title: Minimally Supervised Event Causality Identification

abstract: This paper develops a minimally supervised approach, based on focused distributional similarity methods and discourse connectives, for identifying of causality relations between events in context. While it has been shown that distributional similarity can help identifying causality, we observe that discourse connectives and the particular discourse relation they evoke in context provide additional information towards determining causality between events. We show that combining discourse relation predictions and distributional similarity methods in a global inference procedure provides additional improvements towards determining event causality.


title: One-network Adversarial Fairness

abstract: There is currently a great expansion of the impact of machine learning algorithms on our lives, prompting the need for objectives other than pure performance, including fairness. Fairness here means that the outcome of an automated decisionmaking system should not discriminate between subgroups characterized by sensitive attributes such as gender or race. Given any existing differentiable classifier, we make only slight adjustments to the architecture including adding a new hidden layer, in order to enable the concurrent adversarial optimization for fairness and accuracy. Our framework provides one way to quantify the tradeoff between fairness and accuracy, while also leading to strong empirical performance.


title: Security Requirements Analysis of ADS-B Networks

abstract: Due to their many advantages over their hardwarebased counterparts, Software Defined Radios are becoming the new paradigm for radio and radar applications. In particular, Automatic Dependent Surveillance-Broadcast (ADS-B) is an emerging software defined radar technology, which has been already deployed in Europe and Australia. Deployment in the US is underway as part of the Next Generation Transportation Systems (NextGen). In spite of its several benefits, this technology has been widely criticized for being designed without security in mind, making it vulnerable to numerous attacks. Most approaches addressing this issue fail to adopt a holistic viewpoint, focusing only on part of the problem. In this paper, we propose a methodology that uses semantic technologies to address the security requirements definition from a systemic perspective. More specifically, knowledge engineering focused on misuse scenarios is applied for building customized resilient software defined radar applications, as well as classifying cyber attack severity according to measurable security metrics. We showcase our ideas using an ADS-B-related scenario developed to evaluate our research. Keywords— Ontologies, Misuse case, Cybersecurity, ADS-B


title: Self-similarity of complex networks

abstract: Complex networks have been studied extensively owing to their relevance to many real systems such as the world-wide web, the Internet, energy landscapes and biological and social networks. A large number of real networks are referred to as 'scale-free' because they show a power-law distribution of the number of links per node. However, it is widely believed that complex networks are not invariant or self-similar under a length-scale transformation. This conclusion originates from the 'small-world' property of these networks, which implies that the number of nodes increases exponentially with the 'diameter' of the network, rather than the power-law relation expected for a self-similar structure. Here we analyse a variety of real complex networks and find that, on the contrary, they consist of self-repeating patterns on all length scales. This result is achieved by the application of a renormalization procedure that coarse-grains the system into boxes containing nodes within a given 'size'. We identify a power-law relation between the number of boxes needed to cover the network and the size of the box, defining a finite self-similar exponent. These fundamental properties help to explain the scale-free nature of complex networks and suggest a common self-organization dynamics.


A Unified Framework for Multi-Oriented Text Detection and Recognition


title: A reranking approach for context-based concept fusion in video indexing and retrieval

abstract: We propose to incorporate hundreds of pre-trained concept detectors to provide contextual information for improving the performance of multimodal video search. The approach takes initial search results from established video search methods (which typically are conservative in usage of concept detectors) and mines these results to discover and leverage co-occurrence patterns with detection results for hundreds of other concepts, thereby refining and reranking the initial video search result. We test the method on TRECVID 2005 and 2006 automatic video search tasks and find improvements in mean average precision (MAP) of 15%-30%. We also find that the method is adept at discovering contextual relationships that are unique to news stories occurring in the search set, which would be difficult or impossible to discover even if external training data were available.


title: Real time video surveillance system using motion detection

abstract: In present scenario, the security concerns have grown tremendously. The security of restricted areas such as borders or buffer zones is of utmost importance; in particular with the worldwide increase of military conflicts, illegal immigrants, and terrorism over the past decade. Monitoring such areas rely currently on technology and man power, however automatic monitoring has been advancing in order to avoid potential human errors that can be caused by different reasons. The purpose of this project is to design a surveillance system which would detect motion in a live video feed and record the video feed only at the moment where the motion was detected also to track moving object based on background subtraction using video surveillance. The moving object is identified using the image subtraction method.


title: To learn image super-resolution, use a GAN to learn how to do image degradation first

abstract: This paper is on image and face super-resolution. The vast majority of prior work for this problem focus on how to increase the resolution of low-resolution images which are artificially generated by simple bilinear down-sampling (or in a few cases by blurring followed by down-sampling).We show that such methods fail to produce good results when applied to real-world low-resolution, low quality images. To circumvent this problem, we propose a two-stage process which firstly trains a High-to-Low Generative Adversarial Network (GAN) to learn how to degrade and downsample high-resolution images requiring, during training, only unpaired high and low-resolution images. Once this is achieved, the output of this network is used to train a Low-to-High GAN for image super-resolution using this time paired low- and high-resolution images. Our main result is that this network can be now used to efectively increase the quality of real-world low-resolution images. We have applied the proposed pipeline for the problem of face super-resolution where we report large improvement over baselines and prior work although the proposed method is potentially applicable to other object categories.


title: iFogStor: An IoT Data Placement Strategy for Fog Infrastructure

abstract: Internet of Things (IoT) will be one of the driving application for digital data generation in the next years as more than 50 billions of objects will be connected by 2020. IoT data can be processed and used by different devices spread all over the network. The traditional way of centralizing data processing in the Cloud can hardly scale because it cannot satisfy many of the latency critical IoT applications. In addition, it generates a too high network traffic when the number of objects and services increase. Fog infrastructure provides a beginning of an answer to such an issue. In this paper, we present a data placement strategy for Fog infrastructures called iFogStor. The objective of iFogStor is to take profit of the heterogeneity and location of Fog nodes to reduce the overall latency of storing and retrieving data in a Fog. We formulated the data placement problem as a Generalized Assignment Problem (GAP) and proposed two ways to solve it: 1) an exact solution using integer programming and 2) a heuristic one based on geographical zoning to reduce the solving time. Both solutions proved very good performance as they reduced the latency by more than 86% as compared to a Cloud based solution and by 60% as compared to a naive Fog solution. Using geographical zoning heuristic can allow solving problems with large number of Fog nodes efficiently and in a couple of seconds making iFogStor feasible in runtime and scalable.


title: 6-DOF VR videos with a single 360-camera

abstract: Recent breakthroughs in consumer level virtual reality (VR) headsets are creating a growing user-base in demand for immersive, full 3D VR experiences. While monoscopic 360-videos are perhaps the most prevalent type of content for VR headsets, they lack 3D information and thus cannot be viewed with full 6 degree-of-freedom (DOF). We present an approach that addresses this limitation via a novel warping algorithm that can synthesize new views both with rotational and translational motion of the viewpoint. This enables the ability to perform VR playback of input monoscopic 360-videos files in full stereo with full 6-DOF of head motion. Our method synthesizes novel views for each eye in accordance with the 6-DOF motion of the headset. Our solution tailors standard structure-from-motion and dense reconstruction algorithms to work accurately for 360-videos and is optimized for GPUs to achieve VR frame rates (>120 fps). We demonstrate the effectiveness our approach on a variety of videos with interesting content.


title: K-Tracer: A System for Extracting Kernel Malware Behavior

abstract: Kernel rootkits can provide user level-malware programs with the additional capabilities of hiding their malicious activities by altering the legitimate kernel behavior of an operating system. While existing research has studied rootkit hooking behavior in an effort to help develop defense and remediation mechanisms, automated analysis of the actual malicious goals and capabilities of rootkits has not been adequately investigated. In this paper, we present an approach based on a combination of backward slicing and chopping techniques that enables automatic discovery of the system data manipulation behaviors of rootkits. We have built a system called K-Tracer that can dynamically analyze Windows kernel-level code and extract malicious behaviors from rootkits, including sensitive data access, modification and triggers. Our system overcomes several challenges of analyzing the Windows Kernel. We have performed experiments on several kernel malware samples and shown that our system can successfully extract all malicious data manipulation behaviors from them. We also discuss the limitations of our current system on newer rootkit strategies, and provide insight into how it can be extended to handle these


title: Frequency-tuned salient region detection

abstract: Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.


