title: Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain

abstract: We develop an efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.


title: Machine Learning the Cryptocurrency Market

abstract: Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for $1,681$ cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that non-trivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.


title: Recognizing Stress Using Semantics and Modulation of Speech and Gestures

abstract: This paper investigates how speech and gestures convey stress, and how they can be used for automatic stress recognition. As a first step, we look into how humans use speech and gestures to convey stress. In particular, for both speech and gestures, we distinguish between stress conveyed by the intended semantic message (e.g. spoken words for speech, symbolic meaning for gestures), and stress conveyed by the modulation of either speech and gestures (e.g. intonation for speech, speed and rhythm for gestures). As a second step, we use this decomposition of stress as an approach for automatic stress prediction. The considered components provide an intermediate representation with intrinsic meaning, which helps bridging the semantic gap between the low level sensor representation and the high level context sensitive interpretation of behavior. Our experiments are run on an audiovisual dataset with service-desk interactions. The final goal is having a surveillance system that would notify when the stress level is high and extra assistance is needed. We find that speech modulation is the best performing intermediate level variable for automatic stress prediction. Using gestures increases the performance and is mostly beneficial when speech is lacking. The two-stage approach with intermediate variables performs better than baseline feature level or decision level fusion.


title: Electromagnetic Analysis: Concrete Results

abstract: Although the possibility of attacking smart-cards by analyzing their electromagnetic power radiation repeatedly appears in research papers, all accessible references evade the essence of reporting conclusive experiments where actual cryptographic algorithms such as des or RSA were successfully attacked. ::: ::: This work describes electromagnetic experiments conducted on three different cmos chips, featuring different hardware protections and executing a des, an alleged comp128 and an rsa. In all cases the complete key material was successfully retrieved.


title: Selecting the Most Influential Nodes in Social Networks

abstract: A set covering greedy algorithm is proposed for solving the influence maximization problem in social networks. Two information diffusion models are considered: Independent Cascade Model and Linear Threshold Model. The proposed algorithm is compared with traditional maximization algorithms such as simple greedy and degree centrality using three data sets. In addition, an algorithm for mapping social networks is proposed, which allows visualizing the infection process and how the different algorithms evolve. The proposed approach is useful for mining large social networks.


title: Securing Application with Software Partitioning: A case study using SGX

abstract: Application size and complexity are the underlying cause of numerous security vulnerabilities in code. In order to mitigate the risks arising from such vulnerabilities, various techniques have been proposed to isolate the execution of sensitive code from the rest of the application and from other software on the platform (e.g. the operating system). However, even with these partitioning techniques, it is not immediately clear exactly how they can and should be used to partition applications. What overall partitioning scheme should be followed; what granularity of the partitions should be. To some extent, this is dependent on the capabilities and performance of the partitioning technology in use. For this work, we focus on the upcoming Intel Software Guard Extensions (SGX) technology as the state-of-the-art in this field. SGX provides a trusted execution environment, called an enclave, that protects the integrity of the code and the confidentiality of the data inside it from other software, including the operating system. We present a novel framework consisting of four possible schemes under which an application can be partitioned. These schemes range from coarse-grained partitioning, in which the full application is included in a single enclave, through ultra-fine partitioning, in which each application secret is protected in an individual enclave. We explain the specific security benefits provided by each of the partitioning schemes and discuss how the performance of the application would be affected. To compare the different partitioning schemes, we have partitioned OpenSSL using four different schemes. We discuss SGX properties together with the implications of our design choices in this paper.


title: Atorvastatin lowers plasma low-density lipoprotein cholesterol and C-reactive protein in Japanese type 2 diabetic patients.

abstract: We investigated the meaning and the worth of lowering low-density lipoprotein cholesterol (LDL-C) to less than 100 mg/dL in Japanese type 2 diabetic patients using atorvastatin. As a multicenter open-labeled study, 84 type 2 diabetic Japanese patients with hypercholesterolemia were enrolled between September 2003 and April 2004. Subjects received 16 weeks of treatment with atorvastatin. High-sensitive C-reactive protein (hs-CRP), plasminogen activator inhibitor 1, monocyte chemotactic protein 1, interleukin 6, urine albumin-creatinine ratio, hemoglobin A1c, total cholesterol, and LDL-C were measured at baseline and after 8 and 16 weeks of treatment. According to the Adult Treatment Panel III of the National Cholesterol Education Program, we divided the subjects into responders (final LDL-C <100 mg/dL) and nonresponders (final LDL-C ≥100 mg/dL). After 16 weeks of atorvastatin treatment, as well as a reduction of total cholesterol and LDL-C, a significant reduction of hs-CRP was observed. Plasminogen activator inhibitor 1, monocyte chemotactic protein 1, and interleukin 6 were not changed. After stratification, hs-CRP declined only in responders. We concluded that atorvastatin not only improved hypercholesterolemia, but also reduced CRP even in Japanese diabetic patients. The results of this stratified study suggest that achievement of the Adult Treatment Panel III treatment goal of LDL-C might assure a reduction of inflammation, which is associated with cardiovascular events.


title: Flexible Attribute-Based Encryption Applicable to Secure E-Healthcare Records

abstract: In e-healthcare record systems (EHRS), attribute-based encryption (ABE) appears as a natural way to achieve fine-grained access control on health records. Some proposals exploit key-policy ABE (KP-ABE) to protect privacy in such a way that all users are associated with specific access policies and only the ciphertexts matching the users' access policies can be decrypted. An issue with KP-ABE is that it requires an a priori formulation of access policies during key generation, which is not always practicable in EHRS because the policies to access health records are sometimes determined after key generation. In this paper, we revisit KP-ABE and propose a dynamic ABE paradigm, referred to as access policy redefinable ABE (APR-ABE). To address the above issue, APR-ABE allows users to redefine their access policies and delegate keys for the redefined ones; hence, a priori precise policies are no longer mandatory. We construct an APR-ABE scheme with short ciphertexts and prove its full security in the standard model under several static assumptions.


title: Home Buprenorphine/Naloxone Induction in Primary Care

abstract: Buprenorphine can be used for the treatment of opioid dependence in primary care settings. National guidelines recommend directly observed initial dosing followed by multiple in-clinic visits during the induction week. We offered buprenorphine treatment at a public hospital primary care clinic using a home, unobserved induction protocol. Participants were opioid-dependent adults eligible for office-based buprenorphine treatment. The initial physician visit included assessment, education, induction telephone support instructions, an illustrated home induction pamphlet, and a 1-week buprenorphine/naloxone prescription. Patients initiated dosing off-site at a later time. Follow-up with urine toxicology testing occurred at day 7 and thereafter at varying intervals. Primary outcomes were treatment status at week 1 and induction-related events: severe precipitated withdrawal, other buprenorphine-prompted withdrawal symptoms, prolonged unrelieved withdrawal, and serious adverse events (SAEs). Patients (N = 103) were predominantly heroin users (68%), but also prescription opioid misusers (18%) and methadone maintenance patients (14%). At the end of week 1, 73% were retained, 17% provided induction data but did not return to the clinic, and 11% were lost to follow-up with no induction data available. No cases of severe precipitated withdrawal and no SAEs were observed. Five cases (5%) of mild-to-moderate buprenorphine-prompted withdrawal and eight cases of prolonged unrelieved withdrawal symptoms (8% overall, 21% of methadone-to-buprenorphine inductions) were reported. Buprenorphine-prompted withdrawal and prolonged unrelieved withdrawal symptoms were not associated with treatment status at week 1. Home buprenorphine induction was feasible and appeared safe. Induction complications occurred at expected rates and were not associated with short-term treatment drop-out.


title: Background–Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering

abstract: Background estimation and foreground segmentation are important steps in many high-level vision tasks. Many existing methods estimate background as a low-rank component and foreground as a sparse matrix without incorporating the structural information. Therefore, these algorithms exhibit degraded performance in the presence of dynamic backgrounds, photometric variations, jitter, shadows, and large occlusions. We observe that these backgrounds often span multiple manifolds. Therefore, constraints that ensure continuity on those manifolds will result in better background estimation. Hence, we propose to incorporate the spatial and temporal sparse subspace clustering into the robust principal component analysis (RPCA) framework. To that end, we compute a spatial and temporal graph for a given sequence using motion-aware correlation coefficient. The information captured by both graphs is utilized by estimating the proximity matrices using both the normalized Euclidean and geodesic distances. The low-rank component must be able to efficiently partition the spatiotemporal graphs using these Laplacian matrices. Embedded with the RPCA objective function, these Laplacian matrices constrain the background model to be spatially and temporally consistent, both on linear and nonlinear manifolds. The solution of the proposed objective function is computed by using the linearized alternating direction method with adaptive penalty optimization scheme. Experiments are performed on challenging sequences from five publicly available datasets and are compared with the 23 existing state-of-the-art methods. The results demonstrate excellent performance of the proposed algorithm for both the background estimation and foreground segmentation.


