title: Electromagnetic design considerations for a 50,000 rpm 1kW Switched Reluctance Machine using a flux bridge

abstract: The Switched Reluctance Machine (SRM) is a robust machine and is a candidate for ultra high speed applications. Until now the area of ultra high speed machines has been dominated by permanent magnet machines (PM). The PM machine has a higher torque density and some other advantages compared to SRMs. However, the soaring prices of the rare earth materials are driving the efforts to find an alternative to PM machines without significantly impacting the performance. At the same time significant progress has been made in the design and control of the SRM. This paper reviews the progress of the SRM as a high speed machine and proposes a novel rotor structure design to resolve the challenge of high windage losses at ultra high speed. It then elaborates on the path of modifying the design to achieve optimal performance. The simulation result of the final design is verified on FEA software. Finally, a prototype machine with similar design is built and tested to verify the simulation model. The experimental waveform indicates good agreement with the simulation result. Therefore, the performance of the prototype machine is analyzed and presented at the end of this paper.


title: Bio-inspired Analysis of Deep Learning on Not-So-Big Data Using Data-Prototypes

abstract: Deep artificial neural networks are feed-forward architectures capable of very impressive performances in diverse domains. Indeed stacking multiple layers allows a hierarchical composition of local functions, providing efficient compact mappings. Compared to the brain, however, such architectures are closer to a single pipeline and require huge amounts of data, while concrete cases for either human or machine learning systems are often restricted to not-so-big data sets. ::: Furthermore, interpretability of the obtained results is a key issue: since deep learning applications are increasingly present in society, ::: it is important that the underlying processes be accessible and understandable to every one. ::: In order to target these challenges, in this contribution we analyze how considering prototypes in a rather generalized sense (with respect to the state of the art) ::: allows to reasonably work with small data sets while providing an interpretable view of the obtained results. ::: Some mathematical interpretation of this proposal is discussed. ::: Sensitivity to hyperparameters is a key issue for reproducible deep learning results, and is carefully considered in our methodology. ::: Performances and limitations of the proposed setup are explored in details, under different hyperparameters sets, in an analogous way as biological experiments are conducted. ::: We obtain a rather simple architecture, easy to explain, and which allows, combined with a standard method, to target both performances and interpretability.


title: Human primary liver cancer–derived organoid cultures for disease modeling and drug screening

abstract: Tumor organoids derived from the most common subtypes of primary liver cancer recapitulate the histologic and molecular features of the tissues of origin, even after long-term culture. These in vitro models, as well as those for colorectal cancer reported in Crespo et al. in a previous issue, are amenable for drug screening and allow the identification of therapeutic approaches with potential for cancer treatment. Human liver cancer research currently lacks in vitro models that can faithfully recapitulate the pathophysiology of the original tumor. We recently described a novel, near-physiological organoid culture system, wherein primary human healthy liver cells form long-term expanding organoids that retain liver tissue function and genetic stability. Here we extend this culture system to the propagation of primary liver cancer (PLC) organoids from three of the most common PLC subtypes: hepatocellular carcinoma (HCC), cholangiocarcinoma (CC) and combined HCC/CC (CHC) tumors. PLC-derived organoid cultures preserve the histological architecture, gene expression and genomic landscape of the original tumor, allowing for discrimination between different tumor tissues and subtypes, even after long-term expansion in culture in the same medium conditions. Xenograft studies demonstrate that the tumorogenic potential, histological features and metastatic properties of PLC-derived organoids are preserved in vivo. PLC-derived organoids are amenable for biomarker identification and drug-screening testing and led to the identification of the ERK inhibitor SCH772984 as a potential therapeutic agent for primary liver cancer. We thus demonstrate the wide-ranging biomedical utilities of PLC-derived organoid models in furthering the understanding of liver cancer biology and in developing personalized-medicine approaches for the disease.


title: Coexistence of Wireless Technologies in the 5 GHz Bands: A Survey of Existing Solutions and a Roadmap for Future Research

abstract: As the 2.4 GHz spectrum band has become significantly congested, there is growing interest from the Wi-Fi proponents, cellular operators, and other stakeholders to use the spectrum in the 5 GHz bands. The 5 GHz bands have emerged as the most coveted bands for launching new wireless applications and services, because of their relatively favorable propagation characteristics and the relative abundance of spectrum therein. To meet the exploding demand for more unlicensed spectrum, regulators across the world such as the United States Federal Communications Commission and the European Electronic Communications Committee have recently started considerations for opening up additional spectrum in the 5 GHz bands for use by unlicensed devices. Moreover, to boost cellular network capacity, wireless service providers are considering the deployment of unlicensed long term evaluation (LTE) in the 5 GHz bands. This and other emerging wireless technologies and applications have resulted in likely deployment scenarios where multiple licensed and unlicensed networks operate in overlapping spectrum. This paper provides a comprehensive overview of the various coexistence scenarios in the 5 GHz bands. In this paper, we discuss coexistence issues between a number of important wireless technologies—viz., LTE and Wi-Fi, radar and Wi-Fi, dedicated short range communication (DSRC) and Wi-Fi, and coexistence among various 802.11 protocols operating in the 5 GHz bands. Additionally, we identify and provide brief discussions on an impending coexistence issue—one between Cellular V2X and DSRC/Wi-Fi. We summarize relevant standardization initiatives, explain existing coexistence solutions, and discuss open research problems.


title: Discrete wavelet transform-based time series analysis and mining

abstract: Time series are recorded values of an interesting phenomenon such as stock prices, household incomes, or patient heart rates over a period of time. Time series data mining focuses on discovering interesting patterns in such data. This article introduces a wavelet-based time series data analysis to interested readers. It provides a systematic survey of various analysis techniques that use discrete wavelet transformation (DWT) in time series data mining, and outlines the benefits of this approach demonstrated by previous studies performed on diverse application domains, including image classification, multimedia retrieval, and computer network anomaly detection.


title: Vision Based Front and Rear Vehicle Collision Warning System

abstract: In the current driving environment, the top priority is the safety of person. There are two methods proposed to solve safety problems. One is active sensors method and another is passive sensor method. Though with high accuracy, active sensors method has many disadvantages such as high cost, failure to adapt to complex change of environments, and problems relating to laws. Thus there is no way to popularize it. In contrast, passive sensor method is more suitable to current assist systems in virtue of low cost, ability to acquire lots of information. In this paper, the passive sensor method is applied to front and rear vision-based collision warning application. Meanwhile, time-to-contact is used to collision judgment analysis and dedicated short range communications is used to give alert information to near vehicle.


title: Quantifying the Ripple: Word-of-Mouth and Advertising Effectiveness

abstract: ABSTRACT In this article the authors demonstrate how a customer lifetime value approach can provide a better assessment of advertising effectiveness that takes into account postpurchase behaviors such as word-of-mouth. Although for many advertisers word-of-mouth is viewed as an alternative to advertising, the authors show that it is possible to quantify the way in which word-of-mouth often complements and extends the effects of advertising. The authors provide a simple approach to the measurement of postpurchase word-of-mouth sales effects and demonstrate how firms may be underestimating advertising effectiveness by ignoring such effects. Their approach illustrates how customer lifetime value models can provide an important tool to assess the long-term effects of advertising campaigns.


title: Swarm: Hyper Awareness, Micro Coordination, and Smart Convergence through Mobile Group Text Messaging

abstract: Text messaging has become an integral part of mobile communication, with a significant impact on cultural and social norms in many countries. Our goal is to explore how group-based text messaging enables continuous social awareness, group coordination and smart convergence on social events. We implemented a group text messaging system, Swarm, and deployed it to several communication groups for ten months. Through usage logs, questionnaires, text analysis, and direct observation we examined how people integrate group text messaging into their day to day social lives. Swarm was used primarily for lightweight distribution of context information (availability, location, and event status) in order to facilitate social convergence. We discuss the lessons learned from our deployment, and the implications for the design of mobile, group communication systems.


title: Filter-Pruned 3D Convolutional Neural Network for Drowsiness Detection

abstract: Human drowsiness while operating motor vehicles or heavy machinery can have potentially lethal consequences for the operator and others in their immediate vicinity. In this study, we developed a visual-based drowsiness detection system that can analyze videos and make predictions on attention status. A 3D convolutional neural network (CNN) was built for spatio-temporal feature extraction in consecutive frames, and temporal smoothing was used to remove noisy predictions. As a part of an assistance system, a real-time, lightweight and computationally-efficient system is preferable. Thus, we proposed a Scale Module that can be easily integrated into the convolutional layer and estimate the importance of filters. Our results show that scale values calculated from the Scale Module are good indicators for filter pruning, and that filters with small scale values can be removed with negligible loss in the model's performance.


