Ei’s refractory period has expired, then Ei fires. When Ei fires, it sends pulses to other active elements. If Ei fires at time t, a pulse reaches element Ek at time t+ τik where τik is the transmission time from Ei to Ek. The AEM language uses five commands – Element, Connection, Fire, Program and Meta – to write AEM programs, where time is explicitly specified and multiple commands may simultaneously execute. An Element command creates, at the time specified in the command, an active element with a threshold value, a refractory period and a most recent firing time. A Connection command sets, at the time specified in the command, a pulse amplitude, a pulse width and a transmission time from element Ei to element Ek. The Fire command fires an input element at a particular time. The Program command defines the execution of multiple commands with a single command. Element and Connection commands establish the AEM architecture and firing activity. The Meta command can change the AEM architecture during AEM program execution. This model uses quantum random input to generate random firing patterns that deterministically execute a universal Turing machine (UTM) program η. Random firing interpretations are constructed with dynamic level sets on boolean functions that compute η. The quantum randomness is an essential component for building the random firing patterns that are Turing incomputable. 4 It is assumed that the following are all kept perfectly secret: the state and tape of the UTM, represented by the active elements and connections; the quantum random bits determining how η is computed for each computational step; and the dynamic connections in the AEM. Formally, let f1j , f2j , . . . fmj represent the random firing pattern computing η during the jth computational step and assume an adversary can eavesdrop on f1j , f2j , . . . fmj . Let q denote the current state of the UTM, ak a UTM alphabet symbol and qk a UTM state. Perfect secrecy means that probabilities P (q = qk|f1j = b1 . . . fmj = bm) = P (q = qk) and P (Tk = ak|f1j = b1 . . . fmj = bm) = P (Tk = ak) for each bi ∈ {0, 1} and each Tk which is the contents of the kth tape square. If these secrecy conditions hold, then there doesn’t exist a “reverse engineer” Turing machine that can map the random firing patterns back to an unbounded sequence of UTM instructions. For an unbounded number of computational steps, define function g : N→ {0, 1} where g((j − 1)m+ r) = f(r+1)j and 0 ≤ r < m. Then g is incomputable. Proposed methods of hypercomputation currently have no physical realization. 5 The methods described here can be physically realized using an off-the-shelf quantum random generator device with a USB plug connected to a laptop computer executing a finite AEM program.


title: Magneto-Dielectric Nanocomposite for Antenna Miniaturization and SAR Reduction

abstract: A 900-MHz meander planar inverted-F antenna (PIFA) on a magneto-dielectric nanocomposite (MDNC) substrate for mobile communication is presented. Cobalt nanoparticles were synthesized with polymer matrix, and its properties were measured up to 4 GHz. Bandwidth, gain, and radiation efficiency of antenna on different substrates (MDNC, High K material, and FR4) were compared, and it is demonstrated that MDNC is beneficial for antenna miniaturization with acceptable antenna performance. Head effects due to the antenna were studied, and specific absorption rate (SAR) was calculated. The simulation results demonstrate that the MDNC reduces the head effects and the magnetic loss of MDNC helps to decrease SAR due to the antenna.


A huge benign ovarian mucinous cystadenoma in post-menopausal woman


title: Gait Training in Virtual Reality: Short-Term Effects of Different Virtual Manipulation Techniques in Parkinson’s Disease

abstract: It is well documented that there is a strong relationship between gait asymmetry and the freezing of gait (FOG) in Parkinson’s Disease. The purpose of this pilot study was to find a “virtual reality (VR)- based” gait manipulation strategy to improve gait symmetry by equalizing step length. Fifteen male PD patients (mean age of 67.6 years) with FOG were assessed on a GAITRite® walkway. Natural gait was compared with walking conditions during “VR-based” gait modulation tasks that aimed at equalizing gait symmetry using visual or proprioceptive signals. Compared to natural gait, VR manipulation tasks significantly increased step width and swing time variability for both body sides. Within the VR conditions, only the task with “proprioceptive-visual dissociation” by artificial backward shifting of the foot improved spatial asymmetry significantly with comparable step lengths of both sides. Specific, hypothesis-driven VR tasks represent an efficient tool to manipulate gait features as gait symmetry in PD potentially preventing FOG. This pilot study offers promising “VR-based” approaches for rehabilitative training strategies to achieve gait symmetry and prevent FOG.


