nils-herrmann/clean_scirepeval huggingface dataset:
Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control

The aim of this study was to review existing research into driver control transitions and to determine the time it takes drivers to resume control from a highly automated vehicle in noncritical scenarios.



Contemporary research has moved from an inclusive design approach to adhering only to mean/median values when designing control transitions in automated driving. Research into control transitions in highly automated driving has focused on urgent scenarios where drivers are given a relatively short time span to respond to a request to resume manual control. We found a paucity in research into more frequent scenarios for control transitions, such as planned exits from highway systems.



Twenty-six drivers drove two scenarios with an automated driving feature activated. Drivers were asked to read a newspaper, or to monitor the system, and to relinquish, or resume, control from the automation when prompted by vehicle systems.



Significantly longer control transition times were found between driving with and without secondary tasks. Control transition times were substantially longer than those reported in the peer-reviewed literature.



We found that drivers take longer to resume control when under no time pressure compared with that reported in the literature. Moreover, we found that drivers occupied by a secondary task exhibit larger variance and slower responses to requests to resume control. Workload scores implied optimal workload.



Intra- and interindividual differences need to be accommodated by vehicle manufacturers and policy makers alike to ensure inclusive design of contemporary systems and safety during control transitions.
Simulation of bulk current injection test using integrated circuit immunity macro model and electromagnetic analysis

This paper provides a technique to predict bulk current injection (BCI) test results. In order to define the threshold of failure, the direct power injection based integrated circuit (IC) immunity macro model for conducted immunity is adopted. Injected radio frequency disturbance that reaches to an integrated circuit is calculated by using electromagnetic analysis with a high accuracy injection probe model. 3D model of equipment under test can provide the terminal voltage of IC which reference is the ground terminal of IC, not BCI test setups reference ground plane. The proposed method is applied to BCI tests and the simulated results have good correlation with experimental results.
Anti-phishing detection of phishing attacks using genetic algorithm

An approach to detection of phishing hyperlinks using the rule based system formed by genetic algorithm is proposed, which can be utilized as a part of an enterprise solution to anti-phishing. A legitimate webpage owner can use this approach to search the web for suspicious hyperlinks. In this approach, genetic algorithm is used to evolve rules that are used to differentiate phishing link from legitimate link. Evaluating the parameters like evaluation function, crossover and mutation, GA generates a ruleset that matches only the phishing links. This ruleset is stored in a database and a link is reported as a phishing link if it matches any of the rules in the rule based system and thus it keeps safe from fake hackers. Preliminary experiments show that this approach is effective to detect phishing hyperlink with minimal false negatives at a speed adequate for online application.
The VerbCorner Project: Toward an Empirically-Based Semantic Decomposition of Verbs

This research describes efforts to use crowdsourcing to improve the validity of the semantic predicates in VerbNet, a lexicon of about 6300 English verbs. The current semantic predicates can be thought of semantic primitives, into which the concepts denoted by a verb can be decomposed. For example, the verb spray (of the Spray class), involves the predicates MOTION, NOT, and LOCATION, where the event can be decomposed into an AGENT causing a THEME that was originally not in a particular location to now be in that location. Although VerbNet’s predicates are theoretically well-motivated, systematic empirical data is scarce. This paper describes a recently-launched attempt to address this issue with a series of human judgment tasks, posed to subjects in the form of games.
Development and Use of a Gold-Standard Data Set for Subjectivity Classifications

This paper presents a case study of analyzing and improving intercoder reliability in discourse tagging using statistical techniques. Bias-corrected tags are formulated and successfully used to guide a revision of the coding manual and develop an automatic classifier. 1  This paper presents a case study of analyzing and improving intercoder reliability in discourse tagging using the statistical techniques presented in (Bruce and Wiebe, 1998; Bruce and Wiebe, to appear). Our approach is data driven: we refine our understanding and presentation of the classification scheme guided by the results of the intercoder analysis. We also present the results of a probabilistic classifier developed on the resulting annotations. Much research in discourse processing has focused on task-oriented and instructional dialogs. The task addressed here comes to the fore in other genres, especially news reporting. The task is to distinguish sentences used to objectively present factual information from sentences used to present opinions and evaluations. There are many applications for which this distinction promises to be important, including text categorization and summarization. This research takes a large step toward developing a reliably annotated gold standard to support experimenting with such applications. This research is also a case study of analyzing and improving manual tagging that is applicable to any tagging task. We perform a statistical analysis that provides information that complements the information provided by
Super High Gain Substrate Integrated Clamped-Mode Printed Log-Periodic Dipole Array Antenna

