title: Permacoin: Repurposing Bitcoin Work for Data Preservation

abstract: Bit coin is widely regarded as the first broadly successful e-cash system. An oft-cited concern, though, is that mining Bit coins wastes computational resources. Indeed, Bit coin's underlying mining mechanism, which we call a scratch-off puzzle (SOP), involves continuously attempting to solve computational puzzles that have no intrinsic utility. We propose a modification to Bit coin that repurposes its mining resources to achieve a more broadly useful goal: distributed storage of archival data. We call our new scheme Perm coin. Unlike Bit coin and its proposed alternatives, Perm coin requires clients to invest not just computational resources, but also storage. Our scheme involves an alternative scratch-off puzzle for Bit coin based on Proofs-of-Retrievability (PORs). Successfully minting money with this SOP requires local, random access to a copy of a file. Given the competition among mining clients in Bit coin, this modified SOP gives rise to highly decentralized file storage, thus reducing the overall waste of Bit coin. Using a model of rational economic agents we show that our modified SOP preserves the essential properties of the original Bit coin puzzle. We also provide parameterizations and calculations based on realistic hardware constraints to demonstrate the practicality of Perm coin as a whole.


title: An Incentive-Compatible Routing Protocol for Two-Hop Delay-Tolerant Networks

abstract: Delay-tolerant networks (DTNs) rely on the mobility of nodes and their contacts to make up with the lack of continuous connectivity and, thus, enable message delivery from source to destination in a “store-carry-forward” fashion. Since message delivery consumes resource such as storage and power, some nodes may choose not to forward or carry others' messages while relying on others to deliver their locally generated messages. These kinds of selfish behaviors may hinder effective communications over DTNs. In this paper, the authors present an efficient incentive-compatible (IC) routing protocol (ICRP) with multiple copies for two-hop DTNs based on the algorithmic game theory. It takes both the encounter probability and transmission cost into consideration to deal with the misbehaviors of selfish nodes. Moreover, the authors employ the optimal sequential stopping rule and Vickrey–Clarke–Groves (VCG) auction as a strategy to select optimal relay nodes to ensure that nodes that honestly report their encounter probability and transmission cost can maximize their rewards. The authors attempt to find the optimal stopping time threshold adaptively based on realistic probability model and propose an algorithm to calculate the threshold. Based on this threshold, the authors propose a new method to select relay nodes for multicopy transmissions. To ensure that the selected relay nodes can receive their rewards securely, the authors develop a signature scheme based on a bilinear map to prevent the malicious nodes from tampering. Through simulations, the authors demonstrate that ICRP can effectively stimulate nodes to forward/carry messages and achieve higher packet delivery ratio with lower transmission cost.


title: Wireless Medical Sensor Networks in Emergency Response: Implementation and Pilot Results

abstract: This project demonstrates the feasibility of using cost- effective, flexible, and scalable sensor networks to address critical bottlenecks of the emergency response process. For years, emergency medical service providers conducted patient care by manually measuring vital signs, documenting assessments on paper, and communicating over handheld radios. When disasters occurred, the large numbers of casualties quickly and easily overwhelmed the responders. Collaboration with EMS and hospitals in the Baltimore Washington Metropolitan region prompted us to develop miTag (medical information tag), a cost- effective wireless sensor platform that automatically track patients throughout each step of the disaster response process, from disaster scenes, to ambulances, to hospitals. The miTag is a highly extensible platform that supports a variety of sensor add-ons - GPS, pulse oximetry, blood pressure, temperature, ECG - and relays data over a self-organizing wireless mesh network Scalability is the distinguishing characteristic of miTag: its wireless network scales across a wide range of network densities, from sparse hospital network deployments to very densely populated mass casualty sites. The miTag system is out-of-the-box operational and includes the following key technologies: 1) cost-effective sensor hardware, 2) self-organizing wireless network and 3) scalable server software that analyzes sensor data and delivers real-time updates to handheld devices and web portals. The system has evolved through multiple iterations of development and pilot deployments to become an effective patient monitoring solution. A pilot conducted with the Department of Homeland Security indicates miTags can increase the patient care capacity of responders in the field A pilot at Washington Hospital showed miTags are capable of reliably transmitting data inside radio-interference-rich critical care settings.


Clustering versus faceted categories for information exploration


title: Identification and Visualization of the Intellectual Structure in Graphene Research

abstract: After discovering the promising properties of graphene, the research in the field is attracting large grants and sponsors with an exponential rise in the number of papers and applications. This paper presents a global map of graphene research and its intellectual structure through the terms of more than 50,000 documents extracted from Scopus database from 1998 to 2015. The unit of analysis has been the descriptors (including Authors keywords and Indexed keywords) and its co-occurrence, using fractional counting, as unit of measure. The main research lines identified are: Fundamental Research, Functionalization and Biomedical Applications, Technology and Devices, Material Sciences, Energy Storage, Optics and Chemical Properties and Sensors. Using overlay maps we show the graphen research of USA, Europe-28, China and their evolution through longitudinal maps, comparing them. USA begins leading the world output in graphene, but is surpassed by China in 2011 and Europe in 2014, as a result of stimulus policies and financial support. The output of China is so intense from that year on, that it is difficult to understand the recent world scientific production in graphene without its participation, marking the research trends in some cases. We believe that this information may be very useful for the scientific community involved in this field as it reflects a large-scale analysis how research has changed over time, and for helping policimakers in research planning. This study is moreover intended to offer a useful tool for the graphene scientific community, revealing at a glance the main research lines. The methodology can be replicated in any other field of science to explore its intellectual structure and its evolution.


title: A Laparoscopic Grasping Tool With Force Sensing Capability

abstract: This paper presents a laparoscopic grasping tool for minimally invasive surgery with the capability of multiaxis force sensing. The tool is able to sense three-axis Cartesian manipulation force and a single-axis grasping force. The forces are measured by a wrist force sensor located at the distal end of the tool, and two torque sensors at the tool base, respectively. We propose an innovative design of a miniature force sensor achieving structural simplicity and potential cost effectiveness. A prototype is manufactured and experiments are conducted in a simulated surgical environment by using an open platform for surgical robot research, called Raven-II.


title: Restoration of Complicated Epicanthus: Modified Reverse Skin Redraping With Mini-epicanthoplasty for Rescue in Unsatisfied Epicanthoplasty Patients

abstract: We had reported the surgical outcome of reverse skin redraping technique for restoration of previously performed epicanthoplasty. In this study, we introduce a modified reverse skin redraping technique that added mini-epicanthoplasty in patients with unsatisfactory results after epicanthoplasty.Three hundred twenty-four patients (288 female and 36 male patients) who had unsatisfied results with previous epicanthoplasty and that were treated with our modified restoration surgery were included in this study.The mean preoperative interepicanthal distance was 33.6 mm, and the mean postoperative interepicanthal distance was 36.9 mm; the mean difference in the interepicanthal distance before and after restoration surgery was 3.3 mm. Satisfactory aesthetic results were obtained with improvements in areas of asymmetry, overexposure of the caruncle, and the appearance of the previous scar. Only minor complications developed in 15 patients (4.6%) that were resolved with minor revisions. No severe complications requiring reoperations were noted.Our modified method involving reverse skin redraping and mini-epicanthoplasty is simple and reproducible and is useful for resolution of unsatisfactory results to obtain a naturally shaped epicanthus.


title: Review of energy harvesting techniques and applications for microelectronics (Keynote Address)

abstract: The trends in technology allow the decrease in both size and power consumption of complex digital systems. This decrease in size and power gives rise to new paradigms of computing and use of electronics, with many small devices working collaboratively or at least with strong communication capabilities. Examples of these new paradigms are wearable devices and wireless sensor networks. Currently, these devices are powered by batteries. However, batteries present several disadvantages: the need to either replace or recharge them periodically and their big size and weight compared to high technology electronics. One possibility to overcome these power limitations is to extract (harvest) energy from the environment to either recharge a battery, or even to directly power the electronic device. This paper presents several methods to design an energy harvesting device depending on the type of energy avaliable.


title: Residual enhanced visual vector as a compact signature for mobile visual search

abstract: Many mobile visual search (MVS) systems transmit query data from a mobile device to a remote server and search a database hosted on the server. In this paper, we present a new architecture for searching a large database directly on a mobile device, which can provide numerous benefits for network-independent, low-latency, and privacy-protected image retrieval. A key challenge for on-device retrieval is storing a large database in the limited RAM of a mobile device. To address this challenge, we develop a new compact, discriminative image signature called the Residual Enhanced Visual Vector (REVV) that is optimized for sets of local features which are fast to extract on mobile devices. REVV outperforms existing compact database constructions in the MVS setting and attains similar retrieval accuracy in large-scale retrieval as a Vocabulary Tree that uses 25x more memory. We have utilized REVV to design and construct a mobile augmented reality system for accurate, large-scale landmark recognition. Fast on-device search with REVV enables our system to achieve latencies around 1s per query regardless of external network conditions. The compactness of REVV allows it to also function well as a low-bitrate signature that can be transmitted to or from a remote server for an efficient expansion of the local database search when required.