title: TrustRank: a Cold-Start tolerant recommender system

abstract: The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. User- and item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are known to have Cold-Start problems, i.e., they are unable to effectively handle Cold-Start users who have an extremely limited number of purchase records. In this paper, we develop TrustRank, a novel recommender system which handles the Cold-Start problem by leveraging the user-trust networks which are commonly available for e-commerce applications. A user-trust network is formed by friendships or trust relationships that users specify among them. While it is straightforward to conjecture that a user-trust network is helpful for improving the accuracy of recommendations, a key challenge for using user-trust networ...


title: Longitudinal analysis of the role of perceived self-efficacy for self-regulated learning in academic continuance and achievement.

abstract: The present study examined the developmental course of perceived efficacy for self-regulated learning and its contribution to academic achievement and likelihood of remaining in school in a sample of 412 Italian students (48% males and 52% females ranging in age from 12 to 22 years). Latent growth curve analysis revealed a progressive decline in self-regulatory efficacy from junior to senior high school, with males experiencing the greater reduction. The lower the decline in self-regulatory efficacy, the higher the high school grades and the greater the likelihood of remaining in high school controlling for socioeconomic status. Reciprocal cross-lagged models revealed that high perceived efficacy for self-regulated learning in junior high school contributed to junior high school grades and self-regulatory efficacy in high school, which partially mediated the relation of junior high grades on high school grades and the likelihood of remaining in school. Socioeconomic status contributed to high school grades only mediationally through junior high grades and to school drop out both directly and mediationally through junior high grades.


title: Attention-based mixture density recurrent networks for history-based recommendation

abstract: Recommendation system has been widely used in search, online advertising, e-Commerce, etc. Most products and services can be formulated as a personalized recommendation problem. Based on users' past behavior, the goal of personalized history-based recommendation is to dynamically predict the user's propensity (online purchase, click, etc.) distribution over time given a sequence of previous activities. In this paper, with an e-Commerce use case, we present a novel and general recommendation approach that uses a recurrent network to summarize the history of users' past purchases, with a continuous vectors representing items, and an attention-based recurrent mixture density network, which outputs each mixture component dynamically, to accurate model the predictive distribution of future purchase. We evaluate the proposed approach on two publicly available datasets, MovieLens-20M and RecSys15. Both experiments show that the proposed approach, which explicitly models the multi-modal nature of the predictive distribution, is able to greatly improve the performance over various baselines in terms of precision, recall and nDCG. The new modeling framework proposed can be easily adopted to many domain-specific problems, such as item recommendation in e-Commerce, ads targeting in online advertising, click-through-rate modeling, etc.


title: A hypo-optimum feature selection strategy for mouse dynamics in continuous identity authentication and monitoring

abstract: Mouse dynamics has recently become an interesting new topic in computer security and biometrics due to its non-intrusiveness and convenience. While several pattern recognition methods have been proposed to verify a user based on characteristics of mouse dynamics, they are not applicable to continuous identity authentication and monitoring because most features adopted are statistical-based. This paper compares two hypo-optimum feature selection and evaluation methods to obtain the best combination of features for continuous identity authentication and monitoring. Experiments show that most of the selected feature parameters (12 out of 14) are real time computable which means these features are suitable for online monitoring. Classification results by SVM (Support Vector Machine) show that the performance of feature-selected samples are encouraging with the FAR of 1.86% and FRR of 3.46%, suggesting continuous identity authentication and monitoring with high accuracy is achievable.