title: A Survey on Context-aware Web Service Systems

abstract: Purpose – This survey aims to study and analyze current techniques and methods for context‐aware web service systems, to discuss future trends and propose further steps on making web services systems context‐aware.Design/methodology/approach – The paper analyzes and compares existing context‐aware web service‐based systems based on techniques they support, such as context information modeling, context sensing, distribution, security and privacy, and adaptation techniques. Existing systems are also examined in terms of application domains, system type, mobility support, multi‐organization support and level of web services implementation.Findings – Supporting context‐aware web service‐based systems is increasing. It is hard to find a truly context‐aware web service‐based system that is interoperable and secure, and operates on multi‐organizational environments. Various issues, such as distributed context management, context‐aware service modeling and engineering, context reasoning and quality of context, se...


title: Language as a Latent Variable: Discrete Generative Models for Sentence Compression

abstract: In this work we explore deep generative models of text in which the latent representation of a document is itself drawn from a discrete language model distribution. We formulate a variational auto-encoder for inference in this model and apply it to the task of compressing sentences. In this application the generative model first draws a latent summary sentence from a background language model, and then subsequently draws the observed sentence conditioned on this latent summary. In our empirical evaluation we show that generative formulations of both abstractive and extractive compression yield state-of-the-art results when trained on a large amount of supervised data. Further, we explore semi-supervised compression scenarios where we show that it is possible to achieve performance competitive with previously proposed supervised models while training on a fraction of the supervised data.


title: Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution

abstract: We introduce a simple, non-linear mention-ranking model for coreference resolution that attempts to learn distinct feature representations for anaphoricity detection and antecedent ranking, which we encourage by pre-training on a pair of corresponding subtasks. Although we use only simple, unconjoined features, the model is able to learn useful representations, and we report the best overall score on the CoNLL 2012 English test set to date.


title: Object Recognition with and without Objects

abstract: While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image. In this paper, we train deep neural networks on the foreground (object) and background (context) regions of images respectively. Considering human recognition in the same situations, networks trained on the pure background without objects achieves highly reasonable recognition performance that beats humans by a large margin if only given context. However, humans still outperform networks with pure object available, which indicates networks and human beings have different mechanisms in understanding an image. Furthermore, we straightforwardly combine multiple trained networks to explore different visual cues learned by different networks. Experiments show that useful visual hints can be explicitly learned separately and then combined to achieve higher performance, which verifies the advantages of the proposed framework.


title: Hyper-Threading Technology: Impact on

abstract: Intel’s recently introduced Hyper-Threading Technology promises to increase applicationand system-level performance through increased utilization of processor resources. It achieves this goal by allowing the processor to simultaneously maintain the context of multiple instruction streams and execute multiple instruction streams or threads. These multiple streams afford the processor added flexibility in internal scheduling, lowering the impact of external data latency, raising utilization of internal resources, and increasing overall performance. We compare the performance of an Intel Xeon processor enabled with Hyper-Threading Technology to that of a dual Xeon processor that does not have HyperThreading Technology on a range of compute-intensive, data-parallel applications threaded with OpenMP. The applications include both real-world codes and handcoded “kernels” that illustrate performance characteristics of Hyper-Threading Technology. The results demonstrate that, in addition to functionally decomposed applications, the technology is effective for  Intel is a registered trademark of Intel Corporation or its subsidiaries in the United States and other countries.  Xeon is a trademark of Intel Corporation or its subsidiaries in the United States and other countries. 1 OpenMP is an industry-standard specification for multithreading data-intensive and other highly structured applications in C, C++, and Fortran. See www.openmp.org for more information. many data-parallel applications. Using hardware performance counters, we identify some characteristics of applications that make them especially promising candidates for high performance on threaded processors. Finally, we explore some of the issues involved in threading codes to exploit Hyper-Threading Technology, including a brief survey of both existing and still-needed tools to support multi-threaded software development. INTRODUCTION While the most visible indicator of computer performance is its clock rate, overall system performance is also proportional to the number of instructions retired per clock cycle. Ever-increasing demand for processing speed has driven an impressive array of architectural innovations in processors, resulting in substantial improvements in clock rates and instructions per cycle. One important innovation, super-scalar execution, exploits multiple execution units to allow more than one operation to be in flight simultaneously. While the performance potential of this design is enormous, keeping these units busy requires super-scalar processors to extract independent work, or instructionlevel parallelism (ILP), directly from a single instruction stream. Modern compilers are very sophisticated and do an admirable job of exposing parallelism to the processor; nonetheless, ILP is often limited, leaving some internal processor resources unused. This can occur for a number of reasons, including long latency to main memory, branch mis -prediction, or data dependences in the instruction stream itself. Achieving additional performance often requires tedious performance Intel Technology Journal Q1, 2002. Vol. 6 Issue 1. Hyper-Threading Technology: Impact on Compute-Intensive Workloads 2 analysis, experimentation with advanced compiler optimization settings, or even algorithmic changes. Feature sets, rather than performance, drive software economics. This results in most applications never undergoing performance tuning beyond default comp iler optimization. An Intel processor with Hyper-Threading Technology offers a different approach to increasing performance. By presenting itself to the operating system as two logical processors, it is afforded the benefit of simultaneously scheduling two potentially independent instruction streams [1]. This explicit parallelism complements ILP to increase instructions retired per cycle and increase overall system utilization. This approach is known as simultaneous multi-threading, or SMT. Because the operating system treats an SMT processor as two separate processors, Hyper-Threading Technology is able to leverage the existing base of multithreaded applications and deliver immediate performance gains. To assess the effectiveness of this technology, we first measure the performance of existing multi-threaded applications on systems containing the Intel Xeon processor with Hyper-Threading Technology. We then examine the system’s performance characteristics more closely using a selection of hand-coded application kernels. Finally, we consider the issues and challenges application developers face in creating new threaded applications, including existing and needed tools for efficient multi-threaded development. APPLICATION SCOPE While many existing applications can benefit from Hyper-Threading Technology, we focus our attention on single-process, numerically intensive applications. By numerically intensive, we mean applications that rarely wait on external inputs, such as remote data sources or network requests, and instead work out of main system memory. Typical examples include mechanical design analysis, multi-variate optimization, electronic design automation, genomics, photo-realistic rendering, weather forecasting, and computational chemistry. A fast turnaround of results normally provides significant value to the users of these applications  Intel is a registered trademark of Intel Corporation or its subsidiaries in the United States and other countries.  Xeon is a trademark of Intel Corporation or its subsidiaries in the United States and other countries. through better quality products delivered more quickly to market. The data-intensive nature of these codes, paired with the demand for better performance, makes them ideal candidates for multi-threaded speed-up on shared memory multi-processor (SMP) systems. We considered a range of applications, threaded with OpenMP, that show good speed-up on SMP systems. The applications and their problem domains are listed in Table 1. Each of these applications achieves 100% processor utilization from the operating system’s point of view. Despite external appearances, however, internal processor resources often remain underutilized. For this reason, these applications appeared to be good candidates for additional speed-up via Hyper-Threading Technology. Table 1: Applications type


Speech recognition with DNN-LAS


title: Laban movement analysis and hidden Markov models for dynamic 3D gesture recognition

abstract: In this paper, we propose a new approach for body gesture recognition. The body motion features considered quantify a set of Laban Movement Analysis (LMA) concepts. These features are used to build a dictionary of reference poses, obtained with the help of a k-medians clustering technique. Then, a soft assignment method is applied to the gesture sequences to obtain a gesture representation. The assignment results are used as input in a Hidden Markov Models (HMM) scheme for dynamic, real-time gesture recognition purposes. The proposed approach achieves high recognition rates (more than 92% for certain categories of gestures), when tested and evaluated on a corpus including 11 different actions. The high recognition rates obtained on two other datasets (Microsoft Gesture dataset and UTKinect-Human Detection dataset) show the relevance of our method.


title: Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network

abstract: This paper applies a deep long-short term memory (LSTM) structure to classify dialogue acts in open-domain conversations.


title: On RTL scan design

abstract: This paper presents a methodology to insert scan paths in a functional Register Transfer Level (RTL) specification of a design that can exploit existing functional paths between sequential elements in the original circuit for establishing scan chains. The primary objective for RTL scan insertion is to reduce the time taken for DFT, and thus reduce the time to market. Additionally, building scan chains at the functional RT-Level is expected to reduce the total area overhead introduced by full scan without compromising the fault coverage achieved. In addition, it often eliminates the delay associated with the additional multiplexer as a part of a conventional scan-cell in high performance designs. Experimental results presented in this paper demonstrate that the proposed method achieves the above objectives while also achieving higher fault coverages for most of the benchmark circuits considered.


title: Brownout: building more robust cloud applications

abstract: Self-adaptation is a first class concern for cloud applications, which should be able to withstand diverse runtime changes. Variations are simultaneously happening both at the cloud infrastructure level - for example hardware failures - and at the user workload level - flash crowds. However, robustly withstanding extreme variability, requires costly hardware over-provisioning. In this paper, we introduce a self-adaptation programming paradigm called brownout. Using this paradigm, applications can be designed to robustly withstand unpredictable runtime variations, without over-provisioning. The paradigm is based on optional code that can be dynamically deactivated through decisions based on control theory. We modified two popular web application prototypes - RUBiS and RUBBoS - with less than 170 lines of code, to make them brownout-compliant. Experiments show that brownout self-adaptation dramatically improves the ability to withstand flash-crowds and hardware failures.


