nils-herrmann/clean_scirepeval huggingface dataset:
Arabic Textual Entailment with Word Embeddings

Determining the textual entailment between texts is important in many NLP tasks, such as summarization, question answering, and information extraction and retrieval. Various methods have been suggested based on external knowledge sources; however, such resources are not always available in all languages and their acquisition is typically laborious and very costly. Distributional word representations such as word embeddings learned over large corpora have been shown to capture syntactic and semantic word relationships. Such models have contributed to improving the performance of several NLP tasks. In this paper, we address the problem of textual entailment in Arabic. We employ both traditional features and distributional representations. Crucially, we do not depend on any external resources in the process. Our suggested approach yields state of the art performance on a standard data set, ArbTE, achieving an accuracy of 76.2 % compared to current state of the art of 69.3 %.
Task-Relevant Object Discovery and Categorization for Playing First-person Shooter Games

We consider the problem of learning to play first-person shooter (FPS) video games using raw screen images as observations and keyboard inputs as actions. The high-dimensionality of the observations in this type of applications leads to prohibitive needs of training data for model-free methods, such as the deep Q-network (DQN), and its recurrent variant DRQN. Thus, recent works focused on learning low-dimensional representations that may reduce the need for data. This paper presents a new and efficient method for learning such representations. Salient segments of consecutive frames are detected from their optical flow, and clustered based on their feature descriptors. The clusters typically correspond to different discovered categories of objects. Segments detected in new frames are then classified based on their nearest clusters. Because only a few categories are relevant to a given task, the importance of a category is defined as the correlation between its occurrence and the agent’s performance. The result is encoded as a vector indicating objects that are in the frame and their locations, and used as a side input to DRQN. Experiments on the game Doom provide a good evidence for the benefit of this approach.
iMAP-CampUS: Developing an Intelligent Mobile Augmented Reality Program on Campus as a Ubiquitous System

Augmented Reality (AR) offers a combination of physical and virtual objects, drawing on the strengths of each. Therefore, it is different from virtual reality since it permits users to sight the real world enhanced with virtual objects. Thus, AR technology holds potential to support users with valuable information about the surrounding area. In this paper, we demonstrate the development of an AR app that provides the students with rich information about the buildings nearby. The application, which we name "iMAP-CampUS", is designed to assist students at Macquarie University in locating places of interest close to them by moving the camera of the device in all probable directions to overlay information of places around them. The iMAP-CampUS has been developed for both iOS and Android platforms and run on smartphones and tablets with different screen sizes.
Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach

Abstruct-Most applications of hyperspectral imagery require processing techniques which achieve two fundamental goals: 1) detect and classify the constituent materials for each pixel in the scene; 2) reduce the data volumeldimensionality, without loss of critical information, so that it can be processed efficiently and assimilated by a human analyst. In this paper, we describe a technique which simultaneously reduces the data dimensionality, suppresses undesired or interfering spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel vector onto a subspace which is orthogonal to the undesired signatures. This operation is an optimal interference suppression process in the least squares sense. Once the interfering signatures have been nulled, projecting the residual onto the signature of interest maximizes the signal-to-noise ratio and results in a single component image that represents a classification for the signature of interest. The orthogonal subspace projection (OSP) operator can be extended to k signatures of interest, thus reducing the dimensionality of k and classifying the hyperspectral image simultaneously. The approach is applicable to both spectrally pure as well as mixed pixels.
Experimental Assessment of Private Information Disclosure in LTE Mobile Networks

Open source software running on SDR (Software Defined Radio) devices now allow building a full-fledged mobile network at low cost. These novel tools open up for exciting possibilities to analyse and verify by experiments the behaviour of existing and emerging mobile networks in new lab environments, for instance at universities. We use SDR equipment and open source software to analyse the feasibility of disclosing private information that is sent over the LTE access network. We verify by experiments that subscriber identity information can be obtained both passively, by listening on the radio link, and actively, by running considerable low detectable rogue base stations to impersonate the commercial network. Moreover, we implement a downgrade attack (to non-LTE networks) with minimal changes to the open source software.
EARLY DEVELOPMENTS OF A PARALLELLY ACTUATED HUMANOID , SAFFIR