title: Discriminative Gaifman Models

abstract: We present discriminative Gaifman models, a novel family of relational machine learning models. Gaifman models learn feature representations bottom up from representations of locally connected and bounded-size regions of knowledge bases (KBs). Considering local and bounded-size neighborhoods of knowledge bases renders logical inference and learning tractable, mitigates the problem of overfitting, and facilitates weight sharing. Gaifman models sample neighborhoods of knowledge bases so as to make the learned relational models more robust to missing objects and relations which is a common situation in open-world KBs. We present the core ideas of Gaifman models and apply them to large-scale relational learning problems. We also discuss the ways in which Gaifman models relate to some existing relational machine learning approaches.


title: Effects of Prosocial, Neutral, and Violent Video Games on Children's Helpful and Hurtful Behaviors

abstract: Recent research reveals that playing prosocial video games increases prosocial cognitions, positive affect, and helpful behaviors [Gentile et al., 2009; Greitemeyer and Osswald, 2009, 2010, 2011]. These results are consistent with the social-cognitive models of social behavior such as the general learning model [Buckley and Anderson, 2006]. However, no experimental studies have examined such effects on children. Previous research on violent video games suggests that short-term effects of video games are largely based on priming of existing behavioral scripts. Thus, it is unclear whether younger children will show similar effects. This research had 9-14 years olds play a prosocial, neutral, or violent video game, and assessed helpful and hurtful behaviors simultaneously through a new tangram measure. Prosocial games increased helpful and decreased hurtful behavior, whereas violent games had the opposite effects. Aggr. Behav. 38:281-287, 2012. © 2012 Wiley Periodicals, Inc. Language: en


User engineering principles for interactive systems


title: Synthesis of full-adder circuit using reversible logic

abstract: A reversible gate has the equal number of inputs and outputs and one-to-one mappings between input vectors and output vectors; so that, the input vector states can be always uniquely reconstructed from the output vector states. This correspondence introduces a reversible full-adder circuit that requires only three reversible gates and produces least number of "garbage outputs ", that is two. After that, a theorem has been proposed that proves the optimality of the propounded circuit in terms of number of garbage outputs. An efficient algorithm is also introduced in this paper that leads to construct a reversible circuit.


title: A review of deep learning with special emphasis on architectures, applications and recent trends

abstract: Abstract Deep learning (DL) has solved a problem that a few years ago was thought to be intractable - the automatic recognition of patterns in spatial and temporal data with an accuracy superior to that of humans. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners who are inundated with all types of data. As public awareness of the efficacy of DL increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge in the field. Where does one start? How does one determine if a particular DL model is applicable to their problem? How does one train and deploy them? With these questions in mind, we present an overview of some of the key DL architectures. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is critical to many applications, a section dwells on using DL for fault detection and mitigation. This is followed by an exploratory survey of several areas where DL emerged as a game-changer: fraud detection in financial applications, financial time-series forecasting, predictive and prescriptive analytics, medical image processing, power systems research and recommender systems. The thrust of this review is to outline emerging applications of DL and provide a reference to researchers seeking to use DL in their work for pattern recognition with unparalleled learning capacity and the ability to scale with data.


title: The genomic landscape of Neanderthal ancestry in present-day humans

abstract: Genomic studies have shown that Neanderthals interbred with modern humans, and that non-Africans today are the products of this mixture. The antiquity of Neanderthal gene flow into modern humans means that genomic regions that derive from Neanderthals in any one human today are usually less than a hundred kilobases in size. However, Neanderthal haplotypes are also distinctive enough that several studies have been able to detect Neanderthal ancestry at specific loci. We systematically infer Neanderthal haplotypes in the genomes of 1,004 present-day humans. Regions that harbour a high frequency of Neanderthal alleles are enriched for genes affecting keratin filaments, suggesting that Neanderthal alleles may have helped modern humans to adapt to non-African environments. We identify multiple Neanderthal-derived alleles that confer risk for disease, suggesting that Neanderthal alleles continue to shape human biology. An unexpected finding is that regions with reduced Neanderthal ancestry are enriched in genes, implying selection to remove genetic material derived from Neanderthals. Genes that are more highly expressed in testes than in any other tissue are especially reduced in Neanderthal ancestry, and there is an approximately fivefold reduction of Neanderthal ancestry on the X chromosome, which is known from studies of diverse species to be especially dense in male hybrid sterility genes. These results suggest that part of the explanation for genomic regions of reduced Neanderthal ancestry is Neanderthal alleles that caused decreased fertility in males when moved to a modern human genetic background.


title: A Deep Learning Approach for Multimodal Deception Detection

abstract: Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multi-modal neural model for deception detection. By combining features from different modalities such as video, audio, and text along with Micro-Expression features, we show that detecting deception in real life videos can be more accurate. Experimental results on a dataset of real-life deception videos show that our model outperforms existing techniques for deception detection with an accuracy of 96.14% and ROC-AUC of 0.9799.


title: Neural Networks for Information Retrieval

abstract: Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many approaches to many IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. The aim of this full- day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR.


title: Will Travel for Food: Spatial Discounting in Two New World Monkeys

abstract: Nonhuman animals steeply discount the future, showing a preference for small, immediate over large, delayed rewards. Currently unclear is whether discounting functions depend on context. Here, we examine the effects of spatial context on discounting in cotton-top tamarins (Saguinus oedipus) and common marmosets (Callithrix jacchus), species known to differ in temporal discounting. We presented subjects with a choice between small, nearby rewards and large, distant rewards. Tamarins traveled farther for the large reward than marmosets, attending to the ratio of reward differences rather than their absolute values. This species difference contrasts with performance on a temporal task in which marmosets waited longer than tamarins for the large reward. These comparative data indicate that context influences choice behavior, with the strongest effect seen in marmosets who discounted more steeply over space than over time. These findings parallel details of each species' feeding ecology. Tamarins range over large distances and feed primarily on insects, which requires using quick, impulsive action. Marmosets range over shorter distances than tamarins and feed primarily on tree exudates, a clumped resource that requires patience to wait for sap to exude. These results show that discounting functions are context specific, shaped by a history of ecological pressures.


title: Deep Variance Network: An Iterative, Improved CNN Framework for Unbalanced Training Datasets

abstract: Abstract Convolutional neural network (CNN) has demonstrated its superior ability to achieve amazing accuracy in computer vision field. Nevertheless, for practical domain-specific image recognition tasks, it still remains difficult to obtain massive high-quality labeled datasets due to the strong requirements for extensive, tedious manual processing. Inspired by the well-known observation that human brain can accurately recognize objects without relying on massive congeneric examples, we propose a novel deep variance network (DVN) to further enhance the generalization ability of CNN in this paper, which could still produce higher recognition accuracy even with unbalanced training datasets than original CNN. The key idea of our DVN is built upon the intrinsic exploitation of inter-class homogeneity and intra-class heterogeneity. Towards such goal, we make the first attempt to incorporate a hierarchical Bayesian model into the powerful CNN framework, which can transfer the joint feature distribution from certain object’s complete training dataset to other object’s incomplete training dataset in an iterative way. In each training cycle, the CNN-resulted features are clustered into discrimination-related subspaces to guide the learning and adaptive adjustment of homogeneity and heterogeneity over unbalanced training datasets. In practice, we furnish several state-of-the-art deep networks with our proposed DVN, and conduct extensive experiments and comprehensive evaluations over CIFAR-10, MNIST, and SVHN benchmarks. The experiments have shown that, most of the furnished deep networks can benefit from our DVN, wherein they gain at most 6.9% accuracy improvement over CIFAR-10 benchmark, 52.83% error reduction over MNIST benchmark, and an improvement of 6.2% over SVHN datasets.


title: Deep Active Learning for Dialogue Generation

abstract: We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.