title: Advanced image fusion to overlay coronary sinus anatomy with real-time fluoroscopy to facilitate left ventricular lead implantation in CRT

abstract: Methods: Twelve patients (11 male, 59 +/- 16.8 years) undergoing CRT had three-dimensional (3D) whole-heart imaging (six CT, six CMR). Eight patients had at least one previously failed LV lead implant. Using segmentation software, anatomical models of the cardiac chambers, CS, and its branches were overlaid onto the live fluoroscopy using a prototype version of the Philips EP Navigator software to guide lead implantation. Results: We achieved high-fidelity segmentations of cardiac chambers, coronary vein anatomy, and accurate registration between the 3D anatomical models and the live fluoroscopy in all 12 patients confirmed by balloon occlusion angiography. The CS was cannulated successfully in every patient and in 11, an LV lead was implanted successfully. (One patient had no acceptable lead values due to extensive myocardial scar.) Conclusion: Using overlaid 3D segmentations of the CS and cardiac chambers, it is feasible to guide CRT implantation in real time by fusing advanced imaging and fluoroscopy. This enabled successful CRT in a group of patients with previously failed implants. This technology has the potential to facilitate CRT and improve implant success. (PACE 2011; 34:226-234).


title: Instant dehazing of images using polarization

abstract: We present an approach to easily remove the effects of haze from images. It is based on the fact that usually airlight scattered by atmospheric particles is partially polarized. Polarization filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, works under a wide range of atmospheric and viewing conditions. We analyze the image formation process, taking into account polarization effects of atmospheric scattering. We then invert the process to enable the removal of haze from images. The method can be used with as few as two images taken through a polarizer at different orientations. This method works instantly, without relying on changes of weather conditions. We present experimental results of complete dehazing in far from ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color. As a by product, the method also yields a range (depth) map of the scene, and information about properties of the atmospheric particles.


Affordance, conventions, and design


title: Review on Electric Vehicle, Battery Charger, Charging Station and Standards

abstract: Electric vehicles are a new and upcoming technology in the transportation and power sector that have many benefits in terms of economic and environmental. This study presents a comprehensive review and evaluation of various types of electric vehicles and its associated equipment in particular battery charger and charging station. A comparison is made on the commercial and prototype electric vehicles in terms of electric range, battery size, charger power and charging time. The various types of charging stations and standards used for charging electric vehicles have been outlined and the impact of electric vehicle charging on utility distribution system is also discussed.


title: A DC Voltage-Multiplier Circuit Working as a High-Voltage Pulse Generator

abstract: The intensive use of semiconductor devices enabled the development of a repetitive high-voltage pulse-generator topology from the dc voltage-multiplier (VM) concept. The proposed circuit is based on an odd VM-type circuit, where a number of dc capacitors share a common connection with different voltage ratings in each one, and the output voltage comes from a single capacitor. Standard VM rectifier and coupling diodes are used for charging the energy-storing capacitors, from an ac power supply, and two additional on/off semiconductors in each stage, to switch from the typical charging VM mode to a pulse mode with the dc energy-storing capacitors connected in series with the load. Results from a 2-kV experimental prototype with three stages, delivering a 10- s pulse with a 5-kHz repetition rate into a resistive load, are discussed. Additionally, the proposed circuit is compared against the solid-state Marx generator topology for the same peak input and output voltages.


A Document Retrieval System for Math Queries


title: Traffic light mapping, localization, and state detection for autonomous vehicles

abstract: Detection of traffic light state is essential for autonomous driving in cities. Currently, the only reliable systems for determining traffic light state information are non-passive proofs of concept, requiring explicit communication between a traffic signal and vehicle. Here, we present a passive camera-based pipeline for traffic light state detection, using (imperfect) vehicle localization and assuming prior knowledge of traffic light location. First, we introduce a convenient technique for mapping traffic light locations from recorded video data using tracking, back-projection, and triangulation. In order to achieve robust real-time detection results in a variety of lighting conditions, we combine several probabilistic stages that explicitly account for the corresponding sources of sensor and data uncertainty. In addition, our approach is the first to account for multiple lights per intersection, which yields superior results by probabilistically combining evidence from all available lights. To evaluate the performance of our method, we present several results across a variety of lighting conditions in a real-world environment. The techniques described here have for the first time enabled our autonomous research vehicle to successfully navigate through traffic-light-controlled intersections in real traffic.


title: CAViAR: Context Aware Visual Indoor Augmented Reality for a University Campus

abstract: CAViAR is a mobile software system for indoor environments that provides to the mobile user equipped with a smartphone indoor localization, augmented reality (AR), visual interaction, and indoor navigation. These capabilities are possible with the availability of state of the art AR technologies. The mobile application includes additional features, such as indoor maps, shortest path, inertial navigation, places of interest, location sharing and voice-commanded search. CAViAR was tested in a University Campus as one of the technologies to be used later in an intelligent Campus environment.


title: A Cross-lingual Annotation Projection Approach for Relation Detection

abstract: While extensive studies on relation extraction have been conducted in the last decade, statistical systems based on supervised learning are still limited because they require large amounts of training data to achieve high performance. In this paper, we develop a cross-lingual annotation projection method that leverages parallel corpora to bootstrap a relation detector without significant annotation efforts for a resource-poor language. In order to make our method more reliable, we introduce three simple projection noise reduction methods. The merit of our method is demonstrated through a novel Korean relation detection task.


title: Wearable Multichannel Photoplethysmography Framework for Heart Rate Monitoring During Intensive Exercise

abstract: We propose a multichannel-PPG sensor that uses truncated singular value decomposition (SVD) for accurate estimation of heart rate (HR) during high-intensity exercise. We implemented a sensor to acquire nine channel signals simultaneously, and to enable real-time monitoring of PPG signals and HR. For denoising, we used SVD programmed in the microcontroller unit and showed that the pure pulse is concentrated mostly within the first largest singular value with the related two eigenvectors by extracting the significant feature components of the pulse signals. Our proposed sensor was tested in 32 subjects during exercise of various intensities: walking and medium- and high-intensity jogging. We compared our proposed method with the single-channel method, the multichannel weighting method, and the multichannel best signal selection. Furthermore, we incorporated acceleration signals and a HR tracking algorithm, into our proposed multichannel framework and evaluated its performance using the 12 data sets from the 2015 IEEE Signal Processing Cup Database. Our method showed the lowest absolute errors in HR estimation when compared to the single-channel, multichannel weighting, and multichannel best signal selection methods, irrespective of exercise intensity. We also found that our proposed multichannel framework incorporating acceleration signals and a HR tracking algorithm provided the lowest average absolute error of 0.94 beats/m. The results show that our proposed multichannel framework can be used for accurate HR estimation during high-intensity exercise.


title: A Randomized Algorithm for Approximating the Log Determinant of a Symmetric Positive Definite Matrix

abstract: We introduce a novel algorithm for approximating the logarithm of the determinant of a symmetric positive definite matrix. The algorithm is randomized and proceeds in two steps: first, it finds an approximation to the largest eigenvalue of the matrix after running a few iterations of the so-called"power method"from the numerical linear algebra literature. Then, using this information, it approximates the traces of a small number of matrix powers of a specially constructed matrix, using the method of Avron and Toledo~\cite{AT11}. From a theoretical perspective, we present strong worst-case analysis bounds for our algorithm. From an empirical perspective, we demonstrate that a C++ implementation of our algorithm can approximate the logarithm of the determinant of large matrices very accurately in a matter of seconds.


title: Storekeeper: A Security-Enhanced Cloud Storage Aggregation Service

abstract: Cloud storage services are currently a commodity that allows users to store data persistently, access the data from everywhere, and share it with friends or co-workers. However, due to the proliferation of cloud storage accounts and lack of interoperability between cloud services, managing and sharing cloud-hosted files is a nightmare for many users. To address this problem, specialized cloud aggregator systems emerged that provide users a global view of all files in their accounts and enable file sharing between users from different clouds. Such systems, however, have limited security: not only they fail to provide end-to-end privacy from cloud providers, but they require users to grant full access privileges to individual cloud storage accounts. In this paper, we present Storekeeper, a privacy-preserving cloud aggregation service that enables file sharing on multi-user multi-cloud storage platforms while preserving data confidentiality from cloud providers and from the cloud aggregator service. To provide this property, Storekeeper decentralizes most of the cloud aggregation logic to the client side enabling security sensitive functions to be performed only on the trusted client endpoints. This decentralization brings new challenges related with file update propagation, access control, user authentication, and key management that are addressed by Storekeeper. This is provided at a low cost (7% on average) when compared with the underlining cloud providers.