This paper presents the design of our new 33 degree of freedom full size humanoid robot, SAFFiR (Shipboard Autonomous Fire Fighting Robot). The goal of this research project is to realize a high performance mixed force and position controlled robot with parallel actuation. The robot has two 6 DOF legs and arms, a waist, neck, and 3 DOF hands/fingers. The design is characterized by a central lightweight skeleton actuated with modular ballscrew driven force controllable linear actuators arranged in a parallel fashion around the joints. Sensory feedback on board the robot includes an inertial measurement unit, force and position output of each actuator, as well as 6 axis force/torque measurements from the feet. The lower body of the robot has been fabricated and a rudimentary walking algorithm implemented while the upper body fabrication is completed. Preliminary walking experiments show that parallel actuation successfully minimizes the loads through individual actuators.
Flexible Tactile Sensors Using Screen-Printed P(VDF-TrFE) and MWCNT/PDMS Composites

This paper presents and compares two different types of screen-printed flexible and conformable pressure sensors arrays. In both variants, the flexible pressure sensors are in the form of segmental arrays of parallel plate structure-sandwiching the piezoelectric polymer polyvinylidene fluoride trifluoroethylene [P(VDF-TrFE)] between two printed metal layers of silver (Ag) in one case and the piezoresistive [multiwall carbon nanotube (MWCNT) mixed with poly(dimethylsiloxane (PDMS)] layer in the other. Each sensor module consists of 4 × 4 sensors array with 1-mm × 1-mm sensitive area of each sensor. The screen-printed piezoelectric sensors array exploits the change in polarization level of P(VDF-TrFE) to detect dynamic tactile parameter such as contact force. Similarly, the piezoresistive sensors array exploits the change in resistance of the bulk printed layer of MWCNT/PDMS composite. The two variants are compared on the basis of fabrication by printing on plastic substrate, ease of processing and handling of the materials, compatibility of the dissimilar materials in multilayers structure, adhesion, and finally according to the response to the normal compressive forces. The foldable pressure sensors arrays are completely realized using screen-printing technology and are targeted toward realizing low-cost electronic skin.
Illuminating cell signalling with optogenetic tools

The light-based control of ion channels has been transformative for the neurosciences, but the optogenetic toolkit does not stop there. An expanding number of proteins and cellular functions have been shown to be controlled by light, and the practical considerations in deciding between reversible optogenetic systems (such as systems that use light-oxygen-voltage domains, phytochrome proteins, cryptochrome proteins and the fluorescent protein Dronpa) are well defined. The field is moving beyond proof of concept to answering real biological questions, such as how cell signalling is regulated in space and time, that were difficult or impossible to address with previous tools.
Which phoneme-to-viseme maps best improve visual-only computer lip-reading?

A critical assumption of all current visual speech recognition systems is that there are visual speech units called visemes which can be mapped to units of acoustic speech, the phonemes. Despite there being a number of published maps it is infrequent to see the effectiveness of these tested, particularly on visual-only lip-reading (many works use audio-visual speech). Here we examine 120 mappings and consider if any are stable across talkers. We show a method for devising maps based on phoneme confusions from an automated lip-reading system, and we present new mappings that show improvements for individual talkers.
A Compact Dual-Band Antenna Enabled by a Complementary Split-Ring Resonator-Loaded Metasurface

In this paper, the design of a compact dual-band antenna with operational bands centered at 1.9 and 2.5 GHz is introduced and validated numerically as well as experimentally. The proposed antenna is comprised by placing a monopole feeding antenna above an engineered multilayer metasurface (MS) which functions as an artificial ground plane. Moreover, complementary split-ring resonator structures are incorporated into the multilayer MS design to realize the dual-band operation of the proposed monopole-MS integrated antenna system. Dispersive analysis of the MS unit cell reveals the underlying radiation mechanism of the antenna. It is observed that the designed MS resonates in both frequency bands with a resonance mode pattern resembling that of the <inline-formula> <tex-math notation="LaTeX">$TM_{10}$ </tex-math></inline-formula> mode of a rectangular patch antenna, thereby yielding a broadside radiation pattern in both bands. The overall footprint of the antenna is only <inline-formula> <tex-math notation="LaTeX">$\mathrm {0.062}{\lambda }_{0}^{2}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">${\lambda }_{0}$ </tex-math></inline-formula> is the free space wavelength at 1.9 GHz. Importantly, by using the proposed artificial ground plane, even with a highly miniaturized form factor, the antenna still maintains a peak gain of above 5 dBi and a front-to-back ratio higher than 6 dB within the two operational bands.
Learning Pixel-Level Semantic Affinity with Image-Level Supervision for Weakly Supervised Semantic Segmentation