title: Multiple Sound Source Mapping for a Mobile Robot by Self-motion Triangulation

abstract: The paper describes a 2D sound source mapping system for a mobile robot. We developed a multiple sound sources localization method for a mobile robot with a 32 channel concentric microphone array. The system can separate multiple moving sound sources using direction localization. Directional localization and separation of different pressure sound sources is achieved using the delay and sum beam forming (DSBF) and the frequency band selection (FBS) algorithm. Sound sources were mapped by using a wheeled robot equipped with the microphone array. The robot localizes sounds direction on the move and estimates sound sources position using triangulation. Assuming the movement of sound sources, the system set a time limit and uses only the last few seconds data. By using the random sample consensus (RANSAC) algorithm for position estimation, we achieved 2D multiple sound source mapping from time limited data with high accuracy. Also, moving sound source separation is experimentally demonstrated with segments of the DSBF enhanced signal derived from the localization process


title: Review on SAW RFID tags

abstract: SAW-tags were invented more than 30 years ago, but only nowadays the conditions are united for mass-application of this technology: the devices in 2.4GHz ISM band can be routinely produced with optical lithography and Internet is available for global access to the tag databases. Perspectives of the “Internet of Things, I-o-T” will demand trillions of cheap tags and sensors. The SAW-tags overcome semiconductor based analogues in many aspects: they are true passive devices, can be “read” at a few meter distance with readers radiating 2 to 3 orders lower power levels, they are cheap and can operate in robust environment. Passive SAW tags are easily combined with sensors. Even the “anti-collision” problem (simultaneous reading of many tags situated nearby) has solutions sufficient for many practical applications. In this paper we discuss state-of-the-art in development of SAW-tags. The design approaches will be reviewed - and the most optimal tag designs are demonstrated, as well as encoding methods. We discuss the ways to reduce size and cost of these devices. A few practical examples of tags with simple time-position coding will be demonstrated with 106 different codes. Phase coded devices allow further radical increase of the number of codes, the price to pay being reduction of the reading distance. We also discuss exciting perspectives of using Ultra Wide Band (UWB) technology emerging now for communication applications in SAW-tag systems. Wide frequency band available in this standard is a great opportunity for SAW-tags to radically reduce their size to about 1×1 mm2 keeping practically infinite number of possible different codes. Finally, the reader technology will be discussed as well as detailed comparison made between SAW-tags and IC-based semiconductor device.


title: Attention-based Multi-Context Guiding for Few-Shot Semantic Segmentation

abstract: Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremendous amounts of data. The scarcity of annotated data becomes even more challenging in semantic segmentation since pixellevel annotation in segmentation task is more labor-intensive to acquire. To tackle this issue, we propose an Attentionbased Multi-Context Guiding (A-MCG) network, which consists of three branches: the support branch, the query branch, the feature fusion branch. A key differentiator of A-MCG is the integration of multi-scale context features between support and query branches, enforcing a better guidance from the support set. In addition, we also adopt a spatial attention along the fusion branch to highlight context information from several scales, enhancing self-supervision in one-shot learning. To address the fusion problem in multi-shot learning, Conv-LSTM is adopted to collaboratively integrate the sequential support features to elevate the final accuracy. Our architecture obtains state-of-the-art on unseen classes in a variant of PASCAL VOC12 dataset and performs favorably against previous work with large gains of 1.1%, 1.4% measured in mIoU in the 1-shot and 5-shot setting.


title: IAT - Image Annotation Tool: Manual

abstract: The annotation of image and video data of large datasets is a fundamental task in multimedia information retrieval and computer vision applications. In order to support the users during the image and video annotation process, several software tools have been developed to provide them with a graphical environment which helps drawing object contours, handling tracking information and specifying object metadata. Here we introduce a preliminary version of the image annotation tools developed at the Imaging and Vision Laboratory.


title: A survey of methods for time series change point detection

abstract: Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.


title: Unified secure DoF analysis of K-user Gaussian interference channels

abstract: We determine the exact sum secure degrees of freedom (d.o.f.) of the K-user Gaussian interference channel. We consider three different secrecy constraints: 1) K-user interference channel with one external eavesdropper (IC-EE), 2) K-user interference channel with confidential messages (IC-CM), and 3) K-user interference channel with confidential messages and one external eavesdropper (IC-CM-EE). We show that for all of these three cases, the exact sum secure d.o.f. is K(K-1)/2K-1. We show converses for IC-EE and IC-CM, which imply a converse for IC-CM-EE. We show achievability for IC-CM-EE, which implies achievability for IC-EE and IC-CM. We develop the converses by relating the channel inputs of interfering users to the reliable rates, and by quantifying the secrecy penalty in terms of the eavesdroppers' observations. Our achievability uses structured signalling, channel prefixing via structured cooperative jamming, and asymptotic real interference alignment.


title: An Elementary Derivation of Mean Wait Time in Polling Systems

abstract: Polling systems are a well-established subject in queueing theory. However, their formal treatments generally rely heavily on relatively sophisticated theoretical tools, such as moment generating functions and Laplace transforms, and solutions often require the solution of large systems of equations. We show that, if you are willing to only have the average waiting of a system time rather than higher moments, it can found through an elementary derivation based only on algebra and some well-known properties of Poisson processes. Our result is simple enough to be easily used in real-world applications, and the simplicity of our derivation makes it ideal for pedagogical purposes.


title: Hybrid scheduling for the parallel solution of linear systems

abstract: We consider the problem of designing a dynamic scheduling strategy that takes into account both workload and memory information in the context of the parallel multifrontal factorization. The originality of our approach is that we base our estimations (work and memory) on a static optimistic scenario during the analysis phase. This scenario is then used during the factorization phase to constrain the dynamic decisions that compute fully irregular partitions in order to better balance the workload. We show that our new scheduling algorithm significantly improves both the memory behaviour and the factorization time. We give experimental results for large challenging real-life 3D problems on 64 and 128 processors.


title: A Procedural Texture Generation Framework Based on Semantic Descriptions

abstract: Procedural textures are normally generated from mathematical models with parameters carefully selected by experienced users. However, for naive users, the intuitive way to obtain a desired texture is to provide semantic descriptions such as "regular," "lacelike," and "repetitive" and then a procedural model with proper parameters will be automatically suggested to generate the corresponding textures. By contrast, it is less practical for users to learn mathematical models and tune parameters based on multiple examinations of large numbers of generated textures. In this study, we propose a novel framework that generates procedural textures according to user-defined semantic descriptions, and we establish a mapping between procedural models and semantic texture descriptions. First, based on a vocabulary of semantic attributes collected from psychophysical experiments, a multi-label learning method is employed to annotate a large number of textures with semantic attributes to form a semantic procedural texture dataset. Then, we derive a low dimensional semantic space in which the semantic descriptions can be separated from one other. Finally, given a set of semantic descriptions, the diverse properties of the samples in the semantic space can lead the framework to find an appropriate generation model that uses appropriate parameters to produce a desired texture. The experimental results show that the proposed framework is effective and that the generated textures closely correlate with the input semantic descriptions.


title: In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

abstract: In this work we present In-Place Activated Batch Normalization (InPlace-ABN) - a novel approach to drastically reduce the training memory footprint of modern deep neural networks in a computationally efficient way. Our solution substitutes the conventionally used succession of BatchNorm + Activation layers with a single plugin layer, hence avoiding invasive framework surgery while providing straightforward applicability for existing deep learning frameworks. We obtain memory savings of up to 50% by dropping intermediate results and by recovering required information during the backward pass through the inversion of stored forward results, with only minor increase (0.8-2%) in computation time. Also, we demonstrate how frequently used checkpointing approaches can be made computationally as efficient as InPlace-ABN. In our experiments on image classification, we demonstrate on-par results on ImageNet-1k with state-of-the-art approaches. On the memory-demanding task of semantic segmentation, we report results for COCO-Stuff, Cityscapes and Mapillary Vistas, obtaining new state-of-the-art results on the latter without additional training data but in a single-scale and -model scenario. Code can be found at this https URL .


title: Threshold Selection for Segmentation of Dense Objects in Tomograms

abstract: Tomographic reconstructions are often segmented to extract valuable quantitative information. In this paper, we consider the problem of segmenting a dense object of constant density within a continuous tomogram, by means of global thresholding. Selecting the proper threshold is a nontrivial problem, for which hardly any automatic procedures exists. We propose a new method that exploits the available projection data to accurately determine the optimal global threshold. Results from simulation experiments show that our algorithm is capable of finding a threshold that is close to the optimal threshold value.


title: Cortico-Striatal-Thalamic Loop Circuits of the Orbitofrontal Cortex: Promising Therapeutic Targets in Psychiatric Illness

abstract: Corticostriatal circuits through the orbitofrontal cortex (OFC) play key roles in complex human behaviours such as evaluation, affect regulation, and reward-based decision-making. Importantly, the medial and lateral OFC circuits have functionally and anatomically distinct connectivity profiles which differentially contribute to the various aspects of goal-directed behaviour. OFC corticostriatal circuits have been consistently implicated across a wide range of psychiatric disorders, including major depressive disorder (MDD), obsessive compulsive disorder (OCD), and substance use disorders (SUDs). Furthermore, psychiatric disorders related to OFC corticostriatal dysfunction can be addressed via conventional and novel neurostimulatory techniques, including deep brain stimulation (DBS), electroconvulsive therapy (ECT), repetitive transcranial stimulation (rTMS), and transcranial direct current stimulation (tDCS). Such techniques elicit changes in OFC corticostriatal activity, resulting in changes in clinical symptomatology. Here we review the available literature regarding how disturbances in medial and lateral OFC corticostriatal functioning lead to psychiatric symptomatology in the aforementioned disorders, and how psychiatric treatments may exert their therapeutic effect by rectifying abnormal OFC corticostriatal activity. First, we review the role of OFC corticostriatal circuits in reward-guided learning, decision-making, affect regulation and reappraisal. Second, we discuss the role of OFC corticostriatal circuit dysfunction across a wide range of psychiatric disorders. Third, we review available evidence that the therapeutic mechanisms of various neuromodulation techniques may directly involve rectifying abnormal activity in medial and lateral OFC corticostriatal circuits. Finally, we examine the potential of future applications of therapeutic brain stimulation targeted at OFC circuitry; specifically, the role of OFC brain stimulation in the growing field of individually-tailored therapies and personalized medicine in psychiatry.


