nils-herrmann/clean_scirepeval huggingface dataset:
Universal design for learning: enhancing achievement and employment of STEM students with disabilities

This article defines universal design for learning (UDL) and presents examples of how universally designed technology hardware and software applications benefit students with disabilities who are majoring in science, technology, engineering, or mathematics (STEM) majors. When digital technologies are developed without incorporating accessible design features, persons with disabilities cannot access required information to interact with the information society. However, when accessible technology and instruction are provided using UDL principles, research indicates that many students benefit with increased achievement. Learning through universally designed and accessible technology is essential for students with disabilities who, without access, would not gain the skills needed to complete their degrees and access employment and a life of self-sufficiency. UDL strategies enhance learning for all students, including students with disabilities who are majoring in STEM, which are among the most rigorous academic disciplines, but also among the most financially rewarding careers.
Speaker Recognition by Machines and Humans: A tutorial review

Identifying a person by his or her voice is an important human trait most take for granted in natural human-to-human interaction/communication. Speaking to someone over the telephone usually begins by identifying who is speaking and, at least in cases of familiar speakers, a subjective verification by the listener that the identity is correct and the conversation can proceed. Automatic speaker-recognition systems have emerged as an important means of verifying identity in many e-commerce applications as well as in general business interactions, forensics, and law enforcement. Human experts trained in forensic speaker recognition can perform this task even better by examining a set of acoustic, prosodic, and linguistic characteristics of speech in a general approach referred to as structured listening. Techniques in forensic speaker recognition have been developed for many years by forensic speech scientists and linguists to help reduce any potential bias or preconceived understanding as to the validity of an unknown audio sample and a reference template from a potential suspect. Experienced researchers in signal processing and machine learning continue to develop automatic algorithms to effectively perform speaker recognition?with ever-improving performance?to the point where automatic systems start to perform on par with human listeners. In this article, we review the literature on speaker recognition by machines and humans, with an emphasis on prominent speaker-modeling techniques that have emerged in the last decade for automatic systems. We discuss different aspects of automatic systems, including voice-activity detection (VAD), features, speaker models, standard evaluation data sets, and performance metrics. Human speaker recognition is discussed in two parts?the first part involves forensic speaker-recognition methods, and the second illustrates how a na?ve listener performs this task from a neuroscience perspective. We conclude this review with a comparative study of human versus machine speaker recognition and attempt to point out strengths and weaknesses of each.
Optimized Day-Ahead Pricing With Renewable Energy Demand-Side Management for Smart Grids

Internet of Things (IoT) has recently emerged as an enabling technology for context-aware and interconnected “smart things.” Those smart things along with advanced power engineering and wireless communication technologies have realized the possibility of next generation electrical grid, smart grid, which allows users to deploy smart meters, monitoring their electric condition in real time. At the same time, increased environmental consciousness is driving electric companies to replace traditional generators with renewable energy sources which are already productive in user’s homes. One of the most incentive ways is for electric companies to institute electricity buying-back schemes to encourage end users to generate more renewable energy. Different from the previous works, we consider renewable energy buying-back schemes with dynamic pricing to achieve the goal of energy efficiency for smart grids. We formulate the dynamic pricing problem as a convex optimization dual problem and propose a day-ahead time-dependent pricing scheme in a distributed manner which provides increased user privacy. The proposed framework seeks to achieve maximum benefits for both users and electric companies. To our best knowledge, this is one of the first attempts to tackle the time-dependent problem for smart grids with consideration of environmental benefits of renewable energy. Numerical results show that our proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.
A High-Efficiency High Step-Up Converter With Low Switch Voltage Stress for Fuel-Cell System Applications