The deficiency of segmentation labels is one of the main obstacles to semantic segmentation in the wild. To alleviate this issue, we present a novel framework that generates segmentation labels of images given their image-level class labels. In this weakly supervised setting, trained models have been known to segment local discriminative parts rather than the entire object area. Our solution is to propagate such local responses to nearby areas which belong to the same semantic entity. To this end, we propose a Deep Neural Network (DNN) called AffinityNet that predicts semantic affinity between a pair of adjacent image coordinates. The semantic propagation is then realized by random walk with the affinities predicted by AffinityNet. More importantly, the supervision employed to train AffinityNet is given by the initial discriminative part segmentation, which is incomplete as a segmentation annotation but sufficient for learning semantic affinities within small image areas. Thus the entire framework relies only on image-level class labels and does not require any extra data or annotations. On the PASCAL VOC 2012 dataset, a DNN learned with segmentation labels generated by our method outperforms previous models trained with the same level of supervision, and is even as competitive as those relying on stronger supervision.
BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations

The presented work aims at generating a systematically annotated corpus that can support the enhancement of sentiment analysis tasks in Telugu using wordlevel sentiment annotations. From , we extracted 11,000 adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by language experts. We discuss the methodology followed for the polarity annotations and validate the developed resource. This work aims at developing a benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a model where lexeme annotations are applied for sentiment predictions. The fundamental aim of this paper is to validate and study the possibility of utilizing machine learning algorithms, word-level sentiment annotations in the task of automated sentiment identification. Furthermore, accuracy is improved by annotating the bi-grams extracted from the target corpus.
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff

Lossy compression algorithms are typically designed and analyzed through the lens of Shannon’s rate-distortion theory, where the goal is to achieve the lowest possible distortion (e.g., low MSE or high SSIM) at any given bit rate. However, in recent years, it has become increasingly accepted that “low distortion” is not a synonym for “high perceptual quality”, and in fact optimization of one often comes at the expense of the other. In light of this understanding, it is natural to seek for a generalization of rate-distortion theory which takes perceptual quality into account. In this paper, we adopt the mathematical definition of perceptual quality recently proposed by Blau & Michaeli (2018), and use it to study the three-way tradeoff between rate, distortion, and perception. We show that restricting the perceptual quality to be high, generally leads to an elevation of the rate-distortion curve, thus necessitating a sacrifice in either rate or distortion. We prove several fundamental properties of this triple-tradeoff, calculate it in closed form for a Bernoulli source, and illustrate it visually on a toy MNIST example.
An anthropomorphic hand exoskeleton to prevent astronaut hand fatigue during extravehicular activities

This correspondence presents a prototype of a powered hand exoskeleton that is designed to fit over the gloved hand of an astronaut and offset the stiffness of the pressurized space suit. This will keep the productive time spent in extravehicular activity from being constrained by hand fatigue. The exoskeleton has a three-finger design, the third and fourth fingers being combined to lighten and simplify the assembly. The motions of the hand are monitored by an array of pressure sensors mounted between the exoskeleton and the hand. Controller commands are determined by a state-of-the-art programmable microcontroller using pressure sensor input. These commands are applied to a PWM driven dc motor array which provides the motive power to move the exoskeleton fingers. The resultant motion of the exoskeleton allows the astronaut to perform both precision grasping tasks with the thumb and forefinger, as well as a power grasp with the entire hand.
PuppetGAN: Transferring Disentangled Properties from Synthetic to Real Images

In this work we propose a model that enables controlled manipulation of visual attributes of real “target” images (e.g. lighting, expression or pose) using only implicit supervision with synthetic “source” exemplars. Specifically, our model learns a shared low-dimensional representation of input images from both domains in which a property of interest is isolated from other content features of the input. By using triplets of synthetic images that demonstrate modification of the visual property that we would like to control (for example mouth opening) we are able to perform disentanglement of image representations with respect to this property without using explicit attribute labels in either domain. Since our technique relies on triplets instead of explicit labels, it can be applied to shape, texture, lighting, or other properties that are difficult to measure or represent as explicit conditioners. We quantitatively analyze the degree to which trained models learn to isolate the property of interest from other content features with a proof-of-concept digit dataset and demonstrate results in a far more difficult setting, learning to manipulate real faces using a synthetic 3D faces dataset. We also explore limitations of our model with respect to differences in distributions of properties observed in two domains.
lncDML: Identification of long non-coding RNAs by Deep Metric Learning

The next-generation sequencing technologies provide a great deal of transcripts for bioinformatics research. Specially, because of the regulation of long non-coding RNAs (lncRNAs) in various cellular processes, the research on IncRNAs is in full swing. And the solution of IncRNAs identification is the basis for the in-depth study of its functions. In this study, we present an approach to identify the IncRNAs from large scale transcripts, named IncDML which is completely different from previous identification methods. In our model, we extract signal to noise ratio (SNR) and k-mer from transcripts sequences as features. Firstly, we just use the SNR to cluster the original dataset to three parts. In this process, we achieve preliminary identification effect to some extent. Then abandoning traditional feature selection, we directly measure the relationship between each pair of samples by deep metric learning for each part of data. Finally, a novel classifier based on complex network is applied to achieve the final identification. The experiment results show that IncDML is a very effective method for identifying IncRNAs.
The sounds of silence: language, cognition, and anxiety in selective mutism.