A compact, low profile, super high antenna gain, and broadband antenna, based on the printed log-periodic dipole array (PLPDA) antenna and the substrate integrated waveguide (SIW) technologies, has been proposed. It is so called substrate integrated waveguide clamped-mode printed log-periodic dipole array (SIW clamped-mode PLPDA) antenna. The arrangement of the dipole elements and feed lines in the SIW clamped-mode PLPDA antenna is designed and optimized. The proposed SIW clamped-mode PLPDA antenna was designed, fabricated, and tested, while a single SIW PLPDA antenna and a 2-element SIW PLPDA array were designed for comparison. The impedance bandwidth and the far-field radiation patterns in both E-plane and H-plane were measured. The measured results show that the performance of the SIW clamped-mode PLPDA antenna is similar to that of the 2-element SIW PLPDA array over the working frequency range 25 GHz-40 GHz, while the proposed antenna has only half the number of dipole elements and feed lines.
IoT based smart greenhouse

This work is primarily about the improvement of current agricultural practices by using modern technologies for better yield. This work provides a model of a smart greenhouse, which helps the farmers to carry out the work in a farm automatically without the use of much manual inspection. Greenhouse, ,  being a closed structure protects the plants from extreme weather conditions namely: wind, hailstorm, ultraviolet radiations, and insect and pest attacks. The irrigation of agriculture field is carried out using automatic drip irrigation, which operates according to the soil moisture threshold set accordingly so as optimal amount of water is applied to the plants. Based on data from soil health card, proper amount of nitrogen, phosphorus, potassium and other minerals can be applied by using drip fertigation techniques. Proper water management tanks are constructed and they are filled with water after measuring the current water level using an ultrasonic sensor. Plants are also provided the requisite wavelength light during the night using growing lights. Temperature and air humidity are controlled by humidity and temperature sensors and a fogger is used to control the same. A tube well is controlled using GSM [3] module (missed call or sms). Bee-hive boxes are deployed for pollination and boxes are monitored using ultrasonic sensors to measure honey and send mails to the buyers when they are filled. Further, the readings collected from storage containers are uploaded to cloud service (Google drive) and can be forwarded to an e-commerce company.
Predicting football match results with logistic regression

Many efforts has been made in order to predict football matches result and selecting significant variables in football. Prediction is very useful in helping managers and clubs make the right decision to win leagues and tournaments. In this paper a logistic regression model is built to predict matches results of Barclays' Premier League season 2015/2016 for home win or away win and to determine what are the significant variable to win matches. Our work is different from others as we use only significant variables gathered from researches in the same field rather than making our own guess on what are the significant variables. We also used data gathered from video game FIFA, as Shin and Gasparyan  showed us that including data from the video game could improve prediction quality. The model was built using variations of training data from 2010/2011 season until 2015/2016. Logistic regression is a classification method which can be used to predict sports results and it can gives additional knowledge through regression coefficients. The variables used are “Home Offense”, “Home Defense”, “Away Offense”, and “Away Defense”. We conducted experiments by altering seasons of training data used. Prediction accuracy of built model is 69.5%. We found our work improved significantly compared to the one Snyder  did. We concluded that the significant variables are “Home Defense”, and “Away Defense”.
On the use of windows for harmonic analysis with the discrete Fourier transform

This paper makes available a concise review of data windows and their affect on the detection of harmonic signals in the presence of broad-band noise, and in the presence of nearby strong harmonic interference. We also call attention to a number of common errors in the application of windows when used with the fast Fourier transform. This paper includes a comprehensive catalog of data windows along with their significant performance parameters from which the different windows can be compared. Finally, an example demonstrates the use and value of windows to resolve closely spaced harmonic signals characterized by large differences in amplitude.
A Survey on Food Computing

WEIQING MIN, Key Lab of Intelligent Information Processing, Institute of Computing Technology, CAS, China SHUQIANG JIANG, Key Lab of Intelligent Information Processing, Institute of Computing Technology, CAS, China LINHU LIU, Key Lab of Intelligent Information Processing, Institute of Computing Technology, CAS, China YONG RUI, Lenovo Group, China RAMESH JAIN, Department of Computer Science, University of California, USA
A VLSI architecture for high-performance, low-cost, on-chip learning

