nils-herrmann/clean_scirepeval huggingface dataset:
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences

We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. We solicit and verify questions and answers for this challenge through a 4-step crowdsourcing experiment. Our challenge dataset contains ∼6k questions for +800 paragraphs across 7 different domains (elementary school science, news, travel guides, fiction stories, etc) bringing in linguistic diversity to the texts and to the questions wordings. On a subset of our dataset, we found human solvers to achieve an F1-score of 86.4%. We analyze a range of baselines, including a recent state-of-art reading comprehension system, and demonstrate the difficulty of this challenge, despite a high human performance. The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills.
Design of Permanent Magnet-Assisted Synchronous Reluctance Motors Made Easy

Electric motor design is a multi-variable problem which involves geometric dimensions of the stator and rotor. Presenting a unique solution for a family of optimization criteria has always been a challenge for motor designers. Several numerical tools such as finite element methods (FEM) have been developed to perform a precise analysis and predict the outcome of the design. However, limits in parametric analysis as well as mathematical and computational burden on numerical tools usually prohibit the designer in obtaining a unique solution for the design problem. These limits and demands in optimized solutions motivate the designer to use analytical models in order to perform a comprehensive parametric design. An accurate analytical model is crucial for this purpose. In this paper, an analytical model for permanent magnet assisted synchronous reluctance motor (PMa- SynRM) with four flux barriers and one cutout per pole is developed. Flux densities are found in the air gap, in the cutouts, and in the flux barriers; thus, the back-EMF developed by the permanent magnets is derived. Equations for the d-axis and the q-axis inductances are also obtained. Electromagnetic torque is finally derived using the co-energy method. The developed analytical model highlights the contribution of the reluctance variation and permanent magnets on the developed torque. Simulation results are obtained using both Matlab and Ansoft/Maxwell packages. These outcomes are supported by the experimental results obtained from a laboratory test bed.
Every Filter Extracts A Specific Texture In Convolutional Neural Networks

Many works have concentrated on visualizing and understanding the inner mechanism of convolutional neural networks (CNNs) by generating images that activate some specific neurons, which is called deep visualization. However, it is still unclear what the filters extract from images intuitively. In this paper, we propose a modified code inversion algorithm, called feature map inversion, to understand the function of filter of interest in CNNs. We reveal that every filter extracts a specific texture. The texture from higher layer contains more colours and more intricate structures. We also demonstrate that style of images could be a combination of these texture primitives. Two methods are proposed to reallocate energy distribution of feature maps randomly and purposefully. Then, we inverse the modified code and generate images of diverse styles. With these results, we provide an explanation about why Gram matrix of feature maps  could represent image style.
Vehicles Capable of Dynamic Vision

A survey is given on two decades of developments in the field, encompassing an increase in computing power by four orders of magnitude. The '4-D approach' integrating expectation-based methods from systems dynamics and control engineering with methods from AI has allowed to create vehicles with unprecedented capabilities in the technical realm: Autonomous road vehicle guidance in public traffic on freeways at speeds beyond 130 km/h, on-board-autonomous landing approaches of aircraft, and landmark navigation for AGV's, for road vehicles including turn-offs onto cross-roads, and for helicopters in low-level flight (real-time, hardware-in-the-loop simulations in the latter case).
Quality of Life Theory I. The IQOL Theory: An Integrative Theory of the Global Quality of Life Concept

Quality of life (QOL) means a good life and we believe that a good life is the same as living a life with a high quality. This paper presents the theoretical and philosophical framework of the Danish Quality of Life Survey, and of the SEQOL, QOL5, and QOL1 questionnaires. The notion of a good life can be observed from subjective to the objective, where this spectrum incorporates a number of existing quality of life theories. We call this spectrum the integrative quality-of-life (IQOL) theory and discuss the following aspects in this paper: well being, satisfaction with life, happiness, meaning in life, the biological information system ("balance"), realizing life potential, fulfillment of needs, and objective factors. The philosophy of life outlined in this paper tries to measure the global quality of life with questions derived from the integrative theory of the quality of life. The IQOL theory is an overall theory or meta-theory encompassing eight more factual theories in a subjective-existential-objective spectrum. Other philosophies of life can stress other aspects of life, but by this notion of introducing such an existential depth into the health and social sciences, we believe to have taken a necessary step towards a new humility and respect for the richness and complexity of life.
Implementation of power efficient 8-bit reversible linear feedback shift register for BIST