title: Understanding the search for meaning in life: Personality, cognitive style, and the dynamic between seeking and experiencing meaning

abstract: ABSTRACT Although several theories assert that understanding the search for meaning in life is important, empirical research on this construct is sparse. Three studies provide the first extensive effort to understand the correlates of the search for meaning in a multistudy research program. Assessed were relations between search for meaning and well-being, cognitive style, and the Big Five, Big Three, Approach/Avoidance, and Interest models of personality, with a particular emphasis on understanding the correlates of search for meaning that are independent of presence of meaning. Conceptual models of the relation between search and presence were tested. Findings suggest that people lacking meaning search for it; the search for meaning did not appear to lead to its presence. Study 3 found that basic motive dispositions moderated relations between search for meaning and its presence. Results highlight the importance of basic personality dispositions in understanding the search for meaning and its correlates.


title: CamFlow: Managed Data-sharing for Cloud Services

abstract: A model of cloud services is emerging whereby a few trusted providers manage the underlying hardware and communications whereas many companies build on this infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS applications. From the start, strong isolation between cloud tenants was seen to be of paramount importance, provided first by virtual machines (VM) and later by containers, which share the operating system (OS) kernel. Increasingly it is the case that applications also require facilities to effect isolation and protection of data managed by those applications. They also require flexible data sharing with other applications, often across the traditional cloud-isolation boundaries; for example, when government, consisting of different departments, provides services to its citizens through a common platform. These concerns relate to the management of data. Traditional access control is application and principal/role specific, applied at policy enforcement points, after which there is no subsequent control over where data flows;a crucial issue once data has left its owner’s control by cloud-hosted applications andwithin cloud-services. Information Flow Control (IFC), in addition, offers system-wide, end-to-end, flow control based on the properties of the data. We discuss the potential of cloud-deployed IFC for enforcing owners’ data flow policy with regard to protection and sharing, aswell as safeguarding against malicious or buggy software. In addition, the audit log associated with IFC provides transparency and offers system-wide visibility over data flows. This helps those responsible to meet their data management obligations, providing evidence of compliance, and aids in the identification ofpolicy errors and misconfigurations. We present our IFC model and describe and evaluate our IFC architecture and implementation (CamFlow). This comprises an OS level implementation of IFC with support for application management, together with an IFC-enabled middleware.


title: MC-HOG correlation tracking with saliency proposal

abstract: Designing effective feature and handling the model drift problem are two important aspects for online visual tracking. For feature representation, gradient and color features are most widely used, but how to effectively combine them for visual tracking is still an open problem. In this paper, we propose a rich feature descriptor, MC-HOG, by leveraging rich gradient information across multiple color channels or spaces. Then MC-HOG features are embedded into the correlation tracking framework to estimate the state of the target. For handling the model drift problem caused by occlusion or distracter, we propose saliency proposals as prior information to provide candidates and reduce background interference. In addition to saliency proposals, a ranking strategy is proposed to determine the importance of these proposals by exploiting the learnt appearance filter, historical preserved object samples and the distracting proposals. In this way, the proposed approach could effectively explore the color-gradient characteristics and alleviate the model drift problem. Extensive evaluations performed on the benchmark dataset show the superiority of the proposed method.


UvA-DARE ( Digital Academic Repository ) Imaging studies in pathological gambling : similarities and differences with alcohol dependence


title: Ultra-wideband multi-section power divider on suspended stripline

abstract: In this paper, a design method of an ultra-wideband multi-section power divider on suspended stripline (SSL) is presented. A clear design guideline for ultra-wideband power dividers is provided. As a design example, a 10-section SSL power divider is implemented. The fabricated divider exhibits the minimum insertion loss of 0.3 dB, the maximum insertion loss of 1.5 dB from 1 to 19 GHz. The measured VSWR is typically 1.40:1, and the isolation between output-port is typically 20 dB.


Islamic Funds Play a Disappearing Act


title: Highly Stretchable, Weavable, and Washable Piezoresistive Microfiber Sensors.

abstract: A key challenge in electronic textiles is to develop an intrinsically conductive thread of sufficient robustness and sensitivity. Here, we demonstrate an elastomeric functionalized microfiber sensor suitable for smart textile and wearable electronics. Unlike conventional conductive threads, our microfiber is highly flexible and stretchable up to 120% strain and possesses excellent piezoresistive characteristics. The microfiber is functionalized by enclosing a conductive liquid metallic alloy within the elastomeric microtube. This embodiment allows shape reconfigurability and robustness, while maintaining an excellent electrical conductivity of 3.27 ± 0.08 MS/m. By producing microfibers the size of cotton threads (160 μm in diameter), a plurality of stretchable tubular elastic piezoresistive microfibers may be woven seamlessly into a fabric to determine the force location and directionality. As a proof of concept, the conductive microfibers woven into a fabric glove were used to obtain physiological measurements from the wrist, elbow pit, and less accessible body parts, such as the neck and foot instep. Importantly, the elastomeric layer protects the sensing element from degradation. Experiments showed that our microfibers suffered minimal electrical drift even after repeated stretching and machine washing. These advantages highlight the unique propositions of our wearable electronics for flexible display, electronic textile, soft robotics, and consumer healthcare applications.


title: Words in Context: The Effects of Length, Frequency, and Predictability on Brain Responses During Natural Reading

abstract: Word length, frequency, and predictability count among the most influential variables during reading. Their effects are well-documented in eye movement studies, but pertinent evidence from neuroimaging primarily stem from single-word presentations. We investigated the effects of these variables during reading of whole sentences with simultaneous eye-tracking and functional magnetic resonance imaging (fixation-related fMRI). Increasing word length was associated with increasing activation in occipital areas linked to visual analysis. Additionally, length elicited a U-shaped modulation (i.e., least activation for medium-length words) within a brain stem region presumably linked to eye movement control. These effects, however, were diminished when accounting for multiple fixation cases. Increasing frequency was associated with decreasing activation within left inferior frontal, superior parietal, and occipito-temporal regions. The function of the latter region—hosting the putative visual word form area—was originally considered as limited to sublexical processing. An exploratory analysis revealed that increasing predictability was associated with decreasing activation within middle temporal and inferior frontal regions previously implicated in memory access and unification. The findings are discussed with regard to their correspondence with findings from single-word presentations and with regard to neurocognitive models of visual word recognition, semantic processing, and eye movement control during reading.


title: Feature model extraction from large collections of informal product descriptions

abstract: Feature Models (FMs) are used extensively in software product line engineering to help generate and validate individual product configurations and to provide support for domain analysis. As FM construction can be tedious and time-consuming, researchers have previously developed techniques for extracting FMs from sets of formally specified individual configurations, or from software requirements specifications for families of existing products. However, such artifacts are often not available. In this paper we present a novel, automated approach for constructing FMs from publicly available product descriptions found in online product repositories and marketing websites such as SoftPedia and CNET. While each individual product description provides only a partial view of features in the domain, a large set of descriptions can provide fairly comprehensive coverage. Our approach utilizes hundreds of partial product descriptions to construct an FM and is described and evaluated against antivirus product descriptions mined from SoftPedia.


title: Context Prediction for Unsupervised Deep Learning on Point Clouds

abstract: Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation. While massive point cloud datasets can be captured using modern scanning technology, manually labelling such large 3D point clouds for supervised learning tasks is a cumbersome process. This necessitates effective unsupervised learning methods that can produce representations such that downstream tasks require significantly fewer annotated samples. We propose a novel method for unsupervised learning on raw point cloud data in which a neural network is trained to predict the spatial relationship between two point cloud segments. While solving this task, representations that capture semantic properties of the point cloud are learned. Our method outperforms previous unsupervised learning approaches in downstream object classification and segmentation tasks and performs on par with fully supervised methods.


title: Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey

abstract: Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. In this paper, we present a survey on relevant visual surveillance related researches for anomaly detection in public places, focusing primarily on roads. Firstly, we revisit the surveys done in the last 10 years in this field. Since the underlying building block of a typical anomaly detection is learning, we emphasize more on learning methods applied on video scenes. We then summarize the important contributions made during last six years on anomaly detection primarily focusing on features, underlying techniques, applied scenarios and types of anomalies using single static camera. Finally, we discuss the challenges in the computer vision related anomaly detection techniques and some of the important future possibilities.