A Deep Semantic Natural Language Processing Platform


title: VHDL modules and circuits for underwater optical wireless communication systems

abstract: Underwater wireless optical communication has been used for establish a link between mobile vehicles and/or fixed nodes because light, especially in the blue/green region, allows to achieve higher data-rate than acoustical or electromagnetic waves for moderate distances. The here proposed work has the aim to pave the way for diffuse optical communication allowing to support optical communication in an Underwater Wireless Sensor Network of dense-deployed fixed nodes for specific application, such as monitoring and surveillance, for shallow, coastal and inland water in the case of moderate/limited area. In particular, taking into account already developed protocols for optical wireless communication, the focus is on the hardware implementation of different flexible modules to support both point-to-point and directional communication targeting the use of these technologies in the field of Underwater Wireless Sensor Network and the integration with current terrestrial technologies for Wireless Sensor Network. After a careful analysis of benefits and problems related to optical transmission underwater, design considerations are proposed with the description of the implemented modules. The use of light impulse for communication is motivated by the possibility of targeting high data-rate, low-cost and small dimension components. This paper describes an overall vision of the system: a HDL implementation of flexible modules for the management of optical communication (based on IEEE 802.15.4 and IEEE 802.11 standard) which target the interface with current terrestrial technology for Wireless Sensor Networks; the design and implementation of circuits for underwater optical point-to-point and planar communication. The preliminary results and design consideration are reported considering also future possible developments.


The Use of Case-Based Reasoning for the Monitoring of Financial Fraud Transactions


title: Automatic Generator of Minimal Problem Solvers. ECCV

abstract: Finding solutions to minimal problems for estimating epipolar geometry and camera motion leads to solving systems of algebraic equations. Often, these systems are not trivial and therefore special algorithms have to be designed to achieve numerical robustness and computational efficiency. The state of the art approach for constructing such algorithms is the Grobner basis method for solving systems of polynomial equations. Previously, the Grobner basis solvers were designed ad hoc for concrete problems and they could not be easily applied to new problems. In this paper we propose an automatic procedure for generating Grobner basis solvers which could be used even by non-experts to solve technical problems. The input to our solver generator is a system of polynomial equations with a finite number of solutions. The output of our solver generator is the Matlab or C code which computes solutions to this system for concrete coefficients. Generating solvers automatically opens possibilities to solve more complicated problems which could not be handled manually or solving existing problems in a better and more efficient way. We demonstrate that our automatic generator constructs efficient and numerically stable solvers which are comparable or outperform known manually constructed solvers. The automatic generator is available at http://cmp.felk.cvut.cz/minimal


title: User reputation in a comment rating environment

abstract: Reputable users are valuable assets of a web site. We focus on user reputation in a comment rating environment, where users make comments about content items and rate the comments of one another. Intuitively, a reputable user posts high quality comments and is highly rated by the user community. To our surprise, we find that the quality of a comment judged editorially is almost uncorrelated with the ratings that it receives, but can be predicted using standard text features, achieving accuracy as high as the agreement between two editors! However, extracting a pure reputation signal from ratings is difficult because of data sparseness and several confounding factors in users' voting behavior. To address these issues, we propose a novel bias-smoothed tensor model and empirically show that our model significantly outperforms a number of alternatives based on Yahoo! News, Yahoo! Buzz and Epinions datasets.


title: Inferring 3D layout of building facades from a single image

abstract: In this paper, we propose a novel algorithm that infers the 3D layout of building facades from a single 2D image of an urban scene. Different from existing methods that only yield coarse orientation labels or qualitative block approximations, our algorithm quantitatively reconstructs building facades in 3D space using a set of planes mutually related by 3D geometric constraints. Each plane is characterized by a continuous orientation vector and a depth distribution. An optimal solution is reached through inter-planar interactions. Due to the quantitative and plane-based nature of our geometric reasoning, our model is more expressive and informative than existing approaches. Experiments show that our method compares competitively with the state of the art on both 2D and 3D measures, while yielding a richer interpretation of the 3D scene behind the image.


title: Acoustic-to-Word Attention-Based Model Complemented with Character-Level CTC-Based Model

abstract: This paper addresses end-to-end speech recognition which directly maps acoustic features to a word sequence. The acoustic-to-word model is attractive since it does not require an external language model and an elaborate decoder, resulting in extremely simple and fast decoding. The apparent drawback of this modeling is sparseness of training data, particularly for less frequent words. In this paper, we propose a framework complemented with a character-level model. Joint training of the word-level model with the character-level model enhances the generality of deep learning of feature extraction and classification processes, preventing it from overfitting. Moreover, the character-level model is used to decode out-of-vocabulary (OOV) words that are not covered by the word-level model. Since there are choices of connectionist temporal classification (CTC) and attention-based models in the end-to-end recognition, we also explore optimal combination for the hybrid system. Evaluations on the Corpus of Spontaneous Japanese (CSJ) show that (1) the acoustic-to-word attention-based model outperforms CTC, (2) multitask learning (MTL) with character-level CTC model is effective, and (3) the hybrid system achieves comparable or even better accuracy than the standard DNN-HMM system with a decoding speed faster by a factor of 25.


title: Creation and Evaluation of emotion expression with body movement, sound and eye color for humanoid robots

abstract: The ability to display emotions is a key feature in human communication and also for robots that are expected to interact with humans in social environments. For expressions based on Body Movement and other signals than facial expressions, like Sound, no common grounds have been established so far. Based on psychological research on human expression of emotions and perception of emotional stimuli we created eight different expressional designs for the emotions Anger, Sadness, Fear and Joy, consisting of Body Movements, Sounds and Eye Colors. In a large pre-test we evaluated the recognition ratios for the different expressional designs. In our main experiment we separated the expressional designs into their single cues (Body Movement, Sound, Eye Color) and evaluated their expressivity. The detailed view at the perception of our expressional cues, allowed us to evaluate the appropriateness of the stimuli, check our implementations for flaws and build a basis for systematical revision. Our analysis revealed that almost all Body Movements were appropriate for their target emotion and that some of our Sounds need a revision. Eye Colors could be identified as an unreliable component for emotional expression.


title: Detection and analysis of drive-by-download attacks and malicious JavaScript code

abstract: JavaScript is a browser scripting language that allows developers to create sophisticated client-side interfaces for web applications. However, JavaScript code is also used to carry out attacks against the user's browser and its extensions. These attacks usually result in the download of additional malware that takes complete control of the victim's platform, and are, therefore, called "drive-by downloads." Unfortunately, the dynamic nature of the JavaScript language and its tight integration with the browser make it difficult to detect and block malicious JavaScript code. This paper presents a novel approach to the detection and analysis of malicious JavaScript code. Our approach combines anomaly detection with emulation to automatically identify malicious JavaScript code and to support its analysis. We developed a system that uses a number of features and machine-learning techniques to establish the characteristics of normal JavaScript code. Then, during detection, the system is able to identify anomalous JavaScript code by emulating its behavior and comparing it to the established profiles. In addition to identifying malicious code, the system is able to support the analysis of obfuscated code and to generate detection signatures for signature-based systems. The system has been made publicly available and has been used by thousands of analysts.


title: The turing test track of the 2012 Mario AI Championship: Entries and evaluation

abstract: The Turing Test Track of the Mario AI Championship focused on developing human-like controllers for a clone of the popular game Super Mario Bros. Competitors participated by submitting AI agents that imitate human playing style. This paper presents the rules of the competition, the software used, the voting interface, the scoring procedure, the submitted controllers and the recent results of the competition for the year 2012. We also discuss what can be learnt from this competition in terms of believability in platform games. The discussion is supported by a statistical analysis of behavioural similarities and differences among the agents, and between agents and humans. The paper is co-authored by the organizers of the competition (the first three authors) and the competitors.


title: Alive-Infer: data-driven precondition inference for peephole optimizations in LLVM

abstract: Peephole optimizations are a common source of compiler bugs. Compiler developers typically transform an incorrect peephole optimization into a valid one by strengthening the precondition. This process is challenging and tedious. This paper proposes Alive-Infer, a data-driven approach that infers preconditions for peephole optimizations expressed in Alive. Alive-Infer generates positive and negative examples for an optimization, enumerates predicates on-demand, and learns a set of predicates that separate the positive and negative examples. Alive-Infer repeats this process until it finds a precondition that ensures the validity of the optimization. Alive-Infer reports both a weakest precondition and a set of succinct partial preconditions to the developer. Our prototype generates preconditions that are weaker than LLVM’s preconditions for 73 optimizations in the Alive suite. We also demonstrate the applicability of this technique to generalize 54 optimization patterns generated by Souper, an LLVM IR–based superoptimizer.


title: Precision Motion Control of a Three Degrees of Freedom Hybrid Stage With Dual Actuators

abstract: This paper presents a three degrees of freedom precision hybrid stage that can move and align an object on it for measurement of its three-dimensional image using the confocal scanning microscope. The hybrid stage consists of two individually operating x-y-thetas stages, called the coarse stage and the fine stage. The coarse stage is driven by the three linear motors and produces the initial movement of an object. On the other hand, the fine stage is driven by the four voice coil motors and provides the final alignment of it with sub-micron meter accuracy. For control of the hybrid stage, we propose a precision motion controller. The performances of the precision motion controller of the hybrid stage are evaluated by experiment with a hardware setup.