Built-in Self Test (BIST) is more appropriate for testing any VLSI circuits as it provides a larger range for low power applications. Test pattern generator is the vital module in BIST. Linear Feedback Shift Registers (LFSR) is broadly used in BIST for generating test vectors as it produces highly random test pattern. This paper mainly aims at design and implementation of power efficient LFSR using reversible logic for low power applications. Pareek Gate is used to implement reversible D Flip Flop (DFF). 8 bit reversible LFSR is implemented using 8 DFFs, 8 Feynman Gates (FG) and 3 Double Feynman Gates (DFG). It is found from the analysis that, the proposed approach of designing reversible LFSR reduces the power by 10% when compared to conventional design. Thus the proposed design can be used for designing BIST in low power applications.
WebVRGIS based traffic analysis and visualization system

With several characteristics, such as large scale, diverse predictability and timeliness, the city traffic data falls in the range of definition of Big Data. A Virtual Reality GIS based traffic analysis and visualization system is proposed as a promising and inspiring approach to manage and develop traffic big data. In addition to the basic GIS interaction functions, the proposed system also includes some intelligent visual analysis and forecasting functions. The passenger flow forecasting algorithm is introduced in detail.
Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection

Speaker change detection (SCD) is an important task in dialog modeling. Our paper addresses the problem of text-based SCD, which differs from existing audio-based studies and is useful in various scenarios, for example, processing dialog transcripts where speaker identities are missing (e.g., OpenSubtitle), and enhancing audio SCD with textual information. We formulate text-based SCD as a matching problem of utterances before and after a certain decision point; we propose a hierarchical recurrent neural network (RNN) with static sentence-level attention. Experimental results show that neural networks consistently achieve better performance than feature-based approaches, and that our attention-based model significantly outperforms non-attention neural networks.
Modeling Botnet Propagation Using Time Zones

Time zones play an important and unexplored role in malware epidemics. To understand how time and location affect malware spread dynamics, we studied botnets, or large coordinated collections of victim machines (zombies) controlled by attackers. Over a six month period we observed dozens of botnets representing millions of victims. We noted diurnal properties in botnet activity, which we suspect occurs because victims turn their computers off at night. Through binary analysis, we also confirmed that some botnets demonstrated a bias in infecting regional populations. Clearly, computers that are offline are not infectious, and any regional bias in infections will affect the overall growth of the botnet. We therefore created a diurnal propagation model. The model uses diurnal shaping functions to capture regional variations in online vulnerable populations. The diurnal model also lets one compare propagation rates for different botnets, and prioritize response. Because of variations in release times and diurnal shaping functions particular to an infection, botnets released later in time may actually surpass other botnets that have an advanced start. Since response times for malware outbreaks is now measured in hours, being able to predict short-term propagation dynamics lets us allocate resources more intelligently. We used empirical data from botnets to evaluate the analytical model.
on Home Control Automation Using GSM and Bluetooth

Home automation in essence plays a very vital role in modern era because of its flexibility in using at different places with high precision which will intern save money and time by decreasing human hard work. General idea of home automation shows the quality of human being at house. Prime focus of this technology is to control the household equipment’s like light, fan, door, AC etc. automatically. In hazardous condition, it is useful for old aged and handicapped persons. In this technology, remote, wireless controlled switches, PC, Laptop or smartphone are used for operating. In this paper we detailed a survey on home control automation using GSM and Bluetooth by considering the parameters like efficiency of working, controllers used, type of communication, the apps developed etc. and at last a comparative discussion is given which summarizes the previous literature work.  Home Automation, GSM, Bluetooth, Wireless, Controller, Android Apps
Automated mining of software component interactions for self-adaptation

