nils-herrmann/clean_scirepeval huggingface dataset:
User Interfaces for All: New Perspectives into Human- Computer Interaction

This chapter introduces the notion of User Interfaces for All, elaborates on the motivating rationale and examines its key implications on Human-Computer Interaction. The underlying vision of User Interfaces for All is to provide an approach for the development of computational environments catering for the broadest possible range of human abilities, skills, requirements and preferences. Consequently, User Interfaces for All should not be conceived as an effort to advance a single solution for everybody. Instead, it is a new perspective into Human-Computer Interaction, seeking to unfold and reveal challenges and insights, and to instrument appropriate solutions for alleviating the current obstacles to the access and use of advanced information technologies by the widest possible end-user population.
Reasoning about knowledge

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. To my mother Maxine, who gave me a love of learning; to Susan, who is as happy and amazed as I am that The Book is finally completed; to Josh, Tim, and Teddy, who are impressed that their father is an Author; and to my late father George, who would have been proud. To Gale, for putting up with this over the years; to David and Sara, for sometimes letting Daddy do his work; and to my mother Eva, to whom I can finally say " It's done! ". To Pam, who listened for years to my promises that the book is 90% done; to Aaron, who, I hope, will read this book; to my parents, Zipporah and Pinkhas, who taught me to think; and to my grandparents, who perished in the Holocaust.. iaè knk aè kn yi-m ` , e ` xe ehiad " Behold and see , if there be any sorrow like unto my sorrow. " M. Y. V .
Traffic signal timing via deep reinforcement learning

In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed.
Speech activity detection for NASA apollo space missions: challenges and solutions

Speech Activity Detection(SAD) is a well researched problem for communication, command and control applications, where audio segments are short duration and solution proposed for noisy as well as clean environments. In this study, we investigate the SAD problem using NASA’s Apollo space mission data . Unlike traditional speech corpora, the audio recordings in Apollo are extensive from a longitudinal perspective (i.e., 612 days each). From SAD perspective, the data offers many challenges: (i) noise distortion with variable SNR, (ii) channel distortion, and (iii) extended periods of non-speech activity. Here, we use the recently proposed Combo-SAD, which has performed remarkably well in DARPA RATS evaluations, as our baseline system . Our analysis reveals that the ComboSAD performs well when speech-pause durations are balanced in the audio segment, but deteriorates significantly when speech is sparse or absent. In order to mitigate this problem, we propose a simple yet efficient technique which builds an alternative model of speech using data from a separate corpora, and embeds this new information within the Combo-SAD framework. Our experiments show that the proposed approach has a major impact on SAD performance (i.e., +30% absolute), especially in audio segments that contain sparse or no speech information.
A free simulation experiment to examine the effects of social commerce website quality and customer psychological empowerment on customers ’ satisfaction

This research was designed to theoretically address and empirically examine research issues related to customer’s satisfaction with social commerce. To investigate these research issues, data were collected using a written survey as part of a free simulation experiment. In this experiment, 136 participants were asked to evaluate two social commerce websites using an instrument designed to measure relationships between s-commerce website quality, customer psychological empowerment and customer satisfaction. A total of 278 usable s-commerce site evaluations were collected and analyzed. The results showed that customer satisfaction with social commerce is correlated with social commerce sites quality and customer psychological empowerment.
AI Safety Gridworlds

We present a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. These problems include safe interruptibility, avoiding side effects, absent supervisor, reward gaming, safe exploration, as well as robustness to self-modification, distributional shift, and adversaries. To measure compliance with the intended safe behavior, we equip each environment with a performance function that is hidden from the agent. This allows us to categorize AI safety problems into robustness and specification problems, depending on whether the performance function corresponds to the observed reward function. We evaluate A2C and Rainbow, two recent deep reinforcement learning agents, on our environments and show that they are not able to solve them satisfactorily.
A review of KDD99 dataset usage in intrusion detection and machine learning between 2010 and 2015