title: Consumer Heterogeneity and Paid Search Effectiveness: A Large Scale Field Experiment

abstract: Internet advertising has been the fastest growing advertising channel in recent years with paid search ads comprising the bulk of this revenue. We present results from a series of large scale field experiments done at eBay that were designed to measure the causal effectiveness of paid search ads. Because search clicks and purchase behavior are correlated, we show that returns from paid search are a fraction of conventional non-experimental estimates. As an extreme case, we show that brand-keyword ads have no measurable short-term benefits. For non-brand keywords we find that new and infrequent users are positively influenced by ads but that more frequent users whose purchasing behavior is not influenced by ads account for most of the advertising expenses, resulting in average returns that are negative.


title: Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis

abstract: Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints; 2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is light-weight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our Warping-GAN that significantly outperforms all existing methods on two large datasets.


title: Principled Uncertainty Estimation for Deep Neural Networks.

abstract: When the cost of misclassifying a sample is high, it is useful to have an accurate estimate of uncertainty in the prediction for that sample. There are also multiple types of uncertainty which are best estimated in different ways, for example, uncertainty that is intrinsic to the training set may be well-handled by a Bayesian approach, while uncertainty introduced by shifts between training and query distributions may be better-addressed by density/support estimation. In this paper, we examine three types of uncertainty: model capacity uncertainty, intrinsic data uncertainty, and open set uncertainty, and review techniques that have been derived to address each one. We then introduce a unified hierarchical model, which combines methods from Bayesian inference, invertible latent density inference, and discriminative classification in a single end-to-end deep neural network topology to yield efficient per-sample uncertainty estimation. Our approach addresses all three uncertainty types and readily accommodates prior/base rates for binary detection.


title: Survey of reasoning using Neural networks

abstract: Reason and inference require process as well as memory skills by humans. Neural networks are able to process tasks like image recognition (better than humans) but in memory aspects are still limited (by attention mechanism, size). Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve small memory contexts, but as context becomes larger than a threshold, it is difficult to use them. The Solution is to use large external memory. Still, it poses many challenges like, how to train neural networks for discrete memory representation, how to describe long term dependencies in sequential data etc. Most prominent neural architectures for such tasks are Memory networks: inference components combined with long term memory and Neural Turing Machines: neural networks using external memory resources. Also, additional techniques like attention mechanism, end to end gradient descent on discrete memory representation are needed to support these solutions. Preliminary results of above neural architectures on simple algorithms (sorting, copying) and Question Answering (based on story, dialogs) application are comparable with the state of the art. In this paper, I explain these architectures (in general), the additional techniques used and the results of their application.


title: A clustering method for identifying regions of interest in turbulent combustion tensor fields

abstract: Production of electricity and propulsion systems involve turbulent combustion. Computational modeling of turbulent combustion can improve the efficiency of these processes. However, large tensor datasets are the result of such simulations; these datasets are difficult to visualize and analyze. In this work we present an unsupervised statistical approach for the segmentation, visualization and potentially the tracking of regions of interest in large tensor data. The approach employs a machine learning clustering algorithm to locate and identify areas of interest based on specified parameters such as strain tensor value. Evaluation on two combustion datasets shows this approach can assist in the visual analysis of the combustion tensor field.


Best Practices for Machine Learning Applications


title: Discovering Combos in Fighting Games with Evolutionary Algorithms

abstract: In fighting games, players can perform many different actions at each instant of time, leading to an exponential number of possible sequences of actions. Some of these combinations can lead to unexpected behaviors, which can compromise the game design. One example of these unexpected behaviors is the occurrence of long or infinite combos, a long sequence of actions that does not allow any reactions from the opponent. Finding these sequences is essential to ensure fairness in fighting games, but evaluating all possible sequences is a time consuming task. In this paper, we propose the use of an evolutionary algorithm to find combos on a fighting game. The main idea is to use a genetic algorithm to evolve a population composed of sequences of inputs and, using an adequate fitness function, select the ones that are more suitable to be considered combos. We performed a series of experiments and the results show that the proposed approach was not only successful in finding combos, managing to find unexpected sequences, but also superior to previous methods.


title: PixelSNAIL: An Improved Autoregressive Generative Model

abstract: Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a sequence modeling task, where a recurrent neural network (RNN) models the conditional distribution over the next element conditioned on all previous elements. In this paradigm, the bottleneck is the extent to which the RNN can model long-range dependencies, and the most successful approaches rely on causal convolutions, which offer better access to earlier parts of the sequence than conventional RNNs. Taking inspiration from recent work in meta reinforcement learning, where dealing with long-range dependencies is also essential, we introduce a new generative model architecture that combines causal convolutions with self attention. In this note, we describe the resulting model and present state-of-the-art log-likelihood results on CIFAR-10 (2.85 bits per dim) and $32 \times 32$ ImageNet (3.80 bits per dim). Our implementation is available at this https URL


title: DE-RNN: Forecasting the Probability Density Function of Nonlinear Time Series

abstract: Model-free identification of a nonlinear dynamical system from the noisy observations is of current interest due to its direct relevance to many applications in Industry 4.0. Making a prediction of such noisy time series constitutes a problem of learning the nonlinear time evolution of a probability distribution. Capability of most of the conventional time series models is limited when the underlying dynamics is nonlinear, multi-scale or when there is no prior knowledge at all on the system dynamics. We propose DE-RNN (Density Estimation Recurrent Neural Network) to learn the probability density function (PDF) of a stochastic process with an underlying nonlinear dynamics and compute the time evolution of the PDF for a probabilistic forecast. A Recurrent Neural Network (RNN)-based model is employed to learn a nonlinear operator for the temporal evolution of the stochastic process. We use a softmax layer for a numerical discretization of a smooth PDF, which transforms a function approximation problem to a classification task. A regularized cross-entropy method is introduced to impose a smoothness condition on the estimated probability distribution. A Monte Carlo procedure to compute the temporal evolution of the distribution for a multiple-step forecast is presented. It is shown that the proposed algorithm can learn the nonlinear multi-scale dynamics from the noisy observations and provides an effective tool to forecast time evolution of the underlying probability distribution. Evaluation of the algorithm on three synthetic and two real data sets shows advantage over the compared baselines, and a potential value to a wide range of problems in physics and engineering.


title: Deep Structure Learning for Fraud Detection

abstract: Fraud detection is of great importance because fraudulent behaviors may mislead consumers or bring huge losses to enterprises. Due to the lockstep feature of fraudulent behaviors, fraud detection problem can be viewed as finding suspicious dense blocks in the attributed bipartite graph. In reality, existing attribute-based methods are not adversarially robust, because fraudsters can take some camouflage actions to cover their behavior attributes as normal. More importantly, existing structural information based methods only consider shallow topology structure, making their effectiveness sensitive to the density of suspicious blocks. In this paper, we propose a novel deep structure learning model named DeepFD to differentiate normal users and suspicious users. DeepFD can preserve the non-linear graph structure and user behavior information simultaneously. Experimental results on different types of datasets demonstrate that DeepFD outperforms the state-of-the-art baselines.


title: [The effect of Uygur medicine Hyssopus officinalis L on expression of T-bet, GATA-3 and STAT-3 mRNA in lung tissue of asthma rats].

abstract: AIM ::: To study the Uygur medicine Hyssopus officinalis L on T-bet, GATA-3, STAT-3 mRNA levels of asthma rats in order to explore the mechanism of its treatment of asthma. ::: ::: ::: METHODS ::: Rats were randomly divided into normal control group, asthma model group and dexamethasone group and water extract of Hyssopus officinalis L high and low dose group. The rats were sensitized with OVA, Al(OH)(3); and DPT vaccine and then challenged with inhalation of aerosolized OVA solution for Preparation of asthma model and the level of T-bet, GATA-3, STAT-3 mRNA were detected with RT-PCR. ::: ::: ::: RESULTS ::: The normal control group and model group, model group and treatment group, the expression of T-bet, GATA-3 and STAT-3 mRNA in the lung tissue was statistically significant differences(P<0.05). Compared with model group, after treatment of Hyssopus officinalis L the expression of GATA-3 and STAT-3 mRNA of asthma rats significantly reduced (P<0.05), but the expression of T-bet mRNA was significantly higher (P<0.05).The expression of GATA-3 and STAT-3 mRNA of Hyssopus officinalis L high-dose treatment group was lower than the low-dose treatment group (P<0.05), but T-bet mRNA that was higher(P<0.05). The expression of T-bet mRNA has negative correlation with GATA-3 mRNA (r=-0.696), the expression of STAT-3 mRNA has correlation with T-bet mRNA and GATA-3 mRNA(r=-0.767, 0.772), P<0.05. ::: ::: ::: CONCLUSION ::: Hyssopus officinalis L probably regulates the differentiation of Th1, Th2 and Th17 on transcription level to play the role of anti-inflammatory.