title: Text Classification using Capsules

abstract: Abstract This paper presents an empirical exploration of the use of capsule networks for text classification. While it has been shown that capsule networks are effective for image classification, the research regarding their validity in the domain of text has been initiated recently. In this paper, we show that capsule networks indeed have the potential for text classification and that they have several advantages over convolutional neural networks. As well, we compare our proposed model to the initial studies regarding capsule network-based text classification. We further suggest a simple routing method that effectively reduces the computational complexity of dynamic routing. We utilized seven benchmark datasets to demonstrate that capsule networks, along with the proposed routing method provide comparable results.


title: High dimensional polynomial interpolation on sparse grids

abstract: We study polynomial interpolation on a d-dimensional cube, where d is large. We suggest to use the least solution at sparse grids with the extrema of the Chebyshev polynomials. The polynomial exactness of this method is almost optimal. Our error bounds show that the method is universal, i.e., almost optimal for many different function spaces. We report on numerical experiments for d = 10 using up to 652 065 interpolation points.


title: Query by output

abstract: It has recently been asserted that the usability of a database is as important as its capability. Understanding the database schema, the hidden relationships among attributes in the data all play an important role in this context. Subscribing to this viewpoint, in this paper, we present a novel data-driven approach, called Query By Output (QBO), which can enhance the usability of database systems. The central goal of QBO is as follows: given the output of some query Q on a database D, denoted by Q(D), we wish to construct an alternative query Q′ such that Q(D) and Q′ (D) are instance-equivalent. To generate instance-equivalent queries from Q(D), we devise a novel data classification-based technique that can handle the at-least-one semantics that is inherent in the query derivation. In addition to the basic framework, we design several optimization techniques to reduce processing overhead and introduce a set of criteria to rank order output queries by various notions of utility. Our framework is evaluated comprehensively on three real data sets and the results show that the instance-equivalent queries we obtain are interesting and that the approach is scalable and robust to queries of different selectivities.


title: Deep Reinforcement Learning for Green Security Games with Real-Time Information

abstract: Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing. However, real-time information such as footprints and agents’ subsequent actions upon receiving the information, e.g., rangers following the footprints to chase the poacher, have been neglected in previous work. To fill the gap, we first propose a new game model GSG-I which augments GSGs with sequential movement and the vital element of real-time information. Second, we design a novel deep reinforcement learning-based algorithm, DeDOL, to compute a patrolling strategy that adapts to the real-time information against a best-responding attacker. DeDOL is built upon the double oracle framework and the policy-space response oracle, solving a restricted game and iteratively adding best response strategies to it through training deep Q-networks. Exploring the game structure, DeDOL uses domain-specific heuristic strategies as initial strategies and constructs several local modes for efficient and parallelized training. To our knowledge, this is the first attempt to use Deep Q-Learning for security games.


title: Human Physique and Sexual Attractiveness: Sexual Preferences of Men and Women in Bakossiland, Cameroon

abstract: Men and women living in a rural community in Bakossiland, Cameroon were asked to rate the attractiveness of images of male or female figures manipulated to vary in somatotype, waist-to-hip ratio (WHR), secondary sexual traits, and other features. In Study 1, women rated mesomorphic (muscular) and average male somatotypes as most attractive, followed by ectomorphic (slim) and endomorphic (heavily built) figures. In Study 2, amount and distribution of masculine trunk (chest and abdominal) hair was altered progressively in a series of front-posed male figures. A significant preference for one of these images was found, but the most hirsute figure was not judged as most attractive. Study 3 assessed attractiveness of front-posed male figures which varied only in length of the non-erect penis. Extremes of penile size (smallest and largest of five images) were rated as significantly less attractive than three intermediate sizes. In Study 4, Bakossi men rated the attractiveness of back-posed female images varying in WHR (from 0.5–1.0). The 0.8 WHR figure was rated markedly more attractive than others. Study 5 rated the attractiveness of female skin color. Men expressed no consistent preference for either lighter or darker female figures. These results are the first of their kind reported for a Central African community and provide a useful cross-cultural perspective to published accounts on sexual selection, human morphology and attractiveness in the U.S., Europe, and elsewhere.


title: Distributed Denial-of-Service (DDoS) Threat in Collaborative Environment - A Survey on DDoS Attack Tools and Traceback Mechanisms

abstract: Collaborative applications are feasible nowadays and are becoming more popular due to the advancement in internetworking technology. The typical collaborative applications, in India include the Space research, Military applications, Higher learning in Universities and Satellite campuses, State and Central government sponsored projects, e-governance, e-healthcare systems, etc. In such applications, computing resources for a particular institution/organization spread across districts and states and communication is achieved through internetworking. Therefore the computing and communication resources must be protected against security attacks as any compromise on these resources would jeopardize the entire application/mission. Collaborative environment is prone for various threats, of which Distributed Denial of Service (DDoS) attacks are of major concern. DDoS attack prevents legitimate access to critical resources. A survey by Arbor networks reveals that approximately 1,200 DDoS attacks occur per day. As the DDoS attack is coordinated, the defense for the same has to be a collaborative one. To counter DDoS attacks in a collaborative environment, all the routers need to work collaboratively by exchanging their caveat messages with their neighbors. This paper analyses the security measures in a collaborative environment, identifles the popular DDoS attack tools, and surveys the existing traceback mechanisms to trace the real attacker.


title: Prenatal inflammation is associated with adverse neonatal outcomes.

abstract: OBJECTIVE ::: The purpose of this study was to determine whether prenatal inflammation (as assessed by clinical chorioamnionitis, maternal temperature >38°C, or histologic chorioamnionitis) is associated with a composite adverse neonatal outcome. ::: ::: ::: STUDY DESIGN ::: We performed a prospective cohort study of women at 22 weeks to 33 weeks 6 days' gestation with symptoms of labor (April 2009 to March 2012). Relevant maternal and neonatal exposures and outcomes were recorded. Multivariable logistic regression was performed to determine the association between prenatal inflammation and neonatal outcomes that were controlled for potential confounders. ::: ::: ::: RESULTS ::: We analyzed 871 mother-infant pairs. The preterm birth rate was 42.0%. When we controlled for infant sex and modified the data by gestational age at delivery, prenatal inflammation remains a significant risk factor for adverse neonatal outcomes, despite advancing gestational age: clinical chorioamnionitis at 32 weeks' gestation (odds ratio [OR], 3.12; 95% confidence interval [CI], 1.02-9.52], at 36 weeks' gestation (OR, 8.88; 95% CI, 4.32-18.25), and at 40 weeks' gestation (OR, 25.30; 95% CI, 9.25-69.19); maternal temperature >38°C at 32 weeks' gestation (OR, 3.18; 95% CI, 0.66-15.42), at 36 weeks gestation (OR, 8.40; 95% CI, 3.60-19.61), and at 40 weeks gestation (OR, 22.19; 95% CI, 8.15-60.44); histologic chorioamnionitis at 32 weeks gestation (OR, 1.25; 95% CI, 0.64-2.46), at 36 weeks gestation (OR, 2.56; 95% CI, 1.54-4.23), and at 40 weeks gestation (OR, 5.23; 95% CI, 1.95-13.99). ::: ::: ::: CONCLUSION ::: The protective association with advancing gestational age is diminished when prenatal inflammation is present.


title: Securing Data Transfer in IoT Employing an Integrated Approach of Cryptography & Steganography

abstract: Propelled by the ongoing advancements in mobile communication and wireless technologies, the internet has transformed into Internet of Things (IoT) which refers to a global, dynamic network infrastructure of smart devices coupled with a common pool of self-configuring capabilities that employ seamless integration of intelligent interfaces into information network. Yet it brings forth a new dimension of complexity in terms of many potential challenges specifically in areas of data security comprising data confidentiality and integrity aspects during data transfer phases in IoT. In order to promote secure data transfer in smart IoT environment, a security scheme is advocated in this paper which addresses both the aforesaid issues, employing a combined approach of lightweight Cryptography and Variable LSB Substitution Steganography technique. Further a comparative study is conducted between Simple LSB & Variable LSB Substitution algorithm where it can be concluded that the Variable LSB Substitution scheme offers better security than its Simple LSB Substitution counterpart.


