nils-herrmann/clean_scirepeval huggingface dataset:
Autonomous Indoor Robot Navigation Using Sketched Maps and Routes

Hand drawn sketches are natural means by which a high level description of an environment can be provided. They can be exploited to impart coarse prior information about the scene to a robot, thereby enabling it to perform autonomous navigation and exploration when a full metrical description of the scene is not available beforehand. In this paper, we present a navigation system supplemented by a tablet interface that allows a user to sketch a rough map of an indoor environment and a desired trajectory for the robot to follow. We propose a novel theoretical framework for sketch interpretation based upon the manifold formalism in which associations between the sketch and the real world are modeled as local deformation of a suitable metric manifold. We also present empirical results from experimental evaluations of our approach in real world scenarios both from the perspective of the navigation capability and the usability of the interface.
Establishing pairwise keys in distributed sensor networks

Pairwise key establishment is a fundamental security service in sensor networks; it enables sensor nodes to communicate securely with each other using cryptographic techniques. However, due to the resource constraints on sensors, it is infeasible to use traditional key management techniques such as public key cryptography and key distribution center (KDC). To facilitate the study of novel pairwise key predistribution techniques, this paper presents a general framework for establishing pairwise keys between sensors on the basis of a polynomial-based key predistribution protocol . This paper then presents two efficient instantiations of the general framework: a random subset assignment key predistribution scheme and a grid-based key predistribution scheme. The analysis in this paper indicates that these two schemes have a number of nice properties, including high probability (or guarantee) to establish pairwise keys, tolerance of node captures, and low communication overhead. Finally, this paper presents a technique to reduce the computation at sensors required by these schemes.
A Low-Complexity Pedestrian Detection Framework for Smart Video Surveillance Systems

Pedestrian detection is a key problem in computer vision and is currently addressed with increasingly complex solutions involving compute-intensive features and classification schemes. In this scope, histogram of oriented gradients (HOG) in conjunction with linear support vector machine (SVM) classifier is considered to be the single most discriminative feature that has been adopted as a stand-alone detector as well as a key instrument in advance systems involving hybrid features and cascaded detectors. In this paper, we propose a pedestrian detection framework that is computationally less expensive as well as more accurate than HOG-linear SVM. The proposed scheme exploits the discriminating power of the locally significant gradients in building orientation histograms without involving complex floating point operations while computing the feature. The integer-only feature allows the use of powerful histogram inter-section kernel SVM classifier in a fast lookup-table-based implementation. Resultantly, the proposed framework achieves at least 3% more accurate detection results than HOG on standard data sets while being 1.8 and 2.6 times faster on conventional desktop PC and embedded ARM platforms, respectively, for a single scale pedestrian detection on VGA resolution video. In addition, hardware implementation on Altera Cyclone IV field-programmable gate array results in more than 40% savings in logic resources compared with its HOG-linear SVM competitor. Hence, the proposed feature and classification setup is shown to be a better candidate as the single most discriminative pedestrian detector than the currently accepted HOG-linear SVM.
Credit Assignment Techniques in Stochastic Computation Graphs

Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and reinforcement learning. Previous work has shown that an unbiased estimator of the gradient of the expected loss of SCGs can be derived from a single principle. However, this estimator often has high variance and requires a full model evaluation per data point, making this algorithm costly in large graphs. In this work, we address these problems by generalizing concepts from the reinforcement learning literature. We introduce the concepts of value functions, baselines and critics for arbitrary SCGs, and show how to use them to derive lower-variance gradient estimates from partial model evaluations, paving the way towards general and efficient credit assignment for gradient-based optimization. In doing so, we demonstrate how our results unify recent advances in the probabilistic inference and reinforcement learning literature.
Improving deep neural network design with new text data representations

Using traditional machine learning approaches, there is no single feature engineering solution for all text mining and learning tasks. Thus, researchers must determine and implement the best feature engineering approach for each text classification task; however, deep learning allows us to skip this step by extracting and learning high-level features automatically from low-level text representations. Convolutional neural networks, a popular type of neural network for deep learning, have been shown to be effective at performing feature extraction and classification for many domains including text. Recently, it was demonstrated that convolutional neural networks can be used to train classifiers from character-level representations of text. This approach achieved superior performance compared to classifiers trained on word-level text representations, likely due to the use of character-level representations preserving more information from the data. Training neural networks from character level data requires a large volume of instances; however, the large volume of training data and model complexity makes training these networks a slow and computationally expensive task. In this paper, we propose a new method of creating character-level representations of text to reduce the computational costs associated with training a deep convolutional neural network. We demonstrate that our method of character embedding greatly reduces training time and memory use, while significantly improving classification performance. Additionally, we show that our proposed embedding can be used with padded convolutional layers to enable the use of current convolutional network architectures, while still facilitating faster training and higher performance than the previous approach for learning from character-level text.
Low-Profile Two-Degree-of-Freedom Wrist Exoskeleton Device Using Multiple Spring Blades