Although KDD99 dataset is more than 15 years old, it is still widely used in academic research. To investigate wide usage of this dataset in Machine Learning Research (MLR) and Intrusion Detection Systems (IDS); this study reviews 149 research articles from 65 journals indexed in Science Citation Index Expanded and Emerging Sources Citation Index during the last six years (2010–2015). If we include papers presented in other indexes and conferences, number of studies would be tripled. The number of published studies shows that KDD99 is the most used dataset in IDS and machine learning areas, and it is the de facto dataset for these research areas. To show recent usage of KDD99 and the related sub-dataset (NSL-KDD) in IDS and MLR, the following descriptive statistics about the reviewed studies are given: main contribution of articles, the applied algorithms, compared classification algorithms, software toolbox usage, the size and type of the used dataset for training and testing, and classification output classes (binary, multi-class). In addition to these statistics, a checklist for future researchers that work in this area is provided.
Design and Analysis of Star–Delta Hybrid Windings for High-Voltage Induction Motors

In this paper, a Y-Δ hybrid connection for a high-voltage induction motor is described. Low winding harmonic content is achieved by careful consideration of the interaction between the Y- and Δ-connected three-phase winding sets so that the magnetomotive force (MMF) in the air gap is close to sinusoid. Essentially, the two winding sets operate in a six-phase mode. This paper goes on to verify that the fundamental distribution coefficient for the stator MMF is enhanced compared to a standard three-phase winding set. The design method for converting a conventional double-layer lap winding in a high-voltage induction motor into a Y-Δ hybrid lap winding is described using standard winding theory as often applied to small- and medium-sized motors. The main parameters addressed when designing the winding are the conductor wire gauge, coil turns, and parallel winding branches in the Y and Δ connections. A winding design scheme for a 1250-kW 6-kV induction motor is put forward and experimentally validated; the results show that the efficiency can be raised effectively without increasing the cost.
Imaging human EEG dynamics using independent component analysis

This review discusses the theory and practical application of independent component analysis (ICA) to multi-channel EEG data. We use examples from an audiovisual attention-shifting task performed by young and old subjects to illustrate the power of ICA to resolve subtle differences between evoked responses in the two age groups. Preliminary analysis of these data using ICA suggests a loss of task specificity in independent component (IC) processes in frontal and somatomotor cortex during post-response periods in older as compared to younger subjects, trends not detected during examination of scalp-channel event-related potential (ERP) averages. We discuss possible approaches to component clustering across subjects and new ways to visualize mean and trial-by-trial variations in the data, including ERP-image plots of dynamics within and across trials as well as plots of event-related spectral perturbations in component power, phase locking, and coherence. We believe that widespread application of these and related analysis methods should bring EEG once again to the forefront of brain imaging, merging its high time and frequency resolution with enhanced cm-scale spatial resolution of its cortical sources.
A multi-lane MIPI CSI receiver for mobile camera applications

In this paper, we present a multi-lane Mobile Industry Processor Interface (MIPI) Camera Serial Interface (CSI) receiver which is suitable for high-resolution camera and High-Density (HD) video applications. The presented receiver is designed to achieve parallel processing so that it reduces dynamic power consumption for multi-lane configuration. In order to facilitate parallel processing, we propose two techniques for the multi-lane receiver. The presented design receives data at the maximum rate of 4Gb/s and is verified in a standard CMOS technology.
Management of Car Parking System Using Wireless Sensor Network

To cope with the ever growing problem of traffic management and parking management this paper proposes an advance solution for managing and monitoring free parking space and automated guidance for user to park the car. It aims at implementing smarter and better parking guidance mechanism which reduces significantly vehicle travel time and parking time. In this system all the Infrared sensor nodes (IR sensor) sense the status of the car space and accordingly transfer the information to the AVR controller. Accordingly AVR sensor sense the status of car parking space and displays the information on the LED screen for the user, thereby reducing the time for the driver to find vacant empty space and almost reduce the chances of entering into the unusual space which might lead into the traffic jam.
A Conceptual Model of Personalized Pricing Recommender System Based on Customer Online Behavior

Recommender systems in the last decade opened new interactive channels between buyers and sellers leading to new concepts involved in the marketing strategies and remarkable positive gains in online sales. Businesses intensively aim to maintain customer loyalty, satisfaction and retention; such strategic longterm values need to be addressed by recommender systems in a more tangible and deeper manner. The reason behind the considerable growth of recommender systems is for tracking and analyzing the buyer behavior on the one to one basis to present items on the web that meet his preference, which is the core concept of personalization. Personalization is always related to the relationship between item and user leaving out the contextual information about this relationship. User's buying decision is not only affected by the presented item, but also influenced by its price and the context in which the item is presented, such as time or place. Recently, new system has been designed based on the concept of utilizing price personalization in the recommendation process. This system is newly coined as personalized pricing recommender system (PPRS). We propose personalized pricing recommender system with a novel approach of calculating consumer online real value to determine dynamically his personalized discount, which can be generically applied on the normal price of any recommend item through its predefined discount rules.
Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi

Tracking human vital signs of breathing and heart rates during sleep is important as it can help to assess the general physical health of a person and provide useful clues for diagnosing possible diseases. Traditional approaches (e.g., Polysomnography (PSG)) are limited to clinic usage. Recent radio frequency (RF) based approaches require specialized devices or dedicated wireless sensors and are only able to track breathing rate. In this work, we propose to track the vital signs of both breathing rate and heart rate during sleep by using off-the-shelf WiFi without any wearable or dedicated devices. Our system re-uses existing WiFi network and exploits the fine-grained channel information to capture the minute movements caused by breathing and heart beats. Our system thus has the potential to be widely deployed and perform continuous long-term monitoring. The developed algorithm makes use of the channel information in both time and frequency domain to estimate breathing and heart rates, and it works well when either individual or two persons are in bed. Our extensive experiments demonstrate that our system can accurately capture vital signs during sleep under realistic settings, and achieve comparable or even better performance comparing to traditional and existing approaches, which is a strong indication of providing non-invasive, continuous fine-grained vital signs monitoring without any additional cost.
RBAC-SC: Role-Based Access Control Using Smart Contract

The role-based access control (RBAC) framework is a mechanism that describes the access control principle. As a common interaction, an organization provides a service to a user who owns a certain role that was issued by a different organization. Such trans-organizational RBAC is common in face-to-face communication but not in a computer network, because it is difficult to establish both the security that prohibits the malicious impersonation of roles and the flexibility that allows small organizations to participate and users to fully control their own roles. In this paper, we present an RBAC using smart contract (RBAC-SC), a platform that makes use of Ethereum’s smart contract technology to realize a trans-organizational utilization of roles.  It pioneered smart contracts, which are decentralized applications that serve as “autonomous agents” running exactly as programmed and are deployed on a blockchain. The RBAC-SC uses smart contracts and blockchain technology as versatile infrastructures to represent the trust and endorsement relationship that are essential in the RBAC and to realize a challenge-response authentication protocol that verifies a user’s ownership of roles. We describe the RBAC-SC framework, which is composed of two main parts, namely, the smart contract and the challenge-response protocol, and present a performance analysis. A prototype of the smart contract is created and deployed on Ethereum’s Testnet blockchain, and the source code is publicly available.
A Unit Selection Methodology for Music Generation Using Deep Neural Networks

Several methods exist for a computer to generate music based on data including Markov chains, recurrent neural networks, recombinancy, and grammars. We explore the use of unit selection and concatenation as a means of generating music using a procedure based on ranking, where, we consider a unit to be a variable length number of measures of music. We first examine whether a unit selection method, that is restricted to a finite size unit library, can be sufficient for encompassing a wide spectrum of music. This is done by developing a deep autoencoder that encodes a musical input and reconstructs the input by selecting from the library. We then describe a generative model that combines a deep structured semantic model (DSSM) with an LSTM to predict the next unit, where units consist of four, two, and one measures of music. We evaluate the generative model using objective metrics including mean rank and accuracy and with a subjective listening test in which expert musicians are asked to complete a forcedchoiced ranking task. Our system is compared to a note-level generative baseline model that consists of a stacked LSTM trained to predict forward by one note.
ChEMBL: a large-scale bioactivity database for drug discovery

ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.
MKNN : Modified K-Nearest Neighbor

In this paper, a new classification method for enhancing the performance of K-Nearest Neighbor is proposed which uses robust neighbors in training data. This new classification method is called Modified K-Nearest Neighbor, MKNN. Inspired the traditional KNN algorithm, the main idea is classifying the test samples according to their neighbor tags. This method is a kind of weighted KNN so that these weights are determined using a different procedure. The procedure computes the fraction of the same labeled neighbors to the total number of neighbors. The proposed method is evaluated on five different data sets. Experiments show the excellent improvement in accuracy in comparison with KNN method.
A feature study for classification-based speech separation at very low signal-to-noise ratio