To determine whether oral language, working memory, and social anxiety differentiate children with selective mutism (SM), children with anxiety disorders (ANX), and normal controls (NCs) and explore predictors of mutism severity.



Children ages 6 to 10 years with SM (n = 44) were compared with children with ANX (n = 28) and NCs (n = 19) of similar age on standardized measures of language, nonverbal working memory, and social anxiety. Variables correlating with mutism severity were entered in stepwise regressions to determine predictors of mute behavior in SM.



Children with SM scored significantly lower on standardized language measures than children with ANX and NCs and showed greater visual memory deficits and social anxiety relative to these two groups. Age and receptive grammar ability predicted less severe mutism, whereas social anxiety predicted more severe mutism. These factors accounted for 38% of the variance in mutism severity.



Social anxiety and language deficits are evident in SM, may predict mutism severity, and should be evaluated in clinical assessment. Replication is indicated, as are further studies of cognition and of intervention in SM, using large, diverse samples.
Pomegranate juice, total pomegranate ellagitannins, and punicalagin suppress inflammatory cell signaling in colon cancer cells.

Phytochemicals from fruits such as the pomegranate (Punica granatum L) may inhibit cancer cell proliferation and apoptosis through the modulation of cellular transcription factors and signaling proteins. In previous studies, pomegranate juice (PJ) and its ellagitannins inhibited proliferation and induced apoptosis in HT-29 colon cancer cells. The present study examined the effects of PJ on inflammatory cell signaling proteins in the HT-29 human colon cancer cell line. At a concentration of 50 mg/L PJ significantly suppressed TNFalpha-induced COX-2 protein expression by 79% (SE = 0.042), total pomegranate tannin extract (TPT) 55% (SE = 0.049), and punicalagin 48% (SE = 0.022). Additionally, PJ reduced phosphorylation of the p65 subunit and binding to the NFkappaB response element 6.4-fold. TPT suppressed NFkappaB binding 10-fold, punicalagin 3.6-fold, whereas ellagic acid (EA) (another pomegranate polyphenol) was ineffective. PJ also abolished TNFalpha-induced AKT activation, needed for NFkappaB activity. Therefore, the polyphenolic phytochemicals in the pomegranate can play an important role in the modulation of inflammatory cell signaling in colon cancer cells.
FLAP: An End-to-End Event Log Analysis Platform for System Management

Many systems, such as distributed operating systems, complex networks, and high throughput web-based applications, are continuously generating large volume of event logs. These logs contain useful information to help system administrators to understand the system running status and to pinpoint the system failures. Generally, due to the scale and complexity of modern systems, the generated logs are beyond the analytic power of human beings. Therefore, it is imperative to develop a comprehensive log analysis system to support effective system management. Although a number of log mining techniques have been proposed to address specific log analysis use cases, few research and industrial efforts have been paid on providing integrated systems with an end-to-end solution to facilitate the log analysis routines.
 In this paper, we design and implement an integrated system, called FIU Log Analysis Platform (a.k.a. FLAP), that aims to facilitate the data analytics for system event logs. FLAP provides an end-to-end solution that utilizes advanced data mining techniques to assist log analysts to conveniently, timely, and accurately conduct event log knowledge discovery, system status investigation, and system failure diagnosis. Specifically, in FLAP, state-of-the-art template learning techniques are used to extract useful information from unstructured raw logs; advanced data transformation techniques are proposed and leveraged for event transformation and storage; effective event pattern mining, event summarization, event querying, and failure prediction techniques are designed and integrated for log analytics; and user-friendly interfaces are utilized to present the informative analysis results intuitively and vividly. Since 2016, FLAP has been used by Huawei Technologies Co. Ltd for internal event log analysis, and has provided effective support in its system operation and workflow optimization.
Distributed Computation for Cooperative Control

A cooperative control system consists of multiple, autonomous components interacting to control their environment. Examples include air traffic control systems, automated factories, robot soccer teams and sensor/actuator networks. Designing such systems requires a combination of tools from control theory and distributed systems. In this article, we review some of these tools and then focus on the Computation and Control Language, CCL, which we have developed as a modeling tool and a programming language for cooperative control systems.