Conceptual framework for container-handling process analytics


title: Compensation for acoustic heterogeneities in photoacoustic computed tomography using a variable temporal data truncation reconstruction method

abstract: Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the absorbed optical energy density within tissue. If the object possesses spatially variant acoustic properties that are unaccounted for by the reconstruction algorithm, the estimated image can contain distortions. While reconstruction algorithms have recently been developed for compensating for this effect, they generally require the objects acoustic properties to be known a priori. To circumvent the need for detailed information regarding an objects acoustic properties, we have previously proposed a half-time reconstruction method for PACT. A half-time reconstruction method estimates the PACT image from a data set that has been temporally truncated to exclude the data components that have been strongly aberrated. In this approach, the degree of temporal truncation is the same for all measurements. However, this strategy can be improved upon it when the approximate sizes and locations of strongly heterogeneous structures such as gas voids or bones are known. In this work, we investigate PACT reconstruction algorithms that are based on a variable temporal data truncation (VTDT) approach that represents a generalization of the half-time reconstruction approach. In the VTDT approach, the degree of temporal truncation for each measurement is determined by the distance between the corresponding transducer location and the nearest known bone or gas void location. Reconstructed images from a numerical phantom is employed to demonstrate the feasibility and effectiveness of the approach.


title: Combining static analysis and runtime monitoring to counter SQL-injection attacks

abstract: Our dependence on web applications has steadily increased, and we continue to integrate them into our everyday routine activities. When we are making reservations, paying bills, and shopping on-line, we expect these web applications to be secure and reliable. However, as the availability of these services has increased, there has been a corresponding increase in the number and sophistication of attacks that target them. One of the most serious types of attack against web applications is SQL injection. SQL injection is a class of code-injection attacks in which user input is included in a SQL query in such a way that part of the input is treated as code. Using SQL injection. attackers can leak confidential information, such as credit card numbers, from web applications' databases and even corrupt the database. In this paper, we propose a novel technique to counter SQL-injection. The technique combines conservative static analysis and runtime monitoring to detect and stop illegal queries before they are executed on the database. In its static part, the technique builds a conservative model of the legitimate queries that could be generated by the application. In its dynamic part, the technique inspects the dynamically generated queries for compliance with the statically-built model. We also present a preliminary evaluation of the technique performed on two small web applications. The results of the evaluation are promising---our technique was able to prevent all of the attacks that we performed on the two applications.


title: Decomposing a scene into geometric and semantically consistent regions

abstract: High-level, or holistic, scene understanding involves reasoning about objects, regions, and the 3D relationships between them. This requires a representation above the level of pixels that can be endowed with high-level attributes such as class of object/region, its orientation, and (rough 3D) location within the scene. Towards this goal, we propose a region-based model which combines appearance and scene geometry to automatically decompose a scene into semantically meaningful regions. Our model is defined in terms of a unified energy function over scene appearance and structure. We show how this energy function can be learned from data and present an efficient inference technique that makes use of multiple over-segmentations of the image to propose moves in the energy-space. We show, experimentally, that our method achieves state-of-the-art performance on the tasks of both multi-class image segmentation and geometric reasoning. Finally, by understanding region classes and geometry, we show how our model can be used as the basis for 3D reconstruction of the scene.


title: Double cervix and vagina with septate uterus: an uncommon müllerian malformation.

abstract: BACKGROUND ::: The case of an unusual müllerian anomaly is described. ::: ::: ::: CASE ::: A nulligravida was found to have a septate uterus with double cervix and a vagina with partial longitudinal vaginal septum. The anomaly presented symptomatically, was suggested by clinical examination, and confirmed by magnetic resonance imaging and intraoperative visualization. Visualization of the anomaly was enhanced by magnetic resonance-based three-dimensional reconstruction techniques. ::: ::: ::: CONCLUSION ::: Description of double cervix and vagina in the setting of an anatomically normal or simple septate uterus is uncommon. This anomaly is inconsistent with the generally accepted understanding of müllerian development but fits with an alternative hypothesis. Magnetic resonance imaging and magnetic resonance-based three-dimensional reconstruction was useful in aiding visualization of the anomaly.


title: Neurochemical evidence of functional A10 dopamine terminals innervating the ventromedial axis of the neostriatum: in vitro voltammetric data in rat brain slices

abstract: The neostriatum (CPu) and nucleus accumbens (NAc) receive their primary dopamine (DA) afferents from the A9 and A10 cell groups, respectively. Anatomical evidence has, however, shown that some clusters of A10 afferents innervate the medial (periventricular) aspect of the CPu14. The present study used fast cyclic voltammetry (FCV) at carbon fibre microelectrodes to locate and measure the size of these clusters on the basis of diagnostic differences in DA efflux in A9 and A10 terminal regions. All experiments were conducted in CPu or NAc slices superfused with oxygenated artificial CSF at 32 degrees C. Carbon fibre microelectrodes were placed 80 microns below the slice surface and bipolar stimulating electrodes were located 200 microns away. Except in experiments where the stimulation frequency, pulse width or number of pulses were investigated, DA efflux was evoked using 0.1 ms, 10 mA pulses applied singly (1p) or in 20 pulse trains (20p) at 50 Hz and monitored using FCV. The CPu was first mapped on the basis of the ratio of 20p:1p DA efflux. The CPu consisted mainly (64%) of low ratio (< 3) sites while the NAc core comprised exclusively high ratio (> 6) loci. Population analysis revealed a small percentage (10%) of striatal sites with high (> 6) ratios. These high ratio sites matched the reported distribution of A10 afferent clusters, being found almost entirely along the ventromedial axis of the CPu. Individual clusters of high ratio sites (20p:1p ratios > 6), 'mapped' on the basis of evoked 20p DA efflux, were found to be irregular in outline and around 500 microns across. In order to characterise the clusters further, the influence of stimulation frequency, train duration (number of pulses) and pulse width on DA efflux were examined. Peak DA efflux in the clusters and NAc occurred at 50 Hz while the striatal matrix had a flat frequency response. Both clusters and NAc showed a similar dependence of DA efflux on the number of pulses in the stimulus train. In the CPu matrix, DA efflux was less dependent on the number of pulses. In the striatal matrix, increasing the stimulation pulse width enhanced DA efflux on trains more than on single pulses while, in both NAc and striatal clusters, there was no preferential effect on trains. The medial location of these clusters within the CPu more closely matches the anatomical distribution of the A10 afferents of Gerfen et al. than the more evenly dispersed striosomes.(ABSTRACT TRUNCATED AT 400 WORDS)


title: Downlink capacity and base station density in cellular networks

abstract: There have been a bulk of analytic results about the performance of cellular networks where base stations are regularly located on a hexagonal or square lattice. This regular model cannot reflect the reality, and tends to overestimate the network performance. Moreover, tractable analysis can be performed only for a fixed location user (e.g., cell center or edge user). In this paper, we use the stochastic geometry approach, where base stations can be modeled as a homogeneous Poisson point process. We also consider the user density, and derive the user outage probability that an arbitrary user is under outage owing to low signal-to-interference-plus-noise ratio or high congestion by multiple users. Using the result, we calculate the density of success transmissions in the downlink cellular network. An interesting observation is that the success transmission density increases with the base station density, but the increasing rate diminishes. This means that the number of base stations installed should be more than n-times to increase the network capacity by a factor of n. Our results will provide a framework for performance analysis of the wireless infrastructure with a high density of access points, which will significantly reduce the burden of network-level simulations.


title: Stars and Misfits: Self-Employment and Labor Market Frictions

abstract: Recent evidence has shown that entrants into self-employment are disproportionately drawn from the tails of the earnings and ability distributions. This observation is explained by a multitask model of occupational choice in which frictions in the labor market induce mismatches between firms and workers, and misassignment of workers to tasks. The model also yields distinctive predictions relating prior work histories to earnings and to the probability of entry into self-employment. These predictions are tested with the Korean Labor and Income Panel Study, from which we find considerable support for the model. ::: ::: This paper was accepted by Lee Fleming, entrepreneurship and innovation.


title: 2009a. Quantifying the effects of prey abundance on killer whale reproduction

abstract: Summary 1. Management decisions for threatened and endangered species require risks to be identified and prioritized, based on the degree to which they influence population dynamics. The potential for recovery of small populations at risk may be determined by multiple factors, including intrinsic population characteristics (inbreeding, sex ratios) and extrinsic variables (prey availability, disease, human disturbance). Using Bayesian statistical methods, the impact of each of these risk factors on demographic rates can be quantified and assigned probabilities to express uncertainty. 2. We assessed the impact of a wide range of factors on the fecundity of two threatened populations of killer whales Orcinus orca , specifically whether killer whale production is limited by availability of Chinook salmon Oncorhynchus tshawytscha . Additional variables included anthropogenic factors, climate variables, temporal effects, and population variables (population size, number of males, female age). 3. Our results indicate that killer whale fecundity is highly correlated with the abundance of Chinook salmon. For example, the probability of a female calving differed by 50% between years of low salmon abundance and high salmon abundance. Weak evidence exists for linking fecundity to other variables, such as sea surface temperature. 4. There was strong data support for reproductive senescence in female killer whales. This pattern of rapid maturity and gradual decline of fecundity with age commonly seen in terrestrial mammals has been documented in few marine mammal species. Maximum production for this species occurs between ages 20‐22, and reproductive performance declines gradually to menopause over a period of 25 years. 5. Synthesis and applications . Our results provide strong evidence for reproductive senescence in killer whales, and more importantly, that killer whale fecundity is strongly tied to the abundance of Chinook salmon, a species that is susceptible to environmental variation and has high commercial value to fisheries. This strong predator‐prey relationship highlights the importance of understanding which salmon populations overlap with killer whales seasonally and spatially, so that those salmon populations important as prey for killer whales can be identified and targeted for conservation efforts.