title: Performance Tuning of Storage System using Design of Experiments.

abstract: The disclosure describes a cable clamp which will adapt to and hold firmly cables of various diameters. The clamp includes a "U" shaped body member having a base from which parallel legs project. The outer surface of each of the legs carries a row of teeth. A keeper having a flat plate defining two spaced rectangular openings is placed into engagement with the legs. Relative movement between the plate and the legs is inhibited by the teeth engaging the edges of the openings. Carried on the plate and projecting toward the base of the body member are parallel walls which define angled edges. The edges cam a cable placed between the legs firmly against the base and one leg of the body member.


title: Social stigma and self-esteem: The self-protective properties of stigma.

abstract: Although several psychological theories predict that members of stigmatized groups should have low global self-esteem, empirical research typically does not support this prediction. It is proposed here that this discrepancy may be explained by considering the ways in which membership in a stigmatized group may protect the self-concept It is proposed that members of stigmatized groups may (a) attribute negative feedback to prejudice against their group, (b) compare their outcomes with those of the ingroup, rather than with the relatively advantaged outgroup, and (c) selectively devalue those dimensions on which their group fares poorly and value those dimensions on which their group excels. Evidence for each of these processes and their consequences for self-esteem and motivation is reviewed. Factors that moderate the use of these strategies and implications of this analysis for treatment of stigmas are also discussed. For more than three decades, social psychological research on prejudice, stereotyping, and discrimination has examined both the content of stereotypes about a variety of social groups


title: An efficient Wikipedia semantic matching approach to text document classification

abstract: A traditional classification approach based on keyword matching represents each text document as a set of keywords, without considering the semantic information, thereby, reducing the accuracy of classification. To solve this problem, a new classification approach based on Wikipedia matching was proposed, which represents each document as a concept vector in the Wikipedia semantic space so as to understand the text semantics, and has been demonstrated to improve the accuracy of classification. However, the immense Wikipedia semantic space greatly reduces the generation efficiency of a concept vector, resulting in a negative impact on the availability of the approach in an online environment. In this paper, we propose an efficient Wikipedia semantic matching approach to document classification. First, we define several heuristic selection rules to quickly pick out related concepts for a document from the Wikipedia semantic space, making it no longer necessary to match all the concepts in the semantic space, thus greatly improving the generation efficiency of the concept vector. Second, based on the semantic representation of each text document, we compute the similarity between documents so as to accurately classify the documents. Finally, evaluation experiments demonstrate the effectiveness of our approach, i.e., which can improve the classification efficiency of the Wikipedia matching under the precondition of not compromising the classification accuracy.


title: Hypogonadism and pubertal development in Prader-Willi syndrome

abstract: UNLABELLED ::: Genital abnormalities and disorders of pubertal development such as hypogonadism are common in Prader-Willi Syndrome (PWS). Depending on age, PWS patients present genital hypoplasia and delayed or incomplete gonadal maturation. Nevertheless, only a few evaluations have been made of these findings in this syndrome; in the cases previously reported the diagnosis of PWS has often been based only on clinical criteria and not confirmed by genetic analysis. In this paper we describe both external genital findings and spontaneous pubertal development in 84 patients aged from 2.1 to 35.4 (42 males, 42 females) affected by PWS. Diagnosis was made using the Holm and Cassidy criteria and was confirmed by genetic analysis (methylation test and/or FISH). We evaluated the presence of cryptorchidism, scrotal development, length of penis and volume of testis in males and outlook of labia minora and/or clitoris, age of menarche and features of menses (when present) in females; in both sexes we also evaluated the onset of puberty. All recruited males showed cryptorchidism, which was bilateral in 36 out of 42 patients (86%); 38 patients (90%) underwent orchidopexy. Small testes and scrotal hypoplasia were present in 76% and 69% of cases, respectively. In 76% of females, hypoplasia or absence of labia minora and/or clitoris was described. Spontaneous menarche occurred only in 14/32 cases (44%) over the age of 15 years, but menstrual cycles were often a periodical vaginal spotting. Primary amenorrhea was diagnosed in 56% of cases. Isolated premature pubarche was present in six males and in six females (14% of cases) while one male and two females were affected by precocious puberty (3.6%). ::: ::: ::: CONCLUSION ::: Hypogonadism represents a common clinical feature in PWS, confirming the importance of such a major diagnostic criterion. Cryptorchidism was consistently present in all our cases. Patients with PWS commonly fail to spontaneously complete puberty, although some patients may have early pubarche or, more rarely, precocious puberty. In older subjects, hormonal replacement therapy is not always necessary and it must be reserved for selected patients.


Artificial Neural Network in Medicine


title: A Tissue-Channel Transcutaneous Power Transfer Technique for Implantable Devices

abstract: For implantable devices, miniaturization is a key design consideration, which facilitates keyhole surgery and relieves the surgical pain. Wireless power transfer is a commonly used way for implants, and a high power transfer efficiency (PTE) could help to minimize or even remove the battery inside the human body. Conventionally, the required power is delivered transcutaneously to a medical implant by inductive coupling or capacitive coupling, but the PTE is extremely low when targeting at a very small implant through a thick tissue layer. In this paper, we propose a new technique by utilizing the body tissue as the power transfer channel, where a pair of medical electrodes are attached on the body surface to supply power to a miniaturized implant with a differential input. The PTE is remarkably improved by the current loop through the body tissue without violating the IEEE specific absorption rate standard. Measurement results show that the tissue-channel technique achieves 0.39% PTE for a 1-mm-sized implant locating 5 cm deep inside the tissue.


title: Vehicle Object Detection in Remote Sensing Imagery Based on Multi-Perspective Convolutional Neural Network

abstract: Most traditional object detection approaches have a deficiency of features, slow detection speed, and high false-alarm rate. To solve these problems, we propose a multi-perspective convolutional neural network (Multi-PerNet) to extract remote sensing imagery features. Regions with CNN features (R-CNN) is a milestone in applying CNN method to object detection. With the help of the great feature extraction and classification performance of CNN, the transformation of object detection problem is realized by the Region Proposal method. Multi-PerNet trains a vehicle object detection model in remote sensing imagery based on Faster R-CNN. During model training, sample images and the labels are inputs, and the output is a detection model. First, Multi-PerNet extracts the feature map. Meanwhile, the area distribution and object-area aspect ratio in the sample images are obtained by k-means clustering. Then, the Faster R-CNN region proposal network generates the candidate windows based on the k-means clustering results. Features of candidate windows can be obtained by mapping candidate windows to the feature map. Finally, the candidate window and its features are inputted to the classifier to be trained to obtain the detection model. Experiment results show that the Multi-PerNet model detection accuracy is improved by 10.1% compared with the model obtained by ZF-net and 1.6% compared with the model obtained by PVANet. Moreover, the model size is reduced by 21.3%.


title: Certifying and Removing Disparate Impact

abstract: What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender) and an explicit description of the process. When computers are involved, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the process, we propose making inferences based on the data it uses. We present four contributions. First, we link disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on how well the protected class can be predicted from the other attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.


title: Automated administration of the Wolf Motor Function Test for post-stroke assessment

abstract: The advent of new health sensing technologies has presented us with the opportunity to gain richer data from patients undergoing clinical interventions. Such technologies are particularly suited for applications requiring temporal accuracy. The Wolf Motor Function Test (WMFT) is one such application. This assessment is an instrument used to determine functional ability of the paretic and non-paretic limbs in individuals post-stroke. It consists of 17 tasks, 15 of which are scored according to both time and a functional ability scale. We propose a technique that uses wearable sensors and performance sensors to estimate the timing of seven of these tasks. We have developed a sensing framework and an algorithm to automatically detect total movement time. We have validated the system's accuracy on the seven selected WMFT tasks. We also suggest how this framework can be adapted to the remaining tasks.


title: Direct sGC activation bypasses NO scavenging reactions of intravascular free oxy-hemoglobin and limits vasoconstriction.

abstract: Abstract Aims: Hemoglobin-based oxygen carriers (HBOC) provide a potential alternative to red blood cell (RBC) transfusion. Their clinical application has been limited by adverse effects, in large part thought to be mediated by the intravascular scavenging of the vasodilator nitric oxide (NO) by cell-free plasma oxy-hemoglobin. Free hemoglobin may also cause endothelial dysfunction and platelet activation in hemolytic diseases and after transfusion of aged stored RBCs. The new soluble guanylate cyclase (sGC) stimulator Bay 41-8543 and sGC activator Bay 60-2770 directly modulate sGC, independent of NO bioavailability, providing a potential therapeutic mechanism to bypass hemoglobin-mediated NO inactivation. Results: Infusions of human hemoglobin solutions and the HBOC Oxyglobin into rats produced a severe hypertensive response, even at low plasma heme concentrations approaching 10 μM. These reactions were only observed for ferrous oxy-hemoglobin and not analogs that do not rapidly scavenge NO. Infusions of...