title: Opportunities and challenges of mobile learning for promoting mathematical literacy

abstract: Mathematical literacy plays an important role in supporting individuals to fulfil their professional roles in modern society. The affordances of mobile technologies as well as the emergence of new theories in mobile learning have the potential to promote mathematical literacy. However, implementation of mobile learning in Indonesian society faces challenges related to perceived ethical and learning issues in curriculum-based educational settings. This study aims to investigate the preparedness of teachers in integrating mathematics subject content with mobile technologies, especially in promoting mathematical literacy. An exploratory study has been conducted using mixed methods by performing questionnaire survey and semi-structured interviews to understand teacher's knowledge towards mathematical literacy and identifying opportunities and challenges of mobile learning within instruction. Findings indicate that teachers mostly do not know about mathematical literacy, indicating that the concept of mathematical literacy needs to be promoted. Further, most schools prohibit the use of mobile devices in classrooms as they are wary of inappropriate use of mobile devices which may harm students' mental health and distract them from learning. Study finds this to be the most common cause for teachers' reluctance in using mobile learning.


title: On the use of I-vectors and average voice model for voice conversion without parallel data

abstract: Recently, deep and/or recurrent neural networks (DNNs/RNNs) have been employed for voice conversion, and have significantly improved the performance of converted speech. However, DNNs/RNNs generally require a large amount of parallel training data (e.g., hundreds of utterances) from source and target speakers. It is expensive to collect such a large amount of data, and impossible in some applications, such as cross-lingual conversion. To solve this problem, we propose to use average voice model and i-vectors for long short-term memory (LSTM) based voice conversion, which does not require parallel data from source and target speakers. The average voice model is trained using other speakers' data, and the i-vectors, a compact vector representing the identities of source and target speakers, are extracted independently. Subjective evaluation has confirmed the effectiveness of the proposed approach.


title: Modelling of a self-sustained discharge-excited ArF excimer laser: the influence of photo-ionization and photodetachment by laser light on the discharge development

abstract: The effect of the laser light on the discharge development characteristics in the active medium of an ArF excimer laser is investigated using a one-dimensional fluid model including laser-light-induced photo-ionization and photodetachment processes. These processes are found to have a significant effect at times after the maximum in the laser output signal. For example, the laser-light-driven interactions increase the bulk electron number density by a factor of two and the electric field adjacent to the cathode by a factor of 1.5. Near the anode, the field changes from slightly negative at early times to extremely positive near the end of the laser pulse, resulting in electron drift in the direction of the bulk plasma. This tendency of the field to change sign at the anode is not observed when photon processes are neglected. This strong field enhancement suggests the generation of a filamental instability at the electrodes, as is indicated by experiments.


title: Citation count prediction: learning to estimate future citations for literature

abstract: In most of the cases, scientists depend on previous literature which is relevant to their research fields for developing new ideas. However, it is not wise, nor possible, to track all existed publications because the volume of literature collection grows extremely fast. Therefore, researchers generally follow, or cite merely a small proportion of publications which they are interested in. For such a large collection, it is rather interesting to forecast which kind of literature is more likely to attract scientists' response. In this paper, we use the citations as a measurement for the popularity among researchers and study the interesting problem of Citation Count Prediction (CCP) to examine the characteristics for popularity. Estimation of possible popularity is of great significance and is quite challenging. We have utilized several features of fundamental characteristics for those papers that are highly cited and have predicted the popularity degree of each literature in the future. We have implemented a system which takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period. We consider several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R-square). Experimental results on a real-large data set show that the best predictive model achieves a mean average predictive performance of 0.740 measured in R-square, which significantly outperforms several alternative algorithms.


title: Face recognition using line edge map

abstract: The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.


title: Dynamics of the Microglial/Amyloid Interaction Indicate a Role in Plaque Maintenance

abstract: Microglial cells aggregate around amyloid plaques in Alzheimer's disease, but, despite their therapeutic potential, various aspects of their reactive kinetics and role in plaque pathogenesis remain hypothetical. Through use of in vivo imaging and quantitative morphological measures in transgenic mice, we demonstrate that local resident microglia rapidly react to plaque formation by extending processes and subsequently migrating toward plaques, in which individual transformed microglia somata remain spatially stable for weeks. The number of plaque-associated microglia increased at a rate of almost three per plaque per month, independent of plaque volume. Larger plaques were surrounded by larger microglia, and a subset of plaques changed in size over time, with an increase or decrease related to the volume of associated microglia. Far from adopting a more static role, plaque-associated microglia retained rapid process and membrane movement at the plaque/glia interface. Microglia internalized systemically injected amyloid-binding dye at a much higher rate in the vicinity of plaques. These results indicate a role for microglia in plaque maintenance and provide a model with multiple targets for therapeutic intervention.