In this paper, a novel high step-up converter is proposed for fuel-cell system applications. As an illustration, a two-phase version configuration is given for demonstration. First, an interleaved structure is adapted for reducing input and output ripples. Then, a C¿uk-type converter is integrated to the first phase to achieve a much higher voltage conversion ratio and avoid operating at extreme duty ratio. In addition, additional capacitors are added as voltage dividers for the two phases for reducing the voltage stress of active switches and diodes, which enables one to adopt lower voltage rating devices to further reduce both switching and conduction losses. Furthermore, the corresponding model is also derived, and analysis of the steady-state characteristic is made to show the merits of the proposed converter. Finally, a 200-W rating prototype system is also constructed to verify the effectiveness of the proposed converter. It is seen that an efficiency of 93.3% can be achieved when the output power is 150-W and the output voltage is 200-V with 0.56 duty ratio.
Trajectory generation and control for four wheeled omnidirectional vehicles

This paper describes an algorithm to calculate near-optimal minimum time trajectories for four wheeled omnidirectional vehicles, which can be used as part of a high-level path planner. The algorithm is based on a relaxed optimal control problem. It takes limited friction and vehicle dynamics into account, as encountered in high-performance omnidirectional vehicles. The low computational complexity makes the application in real-time feasible. An implementation of the algorithm on a real vehicle is presented and discussed.
Intelligent Sensorless ABS for In-Wheel Electric Vehicles

The design of electric vehicles (EVs) is increasingly based upon using the in-wheel technology. In this design, the use of a separate electric machine at each corner of the vehicle provides unique opportunities for innovative vehicle control strategies. In this paper, a sensorless antilock braking system (ABS) is proposed that eliminates the need for the installation of separate conventional ABS sensors and saves the costs associated with the installation and maintenance of those sensors for in-wheel EVs. The proposed ABS exploits the information carried by the back electromotive force (EMF) of the electric machines of the in-wheel vehicle to obtain accurate wheel speed estimation at each wheel and conduct road identification simultaneously. A wavelet-packet denoising method is used to maintain the accuracy of wheel speed estimation in the presence of noise. In addition to sensorless wheel speed estimation, the proposed ABS is capable of road identification by analyzing the back EMF signal using discrete wavelet transforms. The design was realized and fully tested using actual ABS hardware. The results of the sensorless technique were compared with a commercial ABS sensor. The experimental results showed that the sensorless ABS can adequately replace the conventional ABS sensor in in-wheel EVs and significantly improve the performance of the ABS.
Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest

The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.
Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.
3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network

We introduce a data-driven approach to aid the repairing and conservation of archaeological objects: ORGAN, an object reconstruction generative adversarial network (GAN). By using an encoder-decoder 3D deep neural network on a GAN architecture, and combining two loss objectives: a completion loss and an Improved Wasserstein GAN loss, we can train a network to effectively predict the missing geometry of damaged objects. As archaeological objects can greatly differ between them, the network is conditioned on a variable, which can be a culture, a region or any metadata of the object. In our results, we show that our method can recover most of the information from damaged objects, even in cases where more than half of the voxels are missing, without producing many errors.
Nonlinear Disturbance Observer-Based Control Design for a Robotic Exoskeleton Incorporating Fuzzy Approximation

To perform power augmentation tasks of a robotic exoskeleton, this paper utilizes fuzzy approximation and designed disturbance observers to compensate for the disturbance torques caused by unknown input saturation, fuzzy approximation errors, viscous friction, gravity, and payloads. The proposed adaptive fuzzy control with updated parameters' mechanism and additional torque inputs by using the disturbance observers are exerted into the robotic exoskeleton via feedforward loops to counteract to the disturbances. Through such an approach, the system does not need any requirement of built-in torque sensing units. In order to validate the proposed framework, extensive experiments are conducted on the upper limb exoskeleton using the state feedback and output feedback control to illustrate the performance of the proposed approaches.
An Overview of the ATSC 3.0 Physical Layer Specification