End-Stage Glaucoma in Stevens-Johnson Syndrome


title: Security and privacy grand challenges for the Internet of Things

abstract: The growth of the Internet of Things (IoT) is driven by market pressures, and while security is being considered, the relationship between the unintended consequences of billions of such devices connecting to the Internet cannot be described with existing mathematical methods. The possibilities for unintended surveillance through lifestyle analysis, unauthorized access to information, and new attack vectors will continue to increase by 2020, when up to 50 billion devices may be connected. This paper discusses various kinds of vulnerabilities that can be expected to arise, and presents a research agenda for mitigating the worst of the impacts. We hope to draw research attention to the potential dangers of IoT so that many of these problems can be avoided.


title: An emotional analysis method based on heart rate variability

abstract: In this paper, we propose a method to evaluate human's emotion and stress based on heart rate variability (HRV). Firstly, experiment scheme has been designed to induce 4 kinds of emotions and the corresponding electrocardiogram (ECG) changes have been measured in a laboratory setting; Secondly, an improved fast denoising method based on wavelet transform threshold denoising was proposed to process the noisy ECG signal, then we realize automatic extraction of HRV sequences; Finally, a wide range of physiological features from various analysis domains, including time, frequency, nonlinear analysis is proposed in order to find the best emotion-relevant features and to correlate them with emotional states, some important conclusions have been obtained.


title: Active calibration target for bistatic radar cross-section measurements: Calibration Target for Bistatic RCS

abstract: Either passive calibration targets are expensive and complex to manufacture or their bistatic radar cross section (RCS) levels are significantly lower than the monostatic RCS levels of targets such as spheres, dihedral, and trihedral corner reflectors. In this paper the performance of an active calibration target with relative high bistatic RCS values is illustrated as a reference target for bistatic RCS measurements. The reference target is simple to manufacture, operates over a wide frequency range, and can be configured to calibrate all four polarizations (VV, HH, HV, and VH). Bistatic RCS measurements of canonical targets, performed in a controlled environment, are calibrated with the reference target and the results are compared to simulated results using FEKO.


title: Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach

abstract: Correct detection and classification of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is of pivotal importance for an automatic external defibrillator and patient monitoring. In this paper, a VF/VT classification algorithm using a machine learning method, a support vector machine, is proposed. A total of 14 metrics were extracted from a specific window length of the electrocardiogram (ECG). A genetic algorithm was then used to select the optimal variable combinations. Three annotated public domain ECG databases (the American Heart Association Database, the Creighton University Ventricular Tachyarrhythmia Database, and the MIT-BIH Malignant Ventricular Arrhythmia Database) were used as training, test, and validation datasets. Different window sizes, varying from 1 to 10 s were tested. An accuracy (Ac) of 98.1%, sensitivity (Se) of 98.4%, and specificity (Sp) of 98.0% were obtained on the in-sample training data with 5 s-window size and two selected metrics. On the out-of-sample validation data, an Ac of 96.3% ± 3.4%, Se of 96.2% ± 2.7%, and Sp of 96.2% ± 4.6% were obtained by fivefold cross validation. The results surpass those of current reported methods.


title: Design and implementation of improved security protocols for DHCP using digital certificates

abstract: DHCP protocol is used to allocate network configuration parameters to each computer in the network. However, there are a number of security issues related to DHCP operation, especially MAC address spoofing. Moreover, existing research still cannot provide sufficient security level. In this paper, we introduce two security enhancement techniques of DHCP protocol for DHCP clients based on digital certificates. Moreover, the proposed technique works well with DHCP clients without digital certificates. The security analysis is provided to guarantee security of the proposed protocols. Furthermore, we implement a prototype of the proposed protocols to show that the proposed protocols are practical.


Guidelines for Calculating Effect Sizes for Practice-Based Research Syntheses


title: Inverse kinematics and design of a novel 6-DoF handheld robot arm

abstract: We present a novel 6-DoF cable driven manipulator for handheld robotic tasks. Based on a coupled tendon approach, the arm is optimized to maximize movement speed and configuration space while reducing the total mass of the arm. We propose a space carving approach to design optimal link geometry maximizing structural strength and joint limits while minimizing link mass. The design improves on similar non-handheld tendon-driven manipulators and reduces the required number of actuators to one per DoF. As the manipulator has one redundant joint, we present a 5-DoF inverse kinematics solution for the end effector pose. The inverse kinematics is solved by splitting the 6-DoF problem into two coupled 3-DoF problems and merging their results. A method for gracefully degrading the output of the inverse kinematics is described for cases where the desired end effector pose is outside the configuration space. This is useful for settings where the user is in the control loop and can help the robot to get closer to the desired location. The design of the handheld robot is offered as open source. While our results and tools are aimed at handheld robotics, the design and approach is useful to non-handheld applications.


title: Distinguishing Historical from Current Problems in Clinical Reports – Which Textual Features Help?

abstract: Determining whether a condition is historical or recent is important for accurate results in biomedicine. In this paper, we investigate four types of information found in clinical text that might be used to make this distinction. We conducted a descriptive, exploratory study using annotation on clinical reports to determine whether this temporal information is useful for classifying conditions as historical or recent. Our initial results suggest that few of these feature values can be used to predict temporal classification.


title: Battery state-of-health estimation for mobile devices

abstract: Insufficient support of electric current sensing on commodity mobile devices leads to inaccurate estimation of their battery's state-of-health (SoH), which, in turn, shuts them off unexpectedly and accelerates their battery fading. In this paper, we design V-BASH, a new battery SoH estimation method based only on their voltages and is compatible to commodity mobile devices. V-BASH is inspired by the physical phenomenon that the relaxing battery voltages correlate to battery SoH. Moreover, it is enabled on mobile devices with a common usage pattern of most users frequently taking a long time to charge their devices. The design of V-BASH is guided by 2, 781 empirically collected relaxing voltage traces with 19 mobile device batteries. We evaluate V-BASH using both laboratory experiments and field tests on mobile devices, showing a


title: Transition-Based Syntactic Linearization

abstract: Syntactic linearization algorithms take a bag of input words and a set of optional constraints, and construct an output sentence and its syntactic derivation simultaneously. The search problem is NP-hard, and the current best results are achieved by bottom-up bestfirst search. One drawback of the method is low efficiency; and there is no theoretical guarantee that a full sentence can be found within bounded time. We propose an alternative algorithm that constructs output structures from left to right using beam-search. The algorithm is based on incremental parsing algorithms. We extend the transition system so that word ordering is performed in addition to syntactic parsing, resulting in a linearization system that runs in guaranteed quadratic time. In standard evaluations, our system runs an order of magnitude faster than a state-of-the-art baseline using best-first search, with improved accuracies.


title: A Case for A Collaborative Query Management System

abstract: Over the past 40 years, database management systems (DBMSs) have evolved to provide a sophisticated variety of data management capabilities. At the same time, tools for managing queries over the data have remained relatively primitive. One reason for this is that queries are typically issued through applications. They are thus debugged once and re-used repeatedly. This mode of interaction, however, is changing. As scientists (and others) store and share increasingly large volumes of data in data centers, they need the ability to analyze the data by issuing exploratory queries. In this paper, we argue that, in these new settings, data management systems must provide powerful query management capabilities, from query browsing to automatic query recommendations. We first discuss the requirements for a collaborative query management system. We outline an early system architecture and discuss the many research challenges associated with building such an engine.


Surgical Simulation Training Systems: Box Trainers, Virtual Reality and Augmented Reality Simulators


title: Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP

abstract: The standard and real-time communication technology is an unalloyed inevitability for the development of Internet of Things (IoT) applications. However, the selection of a standard and effective messaging protocol is a challenging and daunting task for any organisation because it depends on the nature of the IoT system and its messaging requirements. Copious messaging protocols have been developed and employed by various organisations based on their requirements in the last two decades. Though, none of them is able to support all messaging requirements of all types of IoT systems. Messaging protocol is an ongoing dilemma for the IoT industry; consequently, it is important to understand the pros and cons of the widely accepted and emerging messaging protocols for IoT systems to determine their best-fit scenarios. Therefore, this paper presents an evaluation of the four established messaging protocols MQTT, CoAP, AMQP and HTTP for IoT systems. Firstly, it presents the broad comparison among these messaging protocols to introduce their characteristics comparatively. Afterwards, it performs a further in-depth and relative analysis based on some interrelated criteria to gain insight into their strengths and limitations. Thus, based on this detailed evaluation, the user can decide their appropriate usage in various IoT systems according to their requirements and suitability.