The motivation for the architecture described in this paper was to develop inexpensive commercial hardware suitable for solving large, real world problems. Such an architecture must be systems oriented and flexible enough to execute any neural network algorithm, and work cooperatively with existing hardware and software. The early application of neural networks must proceed in conjunction with existing technologies, both hardware and software. Neurocomputation will succeed to the extent that it merges cleanly with existing computer structures. It is an enhancing, not a replacing technology. Using state-of-the-art technology and innovative architectural techniques we can approach the speed and cost of analog systems while retaining much of the flexibility of large, general purpose parallel machines. We do not mean to imply that any point in the neurocomputer design space is necessarily superior to any other, rather, we have aimed at a particular set of applications and have made cost-performance trade-offs accordingly. Our vision is of an architecture that could be considered a general purpose, “microprocessor” of neurocomputing.
Comparison of Approximate Methods for Handling Hyperparameters

I examine two approximate methods for computational implementation of Bayesian hierarchical models, that is, models that include unknown hyperparameters such as regularization constants and noise levels. In the evidence framework, the model parameters are integrated over, and the resulting evidence is maximized over the hyperparameters. The optimized hyperparameters are used to define a gaussian approximation to the posterior distribution. In the alternative MAP method, the true posterior probability is found by integrating over the hyperparameters. The true posterior is then maximized over the model parameters, and a gaussian approximation is made. The similarities of the two approaches and their relative merits are discussed, and comparisons are made with the ideal hierarchical Bayesian solution. In moderately ill-posed problems, integration over hyperparameters yields a probability distribution with a skew peak, which causes signifi-cant biases to arise in the MAP method. In contrast, the evidence framework is shown to introduce negligible predictive error under straightforward conditions. General lessons are drawn concerning inference in many dimensions.
Mobile Robot Control on a Reference Path

In this paper a control design of a nonholonomic mobile robot with a differential drive is presented. On the basis of robot kinematics equations a robot control is designed where the robot is controlled to follow the arbitrary path reference with a predefined velocity profile. The designed control algorithm proved stable and robust to the errors in robot initial positions, to input and output noises and to other disturbances. The obtained control law is demonstrated on a simple trajectory example, however, for a more general applicability a time-optimal motion planning algorithm considering acceleration constraints is presented as well
Towards Context-Aware Interaction Recognition for Visual Relationship Detection

Recognizing how objects interact with each other is a crucial task in visual recognition. If we define the context of the interaction to be the objects involved, then most current methods can be categorized as either: (i) training a single classifier on the combination of the interaction and its context; or (ii) aiming to recognize the interaction independently of its explicit context. Both methods suffer limitations: the former scales poorly with the number of combinations and fails to generalize to unseen combinations, while the latter often leads to poor interaction recognition performance due to the difficulty of designing a contextindependent interaction classifier.,,To mitigate those drawbacks, this paper proposes an alternative, context-aware interaction recognition framework. The key to our method is to explicitly construct an interaction classifier which combines the context, and the interaction. The context is encoded via word2vec into a semantic space, and is used to derive a classification result for the interaction. The proposed method still builds one classifier for one interaction (as per type (ii) above), but the classifier built is adaptive to context via weights which are context dependent. The benefit of using the semantic space is that it naturally leads to zero-shot generalizations in which semantically similar contexts (subject-object pairs) can be recognized as suitable contexts for an interaction, even if they were not observed in the training set. Our method also scales with the number of interaction-context pairs since our model parameters do not increase with the number of interactions. Thus our method avoids the limitation of both approaches. We demonstrate experimentally that the proposed framework leads to improved performance for all investigated interaction representations and datasets.
Low-cost TSH (through-silicon hole) interposers for 3D IC integration

In this investigation, a SiP (system-in-package) which consists of a very low-cost interposer with through-silicon holes (TSHs) and with chips on its top- and bottom-side (a real 3D IC integration) is studied. Emphasis is placed on the fabrication of a test vehicle to demonstrate the feasibility of this SiP technology. The design, materials, and process of the top-chip, bottom-chip, TSH interposer, and final assembly will be presented. Shock and thermal cycling tests will be preformed to demonstrate the integrity of the SiP structure.
Hrm Practices and Mnc Knowledge Transfer

The paper supports the idea that organizations can institute various internal structures, policies and practices to overcome transfer barriers and facilitate the degree of knowledge transfer. I discuss a framework for future empirical research on the relations between human resource management (HRM) practices and knowledge transfer in multinational corporations (MNC). The proposed model is empirically testable, includes a wider range of HRM practices and is not limited to one mode of foreign operations only.
Compact wideband directional antenna with three-dimensional structure for microwave-based head imaging systems