title: Nicky: Toward a Virtual Assistant for Test and Measurement Instrument Recommendations

abstract: Natural language question answering has been an area of active computer science research for decades. Recent advances have led to a new generation of virtual assistants or chatbots, frequently based on semantic modeling of some broadly general domain knowledge. However, answering questions about detailed, highly technical, domain-specific capabilities and attributes remains a difficult and complex problem. In this paper we discuss a prototype conversational virtual assistant designed for choosing test and measurement equipment based on the detailed measurement requirements of the test engineer. Our system allows for multi-stage queries which retain sufficient short-term context to support query refinement as well as compound questions. In addition to the software architecture, we explore an approach to ontology development that leverages inference from reasoners and minimizes the complexity of entering the specifications for a large collection of instruments. Finally, we provide insights into the issues of building this system and provide recommendations for future designs.


title: Designing towards emotional usability in customer interfaces—trustworthiness of cyber-banking system interfaces

abstract: Abstract In this research we investigate the possibility of designing a user interface for electronic commerce systems that will evoke target feelings in the customer. The focus is on the impact of visual design factors on the feeling of trustworthiness because of its significant effect upon the behavior of customers using electronic commerce systems. Four empirical studies were conducted in the domain of cyber-banking systems. The subjects were cyber-banking system developers, bank personnel and potential customers of cyber-banking systems in Korea ranging in age from late teens to early forties. The first study was directed at developing the self-report questionnaire that faithfully reflects the emotional factors related to cyber-banking systems. The resulting questionnaire consisted of the forty bipolar emotive differential scales representative of the emotions most important in interacting with cyber-banking systems, e.g. reliable-not reliable. The second study focused on determining the important visual design factors from the customer's perspective. Fourteen design factors identified from subjects' descriptions were classified into the four design categories of title, menu, main clipart and color. The third study investigated the correlations between the emotional factors and design factors. The design factors were found to have significant effects upon the extent of feelings related to symmetry, trustworthiness, awkwardness and elegance. In the final study, two interfaces were designed based on the results of the third study to differentiate the extent of trustworthiness evoked. The results indicate that it is possible to manipulate the visual design factors of the customer interface in order to induce a target emotion, such as trustworthiness. The paper concludes by discussing the implications of the empirical results on the design and implementation of customer interfaces to electronic commerce systems in general.


title: BotWalk: Efficient Adaptive Exploration of Twitter Bot Networks

abstract: We propose BotWalk, a near-real time adaptive Twitter exploration algorithm to identify bots exhibiting novel behavior. Due to suspension pressure, Twitter bots are constantly changing their behavior to evade detection. Traditional supervised approaches to bot detection are non-adaptive and thus cannot identify novel bot behaviors. We therefore devise an unsupervised approach, which allows us to identify bots as they evolve. We characterize users with a behavioral feature vector which consists of (well-studied in isolation) metadata-, content-, temporal-, and network-based features. We identify a random bot from our seed bank, populated initially by previously-labeled bots, gather this user's followers' features from Twitter in real time, and employ an unsupervised ensemble anomaly detection method in the multi-dimensional behavioral space. These potential bots are folded into the seed bank and the process is then repeated, with the new seeds' features allowing us to adaptively identify novel bot behavior. BotWalk allows for the identification of on average 6,000 potential bots a day. Our method allowed us to detect 7,995 previously undiscovered bots from a sample of 15 seed bots with a precision of 90%.


Using Data Mining Techniques in Customer Segmentation


title: Proposal for Switching Current Reduction Using Reference Layer With Tilted Magnetic Anisotropy in Magnetic Tunnel Junctions for Spin-Transfer Torque (STT) MRAM

abstract: The design and statistical analysis of a magnetic tunnel junction with pinned-layer uniaxial anisotropy, slightly tilted with respect to the perpendicular magnetic anisotropy (PMA) of the free layer, are presented in the presence of thermally induced magnetic noise. The marginal tilting of the pinned-layer easy axis reduces the critical switching current density by almost 80%, as compared to a regular PMA device for a delay of 2 ns with a switching failure probability lower than 10-9. Substantially lower switching current density in spin-transfer torque MRAM with tilted pinned-layer anisotropy enables the use of a higher resistance-area product with a thicker tunnel barrier that compensates for the tunneling-magnetoresistance rolloff due to the relative misalignment of free- and pinned-layer easy axes.


title: Ramp-based soft-start circuit with soft-recovery for DC-DC buck converters

abstract: A soft-start circuit with soft-recovery function for DC-DC converters is presented in this paper. The soft-start strategy is based on a linearly ramped-up reference and an error amplifier with minimum selector implemented with a three-limb differential pair skillfully. The soft-recovery strategy is based on a compact clamp circuit. The ramp voltage would be clamped once the feedback voltage is detected lower than a threshold, which could control the output to be recovered slowly and linearly. A monolithic DC-DC buck converter with proposed circuit has been fabricated with a 0.5μm CMOS process for validation. The measurement result shows that the ramp-based soft-start and soft-recovery circuit have good performance and agree well with the theoretical analysis.


title: Adversarial machine learning

abstract: In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.


title: Evaluating and modeling power consumption of multi-core processors

abstract: Recently, energy-efficient computing has become a major interest, both in the mobile and IT sectors. With the advent of multi-core processors and their energy-saving mechanisms, there is a necessity to model their power consumption. The existing models for multi-core processors are based on the assumption that the power consumption of multiple cores performing parallel computations is equal to the sum of the power of each of those active cores. In this paper, we analyze this assumption and show that it leads to lack of accuracy when applied to modern processors such as quad-core. Based on our analysis, we present a methodology for estimating the power consumption of multi-core processors. Unlike existing models, we take into account resource sharing and power saving mechanisms. We show that our approach provides an accuracy within a maximum error of 5%.


title: Recipe recommendation using ingredient networks

abstract: The recording and sharing of cooking recipes, a human activity dating back thousands of years, naturally became an early and prominent social use of the web. The resulting online recipe collections are repositories of ingredient combinations and cooking methods whose large-scale and variety yield interesting insights about both the fundamentals of cooking and user preferences. At the level of an individual ingredient we measure whether it tends to be essential or can be dropped or added, and whether its quantity can be modified. We also construct two types of networks to capture the relationships between ingredients. The complement network captures which ingredients tend to co-occur frequently, and is composed of two large communities: one savory, the other sweet. The substitute network, derived from user-generated suggestions for modifications, can be decomposed into many communities of functionally equivalent ingredients, and captures users' preference for healthier variants of a recipe. Our experiments reveal that recipe ratings can be well predicted with features derived from combinations of ingredient networks and nutrition information.


title: The most dangerous code in the world: validating SSL certificates in non-browser software

abstract: SSL (Secure Sockets Layer) is the de facto standard for secure Internet communications. Security of SSL connections against an active network attacker depends on correctly validating public-key certificates presented when the connection is established. We demonstrate that SSL certificate validation is completely broken in many security-critical applications and libraries. Vulnerable software includes Amazon's EC2 Java library and all cloud clients based on it; Amazon's and PayPal's merchant SDKs responsible for transmitting payment details from e-commerce sites to payment gateways; integrated shopping carts such as osCommerce, ZenCart, Ubercart, and PrestaShop; AdMob code used by mobile websites; Chase mobile banking and several other Android apps and libraries; Java Web-services middleware including Apache Axis, Axis 2, Codehaus XFire, and Pusher library for Android and all applications employing this middleware. Any SSL connection from any of these programs is insecure against a man-in-the-middle attack. The root causes of these vulnerabilities are badly designed APIs of SSL implementations (such as JSSE, OpenSSL, and GnuTLS) and data-transport libraries (such as cURL) which present developers with a confusing array of settings and options. We analyze perils and pitfalls of SSL certificate validation in software based on these APIs and present our recommendations.


title: Facial Expression Recognition using Convolutional Neural Networks: State of the Art

abstract: The ability to recognize facial expressions automatically enables novel applications in human-computer interaction and other areas. Consequently, there has been active research in this field, with several recent works utilizing Convolutional Neural Networks (CNNs) for feature extraction and inference. These works differ significantly in terms of CNN architectures and other factors. Based on the reported results alone, the performance impact of these factors is unclear. In this paper, we review the state of the art in image-based facial expression recognition using CNNs and highlight algorithmic differences and their performance impact. On this basis, we identify existing bottlenecks and consequently directions for advancing this research field. Furthermore, we demonstrate that overcoming one of these bottlenecks - the comparatively basic architectures of the CNNs utilized in this field - leads to a substantial performance increase. By forming an ensemble of modern deep CNNs, we obtain a FER2013 test accuracy of 75.2%, outperforming previous works without requiring auxiliary training data or face registration.