title: A Way to Boost Semi-NMF for Document Clustering

abstract: Semi-Non Negative Matrix Factorization (Semi-NMF) is one of the most popular extensions of NMF, it extends the applicable range of NMF models, to data having mixed signs, as well as strengthens their relation to clustering. However, Semi-NMF has been found to perform somewhat less than NMF, in terms of clustering, when applied to positive data such as text, which we focus on. Inspired by the recent success of neural word embedding models, e.g., word2vec, in learning high quality real valued vector representations of words, we propose to integrate a word embedding model into Semi-NMF. This allows Semi-NMF to capture more semantic relationships among words and, thereby, to infer document factors that are even better for clustering. The combination of Semi-NMF and word embedding noticeably improves the performance of NMF models, in terms of both clustering and embedding, as illustrated in our experiments.


title: CroMatcher: An ontology matching system based on automated weighted aggregation and iterative final alignment

abstract: In order to perform ontology matching with high accuracy, while at the same time retaining applicability to most diverse input ontologies, the matching process generally incorporates multiple methods. Each of these methods is aimed at a particular ontology component, such as annotations, structure, properties or instances. Adequately combining these methods is one of the greatest challenges in designing an ontology matching system. In a parallel composition of basic matchers, the ability to dynamically set the weights of the basic matchers in the final output, thus making the weights optimal for the given input, is the key breakthrough for obtaining first-rate matching performance. In this paper we present CroMatcher, an ontology matching system, introducing several novelties to the automated weight calculation process. We apply substitute values for matchers that are inapplicable for the particular case and use thresholds to eliminate low-probability alignment candidates. We compare the alignments produced by the matchers and give less weight to the matchers producing mutually similar alignments, whereas more weight is given to those matchers whose alignment is distinct and rather unique. We also present a new, iterative method for producing one-to-one final alignment of ontology structures, which is a significant enhancement of similar non-iterative methods proposed in the literature. CroMatcher has been evaluated against other state-of-the-art matching systems at the OAEI evaluation contest. In a large number of test cases it achieved the highest score, which puts it among the state-of-the-art leaders.


title: Interactive and anisotropic geometry processing using the screened Poisson equation

abstract: We present a general framework for performing geometry filtering through the solution of a screened Poisson equation. We show that this framework can be efficiently adapted to a changing Riemannian metric to support curvature-aware filtering and describe a parallel and streaming multigrid implementation for solving the system. We demonstrate the practicality of our approach by developing an interactive system for mesh editing that allows for exploration of a large family of curvature-guided, anisotropic filters.


title: Anomaly Detection in IP Networks

abstract: Network anomaly detection is a vibrant research area. Researchers have approached this problem using various techniques such as artificial intelligence, machine learning, and state machine modeling. In this paper, we first review these anomaly detection methods and then describe in detail a statistical signal processing technique based on abrupt change detection. We show that this signal processing technique is effective at detecting several network anomalies. Case studies from real network data that demonstrate the power of the signal processing approach to network anomaly detection are presented. The application of signal processing techniques to this area is still in its infancy, and we believe that it has great potential to enhance the field, and thereby improve the reliability of IP networks.


title: Less guilty by reason of adolescence: developmental immaturity, diminished responsibility, and the juvenile death penalty. Am Psychol

abstract: The authors use a developmental perspective to examine questions about the criminal culpability of juveniles and the juvenile death penalty. Under principles of criminal law, culpability is mitigated when the actor's decision-making capacity is diminished, when the criminal act was coerced, or when the act was out of character. The authors argue that juveniles should not be held to the same standards of criminal responsibility as adults, because adolescents' decision-making capacity is diminished, they are less able to resist coercive influence, and their character is still undergoing change. The uniqueness of immaturity as a mitigating condition argues for a commitment to a legal environment under which most youths are dealt with in a separate justice system and none are eligible for capital punishment.


title: Designing and orchestrating reproducible experiments on federated networking testbeds

abstract: Abstract In addition to theoretical analysis and simulations, the evaluation of new networking technologies in a real-life context and scale is critical to their global adoption and deployment. Federations of experimental platforms (aka testbeds) offer a controlled and cost-effective solution to perform such an evaluation. Most recent efforts in that area focused on building those facilities and providing experimenters with tools to allow the discovery and provisioning of their shared resources. Many challenges remain in order to support the complete experiment life cycle in a federated environment. We propose OMF-F, a framework which allows the definition of networking experiments and their execution over shared resources provided by different federated administrative domains. OMF-F provides a domain-specific language enabling rich event-based experiment descriptions. It defines a specific resource model and protocol, which together with its publish-subscribe messaging system allows automatic experiment orchestrations at a large scale. OMF-F further provides interfaces to operate with existing resource discovery and provisioning tools for federated testbeds. Our contributions in this paper are threefold. First we provide detailed descriptions of OMF-F’s design, its architecture, and its involved entities. Then, we present a quantitative evaluation of its underlying messaging and event-handling systems. Finally, we discuss two real examples of OMF-F deployed and used on federated domains to define and execute experiments.


title: RNA-SeQC: RNA-seq metrics for quality control and process optimization

abstract: Summary: RNA-seq, the application of next-generation sequencing to RNA, provides transcriptome-wide characterization of cellular activity. Assessment of sequencing performance and library quality is critical to the interpretation of RNA-seq data, yet few tools exist to address this issue. We introduce RNA-SeQC, a program which provides key measures of data quality. These metrics include yield, alignment and duplication rates; GC bias, rRNA content, regions of alignment (exon, intron and intragenic), continuity of coverage, 3′/5′ bias and count of detectable transcripts, among others. The software provides multi-sample evaluation of library construction protocols, input materials and other experimental parameters. The modularity of the software enables pipeline integration and the routine monitoring of key measures of data quality such as the number of alignable reads, duplication rates and rRNA contamination. RNA-SeQC allows investigators to make informed decisions about sample inclusion in downstream analysis. In summary, RNA-SeQC provides quality control measures critical to experiment design, process optimization and downstream computational analysis. ::: ::: Availability and implementation: See www.genepattern.org to run online, or www.broadinstitute.org/rna-seqc/ for a command line tool. ::: ::: Contact: ddeluca@broadinstitute.org ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.


title: R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

abstract: The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017. During the period of time, we have collected approximately 2 million of benign and malicious Android apps for our experiments with the help from our research partner Leopard Mobile Inc. Our experiment results demonstrate that the proposed system has accurate security analysis on contracts. Furthermore, we keep our research results and experiment materials on this http URL


The SMART automatic document retrieval systems—an illustration


title: Decision Making for Predictive Maintenance in Asset Information Management

abstract: Asset management is a process of identification, design, construction, operation, and maintenance of physical assets (Wenzler, 2005). An asset-centric approach is vital for the success of an asset intensive organisation as the effective management of assets is a major determinant of organisational success. One key issue in asset information management is the availability of information at the right time, in the right format, before the right person, against the right query, and at the right level. This paper provides a comprehensive and in-depth critical analysis from literature which fulfils an identified need of fusing asset information for predictive maintenance so that decision making can be improved. The critical literature review included also highlights the need for an expert system which integrates reliable information with effective decision-support, under the umbrella of Asset Management. Various elements of asset management were critically reviewed, highlighting the need for more robust Predictive maintenance management for assets. We argue that this is best achieved by a system that, in particular, incorporates Expert System to enhance the quality of predictive maintenance through accurate decision analysis. In addition, it should have fuzzy logic reasoning ability that assists in the decision-making process. Our analysis leads us to propose that Expert System when combined with fuzzy logic provides a better way of decision making in predictive maintenance management of assets.


title: At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks

abstract: In this paper we analyze the relationship between the computational capabilities of randomly connected networks of threshold gates in the time-series domain and their dynamical properties. In particular we propose a complexity measure which we find to assume its highest values near the edge of chaos, i.e. the transition from ordered to chaotic dynamics. Furthermore we show that the proposed complexity measure predicts the computational capabilities very well: only near the edge of chaos are such networks able to perform complex computations on time series. Additionally a simple synaptic scaling rule for self-organized criticality is presented and analyzed.


title: Feed-Forward Support Vector Machine Without Multipliers

abstract: In this letter, we propose a coordinate rotation digital computer (CORDIC)-like algorithm for computing the feed-forward phase of a support vector machine (SVM) in fixed-point arithmetic, using only shift and add operations and avoiding resource-consuming multiplications. This result is obtained thanks to a hardware-friendly kernel, which greatly simplifies the SVM feed-forward phase computation and, at the same time, maintains good classification performance respect to the conventional Gaussian kernel.


title: Two-step translation with grammatical post-processing

abstract: This paper describes an experiment in which we try to automatically correct mistakes in grammatical agreement in English to Czech MT outputs. We perform several rule-based corrections on sentences parsed to dependency trees. We prove that it is possible to improve the MT quality of majority of the systems participating in WMT shared task. We made both automatic (BLEU) and manual evaluations.