Speech separation is a challenging problem at low signal-to-noise ratios (SNRs). Separation can be formulated as a classification problem. In this study, we focus on the SNR level of -5 dB in which speech is generally dominated by background noise. In such a low SNR condition, extracting robust features from a noisy mixture is crucial for successful classification. Using a common neural network classifier, we systematically compare separation performance of many monaural features. In addition, we propose a new feature called Multi-Resolution Cochleagram (MRCG), which is extracted from four cochlea-grams of different resolutions to capture both local information and spectrotemporal context. Comparisons using two non-stationary noises show a range of feature robustness for speech separation with the proposed MRCG performing the best. We also find that ARMA filtering, a post-processing technique previously used for robust speech recognition, improves speech separation performance by smoothing the temporal trajectories of feature dimensions.
Substrate Integrated Waveguide Filters Based on a Dielectric Layer With Periodic Perforations

This paper presents a novel class of substrate integrated waveguide (SIW) filters, based on periodic perforations of the dielectric layer. The perforations allow to reduce the local effective dielectric permittivity, thus creating waveguide sections below cutoff. This effect is exploited to implement immittance inverters through analytical formulas, providing simple design rules for the direct synthesis of the filters. The proposed solution is demonstrated through the design and testing of several filters with different topologies (including half-mode SIW and folded structures). The comparison with classical iris-type SIW filters demonstrates that the proposed filters exhibit better performance in terms of sensitivity to fabrication inaccuracies and rejection bandwidth, at the cost of a slightly larger size.
Extractive Summarization Using Supervised and Semi-Supervised Learning

It is difficult to identify sentence importance from a single point of view. In this paper, we propose a learning-based approach to combine various sentence features. They are categorized as surface, content, relevance and event features. Surface features are related to extrinsic aspects of a sentence. Content features measure a sentence based on contentconveying words. Event features represent sentences by events they contained. Relevance features evaluate a sentence from its relatedness with other sentences. Experiments show that the combined features improved summarization performance significantly. Although the evaluation results are encouraging, supervised learning approach requires much labeled data. Therefore we investigate co-training by combining labeled and unlabeled data. Experiments show that this semisupervised learning approach achieves comparable performance to its supervised counterpart and saves about half of the labeling time cost.
Development and control of a wearable robot for rehabilitation of elbow and shoulder joint movements

We have been developing an exoskeleton robot (ExoRob) for assisting daily upper limb movements (i.e., shoulder, elbow and wrist). In this paper we have focused on the development of a 2DOF ExoRob to rehabilitate elbow joint flexion/extension and shoulder joint internal/external rotation, as a step toward the development of a complete (i.e., 3DOF) shoulder motion assisted exoskeleton robot. The proposed ExoRob is designed to be worn on the lateral side of the upper arm in order to provide naturalistic movements at the level of elbow (flexion/extension) and shoulder joint internal/external rotation. This paper also focuses on the modeling and control of the proposed ExoRob. A kinematic model of ExoRob has been developed based on modified Denavit-Hartenberg notations. In dynamic simulations of the proposed ExoRob, a novel nonlinear sliding mode control technique with exponential reaching law and computed torque control technique is employed, where trajectory tracking that corresponds to typical rehab (passive) exercises has been carried out to evaluate the effectiveness of the developed model and controller. Simulated results show that the controller is able to drive the ExoRob efficiently to track the desired trajectories, which in this case consisted in passive arm movements. Such movements are used in rehabilitation and could be performed very efficiently with the developed ExoRob and the controller. Experiments were carried out to validate the simulated results as well as to evaluate the performance of the controller.
Customer Satisfaction and Shareholder ’ s Value : The Role of Employee Satisfaction

This paper explores the relationship between employee satisfaction, customer satisfaction and shareholders value theoretically. A conceptual model discusses about the relationship of these three variables. In first section the link between tow variables employee satisfaction and customer satisfaction is examined. In the second section the link between customer satisfaction and shareholders value is examined while in third section the link between customer satisfaction and shareholder’s value is examine. All these three models represent positive relationship among the related variables. Paper concludes that customer satisfaction and loyalty would continue as long as employees are satisfied and they deliver the required quality of goods and services. Customer satisfaction and employee’s satisfaction will increase shareholders value.