title: Patterns of Mean-Level Change in Personality Traits Across the Life Course: A Meta-Analysis of Longitudinal Studies

abstract: The present study used meta-analytic techniques (number of samples = 92) to determine the patterns of mean-level change in personality traits across the life course. Results showed that people increase in measures of social dominance (a facet of extraversion), conscientiousness, and emotional stability, especially in young adulthood (age 20 to 40). In contrast, people increase on measures of social vitality (a 2nd facet of extraversion) and openness in adolescence but then decrease in both of these domains in old age. Agreeableness changed only in old age. Of the 6 trait categories, 4 demonstrated significant change in middle and old age. Gender and attrition had minimal effects on change, whereas longer studies and studies based on younger cohorts showed greater change.


title: Information-seeking , curiosity , and attention : computational and neural mechanisms

abstract: Intelligent animals devote much time and energy to exploring and obtaining information, but the underlying mechanisms are poorly understood. We review recent developments on this topic that have emerged from the traditionally separate fields of machine learning, eye movements in natural behavior, and studies of curiosity in psychology and neuroscience. These studies show that exploration may be guided by a family of mechanisms that range from automatic biases toward novelty or surprise to systematic searches for learning progress and information gain in curiosity-driven behavior. In addition, eye movements reflect visual information searching in multiple conditions and are amenable for cellular-level investigations. This suggests that the oculomotor system is an excellent model system for understanding information-sampling mechanisms.


title: Simulation and analysis of distributed PV generation in a LV network using MATLAB-Simulink

abstract: Power quality of electric networks with high penetration of photovoltaic systems has been analyzed in different publications. It has been noted that inverters may increase their harmonic emissions when connected to weak networks, this harmonics may affect other equipments connected to the network and decreasing their performance. This document shows the effects of connecting multiple photovoltaic generators in a network through simulations using MATLAB-SimulinkSimPowerSystems.


title: Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster

abstract: This paper introduces how ClaimBuster, a fact-checking platform, uses natural language processing and supervised learning to detect important factual claims in political discourses. The claim spotting model is built using a human-labeled dataset of check-worthy factual claims from the U.S. general election debate transcripts. The paper explains the architecture and the components of the system and the evaluation of the model. It presents a case study of how ClaimBuster live covers the 2016 U.S. presidential election debates and monitors social media and Australian Hansard for factual claims. It also describes the current status and the long-term goals of ClaimBuster as we keep developing and expanding it.


title: Collaboration and Computational Thinking: A classroom structure

abstract: Building a classroom environment based on student-led, meaningful collaboration is an ideal being promoted by educators and administrators seeking to equip students with 21st century workforce skills. More and more often, teachers are challenged to design lessons that utilize students' innate desire to communicate with one another rather than more traditional direct instruction. With a heavy focus on student-driven instruction and a reduced role for direct instruction, Carson Middle School's Game Design I course is able to utilize collaboration as a means for allowing students to not only learn but master and retain Computational Thinking Patterns and apply them in formal summative assessments. Through the collection of these data points throughout the course of a semester of learning, an educator can fully appreciate the learning of Computational Thinking skills that occurs as students build a sequence of games and simulations.


title: IdentityLink: user-device linking through visual and RF-signal cues

abstract: Mobile devices have become people's indispensable companion, since they allow each individual to be constantly connected with the outside world. In order to keep connected, the devices periodically send out data, which reveal some information about the device owner. Data sent by these devices can be captured by any external observer. Since the observer can observe only the wireless data, the actual person using the device is unknown. In this work, we propose IdentityLink, an approach leveraging the captured wireless data and computer vision to infer the user-device links, i.e., inferring which device is carried by which user. Knowing the user-device links opens up new opportunities for applications such as identifying unauthorized personnel in enterprises or finding criminals by law enforcement. By conducting experiments in a realistic scenario, we demonstrate how IdentityLink can be effectively applied to real practice.


title: Appearance contrast for fast, robust trail-following

abstract: We describe a framework for finding and tracking “trails” for autonomous outdoor robot navigation. Through a combination of visual cues and ladar-derived structural information, the algorithm is able to follow paths which pass through multiple zones of terrain smoothness, border vegetation, tread material, and illumination conditions. Our shape-based visual trail tracker assumes that the approaching trail region is approximately triangular under perspective. It generates region hypotheses from a learned distribution of expected trail width and curvature variation, and scores them using a robust measure of color and brightness contrast with flanking regions. The structural component analogously rewards hypotheses which correspond to empty or low-density regions in a groundstrike-filtered ladar obstacle map. Our system's performance is analyzed on several long sequences with diverse appearance and structural characteristics. Ground-truth segmentations are used to quantify performance where available, and several alternative algorithms are compared on the same data.


title: Benchmarking non-photorealistic rendering of portraits

abstract: We present a set of images for helping NPR practitioners evaluate their image-based portrait stylisation algorithms. Using a standard set both facilitates comparisons with other methods and helps ensure that presented results are representative. We give two levels of difficulty, each consisting of 20 images selected systematically so as to provide good coverage of several possible portrait characteristics. We applied three existing portrait-specific stylisation algorithms, two general-purpose stylisation algorithms, and one general learning based stylisation algorithm to the first level of the benchmark, corresponding to the type of constrained images that have often been used in portrait-specific work. We found that the existing methods are generally effective on this new image set, demonstrating that level one of the benchmark is tractable; challenges remain at level two. Results revealed several advantages conferred by portrait-specific algorithms over general-purpose algorithms: portrait-specific algorithms can use domain-specific information to preserve key details such as eyes and to eliminate extraneous details, and they have more scope for semantically meaningful abstraction due to the underlying face model. Finally, we provide some thoughts on systematically extending the benchmark to higher levels of difficulty.


title: Exploring cell tower data dumps for supervised learning-based point-of-interest prediction

abstract: Exploring massive mobile data for location-based services (LBS) becomes one of the key challenges in mobile data mining. In this paper, we propose a framework that uses large-scale cell tower data dumps and extracts points-of-interest (POIs) from a social network web site called Weibo, and provides new LBS based on these two data sets, i.e., predicting the existence of POIs and the number of POIs in a certain area. We use Voronoi diagram to divide a city area into non-overlapping regions, and a k-means clustering algorithm to aggregate neighboring cell towers into region groups. A supervised learning algorithm is adopted to build up a model between the number of connections of cell towers and the POIs in different region groups, where a classification or regression model is used to predict the POI existence or the number of POIs, respectively. We studied 12 state-of-the-art classification and regression algorithms, and the experimental results demonstrate the feasibility and effectiveness of the proposed framework.


title: Optogenetics: 10 years of microbial opsins in neuroscience

abstract: Over the past decade, modern optogenetics has emerged from the convergence of developments in microbial opsin engineering, genetic methods for targeting, and optical strategies for light delivery. In this Historical Commentary, Karl Deisseroth reflects on the optogenetic landscape, from the important steps but slow progress in the beginning to the acceleration in discovery seen in recent years.


title: Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification

abstract: Recent work on scene classification still makes use of generic CNN features in a rudimentary manner. In this paper, we present a novel pipeline built upon deep CNN features to harvest discriminative visual objects and parts for scene classification. We first use a region proposal technique to generate a set of high-quality patches potentially containing objects, and apply a pre-trained CNN to extract generic deep features from these patches. Then we perform both unsupervised and weakly supervised learning to screen these patches and discover discriminative ones representing category-specific objects and parts. We further apply discriminative clustering enhanced with local CNN fine-tuning to aggregate similar objects and parts into groups, called meta objects. A scene image representation is constructed by pooling the feature response maps of all the learned meta objects at multiple spatial scales. We have confirmed that the scene image representation obtained using this new pipeline is capable of delivering state-of-the-art performance on two popular scene benchmark datasets, MIT Indoor 67 [22] and Sun397 [31].


title: Entity linking by focusing DBpedia candidate entities

abstract: Recently, Entity Linking and Retrieval turned out to be one of the most interesting tasks in Information Extraction due to its various applications. Entity Linking (EL) is the task of detecting mentioned entities in a text and linking them to the corresponding entries of a Knowledge Base. EL is traditionally composed of three major parts: i)spotting, ii)candidate generation, and iii)candidate disambiguation. The performance of an EL system is highly dependent on the accuracy of each individual part. In this paper, we focus on these three main building blocks of EL systems and try to improve on the results of one of the open source EL systems, namely DBpedia Spotlight. We propose to use text pre-processing and parameter tuning to "focus" a general-purpose EL system to perform better on different kinds of input text. Also, one of the main drawbacks of EL systems is identifying where a name does not refer to any known entity. To improve this so-called NIL-detection, we define different features using a set of texts and their known entities and design a classifier to automatically classify DBpedia Spotlight's output entities as "NIL" or "Not NIL". The proposed system has participated in the SIGIR ERD Challenge 2014 and the performance analysis of this system on the challenge's datasets shows that the proposed approaches successfully improve the accuracy of the baseline system.


title: Types of Common-Sense Knowledge Needed for Recognizing Textual Entailment

abstract: Understanding language requires both linguistic knowledge and knowledge about how the world works, also known as common-sense knowledge. We attempt to characterize the kinds of common-sense knowledge most often involved in recognizing textual entailments. We identify 20 categories of common-sense knowledge that are prevalent in textual entailment, many of which have received scarce attention from researchers building collections of knowledge.