Multispectral ―Iris Analysis: A Preliminary Study


title: Classification with color and texture: jointly or separately?

abstract: Abstract Current approaches to color texture analysis can be roughly divided into two categories: methods that process color and texture information separately, and those that consider color and texture a joint phenomenon. In this paper, both approaches are empirically evaluated with a large set of natural color textures. The classification performance of color indexing methods is compared to gray-scale and color texture methods, and to combined color and texture methods, in static and varying illumination conditions. Based on the results, we argue that color and texture are separate phenomena that can, or even should, be treated individually.


title: Musicians and music making as a model for the study of brain plasticity

abstract: Playing a musical instrument is an intense, multisensory, and motor experience that usually commences at an early age and requires the acquisition and maintenance of a range of sensory and motor skills over the course of a musician’s lifetime. Thus, musicians offer an excellent human model for studying behavioral-cognitive as well as brain effects of acquiring, practicing, and maintaining these specialized skills. Research has shown that repeatedly practicing the association of motor actions with specific sound and visual patterns (musical notation), while receiving continuous multisensory feedback will strengthen connections between auditory and motor regions (e.g., arcuate fasciculus) as well as multimodal integration regions. Plasticity in this network may explain some of the sensorimotor and cognitive enhancements that have been associated with music training. Furthermore, the plasticity of this system as a result of long term and intense interventions suggest the potential for music making activities (e.g., forms of singing) as an intervention for neurological and developmental disorders to learn and relearn associations between auditory and motor functions such as vocal motor functions.


title: SafetyNet: Detecting and Rejecting Adversarial Examples Robustly

abstract: We describe a method to produce a network where current methods such as DeepFool have great difficulty producing adversarial samples. Our construction suggests some insights into how deep networks work. We provide a reasonable analyses that our construction is difficult to defeat, and show experimentally that our method is hard to defeat with both Type I and Type II attacks using several standard networks and datasets. This SafetyNet architecture is used to an important and novel application SceneProof, which can reliably detect whether an image is a picture of a real scene or not. SceneProof applies to images captured with depth maps (RGBD images) and checks if a pair of image and depth map is consistent. It relies on the relative difficulty of producing naturalistic depth maps for images in post processing. We demonstrate that our SafetyNet is robust to adversarial examples built from currently known attacking approaches.


title: Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection

abstract: We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. To train our network, we collected over 800,000 grasp attempts over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing.


title: Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring

abstract: Many devices and solutions for remote electrocardiogram (ECG) monitoring have been proposed in the literature. These solutions typically have a large marginal cost per added sensor and are not seamlessly integrated with other smart home solutions. Here, we propose an ECG remote monitoring system that is dedicated to non-technical users in need of long-term health monitoring in residential environments and is integrated in a broader Internet-of-Things (IoT) infrastructure. Our prototype consists of a complete vertical solution with a series of advantages with respect to the state of the art, considering both the prototypes with integrated front end and prototypes realized with off-the-shelf components: 1) ECG prototype sensors with record-low energy per effective number of quantized levels; 2) an architecture providing low marginal cost per added sensor/user; and 3) the possibility of seamless integration with other smart home systems through a single IoT infrastructure.


title: Cognitive Radio Networks with Energy Harvesting

abstract: We consider a cognitive radio network with an energy-harvesting secondary transmitter to improve both energy efficiency and spectral efficiency. The goal of this paper is to determine an optimal spectrum sensing policy that maximizes the expected total throughput subject to an energy causality constraint and a collision constraint. The energy causality constraint comes from the fact that the total consumed energy should be equal to or less than the total harvested energy, while the collision constraint is required to protect the primary user. We first show that the system can be divided into a spectrum-limited regime and an energy-limited regime depending on where the detection threshold for the spectrum sensor lies. Assuming infinite battery capacity, we derive the optimal detection threshold that maximizes the expected total throughput subject to the energy causality constraint and the collision constraint. Analytical and numerical results show that the system is energy-limited if the energy arrival rate is lower than the expected energy consumption for a single spectrum access. They also show that a decreasing probability of accessing the occupied spectrum does not always result in decreased probability of accessing the idle spectrum in the energy-limited regime.


title: Adaptive multimedia streaming in information-centric networks

abstract: ICN has received a lot of attention in recent years, and is a promising approach for the Future Internet design. As multimedia is the dominating traffic in today's and (most likely) the Future Internet, it is important to consider this type of data transmission in the context of ICN. In particular, the adaptive streaming of multimedia content is a promising approach for usage within ICN, as the client has full control over the streaming session and has the possibility to adapt the multimedia stream to its context (e.g. network conditions, device capabilities), which is compatible with the paradigms adopted by ICN. In this article we investigate the implementation of adaptive multimedia streaming within networks adopting the ICN approach. In particular, we present our approach based on the recently ratified ISO/IEC MPEG standard Dynamic Adaptive Streaming over HTTP and the ICN representative Content-Centric Networking, including baseline evaluations and open research challenges.


title: On space-time-frequency coding over MIMO-OFDM systems

abstract: A sphere packing lower bound and a pairwise error upper bound are derived on error probability of space-time-frequency coded OFDM systems over multiple antenna block-fading channels. These bounds are useful in performance analysis and design of the codes. For example, the sphere packing bound provides a tool to investigate the performance limits of frequency-selective block-fading channels in terms of parameters such as number of antennas, coherence time, coherence bandwidth, delay constraint, and system bandwidth. Also, it can be used to specify the merit of a code in the sense of how far the code performs from fundamental limits. Comparisons with several known results are provided to support this assertion. On the other hand, the pairwise error upper bound can be used as a basis for a unified approach to analysis and design of space-time-frequency codes over any Rician or Rayleigh, block-fading channel in the presence of any type of correlation profile. Expressions for maxima of temporal diversity, spectral diversity, and spatial diversity are derived in terms of the system parameters. It is shown that under certain circumstances, one can not obtain the maxima of all types of diversities jointly. Moreover, the effect of system parameters and channel correlation profile on outage probability is investigated. Specifically, it is shown that unlike the spatial correlation, the temporal and spectral correlation does not affect the ergodic channel capacity (per dimension). However, they increase the variance of instantaneous channel capacity and hence, increase the outage probability.


title: Proposing Ayurvedic Traya Upastambha as an Important Behavioral Health Care Strategy

abstract: “Traya Upastamba” is a concept predominantly described in classical treatises of Ayurveda. The word literally means ::: three pillars or triad of life. It encompasses three important pillars such as Aahara (Dietary Habits and Nutrition), Nidra ::: (Sleep), and Bramhacharya (Celibacy/Continence/Abstinence). These three important pillars can better way be compared ::: with health behavior of modern day and in fact are the important behavioral determinants of health. In this context a ::: narrative review of various literatures pertaining to food and nutrition, sleep and sexual behavior was carried out. At the ::: same time the original concept of Trya Upastambha was adopted from classical treatises of Ayurveda. It was observed ::: that the dietary patterns and nutrition are associated with a number of health related conditions. In one of the studies it ::: was found that snacking, intake of meat, fried foods, sweet foods, and instant foods were associated with higher bone ::: mineral density (BMD) on the contrary regular lunch hours, skipping meals, calorie restriction, and eating moderate ::: amounts were associated with lower BMD. At the same time adequate sleep is a central part of healthy life style. This ::: brings proper healthy body and brain function. Similarly sexual behaviors, attraction, and identity vary by age, marital or ::: cohabiting status, education, and race and Hispanic origin. These behaviors and characteristics are relevant to birth and ::: pregnancy rates, as well as to the incidence of sexually transmitted infections (STIs), including human immunodeficiency ::: virus (HIV). Understanding of Traya Upastambha is of paramount importance for the management, control and prevention ::: of a good number health problem.


title: Analyzing intention in utterances

abstract: This paper describes a model of cooperative behavior and describes how such a model can be applied in a natural language understanding system. We assume that agents attempt to recognize the plans of other agents and, then, use this plan when deciding what response to make. In particular, we show that, given a setting in which purposeful dialogues occur, this model can account for responses that provide more information that explicitly requested and for appropriate responses to both short sentence fragments and indirect speech acts.