This paper provides an overview of the physical layer specification of Advanced Television Systems Committee (ATSC) 3.0, the next-generation digital terrestrial broadcasting standard. ATSC 3.0 does not have any backwards-compatibility constraint with existing ATSC standards, and it uses orthogonal frequency division multiplexing-based waveforms along with powerful low-density parity check (LDPC) forward error correction codes similar to existing state-of-the-art. However, it introduces many new technological features such as 2-D non-uniform constellations, improved and ultra-robust LDPC codes, power-based layered division multiplexing to efficiently provide mobile and fixed services in the same radio frequency (RF) channel, as well as a novel frequency pre-distortion multiple-input single-output antenna scheme. ATSC 3.0 also allows bonding of two RF channels to increase the service peak data rate and to exploit inter-RF channel frequency diversity, and to employ dual-polarized multiple-input multiple-output antenna system. Furthermore, ATSC 3.0 provides great flexibility in terms of configuration parameters (e.g., 12 coding rates, 6 modulation orders, 16 pilot patterns, 12 guard intervals, and 2 time interleavers), and also a very flexible data multiplexing scheme using time, frequency, and power dimensions. As a consequence, ATSC 3.0 not only improves the spectral efficiency and robustness well beyond the first generation ATSC broadcast television standard, but also it is positioned to become the reference terrestrial broadcasting technology worldwide due to its unprecedented performance and flexibility. Another key aspect of ATSC 3.0 is its extensible signaling, which will allow including new technologies in the future without disrupting ATSC 3.0 services. This paper provides an overview of the physical layer technologies of ATSC 3.0, covering the ATSC A/321 standard that describes the so-called bootstrap, which is the universal entry point to an ATSC 3.0 signal, and the ATSC A/322 standard that describes the physical layer downlink signals after the bootstrap. A summary comparison between ATSC 3.0 and DVB-T2 is also provided.
Deep Neural Networks for traffic flow prediction

Traffic flow prediction is an essential function of traffic information systems. Conventional approaches, using artificial neural networks with narrow network architecture and poor training samples for supervised learning, have been only partially successful. In this paper, a deep-learning neural-network based on TensorFlow™ is suggested for the prediction traffic flow conditions, using real-time traffic data. Until now, no research has applied the TensorFlow™ deep learning neural network model to the estimation of traffic conditions. The suggested supervised model is trained by a deep learning algorithm, which uses real traffic data aggregated every five minutes. Results demonstrate that the model's accuracy rate is around 99%.
Mapping usability heuristics and design principles for touchscreen-based mobile devices

Touchscreen-based mobile devices (TMDs) are one of the most popular and widespread kind of electronic device. Many manufacturers have published its own design principles as a guideline for developers. Each platform has specific constrains and recommendations for software development; specially in terms of user interface. Four sets of design principles from iOS, Windows Phone, Android and Tizen OS has been mapped against a set of usability heuristics for TMDs. The map shows that the TMDs usability heuristics cover almost every design pattern with the addition of two new dimensions: user experience and cognitive load. These new dimensions will be considered when updating the proposal of usability heuristics for TMDs.
EGameFlow: A scale to measure learners' enjoyment of e-learning games

In an effective e-learning game, the learner’s enjoyment acts as a catalyst to encourage his/her learning initiative. Therefore, the availability of a scale that effectively measures the enjoyment offered by e-learning games assist the game designer to understanding the strength and flaw of the game efficiently from the learner’s points of view. E-learning games are aimed at the achievement of learning objectives via the creation of a flow effect. Thus, this study is based on Sweetser’s & Wyeth’s framework to develop a more rigorous scale that assesses user enjoyment of e-learning games. The scale developed in the present study consists of eight dimensions: Immersion, social interaction, challenge, goal clarity, feedback, concentration, control, and knowledge improvement. Four learning games employed in a university’s online learning course ‘‘Introduction to Software Application” were used as the instruments of scale verification. Survey questionnaires were distributed to students taking the course and 166 valid samples were subsequently collected. The results showed that the validity and reliability of the scale, EGameFlow, were satisfactory. Thus, the measurement is an effective tool for evaluating the level of enjoyment provided by elearning games to their users.
Gender specific classification of road accident patterns through data mining techniques