title: Unilateral Rolling of the Foot did not Affect Non-Local Range of Motion or Balance

abstract: Non-local or crossover (contralateral and non-stretched muscles) increases in range-of-motion (ROM) and balance have been reported following rolling of quadriceps, hamstrings and plantar flexors. Since there is limited information regarding plantar sole (foot) rolling effects, the objectives of this study were to determine if unilateral foot rolling would affect ipsilateral and contralateral measures of ROM and balance in young healthy adults. A randomized within-subject design was used to examine non-local effects of unilateral foot rolling on ipsilateral and contralateral limb ankle dorsiflexion ROM and a modified sit-and-reach-test (SRT). Static balance was also tested during a 30 s single leg stance test. Twelve participants performed three bouts of 60 s unilateral plantar sole rolling using a roller on the dominant foot with 60 s rest intervals between sets. ROM and balance measures were assessed in separate sessions at pre-intervention, immediately and 10 minutes post-intervention. To evaluate repeated measures effects, two SRT pre-tests were implemented. Results demonstrated that the second pre-test SRT was 6.6% higher than the first pre-test (p = 0.009, d = 1.91). There were no statistically significant effects of foot rolling on any measures immediately or 10 min post-test. To conclude, unilateral foot rolling did not produce statistically significant increases in ipsilateral or contralateral dorsiflexion or SRT ROM nor did it affect postural sway. Our statistically non-significant findings might be attributed to a lower degree of roller-induced afferent stimulation due to the smaller volume of myofascia and muscle compared to prior studies. Furthermore, ROM results from studies utilizing a single pre-test without a sufficient warm-up should be viewed critically.


title: Using edit distance and junction feature to detect and recognize arrow road marking

abstract: Arrow road markings usually appear on freeway surface and they convey important navigation information to autonomous driving. But detecting and recognizing them is a tough task because they suffer from numerous deviations like objects' interference and themselves' abrasion, etc. We therefore propose a novel local junction feature (L-junction) to describe each road marking as a junction string, different deviation is dispersed into different junction. We encode those junctions within a range as the same code. To measure the similarity between detected junction string and ground truth junction string, we design a weighted edit distance strategy and assign different deviation with different weight so that our framework is robust enough to deviations in arrow road marking but sensitive to non- arrow road markings' deviations. To test our framework, we collect three freeway datasets with our self-driving car: clean/dirty arrow road marking images (300 images respectively), a video dataset (arrow road marking and non-road marking images (670 images)). Another deep learning framework (Boosting+Convolutional Deep Neural Network (CDNN)) is also implemented for comparison. Extensive experimental results well demonstrate the superior performance of our framework.


title: The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues.

abstract: A parametric model was developed to describe the variation of dielectric properties of tissues as a function of frequency. The experimental spectrum from 10 Hz to 100 GHz was modelled with four dispersion regions. The development of the model was based on recently acquired data, complemented by data surveyed from the literature. The purpose is to enable the prediction of dielectric data that are in line with those contained in the vast body of literature on the subject. The analysis was carried out on a Microsoft Excel spreadsheet. Parameters are given for 17 tissue types.


title: Related paper recommendation to support online-browsing of research papers

abstract: An online-browsing support system for research papers has been developed that extracts technical terms from a paper and presents links to useful pages such as those explaining the terms. A method to further use the extracted technical terms is proposed to recommend papers to a user that are related to the paper he or she is browsing. Specifically, the proposed method generates a bipartite graph consisting of papers retrieved by the extracted technical terms, which are called related papers, and technical terms appearing in these related papers. It then ranks the related papers using the HITS algorithm for analyzing the bipartite graph and recommends top-ranked papers to the user. The proposed method was compared with other recommendation methods in terms of effectiveness in an experiment.