Bayesian Averaging of Classifiers and the Overfitting Problem


title: Towards Topic-to-Question Generation

abstract: This paper is concerned with automatic generation of all possible questions from a topic of interest. Specifically, we consider that each topic is associated with a body of texts containing useful information about the topic. Then, questions are generated by exploiting the named entity information and the predicate argument structures of the sentences present in the body of texts. The importance of the generated questions is measured using Latent Dirichlet Allocation by identifying the subtopics which are closely related to the original topic in the given body of texts and applying the Extended String Subsequence Kernel to calculate their similarity with the questions. We also propose the use of syntactic tree kernels for the automatic judgment of the syntactic correctness of the questions. The questions are ranked by considering both their importance in the context of the given body of texts and syntactic correctness. To the best of our knowledge, no previous study has accomplished this task in our setting. A series of experiments demonstrate that the proposed topic-to-question generation approach can significantly outperform the state-of-the-art results.


title: A Linear Algebraic Approach to Datalog Evaluation

abstract: In this paper, we propose a fundamentally new approach to Datalog evaluation. Given a linear Datalog program DB written using N constants and binary predicates, we first translate if-and-only-if completions of clauses in DB into a set Eq(DB) of matrix equations with a non-linear operation where relations in M_DB, the least Herbrand model of DB, are encoded as adjacency matrices. We then translate Eq(DB) into another, but purely linear matrix equations tilde_Eq(DB). It is proved that the least solution of tilde_Eq(DB) in the sense of matrix ordering is converted to the least solution of Eq(DB) and the latter gives M_DB as a set of adjacency matrices. Hence computing the least solution of tilde_Eq(DB) is equivalent to computing M_DB specified by DB. For a class of tail recursive programs and for some other types of programs, our approach achieves O(N^3) time complexity irrespective of the number of variables in a clause since only matrix operations costing O(N^3) or less are used. ::: We conducted two experiments that compute the least Herbrand models of linear Datalog programs. The first experiment computes transitive closure of artificial data and real network data taken from the Koblenz Network Collection. The second one compared the proposed approach with the state-of-the-art symbolic systems including two Prolog systems and two ASP systems, in terms of computation time for a transitive closure program and the same generation program. In the experiment, it is observed that our linear algebraic approach runs 10^1 ~ 10^4 times faster than the symbolic systems when data is not sparse. To appear in Theory and Practice of Logic Programming (TPLP).


A Short History of Modern Dentistry


title: Context augmented Dynamic Bayesian Networks for event recognition

abstract: This paper proposes a new Probabilistic Graphical Model (PGM) to incorporate the scene, event object interaction, and the event temporal contexts into Dynamic Bayesian Networks (DBNs) for event recognition in surveillance videos. We first construct the baseline event DBNs for modeling the events from their own appearance and kinematic observations, and then augment the DBN with contexts to improve its event recognition performance. Unlike the existing context methods, our model incorporates various contexts simultaneously into one unified model. Experiments on real scene surveillance datasets with complex backgrounds show that the contexts can effectively improve the event recognition performance even under great challenges like large intra-class variations and low image resolution.


title: State-of-the-Art in Stock Prediction Techniques

abstract: Stock markets are affected by many uncertainties and interrelated economic and political factors at both local and global levels. The key to successful stock market forecasting is achieving best results with minimum required input data. To determine the set of relevant factors for making accurate predictions is a complicated task and so regular stock market analysis is very essential. More specifically, the stock market’s movements are analyzed and predicted in order to retrieve knowledge that could guide investors on when to buy and sell. It will also help the investor to make money through his investment in the stock market. This paper surveys large number of resources from research papers, web-sources, company reports and other available sources.


title: Railway accidents analysis based on the improved algorithm of the maximal information coefficient

abstract: The maximal information coefficient (MIC), a measure of dependence for two-variable relationships, can be used to discover the relationships between two variables in big data. This paper proposes a new mathematical program model for calculating the value of MIC. A corresponding efficient algorithm is designed to solve the model in big data environment. In order to illustrate the validity of the proposed algorithm, the proposed algorithm is applied into the analysis of railway accidents data. Experimental results show that the proposed algorithm could find important relationships between two variables from big data. And some factors influencing accidents are identified from many factors. In addition, compared with the algorithm proposed by Reshef et al. in 2011, the proposed algorithm has lower time complexity and needs less computation time. Hence the proposed algorithm is more suitable for big data environment.


title: Asymmetric Actor Critic for Image-Based Robot Learning

abstract: Deep reinforcement learning (RL) has proven a powerful technique in many sequential decision making domains. However, Robotics poses many challenges for RL, most notably training on a physical system can be expensive and dangerous, which has sparked significant interest in learning control policies using a physics simulator. While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator. In this work, we exploit the full state observability in the simulator to train better policies which take as input only partial observations (RGBD images). We do this by employing an actor-critic training algorithm in which the critic is trained on full states while the actor (or policy) gets rendered images as input. We show experimentally on a range of simulated tasks that using these asymmetric inputs significantly improves performance. Finally, we combine this method with domain randomization and show real robot experiments for several tasks like picking, pushing, and moving a block. We achieve this simulation to real world transfer without training on any real world data.


title: Optimization of a GCaMP Calcium Indicator for Neural Activity Imaging

abstract: Genetically encoded calcium indicators (GECIs) are powerful tools for systems neuroscience. Recent efforts in protein engineering have significantly increased the performance of GECIs. The state-of-the art single-wavelength GECI, GCaMP3, has been deployed in a number of model organisms and can reliably detect three or more action potentials in short bursts in several systems in vivo . Through protein structure determination, targeted mutagenesis, high-throughput screening, and a battery of in vitro assays, we have increased the dynamic range of GCaMP3 by severalfold, creating a family of “GCaMP5” sensors. We tested GCaMP5s in several systems: cultured neurons and astrocytes, mouse retina, and in vivo in Caenorhabditis chemosensory neurons, Drosophila larval neuromuscular junction and adult antennal lobe, zebrafish retina and tectum, and mouse visual cortex. Signal-to-noise ratio was improved by at least 2- to 3-fold. In the visual cortex, two GCaMP5 variants detected twice as many visual stimulus-responsive cells as GCaMP3. By combining in vivo imaging with electrophysiology we show that GCaMP5 fluorescence provides a more reliable measure of neuronal activity than its predecessor GCaMP3. GCaMP5 allows more sensitive detection of neural activity in vivo and may find widespread applications for cellular imaging in general.


title: Energy-Aware Algorithms for IP Over WDM Optical Networks

abstract: To date, the main limitation to the development of telecom networks mainly depended on two factors, namely the cost of the network equipment and transmission. However, nowadays, the enormous spread and growth of the Internet has resulted in a significant increase in the energy consumption of the network equipment. Particularly, the rise in Internet traffic has led to both the increase of the amount of required equipment and the augmentation of energy consumption of such network devices. Consequently, CapEx and OpEx of modern telecommunication networks render the market penetration of such deployment laborious. This surge in power consumption may ultimately lead to an impediment to this continued growth. Since climate change and environmental protection are also of rising concern, many research efforts are being made to raise awareness of environmental issues and reduce the energy footprint of telecommunication networks. In this context, this study targets the reduction of the energy consumption of an IP over WDM backbone network. Already known lightpath bypass and traffic grooming strategies have been used. Although previously proposed schemes based on lightpath bypass approaches and traffic grooming capabilities have been implemented and consequently are able to reduce the overall power consumption of an optical core network, they do not bear in mind, first, the number of the established lightpaths in the virtual topology and second, the exploitation of the residual capacity of the existing lightpaths. This study overcomes the above drawbacks by introducing three novel heuristic schemes which manage to significantly lower the number of the established lightpaths in the virtual topology and take advantage of the remaining capacity of the optical paths by exploiting the lightpaths’ resources in a more efficient way. These power-aware heuristics will focus on energy efficiency issues related to the planning and the operation phase of a WDM optical network. Extensive simulation results indicate a promising performance improvement in terms of power consumption when these proposed heuristics are compared to similar, already developed, techniques for energy saving.


title: Understanding Data Heterogeneity in the Context of Cyber-Physical Systems Integration

abstract: The current gradual adoption of the Industry 4.0 is the research trend that includes more intensive utilization of cyber-physical systems (CPSs). The computerization of manufacturing will bring many advantages but it is needed to face the heterogeneity problem during an integration of various CPSs for enabling this progress. In this paper, we describe various types of heterogeneity with emphasis to a semantic heterogeneity. The CPSs integration problem is classified into two different challenges. Next, we introduce the approach and the implementation of the semantic heterogeneity reduction with the focus on using Semantic Web technologies for a data integration. Then, the Big Data approach is described for facilitating the implementation. Finally, the possible solution is demonstrated on our proposed semantic Big Data historian.


title: Computer-Aided Diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network

abstract: Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In this paper, a comprehensive computer model is presented for the diagnosis of PD based on motor, non-motor, and neuroimaging features using the recently-developed enhanced probabilistic neural network (EPNN). The model is tested for differentiating PD patients from those with scans without evidence of dopaminergic deficit (SWEDDs) using the Parkinson's Progression Markers Initiative (PPMI) database, an observational, multi-center study designed to identify PD biomarkers for diagnosis and disease progression. The results are compared to four other commonly-used machine learning algorithms: the probabilistic neural network (PNN), support vector machine (SVM), k-nearest neighbors (k-NN) algorithm, and classification tree (CT). The EPNN had the highest classification accuracy at 92.5 % followed by the PNN (91.6 %), k-NN (90.8 %) and CT (90.2 %). The EPNN exhibited an accuracy of 98.6 % when classifying healthy control (HC) versus PD, higher than any previous studies.