Microwave-based head imaging systems demand wideband antennas with compact size. To ensure high scattered signals from the intended target point, the antennas need to have directional radiation patterns. Designing an antenna by addressing all these issues is challenging as the head imaging system utilizes low microwave frequencies (around 1-3 GHz) to maintain high signal penetration in the lossy human head. This paper reports a coplanar waveguide-fed design that overcomes all these limitations. The antenna is constructed in a three-dimensional manner by combining a dipole antenna with a folded parasitic structure printed on two substrate blocks. The antenna mechanism is explained with the description of its design parameters. The antenna covers 101% fractional bandwidth with a stable gain, good radiation efficiency and well directional radiation characteristics. The overall volume of the antenna is 0.24 × 0.1 × 0.05 λm where λm is the wavelength at the lowest frequency of operation.
A Survey of Point-of-interest Recommendation in Location-based Social Networks

Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industrial interest. In this paper, we offer a systematic review of this field, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges in POI recommendation, compared with traditional recommendation problems, e.g., movie recommendation. Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation. Specifically, we propose three taxonomies to classify POI recommendation systems. First, we categorize the systems by the influential factors check-in characteristics, including the geographical information, social relationship, temporal influence, and content indications. Second, we categorize the systems by the methodology, including systems modeled by fused methods and joint methods. Third, we categorize the systems as general POI recommendation and successive POI recommendation by subtle differences in the recommendation task whether to be bias to the recent check-in. For each category, we summarize the contributions and system features, and highlight the representative work. Moreover, we discuss the available data sets and the popular metrics. Finally, we point out the possible future directions in this area and conclude this survey.
PRUNE: Preserving Proximity and Global Ranking for Network Embedding

We investigate an unsupervised generative approach for network embedding. A multi-task Siamese neural network structure is formulated to connect embedding vectors and our objective to preserve the global node ranking and local proximity of nodes. We provide deeper analysis to connect the proposed proximity objective to link prediction and community detection in the network. We show our model can satisfy the following design properties: scalability, asymmetry, unity and simplicity. Experiment results not only verify the above design properties but also demonstrate the superior performance in learning-to-rank, classification, regression, and link prediction tasks.
A study on the bending mechanism of the flexible ureteroscope

Flexible ureteroscope is increasingly used for urologic treatment today. The key mechanism of the ureteroscope is the bending mechanism. It consists of a bending head and a control lever connected through a set of wire. The lighting and imaging optics as well as the surgical tools all go through the bending head. With the demand for improved performance and reduced size, more research on its design is still needed. In this paper, the modeling and simulation of an ureteroscope are presented, which pave the road for design optimization in the future.
Detection of non-flat ground surfaces using V-Disparity images

Ground plane detection plays an important role in stereo vision based obstacle detection methods. Recently, V-Disparity image has been widely used for ground plane detection. The existing approach based on V-Disparity image can detect flat ground successfully but have difficulty in detecting non-flat ground. In this paper, we discuss the representation of non-flat ground in V-Disparity image, based on which we propose a method to detect non-flat ground using V-Disparity image.
Characteristics and Expected Returns in Individual Equity Options

I study excess returns from selling individual equity option portfolios that are leverageadjusted monthly and delta-hedged daily. Strikingly, I find that several measures of risk rise by maturity, although expected returns decrease. Based on my analysis, I identify three new factors –level, slope, and value– in option returns, which together explain the cross-sectional variation in expected returns on option portfolios formed on moneyness, maturity and option value (the spread between historical volatility and the Black-Scholes implied volatility). This three-factor model helps explain expected returns on option portfolios formed on a variety of different characteristics that include carry, VRP, volatility momentum, idiosyncratic volatility, illiquidity, etc. While the level premium appears to be a compensation for marketwide volatility and jump shocks, theories of risk-averse financial intermediaries help us to understand the slope and the value premiums.
Fast Road Sign Detection Using Hough Transform for Assisted Driving of Road Vehicles

A system for real-time traffic sign detection is described in this paper. The system uses restricted Hough transform for circumferences in order to detect circular signs, and for straight lines for triangular ones. Some results obtained from a set of real road images captured under both normal and adverse weather conditions are presented as well in order to illustrate the robustness of the detection system. The average processing time is 30 ms per frame, what makes the system a good approach to work in real time conditions.