ISBN 978-0-7731-0714-4 (on-line) No-Reference Noise and Blur Detection via the Fourier Transform


title: Approximate computing: Challenges and opportunities

abstract: Approximate computing is gaining traction as a computing paradigm for data analytics and cognitive applications that aim to extract deep insight from vast quantities of data. In this paper, we demonstrate that multiple approximation techniques can be applied to applications in these domains and can be further combined together to compound their benefits. In assessing the potential of approximation in these applications, we took the liberty of changing multiple layers of the system stack: architecture, programming model, and algorithms. Across a set of applications spanning the domains of DSP, robotics, and machine learning, we show that hot loops in the applications can be perforated by an average of 50% with proportional reduction in execution time, while still producing acceptable quality of results. In addition, the width of the data used in the computation can be reduced to 10–16 bits from the currently common 32/64 bits with potential for significant performance and energy benefits. For parallel applications we reduced execution time by 50% using relaxed synchronization mechanisms. Finally, our results also demonstrate that benefits compounded when these techniques are applied concurrently. Our results across different applications demonstrate that approximate computing is a widely applicable paradigm with potential for compounded benefits from applying multiple techniques across the system stack. In order to exploit these benefits it is essential to re-think multiple layers of the system stack to embrace approximations ground-up and to design tightly integrated approximate accelerators. Doing so will enable moving the applications into a world in which the architecture, programming model, and even the algorithms used to implement the application are all fundamentally designed for approximate computing.


title: Development of a pneumatic soft actuator with pleated inflatable structures

abstract: AbstractMany soft actuators have been studied for use in robots that come into contact with humans, including communication, entertainment, and medical/health care robots. One reason for this is that soft robots are expected to exhibit intrinsic safety in case an accident occurs. This paper proposes a plastic-film pneumatic actuator with a pleated structure that do not undergo the elastic deformation typical of rubber materials. By utilizing thin plastic films, the mass of an actuator can be significantly reduced, even if the actuators are the same size as a human arm. If the mass of the actuator is reduced, the kinetic energy when contacts with humans mechanically can be reduced considerably without reducing the working speed. More specifically, we propose a pleated structure made of plastic to achieve structural deformation generated from a two-dimensional pleated film. The pleated structure easily generates various bending motions. In this paper, a design method for determining the shape parameters of ...


title: Multi-stream 3D FCN with multi-scale deep supervision for multi-modality isointense infant brain MR image segmentation

abstract: We present a method to address the challenging problem of segmentation of multi-modality isointense infant brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Our method is based on context-guided, multi-stream fully convolutional networks (FCN), which after training, can directly map a whole volumetric data to its volume-wise labels. In order to alleviate the poten-tial gradient vanishing problem during training, we designed multi-scale deep supervision. Furthermore, context infor-mation was used to further improve the performance of our method. Validated on the test data of the MICCAI 2017 Grand Challenge on 6-month infant brain MRI segmentation (iSeg-2017), our method achieved an average Dice Overlap Coefficient of 95.4%, 91.6% and 89.6% for CSF, GM and WM, respectively.


title: Learning-Based Cell Selection Method for Femtocell Networks

abstract: In open-access non-stationary femtocell networks, cellular users (also known as macro users or MU) may join, through a handover procedure, one of the neighboring femtocells so as to enhance their communications/increase their respective channel capacities. To avoid frequent communication disruptions owing to effects such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell selection method. Traditionally, such selection method is usually a measured channel/cell quality metric such as the channel capacity, the load of the candidate cell, the received signal strength (RSS), etc. However, one problem with such approaches is that present measured performance does not necessarily reflect the future performance, thus the need for novel cell selection that can predict the \textit{horizon}. Subsequently, we present in this paper a reinforcement learning (RL), i.e, Q- learning algorithm, as a generic solution for the cell selection problem in a non-stationary femtocell network. After comparing our solution for cell selection with different methods in the literature (least loaded (LL), random and capacity-based), simulation results demonstrate the benefits of using learning in terms of the gained capacity and the number of handovers.


title: SRAM design on 65-nm CMOS technology with dynamic sleep transistor for leakage reduction

abstract: A 70-Mb SRAM is designed and fabricated on a 65-nm CMOS technology. It features a 0.57-μm 2 6T SRAM cell with large noise margin down to 0.7 V for low-voltage operation. The fully synchronized subarray contains an integrated leakage reduction scheme with dynamically controlled sleep transistor. SRAM virtual ground in standby is controlled by programmable bias transistors to achieve good voltage control with fine granularity under process skew. It also has a built-in programmable defect screen circuit for high volume manufacturing. The measurements showed that the SRAM leakage can be reduced by 3-5 x while maintaining the integrity of stored data.


title: Multiparametric Decision Support System for the Prediction of Oral Cancer Reoccurrence

abstract: Oral squamous cell carcinoma (OSCC) constitutes the predominant neoplasm of the head and neck region, featuring particularly aggressive nature, associated with quite unfavorable prognosis. In this work we formulate a Decision Support System (DSS) which integrates a multitude of heterogeneous data (clinical, imaging and genomic), thus, framing all manifestations of the disease. Our primary aim is to identify the factors that dictate OSCC progression and subsequently predict potential relapses (local or metastatic) of the disease. The discrimination potential of each source of data is initially explored separately, and afterwards the individual predictions are combined to yield a consensus decision achieving complete discrimination between patients with and without a disease relapse.


title: Modeling of Ionization in a Spherical Surface Ionizer

abstract: A Monte Carlo method-based model of a hot cavity surface ion source with a spherically shaped ionizer is presented. A numerical code enables studies of ion source efficiency as a function of an extraction voltage, geometry of the ionizer and its temperature, the size of extraction aperture and many other factors. The calculation results for a variety of mentioned parameters are presented. A novel configuration of surface ion source is proposed and discussed — the efficiency of the source with an almost spherically shaped ionizer could be much higher than that of currently used constructions of ion sources.


title: Akyildiz, Characteristics of underground channel for wireless underground sensor networks

abstract: Wireless Underground Sensor Networks (WUSN) constitute one of the promising application areas of the recently developed wireless sensor networking techniques. The main difference between WUSN and the terrestrial wireless sensor networks is the communication medium. The propagation characteristics of electromagnetic (EM) waves in soil and the significant differences between propagation in air prevent a straightforward characterization of the underground wireless channel. To this end, in this paper, advanced channel models are proposed to completely characterize the underground wireless channel and lay out the foundations for efficient communication in this environment. In particular, the underground communication channel is modeled considering not only the propagation of EM waves in soil, but also other affects such as multipath, soil composition, water content, and burial depth. The propagation characteristics are shown through simulation results of path loss between two underground sensors. Moreover, based on the proposed channel model, the resulting bit error rate is analyzed for different network and soil parameters. The theoretical analysis and the simulation results prove the feasibility of wireless communication in underground environment and highlight several important aspects in this field. This work will lead to the provision of a generic framework for underground wireless communication and the realization of WUSN.


title: Comparative analysis of big data transfer protocols in an international high-speed network

abstract: Large-scale scientific installations generate voluminous amounts of data (or big data) every day. These data often need to be transferred using high-speed links (typically with 10 Gb/s or more link capacity) to researchers located around the globe for storage and analysis. Efficiently transferring big data across countries or continents requires specialized big data transfer protocols. Several big data transfer protocols have been proposed in the literature, however, a comparative analysis of these protocols over a long distance international network is lacking in the literature. We present a comparative performance and fairness study of three open-source big data transfer protocols, namely, GridFTP, FDT, and UDT, using a 10 Gb/s high-speed link between New Zealand and Sweden. We find that there is limited performance difference between GridFTP and FDT. GridFTP is stable in terms of handling file system and TCP socket buffer. UDT has an implementation issue that limits its performance. FDT has issues with small buffer size limiting its performance, however, this problem is overcome by using multiple flows. Our work indicates that faster file systems and larger TCP socket buffers in both the operating system and application are useful in improving data transfer rates.