title: Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble

abstract: Inline defect inspection plays a critical role in yield improvement for thin film transistor liquid crystal display (TFT-LCD) manufacturing. In array process, some defects are critical to the quality of LCD panels (target defects), while some are not (non-target defects). This paper proposes a target defect identification system by which the target defects can be automatically identified. The proposed system is composed of five parts: projection-based pixel segmentation, normal pixel removal, feature extraction, target defect identification, and decision making. For the identifier design, a novel one-class kernel classifier called fuzzy support vector data description (F-SVDD) ensemble is proposed. F-SVDD ensemble is proposed to solve two critical problems existing in SVDD, including the overfitting due to outliers, and the multi-cluster distribution. In F-SVDD ensemble, both the best number of the F-SVDD members in the ensemble and the elements of each member can be determined by using partitioning-entropy-based kernel fuzzy c-means (KFCM) algorithm. Experimental results, carried out by real defective images provided by a LCD manufacturer, indicate that the proposed F-SVDD ensemble not only greatly improves the performance of SVDD, but also outperforms other commonly used classifiers such as support vector machine (SVM), in terms of target defect identification rate. In addition, the task of target defect identification for one defective image can be accomplished within 3s by the proposed system.


title: On the efficacy of imperfect public-monitoring of seller reputation in e-commerce

abstract: The main objective is the empirical study of e-transactions for the formation of buyers' beliefs.Theoretical analysis of reputation in on line transactions is presented in terms of game theory.A game with imperfect public monitoring and adverse selection is detailed.Empirical evidence on the determinants of perceived buyers' reputation of virtual shops is shown.Web buyers consider both advertising and social signals to learn seller's characteristics. Reputation is crucial in promoting exchanges in online markets, since it may overcome information inefficiency through successful signals of sellers' quality to less informed customers. To explore this issue, I study web sellers' reliability in business-to-consumer online transactions with reference to reputation games. Customers can gather information in online marketplaces like e-Bay through public feedback systems. In contrast, without a centralized reputation system, it is not clear how potential buyers form their beliefs. For the latter case, I provide empirical evidence on perceived reliability and its determinants for some virtual shops operating worldwide in the clothing retail sector.


title: Self-consistent bounces in two-dimensions

abstract: We compute bounce solutions describing false vacuum decay in a {phi}{sup 4} model in two dimensions in the Hartree approximation, thus going beyond the usual one-loop corrections to the decay rate. We use zero energy mode functions of the fluctuation operator for the numerical computation of the functional determinant and the Green's function. We thus avoid the necessity of discretizing the spectrum, as it is necessary when one uses numerical techniques based on eigenfunctions. Regularization is performed in analogy of standard perturbation theory; the renormalization of the Hartree approximation is based on the two-particle point-irreducible scheme. The iteration towards the self-consistent solution is found to converge for some range of the parameters. Within this range we find the corrections to the leading one-loop approximation to be relatively small, not exceeding 1 order of magnitude in the total transition rate.


Integrating Collaborative Filtering and Sentiment Analysis: A Rating Inference Approach


title: Question classification using support vector machines

abstract: Question classification is very important for question answering. This paper presents our research work on automatic question classification through machine learning approaches. We have experimented with five machine learning algorithms: Nearest Neighbors (NN), Naive Bayes (NB), Decision Tree (DT), Sparse Network of Winnows (SNoW), and Support Vector Machines (SVM) using two kinds of features: bag-of-words and bag-of-ngrams. The experiment results show that with only surface text features the SVM outperforms the other four methods for this task. Further, we propose to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions. We describe how the tree kernel can be computed efficiently by dynamic programming. The performance of our approach is promising, when tested on the questions from the TREC QA track.


title: Context-Aware Event Recommendation in Event-based Social Networks

abstract: The Web has grown into one of the most important channels to communicate social events nowadays. However, the sheer volume of events available in event-based social networks (EBSNs) often undermines the users' ability to choose the events that best fit their interests. Recommender systems appear as a natural solution for this problem, but differently from classic recommendation scenarios (e.g. movies, books), the event recommendation problem is intrinsically cold-start. Indeed, events published in EBSNs are typically short-lived and, by definition, are always in the future, having little or no trace of historical attendance. To overcome this limitation, we propose to exploit several contextual signals available from EBSNs. In particular, besides content-based signals based on the events' description and collaborative signals derived from users' RSVPs, we exploit social signals based on group memberships, location signals based on the users' geographical preferences, and temporal signals derived from the users' time preferences. Moreover, we combine the proposed signals for learning to rank events for personalized recommendation. Thorough experiments using a large crawl of Meetup.com demonstrate the effectiveness of our proposed contextual learning approach in contrast to state-of-the-art event recommenders from the literature.