A self-adaptive software system should be able to monitor and analyze its runtime behavior and make adaptation decisions accordingly to meet certain desirable objectives. Traditional software adaptation techniques and recent "models@runtime" approaches usually require an a priori model for a system's dynamic behavior. Oftentimes the model is difficult to define and labor-intensive to maintain, and tends to get out of date due to adaptation and architecture decay. We propose an alternative approach that does not require defining the system's behavior model beforehand, but instead involves mining software component interactions from system execution traces to build a probabilistic usage model, which is in turn used to analyze, plan, and execute adaptations. Our preliminary evaluation of the approach against an Emergency Deployment System shows that the associations mining model can be used to effectively address a variety of adaptation needs, including (1) safely applying dynamic changes to a running software system without creating inconsistencies, (2) identifying potentially malicious (abnormal) behavior for self-protection, and (3) our ongoing research on improving deployment of software components in a distributed setting for performance self-optimization.
A Framework for Cloud Forensic Readiness in Organizations

Many have argued that cloud computing is one of the fastest growing and most transformative technologies in the history of computing. It has radically changed the way in which information technologies can manage, access, deliver and create services. It has also brought numerous benefits to end-users and organizations. However, this rapid growth in cloud computing adoption has also seen it become a new arena for cybercrime. This has, in turn, led to new technical, legal and organizational challenges. In addition to the large number of attacks which affect cloud computing and the decentralized nature of data processing in the cloud, many concerns have been raised. One of these concerns is how to conduct a proper digital investigation in cloud environments and be ready to collect data proactively before an incident occurs in order to save time, money and effort. This paper proposes the technical, legal and organizational factors that influence digital forensic readiness for Infrastructure as a Service consumers.
Analysis of level design 'push & pull' within 21 games

This paper investigates the differences between 3D level designs in 21 popular games. We have developed a framework to analyze 3D level designs based on patterns extracted from level designers and game play sessions. We then use these patterns to analyze several game play sessions. Results of this analysis reveal methods by which designers push and pull players through levels. We discuss an analysis of these patterns in terms of three level affordance configurations, combat, environmental resistance, and mixed goals in 21 different games using one walkthrough play session per game. By looking at the variety of games, we can further explore the level similarities and differences between games.
Edge-based visual-inertial odometry

In this paper we propose a method for monocular visual-inertial odometry that utilizes image edges as measurements. In contrast to previous feature-based approaches, the proposed method does not employ any assumption on the geometry of the scene (e.g., it does not assume straight lines). It can thus use measurements from all image areas with significant gradient, similarly to direct semi-dense methods. However, in contrast to direct semi-dense approaches, the proposed method's measurement model is invariant to linear changes in the image intensity. The novel edge parameterization and measurement model we propose explicitly account for the fact that edge points can only provide useful information in the direction of the image gradient. We present both Monte-Carlo simulations, as well as results from real-world experimental testing, which demonstrate that the proposed edge-based approach to visual-inertial odometry is consistent, and outperforms the point-based one.
A Distributed Message Delivery Infrastructure for Connected Vehicle Technology Applications

A complex and vast amount of data will be collected from on-board sensors of operational connected vehicles (CVs), infrastructure data sources such as roadway sensors and traffic signals, mobile data sources such as cell phones, social media sources such as Twitter, and news and weather data services. Unfortunately, these data will create a bottleneck at data centers for processing and retrievals of collected data, and will require the deployment of additional message transfer infrastructure between data producers and consumers to support diverse CV applications. In this paper, we present a strategy for creating an efficient and low-latency distributed message delivery system for CV applications using a distributed message delivery platform. This strategy enables large-scale ingestion, curation, and transformation of unstructured data (roadway traffic-related and roadway non-traffic-related data) into labeled and customized topics for a large number of subscribers or consumers, such as CVs, mobile devices, and data centers. We evaluate the performance of this strategy by developing a prototype infrastructure using Apache Kafka, an open source message delivery system, and compared its performance with the latency requirements of CV applications. We present experimental results of the message delivery infrastructure on two different distributed computing testbeds at Clemson University: the Holocron cluster and the Palmetto cluster. Experiments were performed to measure the latency of the message delivery system for a variety of testing scenarios. These experiments reveal that measured latencies are less than the U.S. Department of Transportation recommended latency requirements for CV applications, which prove the efficacy of the system for CV related data distribution and management tasks.
Communication and Security in Health Monitoring Systems -- A Review