Ellipsis Redundancy and Reduction Redundancy


title: Concealed object detection and segmentation over millimetric waves images

abstract: Millimetric Waves Images (MMW) are becoming more and more useful in the passive detection of threaten objects based on plastic substances as explosives or sharp/cutting weapons. Our goal is to achieve segmentation of the body and concealed threats dealing with the inherent problems of this type of images: noise, low resolution and intensity inhomogeneity. In this work we present the results of applying Iterative Steering Kernel Regression (ISKR) method for denoising and Local Binary Fitting (LBF) for segmentation in order to correctly segment bodies and threats over a database of 29 MMW images. These methods, which had not been tested in the literature with these type of images, are compared with previously applied state of the art methods. Experimental results show that the use of the proposed methods in MMW images improve the results that had been obtained before.


title: Reflecting on reflection: framing a design landscape

abstract: Designing for reflection is becoming of increasing interest to HCI researchers, especially as digital technologies move to supporting broader professional and quality of life issues. However, the term 'reflection' is being used and designed for in diverse ways and often with little reference to vast amount of literature on the topic outside of HCI. Here we synthesize this literature into a framework, consisting of aspects such as purposes of reflection, conditions for reflection and levels of reflection (where the levels capture the behaviours and activities associated with reflection). We then show how technologies can support these different aspects and conclude with open questions that can guide a more systematic approach to how we understand and design for support of reflection.


BrainBench: A Brain-Image Test Suite for Distributional Semantic Models


title: A deep framework for bacterial image segmentation and classification

abstract: Bacterial image segmentation and classification is an important problem because bacterial appearance can vary dramatically based on environmental conditions. Further, newly isolated species may exhibit phenotypes previously unseen. Conventionally, biologists identify bacteria using colony morphology, biochemical properties, or molecular phylogenetic approaches. However, these phylogenetic classification approaches do not provide predictive information about the colony morphology that is expected to result from the growth of these bacteria on agar, or how these morphological phenotypes might vary in the presence of other bacterial species. In this paper, we propose a framework to automatically identify and classify regions of bacterial colony images and correspond them across different images from different contexts. Importantly, this approach does not require prior knowledge of species' appearances. Rather, our method assumes that images contain one or more bacteria from a pool of bacteria, and learns morphological features relevant to distinguishing between bacteria in this pool. Our method first segments the image into regions covering the bacterial colonies, agar, plate, and various border artefacts. To achieve this, we use an unsupervised deep learning technique, Convolutional Deep Belief Network (CDBN). This technique provides a deep representation of small image patches. Using this high-level representation instead of raw pixel intensities, we train a support vector machine (SVM). The trained SVM accurately classifies foreground and background patches. Once the foreground patches are identified, we train a supervised deep learning method, Convolutional Neural Network (CNN), that predicts which bacterial colonies from the pool occur in a query image. Experimental results demonstrate that our method outperforms other classical methods on segmentation and classification.


title: Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network

abstract: Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel computer-aided diagnose (CAD) system based on one of the regional deep learning techniques: a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Our proposed YOLO-based CAD system contains four main stages: mammograms preprocessing, feature extraction utilizing multi convolutional deep layers, mass detection with confidence model, and finally mass classification using fully connected neural network (FC-NN). A set of training mammograms with the information of ROI masses and their types are used to train YOLO. The trained YOLO-based CAD system detects the masses and classifies their types into benign or malignant. Our results show that the proposed YOLO-based CAD system detects the mass location with an overall accuracy of 96.33%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 85.52%. Our proposed system seems to be feasible as a CAD system capable of detection and classification at the same time. It also overcomes some challenging breast cancer cases such as the mass existing in the pectoral muscles or dense regions.


title: A Framework for Interaction-driven User Modeling of Mobile News Reading Behaviour

abstract: The news you read is, of course, a highly individual choice and one for which substantial and successful news recommendation techniques have been developed. But as well as what news you read, the way you choose and read that news is also known to be highly individual. We propose a framework for extending the user profile of news readers with features of these interactions. The extensions are dynamic through monitoring an individual's reading and browsing activity. They include factors learned from the user's interaction log and also factors inferred from category level definitions contained in the framework. We report a study in which users' interaction logs with a news app are used to generate user profiles that are verified with self-reported questionnaire data about reading habits. We discuss the implications of our user modeling approach in news personalisation for both recommendation and user interface personalisation for news apps.


title: Stereotype content model across cultures: Towards universal similarities and some differences

abstract: The stereotype content model (SCM) proposes potentially universal principles of societal stereotypes and their relation to social structure. Here, the SCM reveals theoretically grounded, cross-cultural, cross-groups similarities and one difference across 10 non-US nations. Seven European (individualist) and three East Asian (collectivist) nations (N = 1,028) support three hypothesized cross-cultural similarities: (a) perceived warmth and competence reliably differentiate societal group stereotypes; (b) many out-groups receive ambivalent stereotypes (high on one dimension; low on the other); and (c) high status groups stereotypically are competent, whereas competitive groups stereotypically lack warmth. Data uncover one consequential cross-cultural difference: (d) the more collectivist cultures do not locate reference groups (in-groups and societal prototype groups) in the most positive cluster (high-competence/high-warmth), unlike individualist cultures. This demonstrates out-group derogation without obvious reference-group favouritism. The SCM can serve as a pancultural tool for predicting group stereotypes from structural relations with other groups in society, and comparing across societies.


title: The CNN solution to the shortest-path-finder problem

abstract: Algorithms to find the shortest path within the CNN context are a classical problem nowadays. An important attempt to solve this problem by parallel computing using the properties of autowaves was analyzed. Nevertheless, these solutions were unpractical as they required each cell of the system to remember when a wave went through it. In this text, anomalous properties of autowaves are used to solve the shortest-path-finder problem in a very robust, self-content way.


title: Technical accuracy of optical and the electromagnetic tracking systems

abstract: Thousands of operations are annually guided with computer assisted surgery (CAS) technologies. As the use of these devices is rapidly increasing, the reliability of the devices becomes ever more critical. The problem of accuracy assessment of the devices has thus become relevant. During the past five years, over 200 hazardous situations have been documented in the MAUDE database during operations using these devices in the field of neurosurgery alone. Had the accuracy of these devices been periodically assessed pre-operatively, many of them might have been prevented. The technical accuracy of a commercial navigator enabling the use of both optical (OTS) and electromagnetic (EMTS) tracking systems was assessed in the hospital setting using accuracy assessment tools and methods developed by the authors of this paper. The technical accuracy was obtained by comparing the positions of the navigated tool tip with the phantom accuracy assessment points. Each assessment contained a total of 51 points and a region of surgical interest (ROSI) volume of 120x120x100 mm roughly mimicking the size of the human head. The error analysis provided a comprehensive understanding of the trend of accuracy of the surgical navigator modalities. This study showed that the technical accuracies of OTS and EMTS over the pre-determined ROSI were nearly equal. However, the placement of the particular modality hardware needs to be optimized for the surgical procedure. New applications of EMTS, which does not require rigid immobilization of the surgical area, are suggested.


title: A neurobehavioral evolutionary perspective on the mechanisms underlying empathy

abstract: Abstract In mammals, empathy is crucial for living in social groups and caring for others. In this paper, we consider the structural and functional organization of empathy. We propose that empathy subsumes a variety of neurobiological processes and partially dissociable information processing subsystems, each of which has a unique evolutionary history. Even the most advanced and flexible forms of empathy in humans are built on more basic forms and remain connected to core subcortical and neurohormonal mechanisms associated with affective communication, parental care and social attachment processes. Considering empathy within a framework that recognizes both the continuities and the changes within a phylogenetic perspective provides a richer understanding of empathy and related neurobehavioral processes.


title: Dynamics simulation toolbox for industrial robot manipulators

abstract: A new robot toolbox for dynamics simulation based on MATLAB Graphical User Interface (GUI) is developed for educational purposes. It is built on the previous version named as ROBOLAB performing only the kinematics analysis of robot manipulators. The toolbox presented in this paper provides interactive real-time simulation and visualization of the industrial robot manipulator's dynamics based on Langrange–Euler and Newton–Euler formulations. Since most of the industrial robot manipulators are produced with six degrees of freedom (DOF) such as the PUMA 560, the Fanuc ArcMate 120iB and Stanford Arm, the library of the toolbox includes sixteen fundamental 6-DOF robot manipulators with Euler wrist. The software can be used to interactively perform robotics analysis and off-line programming of robot dynamics such as forward and inverse dynamics as well as to interactively teach and simulate the basic principles of robot dynamics in a very realistic way. In order to demonstrate the user-friendly features of the toolbox much better, simulation of the NS robot manipulator (Stanford Arm) is provided as an example. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 319–330, 2010; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20262


title: Stigmergy as a universal coordination mechanism I: Definition and components

abstract: The concept of stigmergy has been used to analyze self-organizing activities in an ever-widening range of domains, including social insects, robotics, web communities and human society. Yet, it is still poorly understood and as such its full power remains underappreciated. The present paper clarifies the issue by defining stigmergy as a mechanism of indirect coordination in which the trace left by an action in a medium stimulates subsequent actions. It then analyses the fundamental concepts used in the definition: action, agent, medium, trace and coordination. It clarifies how stigmergy enables complex, coordinated activity without any need for planning, control, communication, simultaneous presence, or even mutual awareness. The resulting self-organization is driven by a combination of positive and negative feedbacks, amplifying beneficial developments while suppressing errors. Thus, stigmergy is applicable to a very broad variety of cases, from chemical reactions to bodily coordination and Internet-supported collaboration in Wikipedia.


title: A Novel approach to improve rule based Telugu morphological analyzer

abstract: Telugu is an Indian language spoken by more than 50 million people in the country. Language is very rich in literature, and it requires advancements in computational approaches. Applications like machine translation, Speech recognition, speech synthesis and Information retrieval need a powerful morphological generator to give morphological forms of nouns and verbs. The existing Telugu morphological analyzer (TMA) is rule based. The performance of it is further improved by our novel approach which provides a system that gives information about possible decompositions of the word inflected by many morphemes. Using these possible decompositions the root word could be extracted for those words which were unrecognized by rule based morphological analyzer. The experiment is conducted on Telugu text corpus from CIIL Mysore and the improvement in the performance is checked by the rule based morphological analyzer developed by LTRC group, IIIT and HCU,Hyderabad. In this present work we present an unsupervised stemmer for improving the performance of Telugu rule based morph analyzer. The observed increase in performance of rule based is from 77% to 84.2% for words which are in hundreds. It can still be improved if the corpus is increased.