Robotically assisted rehabilitation therapy, which is therapy performed using a robotic device that guides or assists motion and in some cases triggered by a biological signal (such as a myoelectric signal or brain signal) is effective in recovering motor function following impairment. In the field of rehabilitation robots and human support robot, exoskeleton robots have been widely studied both for upper and lower limbs. Applying the exoskeleton to a wrist joint remains challenging because the wrist has two degrees of freedom in a structure smaller than the structures of other upper and lower limb joints and the center of rotation moves according to motion owing to the complex anatomical structure. To tackle this problem, we developed a wrist exoskeleton mechanism that consists of two elastic elements that largely deform during motion in transmitting forces from two linear actuators implemented on the user's forearm and in transforming the forces to wrist flexion/extension and adduction/abduction. The advantages of the presented device are the device's compactness, low weight, and inherent flexibility due to the elastic structure. The present letter presents an overview of the newly developed wrist exoskeleton mechanism, prototype implementation, and preliminary mechanical evaluations. The technical verification of the exoskeleton revealed the feasibility of generating forces that can generate wrist flexion/extension and adduction/abduction movements.
The cognitive activation theory of stress

This paper presents a cognitive activation theory of stress (CATS), with a formal system of systematic definitions. The term "stress" is used for four aspects of "stress", stress stimuli, stress experience, the non-specific, general stress response, and experience of the stress response. These four meanings may be measured separately. The stress response is a general alarm in a homeostatic system, producing general and unspecific neurophysiological activation from one level of arousal to more arousal. The stress response occurs whenever there is something missing, for instance a homeostatic imbalance, or a threat to homeostasis and life of the organism. Formally, the alarm occurs when there is a discrepancy between what should be and what is-between the value a variable should have (set value (SV)), and the real value (actual value (AV)) of the same variable. The stress response, therefore, is an essential and necessary physiological response. The unpleasantness of the alarm is no health threat. However, if sustained, the response may lead to illness and disease through established pathophysiological processes ("allostatic load"). The alarm elicits specific behaviors to cope with the situation. The level of alarm depends on expectancy of the outcome of stimuli and the specific responses available for coping. Psychological defense is defined as a distortion of stimulus expectancies. Response outcome expectancies are defined as positive, negative, or none, to the available responses. This offers formal definitions of coping, hopelessness, and helplessness that are easy to operationalize in man and in animals. It is an essential element of CATS that only when coping is defined as positive outcome expectancy does the concept predict relations to health and disease.
Antibiotic resistance is ancient

The discovery of antibiotics more than 70 years ago initiated a period of drug innovation and implementation in human and animal health and agriculture. These discoveries were tempered in all cases by the emergence of resistant microbes. This history has been interpreted to mean that antibiotic resistance in pathogenic bacteria is a modern phenomenon; this view is reinforced by the fact that collections of microbes that predate the antibiotic era are highly susceptible to antibiotics. Here we report targeted metagenomic analyses of rigorously authenticated ancient DNA from 30,000-year-old Beringian permafrost sediments and the identification of a highly diverse collection of genes encoding resistance to β-lactam, tetracycline and glycopeptide antibiotics. Structure and function studies on the complete vancomycin resistance element VanA confirmed its similarity to modern variants. These results show conclusively that antibiotic resistance is a natural phenomenon that predates the modern selective pressure of clinical antibiotic use.
A 29–30GHz 64-element Active Phased array for 5G Application

A 64-element 29–30GHz active phased array for 5G millimeter wave applications is presented in this paper. The proposed phased array composites of 64-element antennas, 64-chan-nel T/R modules, 4 frequency conversion links, beam controlling circuitry, power management circuits and cooling fans, and are integrated in a in a very compact size(135mmX 77mmX56mm). Hybrid integration of GaAs and Si circuits are employed to achieve better RF performance. The architecture of the proposed phased array and the detail design of the T/R modules and antennas are analyzed. By the OTA (over the air) measurement, the proposed phased array achieves a bandwidth of 1 GHz at the center frequency of 29.5GHz, and the azimuth beam-width is 12 deg with the scanning range of ±45deg. With the excitation of 800MHz 64QAM signals, the transmitter beam achieves a EVM of 5.5%, ACLR of −30.5dBc with the PA working at −10dB back off, and the measured saturated EIRP is 63 dBm.
A multiagent reinforcement learning algorithm using extended optimal response