title: An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting

abstract: We propose the use of dilated filters to construct an aggregation module in a multicolumn convolutional neural network for perspective-free counting. Counting is a common problem in computer vision (e.g. traffic on the street or pedestrians in a crowd). Modern approaches to the counting problem involve the production of a density map via regression whose integral is equal to the number of objects in the image. However, objects in the image can occur at different scales (e.g. due to perspective effects) which can make it difficult for a learning agent to learn the proper density map. While the use of multiple columns to extract multiscale information from images has been shown before, our approach aggregates the multiscale information gathered by the multicolumn convolutional neural network to improve performance. Our experiments show that our proposed network outperforms the state-of-the-art on many benchmark datasets, and also that using our aggregation module in combination with a higher number of columns is beneficial for multiscale counting.


title: A neural network with O(N) neurons for ranking N numbers in O(1/N) time

abstract: In this paper, we propose a neural network for ranking a given set of N numbers in O(1/N) time. The ordering of a set of numbers based on their relative magnitudes, which is analogous to sorting, is a fundamental operation in many algorithms. In comparison with other sorting networks reported in the literature, the proposed network requires fewer neurons, and fewer interconnections between neurons. The interconnections use nonlinear synapses which are composed of comparators, and do not require any weighted interconnections between neurons, as used in conventional neural networks. The proposed network has many applications, including as a component of self-organizing feature maps and other systems where sorting is a frequent operation.


title: Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks

abstract: In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative). The networks containing such relations are named signed social networks (SSNs), and some real-world complex systems can be also modeled with SSNs. Given the information of the observed structure of an SSN, the link prediction aims to estimate the values of the unobserved links. Noticing that most of the previous approaches for link prediction are based on the members’ similarity and the supervised learning method, however, research work on the investigation of the hidden principles that drive the behaviors of social members are rarely conducted. In this paper, the deep belief network (DBN)-based approaches for link prediction are proposed. Including an unsupervised link prediction model, a feature representation method and a DBN-based link prediction method are introduced. The experiments are done on the datasets from three SNSs (social networking services) in different domains, and the results show that our methods can predict the values of the links with high performance and have a good generalization ability across these datasets.


title: Vector Parallelism in JavaScript: Language and Compiler Support for SIMD

abstract: JavaScript is the most widely used web programming language and is increasingly used to implement sophisticated and demanding applications in such domains as graphics, games, video, and cryptography. The performance and energy usage of these applications can benefit from hardware parallelism, including SIMD (Single Instruction, Multiple Data) vector parallel instructions. JavaScript's current support for parallelism is, however, limited and does not directly utilize SIMD capabilities. This paper presents the design and implementation of SIMD language extensions and compiler support that together add fine-grain vector parallelism to JavaScript. The specification for this language extension is in final stages of adoption by the JavaScript standardization committee and our compiler support is available in two open-source production browsers. The design principles seek portability, SIMD performance portability on various SIMD architectures, and compiler simplicity to ease adoption. The design does not require automatic vectorization compiler technology, but does not preclude it either. The SIMD extensions define immutable fixed-length SIMD data types and operations that are common to both ARM and x86 ISAs. The contributions of this work include type speculation and optimizations that generate minimal numbers of SIMD native instructions from high-level JavaScript SIMD instructions. We implement type speculation, optimizations, and code generation in two open-source JavaScript VMs and measure performance improvements between a factor of 1.7× to 8.9× with an average of 3.3× and average energy improvements of 2.9× on micro benchmarks and key graphics kernels on various hardware, browsers, and operating systems. These portable SIMD language extensions significantly improve compute-intensive interactive applications in the browser, such as games and media processing, by exploiting vector parallelism without relying on automatic vectorizing compiler technology, non-portable native code, or plugins.


title: Code compression for embedded VLIW processors using variable-to-fixed coding

abstract: In embedded system design, memory is one of the most restricted resources, posing serious constraints on program size. Code compression has been used as a solution to reduce the code size for embedded systems. Lossless data compression techniques are used to compress instructions, which are then decompressed on-the-fly during execution. Previous work used fixed-to-variable coding algorithms that translate fixed-length bit sequences into variable-length bit sequences. In this paper, we present a class of code compression techniques called variable-to-fixed code compression (V2FCC), which uses variable-to-fixed coding schemes based on either Tunstall coding or arithmetic coding. Though the techniques are suitable for both reduced instruction set computer (RISC) and very long instruction word (VLIW) architectures, they favor VLIW architectures which require a high-bandwidth instruction prefetch mechanism to supply multiple operations per cycle, and fast decompression is critical to overcome the communication bottleneck between memory and CPU. Experimental results for a VLIW embedded processor TMS320C6x show that the compression ratios using memoryless V2FCC and Markov V2FCC are around 82.5% and 70%, respectively. Decompression unit designs for memoryless V2FCC and Markov V2FCC are implemented in TSMC 0.25-/spl mu/m technology.


title: PFunk-H: approximate query processing using perceptual models

abstract: Interactive visualization tools (e.g., crossfilter) are critical to many data analysts by making the discovery and verification of hypotheses quick and seamless. Increasing data sizes has made the scalability of these tools a necessity. To bridge the gap between data sizes and interactivity, many visualization systems have turned to sampling-based approximate query processing frameworks. However, these systems are currently oblivious to human perceptual visual accuracy. This could either lead to overly aggressive sampling when the approximation accuracy is higher than needed or an incorrect visual rendering when the accuracy is too lax. Thus, for both correctness and efficiency, we propose to use empirical knowledge of human perceptual limitations to automatically bound the error of approximate answers meant for visualization. This paper explores a preliminary model of sampling-based approximate query processing that uses perceptual models (encoded as functions) to construct approximate answers intended for visualization. We present initial results that show that the approximate and non-approximate answers for a given query differ by a perceptually indiscernible amount, as defined by perceptual functions.


title: Anemia in pregnancy: Factors influencing knowledge and attitudes among mothers in southeastern Nigeria

abstract: Aim ::: Explored the factors that influence knowledge and attitudes toward anemia in pregnancy (AIP) in southeastern Nigeria.


title: Hepatic resection and transplantation for primary carcinoid tumors of the liver.

abstract: The term “carcinoid” is a generic name for tumors derived from neuroendocrine cells and was first coined by Oberndorfer in 1907.1 Ninety per cent of carcinoid tumors occur within the gastrointestinal tract,2 most commonly in the appendix and the terminal ileum. The liver is a common site for carcinoid metastases. Conversely, primary hepatic carcinoid tumor is a very rare entity, with fewer than 60 cases reported in the English language literature.3–32 Most of these are single case reports, although 4 series, of 6 patients,7 5 patients,28 4 patients,20 and 3 patients29 have been published. ::: ::: The diagnosis of primary hepatic carcinoid tumor is based principally on the histopathological confirmation of carcinoid tumor and the exclusion of a nonhepatic primary tumor. This requires preoperative imaging but most importantly a thorough laparotomy and rigorous follow-up. The suggested diagnostic process is outlined in the flow diagram (Fig. 1). ::: ::: ::: ::: FIGURE 1. Flow diagram representing the suggested diagnostic pathway for primary hepatic carcinoid tumor. ::: ::: ::: ::: In this paper, we describe the clinical course and management of 8 cases of primary hepatic carcinoid tumor diagnosed in accordance with our protocol. These patients have presented to our center during the past 8 years and form the largest single center report, and the only report to include transplantation as a treatment option.


title: Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models

abstract: Author(s): Nimmagadda, Tejaswi; Anandkumar, Anima | Abstract: Deep learning has shown state-of-art classification performance on datasets such as ImageNet, which contain a single object in each image. However, multi-object classification is far more challenging. We present a unified framework which leverages the strengths of multiple machine learning methods, viz deep learning, probabilistic models and kernel methods to obtain state-of-art performance on Microsoft COCO, consisting of non-iconic images. We incorporate contextual information in natural images through a conditional latent tree probabilistic model (CLTM), where the object co-occurrences are conditioned on the extracted fc7 features from pre-trained Imagenet CNN as input. We learn the CLTM tree structure using conditional pairwise probabilities for object co-occurrences, estimated through kernel methods, and we learn its node and edge potentials by training a new 3-layer neural network, which takes fc7 features as input. Object classification is carried out via inference on the learnt conditional tree model, and we obtain significant gain in precision-recall and F-measures on MS-COCO, especially for difficult object categories. Moreover, the latent variables in the CLTM capture scene information: the images with top activations for a latent node have common themes such as being a grasslands or a food scene, and on on. In addition, we show that a simple k-means clustering of the inferred latent nodes alone significantly improves scene classification performance on the MIT-Indoor dataset, without the need for any retraining, and without using scene labels during training. Thus, we present a unified framework for multi-object classification and unsupervised scene understanding.