title: The Hive: An Edge-based Middleware Solution for Resource Sharing in the Internet of Things

abstract: With today's unprecedented proliferation in smart-devices, the Internet of Things Vision has become more of a reality than ever. With the extreme diversity of applications running on these heterogeneous devices, numerous middle-ware solutions have consequently emerged to address IoT-related challenges. These solutions however, heavily rely on the cloud for better data management, integration, and processing. This might potentially compromise privacy, add latency, and place unbearable traffic load. In this paper, we propose The Hive, an edge-based middleware architecture and protocol, that enables heterogeneous edge devices to dynamically share data and resources for enhanced application performance and privacy. We implement a prototype of the Hive, test it for basic robustness, show its modularity, and evaluate its performance with a real world smart emotion recognition application running on edge devices.


title: Deep Learning with Dynamic Computation Graphs

abstract: Neural networks that compute over graph structures are a natural fit for problems in a variety of domains, including natural language (parse trees) and cheminformatics (molecular graphs). However, since the computation graph has a different shape and size for every input, such networks do not directly support batched training or inference. They are also difficult to implement in popular deep learning libraries, which are based on static data-flow graphs. We introduce a technique called dynamic batching, which not only batches together operations between different input graphs of dissimilar shape, but also between different nodes within a single input graph. The technique allows us to create static graphs, using popular libraries, that emulate dynamic computation graphs of arbitrary shape and size. We further present a high-level library of compositional blocks that simplifies the creation of dynamic graph models. Using the library, we demonstrate concise and batch-wise parallel implementations for a variety of models from the literature.


title: Where to Play: Retrieval of Video Segments using Natural-Language Queries

abstract: In this paper, we propose a new approach for retrieval of video segments using natural language queries. Unlike most previous approaches such as concept-based methods or rule-based structured models, the proposed method uses image captioning model to construct sentential queries for visual information. In detail, our approach exploits multiple captions generated by visual features in each image with `Densecap'. Then, the similarities between captions of adjacent images are calculated, which is used to track semantically similar captions over multiple frames. Besides introducing this novel idea of 'tracking by captioning', the proposed method is one of the first approaches that uses a language generation model learned by neural networks to construct semantic query describing the relations and properties of visual information. To evaluate the effectiveness of our approach, we have created a new evaluation dataset, which contains about 348 segments of scenes in 20 movie-trailers. Through quantitative and qualitative evaluation, we show that our method is effective for retrieval of video segments using natural language queries.


Tuberculous Pancreatic Abscess Presenting as Acute Pancreatitis in a Renal Transplant Recipient


title: Vertical integration after stacking (ViaS) process for low-cost and low-stress 3D silicon integration

abstract: A low-cost assembly method is necessary for widespread use of 3D silicon integration. We have been proposing a vertical Si integration process, called Vertical integration after Stacking (ViaS), intended to lower costs, lower stress, and increase yields. The ViaS process uses a polymer insulator and a solder filling technique instead of a SiO 2 insulator and Cu plating. Different from conventional processes, each vertical electrical conductor is continuous from the bottom to the top through the silicon stack and the conductor is surrounded with polymer insulators with a low-Young's modulus. As a result, this ViaS process will greatly decrease the stress in vertical conductors and silicon substrates and increase reliability. In this paper, we present prototyped Si stacks with the ViaS process and the analyzed results on their stress characteristics. The results obtained show significant stress reductions at vertical connections between the layers, which would increase the reliability. These features of 3D stacks by the ViaS process will significantly contribute to expanding the range of 3D-integrated device applications.


title: Mining Time Series Data

abstract: Much of the world’s supply of data is in the form of time series. In the last decade, there has been an explosion of interest in mining time series data. A number of new algorithms have been introduced to classify, cluster, segment, index, discover rules, and detect anomalies/novelties in time series. While these many different techniques used to solve these problems use a multitude of different techniques, they all have one common factor; they require some high level representation of the data, rather than the original raw data. These high level representations are necessary as a feature extraction step, or simply to make the storage, transmission, and computation of massive dataset feasible. A multitude of representations have been proposed in the literature, including spectral transforms, wavelets transforms, piecewise polynomials, eigenfunctions, and symbolic mappings. This chapter gives a high-level survey of time series Data Mining tasks, with an emphasis on time series representations.


title: A 4.5 μW 2.4 GHz wake-up receiver based on complementary current-reuse RF detector

abstract: This paper presents an ultra-low-power wake-up receiver (WuRx) for use in the worldwide recognized 2.4 GHz ISM band. The WuRx features a fully-differential complementary current-reuse RF detector with an embedded impedance matching network. The proposed WuRx does not require power-hungry RF amplification and frequency down-conversion circuits, so the power consumption can be significantly reduced. The measurement results show that the proposed receiver achieves a sensitivity of −50 dBm and has a power consumption of just 4.5 μW in a 0.18-μm CMOS technology. Consequently, it provides a promising solution for a wide range of low-power wireless sensor network and wireless body area network applications.


title: Gate-first high-k/metal gate DRAM technology for low power and high performance products

abstract: It is the first time that the high-k/metal gate technology was used at peripheral transistors for fully integrated and functioning DRAM. For cost effective DRAM technology, capping nitride spacer was used on cell bit-line scheme, and single work function metal gate was employed without strain technology. The threshold voltage was controlled by using single TiN metal gate with La2O3 and SiGe/Si epi technology. The optimized DRAM high-k/metal gate peripheral transistors showed current gains of 65%/55% and DIBL improvements of 52%/46% for nMOSFET and pMOSFET, respectively. The results in process yield, performance, and reliability characteristics of the technology on 4Gb DRAM have shown that the gate-first high-k/metal gate DRAM technology can be regarded as one of the major candidates for next-generation low power DRAM products.


title: Compact Pulse Topology for Adjustable High-Voltage Pulse Generation Using an SOS Diode

abstract: In this paper, a compact circuit topology is presented for pulsed power generation with a semiconductor opening switch (SOS). Such circuits require the generation of a fast forward current through the diode, followed by a reverse current that activates the recovery process. In general, magnetic switches are used to switch the diode current from forward to reverse. Therefore, adjustment of the output voltage of an SOS pulser is difficult. This paper introduces a circuit that allows an adjustable output voltage of the pulser. A solution is realized with a premagnetizing circuit on the primary winding of a pulse transformer (PT), so that a third winding is not needed. The primary capacitor voltage can now be varied while maintaining proper saturation of the PT.


title: Factorization-Based Texture Segmentation

abstract: This paper introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices--one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histograms to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. The experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.


title: MOOGLE: a metamodel-based model search engine

abstract: Models are becoming increasingly important in the software development process. As a consequence, the number of models being used is increasing, and so is the need for efficient mechanisms to search them. Various existing search engines could be used for this purpose, but they lack features to properly search models, mainly because they are strongly focused on text-based search. This paper presents Moogle, a model search engine that uses metamodeling information to create richer search indexes and to allow more complex queries to be performed. The paper also presents the results of an evaluation of Moogle, which showed that the metamodel information improves the accuracy of the search.


title: Determinant factors in climbing ability: Influence of strength, anthropometry, and neuromuscular fatigue

abstract: The goal of this study was to (i) assess the physical and anthropometric differences between three levels of climbers and (ii) predict climbing ability by using a multiple regression model. The participants were divided into novice (n = 15), skilled (n = 16), and elite (n = 10) climbers. Anthropometric characteristics such as height, weight, percentage of body fat and muscle, bi-acromial breath, arm span, and ape index were measured. General and specific strength were assessed through an arm jump test, a bench press test, and a hand and finger grip strength test in maximal and endurance conditions. All variables were combined into components via a principal component analysis (PCA) and the components used in a multiple regression analysis. The major finding of this study is that climbing ability is more related to specific rather than general strength. Only finger grip strength shows a higher level of initial strength between all samples while the arm jump test discriminates between climbers and non-climbers. The PCA reveals three components, labeled as training, muscle, and anthropometry, which together explain 64.22% of the variance. The regression model indicates that trainable variables explained 46% of the total variance in climbing ability, whereas anthropometry and muscle characteristics explain fewer than 4%.


title: Language, cognition, and short-term memory in individuals with Down syndrome

abstract: The developmentally emerging phenotype of language and cognition in individuals with Down syndrome is summarized on the basis of the project's prior work. Identified are a) the emerging divergence of expressive and receptive language, b) the emerging divergence of lexical and syntactic knowledge in each process, and c) the emerging divergence within cognitive skills of auditory short-term memory and visuospatial short-term memory from other visuospatial skills. Expressive syntax and auditory short-term memory are identified as areas of particular difficulty. Evidence for the continued acquisition of language skills in adolescence is presented. The role of the two components of working memory, auditory and visual, in language development is investigated in studies of narrative and longitudinal change in language skills. Predictors of individual differences during six years of language development are evaluated through hierarchical linear modelling. Chronological age, visuospatial short-term memory, and auditory-short term memory are identified as key predictors of performance at study entry, but not individual change over time, for expressive syntax. The same predictors account for variation in comprehension skill at study outset; and change over the six years can be predicted by chronological age and the change in visuospatial short-term memory skills. (Research funded by US National Institutes of Health Grant R01-HD23352 with contributions from the National Down Syndrome Society.)