Stochastic games provides a theoretical framework to multiagent reinforcement learning. Based on the framework, a multiagent reinforcement learning algorithm for zero-sum stochastic games was proposed by Littman and it was extended to general-sum games by Hu and Wellman. Given a stochastic game, if all agents learn with their algorithm, we can expect that the policies of the agents converge to a Nash equilibrium. However, agents with their algorithm always try to converge to a Nash equilibrium independent of the policies used by the other agents. In addition, in case there are multiple Nash equilibria, agents must agree on the equilibrium where they want to reach. Thus, their algorithm lacks adaptability in a sense. In this paper, we propose a multiagent reinforcement learning algorithm. The algorithm uses the extended optimal response which we introduce in this paper. It will converge to a Nash equilibrium when other agents are adaptable, otherwise it will make an optimal response. We also provide some empirical results in three simple stochastic games, which show that the algorithm can realize what we intend.
Side-Channel Attack Standard Evaluation Board SASEBO-W for Smartcard Testing

Side-channel Attack Standard Evaluation BOards (SASEBOs) were developed as uniform evaluation platforms for research purposes. Cryptographic hardware, and control circuits for side-channel experimentation were also developed, and these platforms have been distributed to the government, the industry and academic research labs in order to facilitate research and development directed toward the establishment of an international standard for side-channel attacks. With standardization, testing environments are required to support security evaluation of side-channel attacks. In order to accelerate the adoption of the standard, we developed a new evaluation board named SASEBO-W for testing and evaluating smartcards, which are the most widespread cryptographic modules. The board supports ISO/IEC 7816-3 electrical interface smartcards and provides finegrained control and trigger features with a control circuit implemented on an FPGA. A smartcard OS and cryptographic software for the ATMega 163 card were also developed. In this paper, the features of a variety of SASEBOs and side-channel attack tools are explained, and the analysis results for the power consumption and the electromagnetic radiation of a smartcard as obtained experimentally with SASEBO-W are presented.
Stay on the Wikipedia task: When task-related disagreements slip into personal and procedural conflicts

In Wikipedia, volunteers collaboratively author encyclopedic entries, and therefore managing conflict is a key factor in group success. Behavioral research describes three conflict types: task-related, affective, and process. Affective and process conflict have been consistently found to impede group performance; however, the effect of task conflict is inconsistent. We propose that these inconclusive results are due to under-specification of the task conflict construct, and focus on the transition phase where task-related disagreements escalate into affective and process conflict. We define these transitional phases as distinct constructs: task-affective and task-process conflict, and develop a theoretical model that explains how the various task-related conflict constructs, together with the composition of the wiki editor group, determine the quality of the collaboratively-authored wiki article. Our empirical study of 96 Wikipedia articles involved multiple data-collection methods, including the analysis of Wikipedia system logs, manual content analysis of article's discussion pages, and a comprehensive assessment of articles' quality using the Delphi method. Our results show that when group members' disagreements – originally task-related – escalate into personal attacks or hinge on procedure, these disagreements impede group performance. Implications for research and practice are discussed.
Low power LVDS transmitter with low common mode variation for 1GB/s-per pin operation

This paper presents the design of a low voltage differential signalling (LVDS) transmitter intended using a LCD panel interface and is capable of transmitting 1GB/s per pin data rate. The transmitter is fully LVDS standard compatible which is achieved by employing a common mode feedback circuit to meet all Ac and DC specifications over PVT variation, specifically due to the implementation of feedback circuit, the common mode variation is made very less quiescent current of 5.5mA. Further, the implementation incorporates the idea of sharing of bias blocks among different transmitters in full chip resulting in less active area. The transmitter is implemented in 3.3V, 0.35/spl mu/ CMOS technology. In this implementation the active area (excluding ESD) per transmitter is 0.039 mm square. The initial silicon data (output offset voltage of 1.20V and differential voltage of 320mV) at room temperature shows the consistency of the simulation results.
A 10Gb/s 4.1mW 2-IIR + 1-discrete-tap DFE in 28nm-LP CMOS