title: A benchmark dataset to evaluate sensor displacement in activity recognition

abstract: This work introduces an open benchmark dataset to investigate inertial sensor displacement effects in activity recognition. While sensor position displacements such as rotations and translations have been recognised as a key limitation for the deployment of wearable systems, a realistic dataset is lacking. We introduce a concept of gradual sensor displacement conditions, including ideal, self-placement of a user, and mutual displacement deployments. These conditions were analysed in the dataset considering 33 fitness activities, recorded using 9 inertial sensor units from 17 participants. Our statistical analysis of acceleration features quantified relative effects of the displacement conditions. We expect that the dataset can be used to benchmark and compare recognition algorithms in the future.


title: The CRISPR/Cas Genome-Editing Tool: Application in Improvement of Crops

abstract: The Clustered Regularly Interspaced Short Palindromic Repeats associated Cas9/sgRNA system is a novel targeted genome-editing technique derived from bacterial immune system. It is an inexpensive, easy, most user friendly and rapidly adopted genome editing tool transforming to revolutionary paradigm. This technique enables precise genomic modifications in many different organisms and tissues. Cas9 protein is an RNA guided endonuclease utilized for creating targeted double-stranded breaks with only a short RNA sequence to confer recognition of the target in animals and plants. Development of genetically edited (GE) crops similar to those developed by conventional or mutation breeding using this potential technique makes it a promising and extremely versatile tool for providing sustainable productive agriculture for better feeding of rapidly growing population in a changing climate. The emerging areas of research for the genome editing in plants include interrogating gene function, rewiring the regulatory signaling networks and sgRNA library for high-throughput loss-of-function screening. In this review, we have described the broad applicability of the Cas9 nuclease mediated targeted plant genome editing for development of designer crops. The regulatory uncertainty and social acceptance of plant breeding by Cas9 genome editing have also been described. With this powerful and innovative technique the designer GE non-GM plants could further advance climate resilient and sustainable agriculture in the future and maximizing yield by combating abiotic and biotic stresses.


title: Comparison processes in social judgment: Mechanisms and consequences

abstract: This article proposes an informational perspective on comparison consequences in social judgment. It is argued that to understand the variable consequences of comparison, one has to examine what target knowledge is activated during the comparison process. These informational underpinnings are conceptualized in a selective accessibility model that distinguishes 2 fundamental comparison processes. Similarity testing selectively makes accessible knowledge indicating target–standard similarity, whereas dissimilarity testing selectively makes accessible knowledge indicating target–standard dissimilarity. These respective subsets of target knowledge build the basis for subsequent target evaluations, so that similarity testing typically leads to assimilation whereas dissimilarity testing typically leads to contrast. The model is proposed as a unifying conceptual framework that integrates diverse findings on comparison consequences in social judgment. Human judgment is comparative in nature. When people evaluate a given target, they don’t do so in a vacuum. Rather, such evaluations are made within and in relation to a specific context. In fact, any evaluation is relative in that it refers to a comparison of the evaluated target with a pertinent norm or standard. To charac


title: PlaNet - Photo Geolocation with Convolutional Neural Networks

abstract: Is it possible to determine the location of a photo from just its pixels? While the general problem seems exceptionally difficult, photos often contain cues such as landmarks, weather patterns, vegetation, road markings, or architectural details, which in combination allow to infer where the photo was taken. Previously, this problem has been approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, this model achieves a 50 % performance improvement over the single-image model.


title: Internet-of-Things and big data for smarter healthcare: from device to architecture, applications and analytics

abstract: Abstract The technology and healthcare industries have been deeply intertwined for quite some time. New opportunities, however, are now arising as a result of fast-paced expansion in the areas of the Internet of Things (IoT) and Big Data. In addition, as people across the globe have begun to adopt wearable biosensors, new applications for individualized eHealth and mHealth technologies have emerged. The upsides of these technologies are clear: they are highly available, easily accessible, and simple to personalize; additionally they make it easy for providers to deliver individualized content cost-effectively, at scale. At the same time, a number of hurdles currently stand in the way of truly reliable, adaptive, safe and efficient personal healthcare devices. Major technological milestones will need to be reached in order to address and overcome those hurdles; and that will require closer collaboration between hardware and software developers and medical personnel such as physicians, nurses, and healthcare workers. The purpose of this special issue is to analyze the top concerns in IoT technologies that pertain to smart sensors for health care applications; particularly applications targeted at individualized tele-health interventions with the goal of enabling healthier ways of life. These applications include wearable and body sensors, advanced pervasive healthcare systems, and the Big Data analytics required to inform these devices.


title: Full dimension mimo (FD-MIMO): the next evolution of MIMO in LTE systems

abstract: Full dimension MIMO has attracted significant attention in the wireless industry and academia in the past few years as a candidate technology for the next generation evolution toward beyond fourth generation and fifth generation cellular systems. FD-MIMO utilizes a large number of antennas placed in a 2D antenna array panel for realizing spatially separated transmission links to a large number of mobile stations. The arrangement of these antennas on a 2D panel allows the extension of spatial separation to the elevation domain as well as the traditional azimuth domain. This article discusses features and performance benefits of FD-MIMO along with the ongoing standardization efforts in 3GPP to incorporate FD-MIMO features in the next evolution of LTE. Furthermore, a design of a 2D antenna array, which plays a key role in the implementation of FD-MIMO, is also discussed. Finally, in order to demonstrate the performance benefits of FD-MIMO, system-level evaluation results are provided.


title: When do Recommender Systems Work the Best?: The Moderating Effects of Product Attributes and Consumer Reviews on Recommender Performance

abstract: We investigate the moderating effect of product attributes and consumer reviews on the efficacy of a collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top North American retailer's website with 184,375 users split into a recommender-treated group and a control group with 37,215 unique products in the dataset. By augmenting the dataset with Amazon Mechanical Turk tagged product attributes and consumer review data from the website, we study their moderating influence on recommenders in generating conversion. We first confirm that the use of recommenders increases the baseline conversion rate by 5.9%. We find that the recommenders act as substitutes for high average review ratings with the effect of using recommenders increasing the conversion rate as much as about 1.4 additional average star ratings. Additionally, we find that the positive impacts on conversion from recommenders are greater for hedonic products compared to utilitarian products while search-experience quality did not have any impact. We also find that the higher the price, the lower the positive impact of recommenders, while having lengthier product descriptions and higher review volumes increased the recommender's effectiveness. More findings are discussed in the Results. ::: ::: For managers, we 1) identify the products and product attributes for which the recommenders work well, 2) show how other product information sources on e-commerce sites interact with recommenders. Additionally, the insights from the results could inform novel recommender algorithm designs that are aware of strength and shortcomings. From an academic standpoint, we provide insight into the underlying mechanism behind how recommenders cause consumers to purchase.


title: Design of a wideband filtering power divider with stub-loaded ring resonator

abstract: In this paper, a wideband filtering power divider is designed by employing a stub-loaded ring resonator. The first two resonant modes of the ring resonator are excited to form the filtering passband. To improve the passband skirt, two transmission zeros near the passband are then introduced by mounting an open stub along the ring resonator. After installing two coupled lines at two output ports, one additional transmission pole could be generated and a good in-band response is achieved. Finally, a prototype of the proposed power divider is designed and fabricated. The measured 14-dB fractional bandwidth is about 60 % and the in-band isolation achieved 20 dB from 1.05 GHz to 1.87 GHz.


title: SIMO PPM optical wireless communication system over gamma turbulence channels with generalized pointing errors

abstract: In this work, the performance of a free-space optical communication system using spatial diversity on the receiver's side with optimal combining, is studied. The information signal is conveyed using the L-PPM scheme and the proposed wireless optical system is assumed to be hampered by the atmospheric turbulence effect modeled by the Gamma distribution and the misalignment fading modeled by the recently launched generalized pointing error model. Novel analytical closed-form expressions are derived for the average bit error rate (BER) estimation for such a communication system. Using the derived mathematical expressions, numerical results are illustrated for various turbulence conditions, different pointing error cases and receivers' diversity configurations.


title: Texting, sexting, and attachment in college students' romantic relationships

abstract: In this study, we explored how texting and sexting practices are related to attachment in college students' (n=744) committed romantic relationships. Participants completed a survey containing questions about their texting and sexting practices and attachment styles with relationship partners. Results showed that texting and sexting are relatively common in young adult romantic relationships, and texting and sexting are both significantly related to attachment style. However, whereas text messaging was more common among those with secure attachments (i.e., those with less attachment avoidance), sexting (both texts and pictures) was more common among those with insecure attachments, particularly those with higher attachment avoidance. Whereas anxious attachment predicted variance in sending sex text messages only, attachment avoidance contributed unique variance in sending both sex texts and pictures. This relationship was moderated by gender-avoidant men were more likely than avoidant women to send sex text and picture messages to relationship partners.