The fast development of sensing devices and radios enables more powerful and flexible remote health monitoring systems. Considering the future vision of the Internet of Things (IoT), many requirements and challenges rise to the design and implementation of such systems. Bridging the gap between sensor nodes on the human body and the Internet becomes a challenging task in terms of reliable communications. Additionally, the systems will not only have to provide functionality, but also be highly secure. In this paper, we provide a survey on existing communication protocols and security issues related to pervasive health monitoring, describing their limitations, challenges, and possible solutions. We propose a generic protocol stack design as a first step toward handling interoperability in heterogeneous low-power wireless body area networks.
Overview of rotary steerable system and its control methods

Rotary steerable system (RSS) is a new technique of drilling technology adopted in directional drilling. Compared with conventional directional drilling, RSS has the ability to improve the drilling efficiency, reduce the drilling risk and decrease the drilling cost at the same time. This paper summaries the fundamental structure, classification, directional principle and development process of RSS; and gives detail analysis of the control methods in RSS. According to these summaries and analyses, the advantages and disadvantages of RSS at present are given, and some key suggestions about this technique and the related control methods are also proposed in this paper.
Proposing a New Term Weighting Scheme for Text Categorization

In text categorization, term weighting methods assign appropriate weights to the terms to improve the classification performance. In this study, we propose an effective term weighting scheme, i.e. tf.rf , and investigate several widely-used unsupervised and supervised term weighting methods on two popular data collections in combination with SVM and kNN algorithms. From our controlled experimental results, not all supervised term weighting methods have a consistent superiority over unsupervised term weighting methods. Specifically, the three supervised methods based on the information theory, i.e. tf.χ, tf.ig and tf.or, perform rather poorly in all experiments. On the other hand, our proposed tf.rf achieves the best performance consistently and outperforms other methods substantially and significantly. The popularly-used tf.idf method has not shown a uniformly good performance with respect to different data corpora.
A Wideband SiGe BiCMOS Frequency Doubler With 6.5-dBm Peak Output Power for Millimeter-Wave Signal Sources

This paper presents a balanced frequency doubler with 6.5-dBm peak output power at 204 GHz in 130-nm SiGe BiCMOS technology ( $f_{T}/f_{\max }=210$ /250 GHz). To convert the single-ended input signal to a differential signal for balanced operation, an on-chip transformer-based balun is employed. Detailed design procedure and compensation techniques to lower the imbalance at the output ports, based on mixed mode S parameters are proposed and verified analytically and through electromagnetic simulations. The use of optimized harmonic reflectors at the input port results in a 2-dBm increase in output power without sacrificing the bandwidth of interest. The measured conversion loss of the frequency doubler is 9 dB with 6-dBm input power at 204-GHz output. The measured peak output power is 6.5 dBm with an on-chip power amplifier stage. The 3-dB output power bandwidth is measured to be wider than 50 GHz (170–220 GHz). The total chip area of the doubler is 0.09 mm2 and the dc power consumption is 90 mW from a 1.8-V supply, which corresponds to a 5% collector efficiency.
Identification of induction motor parameters from transient stator current measurements

This paper describes three methods for estimating the lumped model parameters of an induction motor using startup transient data. A three-phase balanced induction motor is assumed. Measurements of the stator currents and voltages are required for the identification procedure, but no measurements from the motor shaft are needed. The first method presented applies simple models with limited temporal domains of validity and obtains parameter estimates by extrapolating the model error bias to zero. This method does not minimize any specific error criterion and is presented as a means of finding a good initial guess for a conventional iterative maximum-likelihood or least-squares estimator. The second method presented minimizes equation errors in the induction motor model in the least-square sense using a Levenburg–Marquardt iteration. The third identification method is a continuation of the Levenburg–Marquardt method, motivated by observed properties of some pathological loss functions. The third method minimizes errors in the observations in the least-squared sense and is, therefore, a maximumlikelihood estimator under appropriate conditions of normality. The performance of the identification schemes is demonstrated with both simulated and measured data, and parameters obtained using the methods are compared with parameters obtained from standard tests.