title: Identifying and Summarizing Systematic Code Changes via Rule Inference

abstract: Programmers often need to reason about how a program evolved between two or more program versions. Reasoning about program changes is challenging as there is a significant gap between how programmers think about changes and how existing program differencing tools represent such changes. For example, even though modification of a locking protocol is conceptually simple and systematic at a code level, diff extracts scattered text additions and deletions per file. To enable programmers to reason about program differences at a high level, this paper proposes a rule-based program differencing approach that automatically discovers and represents systematic changes as logic rules. To demonstrate the viability of this approach, we instantiated this approach at two different abstraction levels in Java: first at the level of application programming interface (API) names and signatures, and second at the level of code elements (e.g., types, methods, and fields) and structural dependences (e.g., method-calls, field-accesses, and subtyping relationships). The benefit of this approach is demonstrated through its application to several open source projects as well as a focus group study with professional software engineers from a large e-commerce company.


title: The prevalence and socio-demographic correlations of depression, anxiety and stress among a group of university students

abstract: Background ::: The mental health of university students is an area of increasing concern worldwide. The objective of this study is to examine the prevalence of depression, anxiety and stress among a group of Turkish university students.


title: Personality and Social Psychology Be yourself, believe in yourself, and be happy: Self-efficacy as a mediator between personality factors and subjective well-being

abstract: Research has shown close connections between personality and subjective well-being (SWB), suggesting that personality traits predispose individuals to experience different levels of SWB. Moreover, numerous studies have shown that self-efficacy is related to both personality factors and SWB. Extending previous research, we show that general self-efficacy functionally connects personality factors and two components of SWB (life satisfaction and subjective happiness). Our results demonstrate the mediating role of self-efficacy in linking personality factors and SWB. Consistent with our expectations, the influence of neuroticism, extraversion, openness, and conscientiousness on life satisfaction was mediated by self-efficacy. Furthermore, self-efficacy mediated the influence of openness and conscientiousness, but not that of neuroticism and extraversion, on subjective happiness. Results highlight the importance of cognitive beliefs in functionally linking personality traits and SWB.


title: Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement

abstract: The back propagation (BP) neural network algorithm is a multi-layer feedforward network trained according to error back propagation algorithm and is one of the most widely applied neural network models. BP network can be used to learn and store a great deal of mapping relations of input-output model, and no need to disclose in advance the mathematical equation that describes these mapping relations. Its learning rule is to adopt the steepest descent method in which the back propagation is used to regulate the weight value and threshold value of the network to achieve the minimum error sum of square. This paper focuses on the analysis of the characteristics and mathematical theory of BP neural network and also points out the shortcomings of BP algorithm as well as several methods for improvement.


title: Automated Behavioral Analysis of Malware: A Case Study of WannaCry Ransomware

abstract: Ransomware, a class of self-propagating malware that uses encryption to hold the victims' data ransom, has emerged in recent years as one of the most dangerous cyber threats, with widespread damage; e.g., zero-day ransomware WannaCry has caused world-wide catastrophe, from knocking U.K. National Health Service hospitals offline to shutting down a Honda Motor Company in Japan[1]. Our close collaboration with security operations of large enterprises reveals that defense against ransomware relies on tedious analysis from high-volume systems logs of the first few infections. Sandbox analysis of freshly captured malware is also commonplace in operation. ::: We introduce a method to identify and rank the most discriminating ransomware features from a set of ambient (non-attack) system logs and at least one log stream containing both ambient and ransomware behavior. These ranked features reveal a set of malware actions that are produced automatically from system logs, and can help automate tedious manual analysis. We test our approach using WannaCry and two polymorphic samples by producing logs with Cuckoo Sandbox during both ambient, and ambient plus ransomware executions. Our goal is to extract the features of the malware from the logs with only knowledge that malware was present. We compare outputs with a detailed analysis of WannaCry allowing validation of the algorithm's feature extraction and provide analysis of the method's robustness to variations of input data\textemdash changing quality/quantity of ambient data and testing polymorphic ransomware. Most notably, our patterns are accurate and unwavering when generated from polymorphic WannaCry copies, on which 63 (of 63 tested) anti-virus (AV) products fail.


title: Faithful to the Original: Fact Aware Neural Abstractive Summarization

abstract: Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts. Our preliminary study reveals nearly 30% of the outputs from a state-of-the-art neural summarization system suffer from this problem. While previous abstractive summarization approaches usually focus on the improvement of informativeness, we argue that faithfulness is also a vital prerequisite for a practical abstractive summarization system. To avoid generating fake facts in a summary, we leverage open information extraction and dependency parse technologies to extract actual fact descriptions from the source text. The dual-attention sequence-to-sequence framework is then proposed to force the generation conditioned on both the source text and the extracted fact descriptions. Experiments on the Gigaword benchmark dataset demonstrate that our model can greatly reduce fake summaries by 80%. Notably, the fact descriptions also bring significant improvement on informativeness since they often condense the meaning of the source text.


title: An Efficient and Secure Automotive Wireless Software Update Framework

abstract: Future vehicles will be wirelessly connected to nearby vehicles, to the road infrastructure, and to the Internet, thereby becoming an integral part of the Internet of Things. New comfort features, safety functions, and a number of new vehicle-specific services will be integrated in future smart vehicles. These include a fast, secure, and reliable way to diagnose and reconfigure a vehicle, as well as the installation of new software (SW) on its integrated electronic control units (ECUs). Such wireless SW updates are beneficial for both automotive carmakers and customers, as they allow us to securely enable new features on the vehicle and to fix SW bugs by installing a new SW version over the air. A secure and dependable wireless SW update process is valuable in the entire lifetime of a modern vehicle as it can be used already during vehicle development and manufacturing process on the assembly line, as well as during vehicle maintenance in a service center. Additionally, future vehicles will allow us to remotely download up-to-date SW on the ECUs. To support this process over the entire vehicle's lifetime, a generic framework is needed. In this paper, SecUp, a generic framework enabling secure and efficient wireless automotive SW updates is proposed. SecUp utilizes IEEE 802.11s as wireless medium to interconnect vehicles and diagnostic devices in a dependable and fast way. Additionally, SecUp is enabling beneficial wireless SW update features such as parallel and partial SW updates to increase the efficiency, and comprises advanced security mechanisms to prevent abuse and attacks.


title: SEPP-ZVS high frequency inverter for induction heating using newly developed SiC-SIT

abstract: SiC-SIT power semiconductor switching devices has an advantage that its switching time is high speed compared to those of other power semiconductor switching devices. We adopt newly developed SiC-SITs which have the maximum ratings 800V/4A and prepare a breadboard of a conventional single-ended push-pull(SEPP) high frequency inverter. This paper describes the characteristics of SiC-SIT on the basis of the experimental results of the breadboard. Its operational frequencies are varied at from 100 kHz to 250kHz with PWM control technique for output power regulation. Its load is induction fluid heating systems for super-heated-steam production.


title: POSTER: A Behavioural Authentication System for Mobile Users

abstract: Active behavioural-based authentication systems are challenge-response based implicit authentication systems that authenticate users using the behavioural features of the users when responding to challenges that are sent from the server. They provide a flexible (no extra hardware) and secure second factor for authentication systems, with applications including protection against identity theft and password compromise of web applications. We propose a novel active behavioural authentication system for mobile devices, called DAC (Draw A Circle), where a challenge specifies a set of constraints on a circle and the response is a user drawn circle that satisfies the constraints. We carefully select a set of features that capture behavioural traits of the user which is used to construct a profile for them, then design a matching algorithm that allows users to be authenticated with approximately 95% accuracy. We discuss our implementation, and present our experimental results that show, (i) the accuracy of authentication system and (ii) non-delegateability of profile, guaranteeing that the user cannot pass their credentials to others.


title: Comparison of Niblack inspired binarization methods for ancient documents

abstract: In this paper, we present a new sliding window based local thresholding technique 'NICK' and give a detailed ::: comparison of some existing sliding-window based thresholding algorithms with our method. The proposed method aims ::: at achieving better binarization results, specifically, for ancient document images. NICK has been inspired from the ::: Niblack's binarization method and exhibits its robustness and effectiveness when evaluated on low quality ancient ::: document images.


title: Beyond the numbers: an analysis of optimistic and pessimistic language in earnings press releases. Working paper

abstract: In this paper, we examine whether managers use optimistic and pessimistic language in earnings press releases to provide information about expected future firm performance to the market, and whether the market responds to optimistic and pessimistic language usage in earnings press releases after controlling for the earnings surprise and other factors likely to influence the market*s response to the earnings announcement. We use textual-analysis software to measure levels of optimistic and pessimistic language for a sample of approximately 24,000 earnings press releases issued between 1998 and 2003. We find a positive (negative) association between optimistic (pessimistic) language usage and future firm performance and a significant incremental market response to optimistic and pessimistic language usage in earnings press releases. Results suggest managers use optimistic and pessimistic language to provide credible information about expected future firm performance to the market, and that the market responds to managers' language usage.