title: Soot: a Java bytecode optimization framework

abstract: This paper presents Soot, a framework for optimizing Java* bytecode. The framework is implemented in Java and supports three intermediate representations for representing Java bytecode: Baf, a streamlined representation of bytecode which is simple to manipulate; Jimple, a typed 3-address intermediate representation suitable for optimization; and Grimp, an aggregated version of Jimple suitable for decompilation. We describe the motivation for each representation, and the salient points in translating from one representation to another. In order to demonstrate the usefulness of the framework, we have implemented intraprocedural and whole program optimizations. To show that whole program bytecode optimization can give performance improvements, we provide experimental results for 12 large benchmarks, including 8 SPECjvm98 benchmarks running on JDK 1.2 for GNU/Linuxtm. These results show up to 8% improvement when the optimized bytecode is run using the interpreter and up to 21% when run using the JIT compiler.


title: LIDAR activities and research priorities in the commercial sector

abstract: Laser altimetry, more commonly referred to in the commercial sector as LiDAR mapping, is becoming a commonplace operational tool in photogrammetry, survey and mapping firms. Its use has grown rapidly over the past five years due to the increasing availability of commercial off-the-shelf sensors, advancements in the design and capabilities of the sensors themselves and an increased awareness of the advantages of using LiDAR technology for elevation data capture by end users. As a result LiDAR mapping has been experiencing strong growth, which in turn has spurred further developments in the technology and even greater demand for the data products. Due to the relatively small size of the lidar mapping sector, capital investment in internally funded research and development appears to be limited. As a result further growth and development of the technology in the commercial sector will depend heavily on the ability to work cooperatively with the academic and research sector to define common research priorities and objectives, especially as relates to specific applications. Education of end users to increase awareness and acceptance of the technology along with establishing approved methodologies and quality control guidelines are also areas of overlap between the commercial and government sectors. Potential research priorities given commercial sector needs will be discussed and ranked with an emphasis on software tools. Undeveloped aspects of research-oriented laser altimetry, especially as relates to waveform capture and analysis will be discussed in the context of potential commercial markets.


title: Vision-based illegal human ladder climbing action recognition in substation

abstract: Nowadays, unattended monitoring system has been widely used in substation for its efficiency and efficacy, and it sometimes may cause safety problems for utility workers. In order to ensure workers' safety, in this paper, we focus on the problem of illegal human ladder climbing action recognition in substation using vision-based algorithm. Specifically, we first detect “forbidden” and “allowing” types of signboards on the ladder to localize the ladder and then define the unsafe area. We use HSV-based algorithm and do hough circle detection to recognize “forbidden” signboard. To recognize “allowing” signboard, we propose HOG-based feature extraction algorithm with SVM classifier, and then use color analysis for further detection. After that, we detect and localize human action applying Visual Background Extractor(ViBE) algorithm. Finally, we can recognize illegal human ladder climbing action based on the relative position between human and signboards. The experiments demonstrate the relative high accuracy of our proposed algorithm.


title: Convolution by Evolution: Differentiable Pattern Producing Networks

abstract: In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN. Unlike a standard CPPN, the topology of a DPPN is evolved but the weights are learned. A Lamarckian algorithm, that combines evolution and learning, produces DPPNs to reconstruct an image. Our main result is that DPPNs can be evolved/trained to compress the weights of a denoising autoencoder from 157684 to roughly 200 parameters, while achieving a reconstruction accuracy comparable to a fully connected network with more than two orders of magnitude more parameters. The regularization ability of the DPPN allows it to rediscover (approximate) convolutional network architectures embedded within a fully connected architecture. Such convolutional architectures are the current state of the art for many computer vision applications, so it is satisfying that DPPNs are capable of discovering this structure rather than having to build it in by design. DPPNs exhibit better generalization when tested on the Omniglot dataset after being trained on MNIST, than directly encoded fully connected autoencoders. DPPNs are therefore a new framework for integrating learning and evolution.