title: Color and Texture Descriptors

abstract: This paper presents an overview of color and texture descriptors that have been approved for the Final Committee Draft of the MPEG-7 standard. The color and texture descriptors that are described in this paper have undergone extensive evaluation and development during the past two years. Evaluation criteria include effectiveness of the descriptors in similarity retrieval, as well as extraction, storage, and representation complexities. The color descriptors in the standard include a histogram descriptor that is coded using the Haar transform, a color structure histogram, a dominant color descriptor, and a color layout descriptor. The three texture descriptors include one that characterizes homogeneous texture regions and another that represents the local edge distribution. A compact descriptor that facilitates texture browsing is also defined. Each of the descriptors is explained in detail by their semantics, extraction and usage. The effectiveness is documented by experimental results.


title: Dynamic Service Provisioning in Elastic Optical Networks With Hybrid Single-/Multi-Path Routing

abstract: Empowered by the optical orthogonal frequency-division multiplexing (O-OFDM) technology, flexible online service provisioning can be realized with dynamic routing, modulation, and spectrum assignment (RMSA). In this paper, we propose several online service provisioning algorithms that incorporate dynamic RMSA with a hybrid single-/multi-path routing (HSMR) scheme. We investigate two types of HSMR schemes, namely HSMR using online path computation (HSMR-OPC) and HSMR using fixed path sets (HSMR-FPS). Moreover, for HSMR-FPS, we analyze several path selection policies to optimize the design. We evaluate the proposed algorithms with numerical simulations using a Poisson traffic model and two mesh network topologies. The simulation results have demonstrated that the proposed HSMR schemes can effectively reduce the bandwidth blocking probability (BBP) of dynamic RMSA, as compared to two benchmark algorithms that use single-path routing and split spectrum. Our simulation results suggest that HSMR-OPC can achieve the lowest BBP among all HSMR schemes. This is attributed to the fact that HSMR-OPC optimizes routing paths for each request on the fly with considerations of both bandwidth utilizations and lengths of links. Our simulation results also indicate that the HSMR-FPS scheme that use the largest slots-over-square-of-hops first path-selection policy obtains the lowest BBP among all HSMR-FPS schemes. We then investigate the proposed algorithms' impacts on other network performance metrics, including network throughput and network bandwidth fragmentation ratio. To the best of our knowledge, this is the first attempt to consider dynamic RMSA based on both online path computation and offline path computation with various path selection policies for multipath provisioning in O-OFDM networks.


title: A Robust Face Detection Method Based on Skin Color and Edges

abstract: In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non- face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions


title: Writer Identification in Noisy Handwritten Documents

abstract: Identifying the writer of a handwritten document based on visual features is difficult, as evidenced by the limited number of subject matter experts proficient in forensic document analysis. Automating writer identification would be beneficial for such experts' workloads. Academic work in identifying writers has focused on clean benchmark datasets: plain white documents with uniform writing instruments. Solutions on this type of data have achieved hitin-top-10 accuracy rates reaching upwards of 98%. Unfortunately, transferring competitive techniques to handwritten documents with noise is nontrivial. This work highlights efforts in unconstrained writer identification in diverse conditions, including but not limited to lined and graph paper, coffee stains, stamps, and different writing implements. The proposed methodology blends both deep learning and traditional computer vision approaches, exploring deep convolutional neural networks (CNNs) for denoising in conjunction with hand-crafted descriptor features. Our identification algorithms are trained on existing clean datasets artificially augmented with noise, and we evaluate them on a commissioned dataset, which features a diverse but balanced set of writers, writing implements, and writing substrates (incorporating various types of noise). Experimenting with mixtures of segmentation methods, novel denoisers, specialized CNNs, and handcrafted features, we exceed the state of the art in writer identification of noisy handwritten documents by over 10%.


title: Representation Learning of Temporal Dynamics for Skeleton-Based Action Recognition

abstract: Motion characteristics of human actions can be represented by the position variation of skeleton joints. Traditional approaches generally extract the spatial-temporal representation of the skeleton sequences with well-designed hand-crafted features. In this paper, in order to recognize actions according to the relative motion between the limbs and the trunk, we propose an end-to-end hierarchical RNN for skeleton-based action recognition. We divide human skeleton into five main parts in terms of the human physical structure, and then feed them to five independent subnets for local feature extraction. After the following hierarchical feature fusion and extraction from local to global, dimensions of the final temporal dynamics representations are reduced to the same number of action categories in the corresponding data set through a single-layer perceptron. In addition, the output of the perceptron is temporally accumulated as the input of a softmax layer for classification. Random scale and rotation transformations are employed to improve the robustness during training. We compare with five other deep RNN variants derived from our model in order to verify the effectiveness of the proposed network. In addition, we compare with several other methods on motion capture and Kinect data sets. Furthermore, we evaluate the robustness of our model trained with random scale and rotation transformations for a multiview problem. Experimental results demonstrate that our model achieves the state-of-the-art performance with high computational efficiency.


title: Infrastructure, programs, and policies to increase bicycling: An international review

abstract: OBJECTIVES ::: To assess existing research on the effects of various interventions on levels of bicycling. Interventions include infrastructure (e.g., bike lanes and parking), integration with public transport, education and marketing programs, bicycle access programs, and legal issues. ::: ::: ::: METHODS ::: A comprehensive search of peer-reviewed and non-reviewed research identified 139 studies. Study methodologies varied considerably in type and quality, with few meeting rigorous standards. Secondary data were gathered for 14 case study cities that adopted multiple interventions. ::: ::: ::: RESULTS ::: Many studies show positive associations between specific interventions and levels of bicycling. The 14 case studies show that almost all cities adopting comprehensive packages of interventions experienced large increases in the number of bicycle trips and share of people bicycling. ::: ::: ::: CONCLUSIONS ::: Most of the evidence examined in this review supports the crucial role of public policy in encouraging bicycling. Substantial increases in bicycling require an integrated package of many different, complementary interventions, including infrastructure provision and pro-bicycle programs, supportive land use planning, and restrictions on car use.


title: Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains.

abstract: Protoplasmic astrocytes are increasingly thought to interact extensively with neuronal elements in the brain and to influence their activity. Recent reports have also begun to suggest that physiologically, and perhaps functionally, diverse forms of these cells may be present in the CNS. Our current understanding of astrocyte form and distribution is based predominantly on studies that used the astrocytic marker glial fibrillary acidic protein (GFAP) and on studies using metal-impregnation techniques. The prevalent opinion, based on studies using these methods, is that astrocytic processes overlap extensively and primarily share the underlying neuropil. However, both of these techniques have serious shortcomings for visualizing the interactions among these structurally complex cells. In the present study, intracellular injection combined with immunohistochemistry for GFAP show that GFAP delineates only approximately 15% of the total volume of the astrocyte. As a result, GFAP-based images have led to incorrect conclusions regarding the interaction of processes of neighboring astrocytes. To investigate these interactions in detail, groups of adjacent protoplasmic astrocytes in the CA1 stratum radiatum were injected with fluorescent intracellular tracers of distinctive emissive wavelengths and analyzed using three-dimensional (3D) confocal analysis and electron microscopy. Our findings show that protoplasmic astrocytes establish primarily exclusive territories. The knowledge of how the complex morphology of protoplasmic astrocytes affects their 3D relationships with other astrocytes, oligodendroglia, neurons, and vasculature of the brain should have important implications for our understanding of nervous system function.


title: Subsampling for efficient and effective unsupervised outlier detection ensembles

abstract: Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Here, we propose and study subsampling as a technique to induce diversity among individual outlier detectors. We show analytically and experimentally that an outlier detector based on a subsample per se, besides inducing diversity, can, under certain conditions, already improve upon the results of the same outlier detector on the complete dataset. Building an ensemble on top of several subsamples is further improving the results. While in the literature so far the intuition that ensembles improve over single outlier detectors has just been transferred from the classification literature, here we also justify analytically why ensembles are also expected to work in the unsupervised area of outlier detection. As a side effect, running an ensemble of several outlier detectors on subsamples of the dataset is more efficient than ensembles based on other means of introducing diversity and, depending on the sample rate and the size of the ensemble, can be even more efficient than just the single outlier detector on the complete data.


title: Individual recognition using gait energy image

abstract: In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches


title: Frequency control and wind turbine technologies

abstract: Increasing levels of wind generation has resulted in an urgent need for the assessment of their impact on frequency control of power systems. Whereas increased system inertia is intrinsically linked to the addition of synchronous generation to power systems, due to differing electromechanical characteristics, this inherent link is not present in wind turbine generators. Regardless of wind turbine technology, the displacement of conventional generation with wind will result in increased rates of change of system frequency. The magnitude of the frequency excursion following a loss of generation may also increase. Amendment of reserve policies or modification of wind turbine inertial response characteristics may be necessary to facilitate increased levels of wind generation. This is particularly true in small isolated power systems.