Adult T-cell leukemia: clinical and hematologic features of 16 cases


title: Periodontal diseases.

abstract: The periodontal diseases are highly prevalent and can affect up to 90% of the worldwide population. Gingivitis, the mildest form of periodontal disease, is caused by the bacterial biofilm (dental plaque) that accumulates on teeth adjacent to the gingiva (gums). However, gingivitis does not affect the underlying supporting structures of the teeth and is reversible. Periodontitis results in loss of connective tissue and bone support and is a major cause of tooth loss in adults. In addition to pathogenic microorganisms in the biofilm, genetic and environmental factors, especially tobacco use, contribute to the cause of these diseases. Genetic, dermatological, haematological, granulomatous, immunosuppressive, and neoplastic disorders can also have periodontal manifestations. Common forms of periodontal disease have been associated with adverse pregnancy outcomes, cardiovascular disease, stroke, pulmonary disease, and diabetes, but the causal relations have not been established. Prevention and treatment are aimed at controlling the bacterial biofilm and other risk factors, arresting progressive disease, and restoring lost tooth support.


title: The efficacy and safety of postoperative adjuvant transarterial embolization and radiotherapy in hepatocellular carcinoma patients with portal vein tumor thrombus

abstract: OBJECTIVE ::: This study aims to find out the safety and efficiency of postoperative adjuvant transarterial chemoembolization (TACE) and radiotherapy (RT) in hepatocellular carcinoma (HCC) patients with portal vein tumor thrombus (PVTT). ::: ::: ::: METHODS ::: From 2009 to 2010, a total of 92 HCC patients with PVTT were enrolled in this retrospective study. Patients were divided into three groups according to their adjuvant therapies (conservative group, n=51; TACE group, n=31; RT group, n=10). ::: ::: ::: RESULTS ::: In our analysis, median survival in patients with postoperative adjuvant TACE (21.91±3.60 months) or RT (14.53±1.61 months) was significantly longer than patients with hepatectomy alone (8.99±1.03 months). But the difference between adjuvant TACE and RT was of no significance (P=0.716). Also a similar result could be observed in median disease-free survival: conservative group (6.51±1.44 months), TACE group (13.98±3.38 months), and RT group (14.03±2.40 months). Treatment strategies (hazard ratio [HR] =0.411, P<0.001) and PVTT type (HR =4.636, P<0.001) were the independent prognostic factors for overall survival. Similarly, the risk factors were the same when multivariate analysis was conducted in disease-free survival (treatment strategies, HR =0.423, P<0.001; PVTT type, HR =4.351, P<0.001) and recurrence (treatment strategies, HR =0.459, P=0.030; PVTT type, HR =2.908, P=0.047). Patients with PVTT type I had longer overall survival than patients with PVTT type II (median survival: 18.43±2.88 months vs 11.59±1.45 months, P=0.035). ::: ::: ::: CONCLUSION ::: Postoperative adjuvant TACE and RT may be a choice for HCC patients with PVTT.


title: Development of an EMG controlled hand exoskeleton for post-stroke rehabilitation

abstract: Rehabilitation after stroke is of crucial importance since patients often have severe motor impairments that affect their daily activities. In the past decades, a number of robotic systems have been proposed for stroke rehabilitation but very few of them are available in the market. Additionally, because of their high costs they are not widely accessible. This paper presents a light-weight hand exoskeleton controlled by patient's surface electromyography (sEMG) signals. This system could empower the patient to intensively exercise the hand and control its movement with his/her muscles. This would personalise the rehabilitation process since the system will respond to patient's own effort to move. sEMG signals were acquired from the healthy forearm and pattern recognition techniques were used to distinguish extension and flexion of the index and middle fingers. The motorized exoskeleton imposed the corresponding movements in the hand wearing it. Individualization of finger movements was accomplished with more than 95% of accuracy and opening and closing motions were established by the exoskeleton in the opposite hand.


title: Automated Aeroponics System Using IoT for Smart Farming

abstract: In recent years, the Internet of Things (IoT) has received much attention in the areas of industry and academia. Currently, IoT technologies are being applied in many fields and is changing lives in many areas such as smart homes, smart cities, smart grids, autonomous cars, and the industrial internet. Howbeit, traditional agriculture is still waiting for many changes to occur in networking technology especially in IoT. Many researchers and engineers are working towards applying IoT technology to traditional agricultural methods. Aeroponics farming is an efficient and effective process for growing plants without using soil. When we apply IoT technology to an aeroponics system, it is expected that there will be many improvements such as decreasing water usage, increasing plant yield, minimizing rate of growth and reducing the workforce. In this paper, we designed and implemented a new automatic aeroponics system using IoT devices. Our system is comprised of three main components: a mobile application, service platform and IoT devices with sensors. The mobile application provides the user a graphical user interface to monitor and adjust the aeroponics system. The service platform is a middleware system that provides information for the mobile application to store the gathered information from IoT devices using sensors within the aeroponics system. The IoT device uses sensors within the aeroponics system to control each pump and access data. Our work is a new application in the agricultural industry and is expected to be a promising application that will help the farmer with increasing productivity in farming and reducing carbon footprint.


title: Decentralized dynamic spectrum access for cognitive radios: cooperative design of a non-cooperative game

abstract: We consider dynamic spectrum access among cognitive radios from an adaptive, game theoretic learning perspective. Spectrum-agile cognitive radios compete for channels temporarily vacated by licensed primary users in order to satisfy their own demands while minimizing interference. For both slowly varying primary user activity and slowly varying statistics of "fast" primary user activity, we apply an adaptive regret based learning procedure which tracks the set of correlated equilibria of the game, treated as a distributed stochastic approximation. This procedure is shown to perform very well compared with other similar adaptive algorithms. We also estimate channel contention for a simple CSMA channel sharing scheme.


title: A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis

abstract: The Orthant-Wise Limited memory Quasi-Newton (OWL-QN) method has been demonstrated to be very effective in solving the l1- regularized sparse learning problem. OWL-QN extends the L-BFGS from solving unconstrained smooth optimization problems to l1-regularized (non-smooth) sparse learning problems. At each iteration, OWL-QN does not involve any l1- regularized quadratic optimization subproblem and only requires matrix-vector multiplications without an explicit use of the (inverse) Hessian matrix, which enables OWL-QN to tackle large-scale problems efficiently. Although many empirical studies have demonstrated that OWL-QN works quite well in practice, several recent papers point out that the existing convergence proof of OWL-QN is flawed and a rigorous convergence analysis for OWL-QN still remains to be established. In this paper, we propose a modified Orthant-Wise Limited memory Quasi-Newton (mOWL-QN) algorithm by slightly modifying the OWL-QN algorithm. As the main technical contribution of this paper, we establish a rigorous convergence proof for the mOWL-QN algorithm. To the best of our knowledge, our work fills the theoretical gap by providing the first rigorous convergence proof for the OWL-QN-type algorithm on solving l1- regularized sparse learning problems. We also provide empirical studies to show that mOWLQN works well and is as efficient as OWL-QN.


title: Control of Magnetic Levitation System Using Fuzzy Logic Control

abstract: This paper presents the investigation on a system model for the stabilisation of a Magnetic Levitation System (Maglev's). Furthermore, the investigation on Proportional Integrated Derivative Controller (PID) also reported here. In this paper shows to design both PID and Fuzzy Logic Control (FLC) based on the system model. Maglev's give the contribution in industry and this system has reduce the power consumption, has increase the power efficiency and reduce the cost maintenance. The common applications for Maglev's are Maglev's Power Generation, Maglev's trains and Maglev's ball bearing less system. In this study, it has also been observed that the basic design of Maglev's is an arrangement of electromagnets placed on top of the plant and makes the ball levitated in the air. The focus of this study is that to design the controller that can cope with Maglev's which highly nonlinear and inherently unstable. The modeling system is simulated using MATLAB simulink. This paper presents the comparison output for both PID Controller and Fuzzy controller to control the ball levitate on the air. The ISE performance index is shown to compare the performance both controller.