Road accident analysis is very challenging task and investigating the dependencies between the attributes become complex because of many environmental and road related factors. In this research work we applied data mining classification techniques to carry out gender based classification of which RndTree and C4.5 using AdaBoost Meta classifier gives high accurate results. The training dataset used for the research work is obtained from Fatality Analysis Reporting System (FARS) which is provided by the University of Alabama's Critical Analysis Reporting Environment (CARE) system. The results reveal that AdaBoost used with RndTree improvised the classifier's accuracy.
Learning to Branch in Mixed Integer Programming

The design of strategies for branching in Mixed Integer Programming (MIP) is guided by cycles of parameter tuning and offline experimentation on an extremely heterogeneous testbed, using the average performance. Once devised, these strategies (and their parameter settings) are essentially input-agnostic. To address these issues, we propose a machine learning (ML) framework for variable branching in MIP. Our method observes the decisions made by Strong Branching (SB), a time-consuming strategy that produces small search trees, collecting features that characterize the candidate branching variables at each node of the tree. Based on the collected data, we learn an easy-to-evaluate surrogate function that mimics the SB strategy, by means of solving a learning-to-rank problem, common in ML. The learned ranking function is then used for branching. The learning is instance-specific, and is performed on-the-fly while executing a branch-and-bound search to solve the instance. Experiments on benchmark instances indicate that our method produces significantly smaller search trees than existing heuristics, and is competitive with a state-of-the-art commercial solver.
Using AHP and TOPSIS approaches in customer-driven product design process

Customer-driven product design process is critically an important part of concurrent engineering (CE). Many new principles and approaches, such as quality function deployment (QFD) and axiomatic design, have been introduced to help designers identify the relationship between customer requirements and design characteristics. However, identification of customer requirements and evaluation of design alternatives are still heavily reliant on designer’s experience and knowledge. This will affect the efficiency and effectiveness of the customer-driven design process and even make the development of design automation become more difficult. This paper presents a framework that integrates the analytical hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) to assist designers in identifying customer requirements and design characteristics, and help achieve an effective evaluation of the final design solution. The proposed approach starts with applying the AHP method to evaluate the relative overall importance of customer requirements and design characteristics. The TOPSIS method is then used to perform competitive benchmarking. Finally, a search strategy is employed to set target values for design characteristics of the recommended design alternative. The performance of the proposed approach is illustrated and validated using a personal digital assistant (PDA) design example. The results show that the proposed approach is capable of helping designers to systematically consider relevant design information and effectively determine the key design objectives and optimal conceptual alternatives.
Instance Weighting for Neural Machine Translation Domain Adaptation

Instance weighting has been widely applied to phrase-based machine translation domain adaptation. However, it is challenging to be applied to Neural Machine Translation (NMT) directly, because NMT is not a linear model. In this paper, two instance weighting technologies, i.e., sentence weighting and domain weighting with a dynamic weight learning strategy, are proposed for NMT domain adaptation. Empirical results on the IWSLT EnglishGerman/French tasks show that the proposed methods can substantially improve NMT performance by up to 2.7-6.7 BLEU points, outperforming the existing baselines by up to 1.6-3.6 BLEU points.
Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation

Deep learning algorithms require large amounts of labeled data which is difficult to attain for medical imaging. Even if a particular dataset is accessible, a learned classifier struggles to maintain the same level of performance on a different medical imaging dataset from a new or never-seen data source domain. Utilizing generative adversarial networks in a semi-supervised learning architecture, we address both problems of labeled data scarcity and data domain overfitting. For cardiac abnormality classification in chest X-rays, we demonstrate that an order of magnitude less data is required with semi-supervised learning generative adversarial networks than with conventional supervised learning convolutional neural networks. In addition, we demonstrate its robustness across different datasets for similar classification tasks.