title: Stigma Consciousness: The Psychological Legacy of Social Stereotypes

abstract: Whereas past researchers have treated targets of stereotypes as though they have uniform reactions to their stereotyped status (e.g., J. Crocker & B. Major, 1989; C. M. Steele & J. Aronson, 1995), it is proposed here that targets differ in the extent to which they expect to be stereotyped by others (i.e., stigma consciousness). Six studies, 5 of which validate the stigma-consciousness questionnaire (SCQ), are presented. The results suggest that the SCQ is a reliable and valid instrument for detecting differences in stigma consciousness. In addition, scores on the SCQ predict perceptions of discriminatio n and the ability to generate convincing examples of such discrimination. The final study highlights a behavioral consequence of stigma consciousness: the tendency for people high in stigma consciousness to forgo opportunities to invalidate stereotypes about their group. The relation of stigma consciousness to past research on targets of stereotypes is considered as is the issue of how stigma consciousness may encourage continued stereotyping.


title: Dipole Antenna Above EBG Substrate for Local SAR Reduction

abstract: This letter investigates the performance of a dipole antenna above an electromagnetic band-gap (EBG) substrate to realize a low specific absorption rate (SAR) antenna. The frequency band considered is 3.5 GHz, which is assigned to fourth-generation mobile communications. One of the motivations for this proposal is that the size of the EBG substrate can be small enough for a handset. The dipole antenna above the EBG substrate was found to reduce the local SAR in a cubic head model by 84% as compared to the local SAR of a perfect electric conductor. The radiation efficiency was also improved by 10%. These results were also confirmed for a realistic head model.


title: Investigation of full-sequence training of deep belief networks for speech recognition

abstract: Recently, Deep Belief Networks (DBNs) have been proposed for phone recognition and were found to achieve highly competitive performance. In the original DBNs, only framelevel information was used for training DBN weights while it has been known for long that sequential or full-sequence information can be helpful in improving speech recognition accuracy. In this paper we investigate approaches to optimizing the DBN weights, state-to-state transition parameters, and language model scores using the sequential discriminative training criterion. We describe and analyze the proposed training algorithm and strategy, and discuss practical issues and how they affect the final results. We show that the DBNs learned using the sequence-based training criterion outperform those with frame-based criterion using both threelayer and six-layer models, but the optimization procedure for the deeper DBN is more difficult for the former criterion.


title: The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification

abstract: Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.


title: Prejudice, stereotyping and discrimination: Theoretical and empirical overview

abstract: This chapter has two main objectives: to review influential ideas and findings in the literature and to outline the organization and content of the volume. The first part of the chapter lays a conceptual and empirical foundation for other chapters in the volume. Specifically, the chapter defines and distinguishes the key concepts of prejudice, stereotypes, and discrimination, highlighting how bias can occur at individual, institutional, and cultural levels. We also review different theoretical perspectives on these phenomena, including individual differences, social cognition, functional relations between groups, and identity concerns. We offer a broad overview of the field, charting how this area has developed over previous decades and identify emerging trends and future directions. The second part of the chapter focuses specifically on the coverage of the area in the present volume. It explains the organization of the book and presents a brief synopsis of the chapters in the volume. Throughout psychology’s history, researchers have evinced strong interest in understanding prejudice, stereotyping, and discrimination (Brewer & Brown, 1998; Dovidio, 2001; Duckitt, 1992; Fiske, 1998), aswell as the phenomenon of intergroup bias more generally (Hewstone, Rubin, & Willis, 2002). Intergroup bias generally refers to the systematic tendency to evaluate one’s own membership group (the ingroup) or its members more favorably than a non-membership group (the outgroup) or its members. These topics have a long history in the disciplines of anthropology and sociology (e.g., Sumner, 1906). However, social psychologists, building on the solid foundations of Gordon Allport’s (1954) masterly volume, The Nature of Prejudice, have developed a systematic and more nuanced analysis of bias and its associated phenomena. Interest in prejudice, stereotyping, and discrimination is currently shared by allied disciplines such as sociology and political science, and emerging disciplines such as neuroscience. The practical implications of this 4 OVERVIEW OF THE TOPIC large body of research are widely recognized in the law (Baldus, Woodworth, & Pulaski, 1990; Vidmar, 2003), medicine (Institute of Medicine, 2003), business (e.g., Brief, Dietz, Cohen, et al., 2000), the media, and education (e.g., Ben-Ari & Rich, 1997; Hagendoorn &


title: A topological insight into restricted Boltzmann machines

abstract: Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features extraction, unsupervised weights initialization, but also as density estimators. Thus, their generative and discriminative capabilities, but also their computational time are instrumental to a wide range of applications. Our main contribution is to look at RBMs from a topological perspective, bringing insights from network science. Firstly, here we show that RBMs and Gaussian RBMs (GRBMs) are bipartite graphs which naturally have a small-world topology. Secondly, we demonstrate both on synthetic and real-world databases that by constraining RBMs and GRBMs to a scale-free topology (while still considering local neighborhoods and data distribution), we reduce the number of weights that need to be computed by a few orders of magnitude, at virtually no loss in generative performance. Thirdly, we show that, given the same number of weights, our proposed sparse models (which by design have a higher number of hidden neurons) achieve better generative capabilities than standard fully connected RBMs and GRBMs (which by design have a smaller number of hidden neurons) at no additional computational costs.


title: Mathematical problems for Moineau pumps

abstract: A progressive cavity pump (PCP) or a Moineau pump has two parts rotating relative to each other and moving in an eccentric track relative to each other. The shapes in an axial cross section can be made of pieces of hypocycloids connected with epicycloids. Alternative it can be a curve with a constant distance to hypocycloids. In the axial direction the cross section is rotated around the z-axis so the external part have at least one thread. For n-teeth shape running inside an n+1 teeth shape the internal part have (n+1)/n times the teeth of the external part. The centre of the internal element is offset relative to the centre of the external element and moving in a circle with the radius e (eccentricity). The speed of displacement of the centres is -n times the speed of the rotation speed of the internal element if the external element is stationary. A further feature can be achieved by making the pump conical. If both elements have a larger cross section in one end the axial force will determine the compression between the two elements. In that configuration one of the elements can be free to move in axial direction and the pump pressure act on a surface can presses the two elements together. By adjusting the area of the surface where the pressure compresses the two parts the closing force for the pump can be adjusted. If the pump has to give a constant flow the cross section of the cavities has to be constant (if thread height is constant) in the z direction. Keeping the cross section of the cavities constant (area between external surface and internal surface)


title: Deep Paraphrase Detection in Indian Languages

abstract: This paper presents an approach to the problem of paraphrase identification in English and Indian languages using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional machine learning approaches used features that involved using resources such as POS taggers, dependency parsers, etc. for English. The lack of similar resources for Indian languages has been a deterrent to the advancement of paraphrase detection task in Indian languages. Deep learning helps in overcoming the shortcomings of traditional machine Learning techniques. In this paper, three approaches have been proposed, a simple CNN that uses word embeddings as input, a CNN that uses WordNet scores as input and RNN based approach with both LSTM and bi-directional LSTM.


title: Sentence-State LSTM for Text Representation

abstract: Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform local and global information exchange between words simultaneously, rather than incremental reading of a sequence of words. Results on various classification and sequence labelling benchmarks show that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers.


title: Learning Curve in Tracheal Allotransplantation

abstract: The first vascularized tracheal allotransplantation was performed in 2008. Immunosuppression was stopped after forearm implantation and grafting of the recipient mucosa to the internal site of the transplant. Nine months after forearm implantation, the allograft was transplanted to the tracheal defect on the radial blood vessels. Since then, four additional patients have undergone tracheal allotransplantation, three (patients 2–4) for long-segment stenosis and one (patient 5) for a low-grade chondrosarcoma. Our goal was to reduce the time between forearm implantation and orthotopic transplantation and to determine a protocol for safe withdrawal of immunosuppressive therapy. Following forearm implantation, all transplants became fully revascularized over 2 months. Withdrawal of immunosuppression began 4 months after graft implantation and was completed within 6 weeks in cases 2–4. Repopulation of the mucosal lining by recipient cells, to compensate for the necrosis of the donor mucosa, was not complete. This resulted in partial loss of the allotransplant in patients 2–4. In patient 5, additional measures promoting recipient cell repopulation were made. The trachea may be used as a composite tissue allotransplant after heterotopic revascularization in the forearm. Measures to maximize recipient cell repopulation may be important in maintaining the viability of the transplant after cessation of immunosuppression.