title: Chromosomal Abnormality in Men with Impaired Spermatogenesis

abstract: Background: Chromosomal abnormalities and Y chromosome microdeletions are regarded as two most frequent genetic causes associated with failure of spermatogenesis in the Caucasian population. Materials and Methods: To investigate the distribution of genetic defects in the Romanian population with azoospermia or severe oligozoospermia, karyotype analysis by G-banding was carried out in 850 idiopathic infertile men and in 49 fertile men with one or more children. Screening for microdeletions in the azoospermia factor (AZF) region of Y chromosome was performed by multiplex polymerase chain reaction (PCR) on a group of 67 patients with no detectable chromosomal abnormality. The results of the two groups were compared by a two-tailed Fisher’s exact test. Results: In our study chromosomal abnormalities were observed in 12.70% and 8.16% of infertile and fertile individuals respectively. Conclusion: Our data suggests that infertile men with severe azoospermia have higher incidences of genetic defects than fertile men and also patients from any other group. Infertile men with normal sperm present a higher rate of polymorphic variants. It is important to know whether there is a genetic cause of male infertility before patients are subjected to intracytoplasmic sperm injection (ICSI) or testicular sperm extraction (TESE)/ICSI treatment.


title: Cyber-physical systems: A security perspective

abstract: A cyber-physical system (CPS) is a composition of independently interacting components, including computational elements, communications and control systems. Applications of CPS institute at different levels of integration, ranging from nation-wide power grids, to medium scale, such as the smart home, and small scale, e.g. ubiquitous health care systems including implantable medical devices. Cyber-physical systems primarily transmute how we interact with the physical world, with each system requiring different levels of security based on the sensitivity of the control system and the information it carries. Considering the remarkable progress in CPS technologies during recent years, advancement in security and trust measures is much needed to counter the security violations and privacy leakage of integration elements. This paper focuses on security and privacy concerns at different levels of the composition and presents system level solutions for ensuring the security and trust of modern cyber-physical systems.


title: A generalized temporal role-based access control model

abstract: Role-based access control (RBAC) models have generated a great interest in the security community as a powerful and generalized approach to security management. In many practical scenarios, users may be restricted to assume roles only at predefined time periods. Furthermore, roles may only be invoked on prespecified intervals of time depending upon when certain actions are permitted. To capture such dynamic aspects of a role, a temporal RBAC (TRBAC) model has been recently proposed. However, the TRBAC model addresses the role enabling constraints only. In This work, we propose a generalized temporal role-based access control (GTRBAC) model capable of expressing a wider range of temporal constraints. In particular, the model allows expressing periodic as well as duration constraints on roles, user-role assignments, and role-permission assignments. In an interval, activation of a role can further be restricted as a result of numerous activation constraints including cardinality constraints and maximum active duration constraints. The GTRBAC model extends the syntactic structure of the TRBAC model and its event and trigger expressions subsume those of TRBAC. Furthermore, GTRBAC allows expressing role hierarchies and separation of duty (SoD) constraints for specifying fine-grained temporal semantics.


title: Competition as an element of gamification for learning: an exploratory longitudinal investigation

abstract: This paper examines the effect of gamification techniques in engaging students in a teaching context, in particular the influence of competition. We conducted a longitudinal survey study (informed by a focus group) -- in an MBA classroom that uses ERPsim, which is a gamified simulation system for teaching the SAP ERP (Enterprise Resource Planning) software solution. Flow theory was used to understand engagement during this method of learning. We examined the effect of antecedents of Flow, such as skill and challenge, and the effect of Flow on its consequences such as satisfaction and student feelings. In line with earlier research, our results showed that losing a competition can have a detrimental effect on students' satisfaction and enjoyment; however, competition is still a key element that highly motivates students to engage in the gamification tasks.


title: Inhibition of ANO1 by luteolin and its cytotoxicity in human prostate cancer PC-3 cells

abstract: Anoctamin 1 (ANO1), a calcium-activated chloride channel, is highly amplified in prostate cancer, the most common form of cancer and leading causes of cancer death in men, and downregulation of ANO1 expression or its functional activity is known to inhibit cell proliferation, migration and invasion in prostate cancer cells. Here, we performed a cell-based screening for the identification of ANO1 inhibitors as potential anticancer therapeutic agents for prostate cancer. Screening of ~300 selected bioactive natural products revealed that luteolin is a novel potent inhibitor of ANO1. Electrophysiological studies indicated that luteolin potently inhibited ANO1 chloride channel activity in a dose-dependent manner with an IC50 value of 9.8 μM and luteolin did not alter intracellular calcium signaling in PC-3 prostate cancer cells. Luteolin inhibited cell proliferation and migration of PC-3 cells expressing high levels of ANO1 more potently than that of ANO1-deficient PC-3 cells. Notably, luteolin not only inhibited ANO1 channel activity, but also strongly decreased protein expression levels of ANO1. Our results suggest that downregulation of ANO1 by luteolin is a potential mechanism for the anticancer effect of luteolin.


title: Can Blockchain Strengthen the Internet of Things?

abstract: This column evaluates blockchain’s roles in strengthening security in the Internet of Things (IoT). Key underlying mechanisms related to the blockchain–IoT security nexus are covered. From a security standpoint, the article highlights how blockchain-based solutions could be, in many aspects, superior to the current IoT ecosystem, which relies mainly on centralized cloud servers. Using practical applications and real-world examples, the article argues that blockchain’s decentralized nature is likely to result in a low susceptibility to manipulation and forgery by malicious participants. Special consideration is given to how blockchain-based identity and access management systems can address some of the key challenges associated with IoT security. The column provides a detailed analysis and description of blockchain’s roles in tracking the sources of insecurity in supply chains related to IoT devices. Using blockchain, it is also possible to contain an IoT security breach in a targeted way after it is discovered. The column also discusses and evaluates initiatives of organizations, interorganizational networks, and industries on the frontlines of blockchain.


title: Neural Optimizer Search with Reinforcement Learning

abstract: We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures. We train a Recurrent Neural Network controller to generate a string in a domain specific language that describes a mathematical update equation based on a list of primitive functions, such as the gradient, running average of the gradient, etc. The controller is trained with Reinforcement Learning to maximize the performance of a model after a few epochs. On CIFAR-10, our method discovers several update rules that are better than many commonly used optimizers, such as Adam, RMSProp, or SGD with and without Momentum on a ConvNet model. We introduce two new optimizers, named PowerSign and AddSign, which we show transfer well and improve training on a variety of different tasks and architectures, including ImageNet classification and Google's neural machine translation system.


title: Victims of Environmental Pollution in the Slipstream of Globalization

abstract: The globalization of the world economy has as one of its side-effects the rapid proliferation of pollution around the globe. Developing countries are especially vulnerable to polluting activities that, predominantly because of market incentives, are still transferred from the north to the south.


title: Large-scale multi-view spectral clustering via bipartite graph

abstract: In this paper, we address the problem of large-scale multi-view spectral clustering. In many real-world applications, data can be represented in various heterogeneous features or views. Different views often provide different aspects of information that are complementary to each other. Several previous methods of clustering have demonstrated that better accuracy can be achieved using integrated information of all the views than just using each view individually. One important class of such methods is multi-view spectral clustering, which is based on graph Laplacian. However, existing methods are not applicable to large-scale problem for their high computational complexity. To this end, we propose a novel large-scale multi-view spectral clustering approach based on the bipartite graph. Our method uses local manifold fusion to integrate heterogeneous features. To improve efficiency, we approximate the similarity graphs using bipartite graphs. Furthermore, we show that our method can be easily extended to handle the out-of-sample problem. Extensive experimental results on five benchmark datasets demonstrate the effectiveness and efficiency of the proposed method, where our method runs up to nearly 3000 times faster than the state-of-the-art methods.


title: A survey on computational intelligence approaches for predictive modeling in prostate cancer

abstract: Focus is on computational intelligence methods in prostate cancer predictive modeling.We survey metaheuristic optimisation methods.We review machine learning methods.We consider cancer data of different modalities.We discuss recent advances, challenges and provide future directions. Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation algorithms (also known as nature inspired algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed.


Juvenile gigantomastia, two cases treated by reduction mammoplasty with nipple-areola complex graft


title: Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval

abstract: This paper presents an effective image retrieval method by combining high-level features from convolutional neural network (CNN) model and low-level features from dot-diffused block truncation coding (DDBTC). The low-level features, e.g., texture and color, are constructed by vector quantization -indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate and average recall rate (ARR), are employed to examine various data sets. As documented in the experimental results, the proposed schemes can achieve superior performance compared with the state-of-the-art methods with either low-or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.