A half-rate decision feedback equalizer (DFE) with two infinite impulse response (IIR) filters and one discrete-time tap is presented. The two IIR filters have different time constants to cancel the long tail of the pulse response. The discrete-tap cancels the first post-cursor inter-symbol interference term. The system can operate with a low transmit swing of 150mVpp-diff and 24 dB channel loss at the Nyquist frequency while consuming 4.1mW at 10 Gb/s. The receiver, including the DFE, clock buffers and clock phase adjustment, occupies an area of 8,760 μm2 and was fabricated in an ST 28nm LP CMOS process.
A Study on Data Mining Frameworks in Cyber Security

Data mining (DM) in cyber security is the process of posing queries and extracting patterns. In this research we focus on a variety of techniques, approaches and different areas of data mining frameworks in Cyber Security from different perspectives, in order to establish a classification and categorization that reflect the important criteria of the data mining frameworks in the state of art. Establishing such a classification impacts deeply on guiding data mining applications towards better operations and performance accordingly Moreover we discuss various types of cyber attacks and how data mining can help in detection and prevention of these attacks. Information security violations such as access control violations as well as a discussion of various threats are presented. Finally we present a comparative analysis between a set of selected frameworks.  Data Mining Techniques; Data Mining Frameworks; Cyber Security, Information Security
Neural Machine Translation Incorporating Named Entity

This study proposes a new neural machine translation (NMT) model based on the encoderdecoder model that incorporates named entity (NE) tags of source-language sentences. Conventional NMT models have two problems enumerated as follows: (i) they tend to have difficulty in translating words with multiple meanings because of the high ambiguity, and (ii) these models’ ability to translate compound words seems challenging because the encoder receives a word, a part of the compound word, at each time step. To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word. Furthermore, the encoder introduces a chunk-level LSTM layer over a word-level LSTM layer and hierarchically encodes a source-language sentence to capture a compound NE as a chunk on the basis of the NE tags. We evaluate the proposed model on an English-to-Japanese translation task with the ASPEC, and English-to-Bulgarian and English-to-Romanian translation tasks with the Europarl corpus. The evaluation results show that the proposed model achieves up to 3.11 point improvement in BLEU.
A Comparative Study of Stereovision Algorithms

Stereo vision has been and continues to be one of the most researched domains of computer vision, having many applications, among them, allowing the depth extraction of a scene. This paper provides a comparative study of stereo vision and matching algorithms, used to solve the correspondence problem. The study of matching algorithms was followed by experiments on the Middlebury benchmarks. The tests focused on a comparison of 6 stereovision methods. In order to assess the performance, RMS and some statistics related were computed. In order to emphasize the advantages of each stereo algorithm considered, two-frame methods have been employed, both local and global. The experiments conducted have shown that the best results are obtained by Graph Cuts. Unfortunately, this has a higher computational cost. If high quality is not an issue in applications, local methods provide reasonable results within a much lower time-frame and offer the possibility of parallel
Social disconnectedness, perceived isolation, and health among older adults.

Previous research has identified a wide range of indicators of social isolation that pose health risks, including living alone, having a small social network, infrequent participation in social activities, and feelings of loneliness. However multiple forms of isolation are rarely studied together making it difficult to determine which aspects of isolation are most deleterious for health. Using population-based data from the National Social Life, Health, and Aging Project, we combine multiple indicators of social isolation into scales assessing social disconnectedness (e.g., small social network, infrequent participation in social activities) and perceived isolation (e.g., loneliness, perceived lack of social support). We examine the extent to which social disconnectedness and perceived isolation have distinct associations with physical and mental health among older adults. Results indicate that social disconnectedness and perceived isolation are independently associated with lower levels of self-rated physical health. However, the association between disconnectedness and mental health may operate through the strong relationship between perceived isolation and mental health. We conclude that health researchers need to consider social disconnectedness and perceived isolation simultaneously.
Mining Solution in R by

We present the package RcmdrPlugin.temis, a graphical user interface for user-friendly text mining in R. Built as a plug-in to the R Commander provided by the Rcmdr package, it brings together several existing packages and provides new features streamlining the process of importing, managing and analyzing a corpus, in addition to saving results and plots to a report file. Beyond common file formats, automated import of corpora from the Dow Jones Factiva content provider and Twitter is supported. Featured analyses include vocabulary and dissimilarity tables, terms frequencies, terms specific of levels of a variable, term co-occurrences, time series, correspondence analysis and hierarchical clustering.