title: An extended set of Haar-like features for rapid object detection

abstract: Recently Viola et al. [2001] have introduced a rapid object detection. scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce a novel set of rotated Haar-like features. These novel features significantly enrich the simple features of Viola et al. and can also be calculated efficiently. With these new rotated features our sample face detector shows off on average a 10% lower false alarm rate at a given hit rate. We also present a novel post optimization procedure for a given boosted cascade improving on average the false alarm rate further by 12.5%.


title: Techniques for diced cartilage with deep temporalis fascia graft.

abstract: Diced cartilage with deep temporalis fascia (DC-F) graft has become a popular technique for reconstruction of the nasal dorsum. Cartilage can be obtained from the septum, ear, or costal cartilage when employing the DC-F technique. The complications seen with DC-F grafts tend to occur early in the surgeon's implementation of this technique. Management of the complications varies depending on the severity of the problem. This article gives an overview of both the technique and the complications commonly encountered.


title: NIFTY: a system for large scale information flow tracking and clustering

abstract: The real-time information on news sites, blogs and social networking sites changes dynamically and spreads rapidly through the Web. Developing methods for handling such information at a massive scale requires that we think about how information content varies over time, how it is transmitted, and how it mutates as it spreads. We describe the News Information Flow Tracking, Yay! (NIFTY) system for large scale real-time tracking of "memes" - short textual phrases that travel and mutate through the Web. NIFTY is based on a novel highly-scalable incremental meme-clustering algorithm that efficiently extracts and identifies mutational variants of a single meme. NIFTY runs orders of magnitude faster than our previous Memetracker system, while also maintaining better consistency and quality of extracted memes. We demonstrate the effectiveness of our approach by processing a 20 terabyte dataset of 6.1 billion blog posts and news articles that we have been continuously collecting for the last four years. NIFTY extracted 2.9 billion unique textual phrases and identified more than 9 million memes. Our meme-tracking algorithm was able to process the entire dataset in less than five days using a single machine. Furthermore, we also provide a live deployment of the NIFTY system that allows users to explore the dynamics of online news in near real-time.


title: Clustering with Multiple Graphs

abstract: In graph-based learning models, entities are often represented as vertices in an undirected graph with weighted edges describing the relationships between entities. In many real-world applications, however, entities are often associated with relations of different types and/or from different sources, which can be well captured by multiple undirected graphs over the same set of vertices. How to exploit such multiple sources of information to make better inferences on entities remains an interesting open problem. In this paper, we focus on the problem of clustering the vertices based on multiple graphs in both unsupervised and semi-supervised settings. As one of our contributions, we propose Linked Matrix Factorization (LMF) as a novel way of fusing information from multiple graph sources. In LMF, each graph is approximated by matrix factorization with a graph-specific factor and a factor common to all graphs, where the common factor provides features for all vertices. Experiments on SIAM journal data show that (1) we can improve the clustering accuracy through fusing multiple sources of information with several models, and (2) LMF yields superior or competitive results compared to other graph-based clustering methods.


title: Robust Smooth Feature Extraction from Point Clouds

abstract: Defining sharp features in a given 3D model facilitates a better understanding of the surface and aids visualizations, reverse engineering, filtering, simplification, non-photo realism, reconstruction and other geometric processing applications. We present a robust method that identifies sharp features in a point cloud by returning a set of smooth curves aligned along the edges. Our feature extraction is a multi-step refinement method that leverages the concept of robust moving least squares to locally fit surfaces to potential features. Using Newton's method, we project points to the intersections of multiple surfaces then grow polylines through the projected cloud. After resolving gaps, connecting corners, and relaxing the results, the algorithm returns a set of complete and smooth curves that define the features. We demonstrate the benefits of our method with two applications: surface meshing and point-based geometry compression.


title: Simple statistical gradient-following algorithms for connectionist reinforcement learning

abstract: This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units. These algorithms, called REINFORCE algorithms, are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reinforcement tasks, and they do this without explicitly computing gradient estimates or even storing information from which such estimates could be computed. Specific examples of such algorithms are presented, some of which bear a close relationship to certain existing algorithms while others are novel but potentially interesting in their own right. Also given are results that show how such algorithms can be naturally integrated with backpropagation. We close with a brief discussion of a number of additional issues surrounding the use of such algorithms, including what is known about their limiting behaviors as well as further considerations that might be used to help develop similar but potentially more powerful reinforcement learning algorithms.


title: The influence of head motion on intrinsic functional connectivity MRI

abstract: Functional connectivity MRI (fcMRI) has been widely applied to explore group and individual differences. A confounding factor is head motion. Children move more than adults, older adults more than younger adults, and patients more than controls. Head motion varies considerably among individuals within the same population. Here we explored the influence of head motion on fcMRI estimates. Mean head displacement, maximum head displacement, the number of micro movements (> 0.1 mm), and head rotation were estimated in 1000 healthy, young adult subjects each scanned for two resting-state runs on matched 3T scanners. The majority of fcMRI variation across subjects was not linked to estimated head motion. However, head motion had significant, systematic effects on fcMRI network measures. Head motion was associated with decreased functional coupling in the default and frontoparietal control networks – two networks characterized by coupling among distributed regions of association cortex. Other network measures increased with motion including estimates of local functional coupling and coupling between left and right motor regions – a region pair sometimes used as a control in studies to establish specificity. Comparisons between groups of individuals with subtly different levels of head motion yielded difference maps that could be mistaken for neuronal effects in other contexts. These effects are important to consider when interpreting variation between groups and across individuals.


title: Sequential latent Dirichlet allocation

abstract: Understanding how topics within a document evolve over the structure of the document is an interesting and potentially important problem in exploratory and predictive text analytics. In this article, we address this problem by presenting a novel variant of latent Dirichlet allocation (LDA): Sequential LDA (SeqLDA). This variant directly considers the underlying sequential structure, i.e. a document consists of multiple segments (e.g. chapters, paragraphs), each of which is correlated to its antecedent and subsequent segments. Such progressive sequential dependency is captured by using the hierarchical two-parameter Poisson–Dirichlet process (HPDP). We develop an efficient collapsed Gibbs sampling algorithm to sample from the posterior of the SeqLDA based on the HPDP. Our experimental results on patent documents show that by considering the sequential structure within a document, our SeqLDA model has a higher fidelity over LDA in terms of perplexity (a standard measure of dictionary-based compressibility). The SeqLDA model also yields a nicer sequential topic structure than LDA, as we show in experiments on several books such as Melville’s ‘Moby Dick’.


title: FactoMineR: an R package for multivariate analysis

abstract: In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and finally supplementary information (supplementary individuals and variables). Moreover, the dimensions issued from the different exploratory data analyses can be automatically described by quantitative and/or categorical variables. Numerous graphics are also available with various options. Finally, a graphical user interface is implemented within the Rcmdr environment in order to propose an user friendly package.


title: Vertebroplasty and kyphoplasty: a comparative review of efficacy and adverse events

abstract: Vertebroplasty and kyphoplasty have become common surgical techniques for the treatment of vertebral compression fractures. Vertebroplasty involves the percutaneous injection of bone cement into the cancellous bone of a vertebral body with the goals of pain alleviation and preventing further loss of vertebral body height. Kyphoplasty utilizes an inflatable balloon to create a cavity for the cement with the additional potential goals of restoring height and reducing kyphosis. Vertebroplasty and kyphoplasty are effective treatment options for the reduction of pain associated with vertebral body compression fractures. Biomechanical studies demonstrate that kyphoplasty is initially superior for increasing vertebral body height and reducing kyphosis, but these gains are lost with repetitive loading. Complications secondary to extravasation of cement include compression of neural elements and venous embolism. These complications are rare but more common with vertebroplasty. Vertebroplasty and kyphoplasty are both safe and effective procedures for the treatment of vertebral body compression fractures.


title: An Exploration of Word Embedding Initialization in Deep-Learning Tasks

abstract: Word embeddings are the interface between the world of discrete units of text processing and the continuous, differentiable world of neural networks. In this work, we examine various random and pretrained initialization methods for embeddings used in deep networks and their effect on the performance on four NLP tasks with both recurrent and convolutional architectures. We confirm that pretrained embeddings are a little better than random initialization, especially considering the speed of learning. On the other hand, we do not see any significant difference between various methods of random initialization, as long as the variance is kept reasonably low. High-variance initialization prevents the network to use the space of embeddings and forces it to use other free parameters to accomplish the task. We support this hypothesis by observing the performance in learning lexical relations and by the fact that the network can learn to perform reasonably in its task even with fixed random embeddings.