title: Smooth second-order sliding modes: Missile guidance application

abstract: A new smooth second-order sliding mode control is proposed and proved using homogeneity-based technique for a system driven by sufficiently smooth uncertain disturbances. The main target application of this technique-the missile-interceptor guidance system against targets performing evasive maneuvers is considered. The smooth second-order sliding mode control-based guidance law is designed and compared with augmented proportional navigation guidance law via computer simulations of a guided missile intercepting a maneuvering ballistic target.


title: Sex-Dimorphic Face Shape Preference in Heterosexual and Homosexual Men and Women

abstract: Studies have used manipulated faces to test the preferences of heterosexual individuals for sexually dimorphic facial cues. In contrast to previous studies, which have generally excluded homosexual participants, we directly compared homosexual and heterosexual male and female preferences for manipulated sexual dimorphism in faces (homosexual males: n = 311; heterosexual males: n = 215; homosexual females: n = 159; heterosexual females: n = 218). Prior studies on sexual orientation and preferences for faces that were paired with masculine and feminine behavioral descriptors suggest that homosexual men prefer more masculine men and that homosexual women demonstrate no preference for either masculinity or femininity in women. In our study, we tested for similarities and differences among heterosexual and homosexual males and females with regard to their preferences for a more specific aspect of faces: sexual dimorphism of face shape. Homosexual men demonstrated stronger preferences for masculinity in male faces than did all of the other groups. Homosexual women demonstrated stronger preferences for masculinity in female faces than did heterosexual women. These results suggest attractiveness judgments of same-sex faces made by homosexual individuals are not a mirror image of those made by heterosexual individuals of the opposite sex. Our data suggest that face preferences of homosexual individuals reflect a system of biologically and socially guided preferences at least as complex as those found among heterosexual individuals.


title: Phoneme recognition using time-delay neural networks

abstract: The authors present a time-delay neural network (TDNN) approach to phoneme recognition which is characterized by two important properties: (1) using a three-layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces, which the TDNN learns automatically using error backpropagation; and (2) the time-delay arrangement enables the network to discover acoustic-phonetic features and the temporal relationships between them independently of position in time and therefore not blurred by temporal shifts in the input. As a recognition task, the speaker-dependent recognition of the phonemes B, D, and G in varying phonetic contexts was chosen. For comparison, several discrete hidden Markov models (HMM) were trained to perform the same task. Performance evaluation over 1946 testing tokens from three speakers showed that the TDNN achieves a recognition rate of 98.5% correct while the rate obtained by the best of the HMMs was only 93.7%. >


Designing for Compliance : ESCHER , Team VALOR ’ s Compliant Biped


title: Collaborative Topic Regression with Social Regularization for Tag Recommendation

abstract: Recently, tag recommendation (TR) has become a very hot research topic in data mining and related areas. However, neither co-occurrence based methods which only use the item-tag matrix nor content based methods which only use the item content information can achieve satisfactory performance in real TR applications. Hence, how to effectively combine the item-tag matrix, item content information, and other auxiliary information into the same recommendation framework is the key challenge for TR. In this paper, we first adapt the collaborative topic regression (CTR) model, which has been successfully applied for article recommendation, to combine both item-tag matrix and item content information for TR. Furthermore, by extending CTR we propose a novel hierarchical Bayesian model, called CTR with social regularization (CTR-SR), to seamlessly integrate the item-tag matrix, item content information, and social networks between items into the same principled model. Experiments on real data demonstrate the effectiveness of our proposed models.


title: Dropout: a simple way to prevent neural networks from overfitting

abstract: Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different "thinned" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.


title: Application Bandwidth and Flow Rates from 3 Trillion Flows Across 45 Carrier Networks

abstract: Geographically broad, application-aware studies of large subscriber networks are rarely undertaken because of the challenges of accessing secured network premises, protecting subscriber privacy, and deploying scalable measurement devices. We present a study examining bandwidth consumption and the rate at which new flows are created in 45 cable, DSL, cellular and WiFi subscriber networks across 26 countries on six continents. Using deep packet inspection, we find that one or two applications can strongly influence the magnitude and duration of daily bandwidth peaks. We analyze bandwidth over 7 days to better understand the potential for network optimization using virtual network functions. We find that on average cellular and non-cellular networks operate at 61% and 57% of peak bandwidth respectively. Since most networks are over provisioned, there is considerable room for optimization.


title: Extracting semantic annotations from twitter

abstract: Tweets contain mentions of numerous entities, persons and events, and often additional information, like an opinion, that can be viewed as an annotation of that entity. However, this information is currently being accumulated only by specific applications without being made available in a generic format. We discuss a natural language processing approach to extract information about entities and their annotations from tweets and transform them into a semantic, reusable knowledge base. We believe this will greatly facilitate access to user-generated Twitter data for many applications.


title: Video Pixel Networks

abstract: We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural architecture reflect the time, space and color structure of video tensors and encode it as a four-dimensional dependency chain. The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state of the art, and the generated videos show only minor deviations from the ground truth. The VPN also produces detailed samples on the action-conditional Robotic Pushing benchmark and generalizes to the motion of novel objects.


title: Content Based Image Retrieval: Review of State of Art and Future Directions

abstract: In this paper we discuss the state of the art of the content based image retrieval highlighting the main components and reviewing various approaches employed at each stage, while enhancing the main challenges and key contributions. Along these lines, some analogy with text retrieval systems will be discussed.


Education and development among tribals in Kerala a study with special reference to Wayanad district


title: The ATR Multilingual Speech-to-Speech Translation System

abstract: In this paper, we describe the ATR multilingual speech-to-speech translation (S2ST) system, which is mainly focused on translation between English and Asian languages (Japanese and Chinese). There are three main modules of our S2ST system: large-vocabulary continuous speech recognition, machine text-to-text (T2T) translation, and text-to-speech synthesis. All of them are multilingual and are designed using state-of-the-art technologies developed at ATR. A corpus-based statistical machine learning framework forms the basis of our system design. We use a parallel multilingual database consisting of over 600 000 sentences that cover a broad range of travel-related conversations. Recent evaluation of the overall system showed that speech-to-speech translation quality is high, being at the level of a person having a Test of English for International Communication (TOEIC) score of 750 out of the perfect score of 990.


Stereotypes and the Media: A Re-evaluation


title: People Counting in High Density Crowds from Still Images

abstract: We present a method of estimating the number of people in high density crowds from still images. The method estimates counts by fusing information from multiple sources. Most of the existing work on crowd counting deals with very small crowds (tens of individuals) and use temporal information from videos. Our method uses only still images to estimate the counts in high density images (hundreds to thousands of individuals). At this scale, we cannot rely on only one set of features for count estimation. We, therefore, use multiple sources, viz. interest points (SIFT), Fourier analysis, wavelet decomposition, GLCM features and low confidence head detections, to estimate the counts. Each of these sources gives a separate estimate of the count along with confidences and other statistical measures which are then combined to obtain the final estimate. We test our method on an existing dataset of fifty images containing over 64000 individuals. Further, we added another fifty annotated images of crowds and tested on the complete dataset of hundred images containing over 87000 individuals. The counts per image range from 81 to 4633. We report the performance in terms of mean absolute error, which is a measure of accuracy of the method, and mean normalised absolute error, which is a measure of the robustness.


title: Automatic extraction of relations between medical concepts in clinical texts

abstract: Objective A supervised machine learning approach to discover relations between medical problems, treatments, and tests mentioned in electronic medical records. ::: ::: Materials and methods A single support vector machine classifier was used to identify relations between concepts and to assign their semantic type. Several resources such as Wikipedia, WordNet, General Inquirer, and a relation similarity metric inform the classifier. ::: ::: Results The techniques reported in this paper were evaluated in the 2010 i2b2 Challenge and obtained the highest F1 score for the relation extraction task. When gold standard data for concepts and assertions were available, F1 was 73.7, precision was 72.0, and recall was 75.3. F1 is defined as 2\*Precision\*Recall/(Precision+Recall). Alternatively, when concepts and assertions were discovered automatically, F1 was 48.4, precision was 57.6, and recall was 41.7. ::: ::: Discussion Although a rich set of features was developed for the classifiers presented in this paper, little knowledge mining was performed from medical ontologies such as those found in UMLS. Future studies should incorporate features extracted from such knowledge sources, which we expect to further improve the results. Moreover, each relation discovery was treated independently. Joint classification of relations may further improve the quality of results. Also, joint learning of the discovery of concepts, assertions, and relations may also improve the results of automatic relation extraction. ::: ::: Conclusion Lexical and contextual features proved to be very important in relation extraction from medical texts. When they are not available to the classifier, the F1 score decreases by 3.7%. In addition, features based on similarity contribute to a decrease of 1.1% when they are not available.