nils-herrmann/clean_scirepeval huggingface dataset:
Missing... presumed at random: cost-analysis of incomplete data.

When collecting patient-level resource use data for statistical analysis, for some patients and in some categories of resource use, the required count will not be observed. Although this problem must arise in most reported economic evaluations containing patient-level data, it is rare for authors to detail how the problem was overcome. Statistical packages may default to handling missing data through a so-called 'complete case analysis', while some recent cost-analyses have appeared to favour an 'available case' approach. Both of these methods are problematic: complete case analysis is inefficient and is likely to be biased; available case analysis, by employing different numbers of observations for each resource use item, generates severe problems for standard statistical inference. Instead we explore imputation methods for generating 'replacement' values for missing data that will permit complete case analysis using the whole data set and we illustrate these methods using two data sets that had incomplete resource use information.
Inkjet-Printed Flexible mm-Wave Van-Atta Reflectarrays: A Solution for Ultralong-Range Dense Multitag and Multisensing Chipless RFID Implementations for IoT Smart Skins

In this effort, the authors implement the first ultralong-range chipless sensing sticker, by providing more than an order of magnitude increase in reading range, compared with the state of the art. The theoretical advantages of the use of millimeter-wave frequencies for high-performance chipless radio-frequency identification (RFID) sensor implementations are first argued before both a new fully inkjet-printed flexible device, based on the Van-Atta reflectarray structure, as well as a new chipless RFID polarimetric interrogation, and time-frequency data-processing approach is then presented and implemented, for operation in the Ka-band. The array, fully inkjet printed on Kapton HN polyimide, was demonstrated as being robust to variations of interrogation angle (between ±70° from normal), as well as to bending. With its demonstrated range, in excess of 30 m, and its proven adequacy for dense multitag and multisensing implementations in indoor environments, the structure may set the foundation for the emergence of flexible printable low-cost sensing smart skins for the Internet of Things.
Development of the Highly Precise Magnetic Current Sensor Module of +/−300 A Utilizing AMR Element With Bias-Magnet

More and more sensitivity improvement is required for current sensors that are used in new area of applications, such as electric vehicle, smart meter, and electricity usage monitoring system. To correspond with the technical needs, a high precision magnetic current sensor module has been developed. The sensor module features an excellent linearity and a small magnetic hysteresis. In addition, it offers 2.5-4.5 V voltage output for 0-300 A positive input current and 0.5-2.5 V voltage output for 0-300 A negative input current under -40 °C-125 °C, VCC = 5 V condition.
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge

We introduce a large dataset of narrative texts and questions about these texts, intended to be used in a machine comprehension task that requires reasoning using commonsense knowledge. Our dataset complements similar datasets in that we focus on stories about everyday activities, such as going to the movies or working in the garden, and that the questions require commonsense knowledge, or more specifically, script knowledge, to be answered. We show that our mode of data collection via crowdsourcing results in a substantial amount of such inference questions. The dataset forms the basis of a shared task on commonsense and script knowledge organized at SemEval 2018 and provides challenging test cases for the broader natural language understanding community.
A Joyful Ode to Automatic Orchestration

Most works in automatic music generation have addressed so far specific tasks. Such a reductionist approach has been extremely successful and some of these tasks have been solved once and for all. However, few works have addressed the issue of generating automatically fully fledged music material, of human-level quality. In this article, we report on a specific experiment in holistic music generation: the reorchestration of Beethoven’s Ode to Joy, the European anthem, in seven styles. These reorchestrations were produced with algorithms developed in the Flow Machines project and within a short time frame. We stress the benefits of having had such a challenging and unifying goal, and the interesting problems and challenges it raised along the way.
Experimental evaluation of eye-blink parameters as a drowsiness measure

Drowsiness and increased tendency to fall asleep during daytime is still a generally underestimated problem. An increased tendency to fall asleep limits the efficiency at work and substantially increases the risk of accidents. Reduced alertness is difficult to assess, particularly under real life settings. Most of the available measuring procedures are laboratory-oriented and their applicability under field conditions is limited; their validity and sensitivity are often a matter of controversy. The spontaneous eye blink is considered to be a suitable ocular indicator for fatigue diagnostics. To evaluate eye blink parameters as a drowsiness indicator, a contact-free method for the measurement of spontaneous eye blinks was developed. An infrared sensor clipped to an eyeglass frame records eyelid movements continuously. In a series of sessions with 60 healthy adult participants, the validity of spontaneous blink parameters was investigated. The subjective state was determined by means of questionnaires immediately before the recording of eye blinks. The results show that several parameters of the spontaneous eye blink can be used as indicators in fatigue diagnostics. The parameters blink duration and reopening time in particular change reliably with increasing drowsiness. Furthermore, the proportion of long closure duration blinks proves to be an informative parameter. The results demonstrate that the measurement of eye blink parameters provides reliable information about drowsiness/sleepiness, which may also be applied to the continuous monitoring of the tendency to fall asleep.
A structural model of technology acceptance

Business organisations are constantly looking for ways to gain an advantage over their competitors (Beyleveld & Schurink, 2005; Castaneda & Toulson, 2013). Historically, their focus was on producing as much as possible without considering exact demand (Turner & Chung, 2005). Recently, businesses have embarked upon finding more efficient ways to deal with large turnovers (Umble, Haft & Umble, 2003). One way of achieving this is by employing an Enterprise Resource Planning (ERP) system. An ERP system is a mandatory, integrated, customised, packaged software-based system that handles most of the system requirements in all business operational functions such as finance, human resources, manufacturing, sales and marketing (Wua, Onga & Hsub, 2008). Although expectations from ERP systems are high, these systems have not always led to significant organisational enhancement (Soh, Kien & Tay-Yap, 2000) and most ERP projects turn out to be over budget, not on time and unsuccessful (Abugabah & Sanzogni, 2010; Hong & Kim, 2002; Kumar, Maheshwari & Kumar, 2003).
Agile methods tailoring - A systematic literature review

The software development industry has been adopting agile methods instead of traditional software development methods because they are more flexible and can bring benefits such as handling requirements changes, productivity gains and business alignment.  This study seeks to evaluate, synthesize, and present aspects of research on agile methods tailoring including the method tailoring approaches adopted and the criteria used for agile practice selection.  The method adopted was a Systematic Literature Review (SLR) on studies published from 2002 to 2014.  56 out of 783 papers have been identified as describing agile method tailoring approaches. These studies have been identified as case studies regarding the empirical research, as solution proposals regarding the research type, and as evaluation studies regarding the research validation type. Most of the papers used method engineering to implement tailoring and were not specific to any agile method on their scope.  Most of agile methods tailoring research papers proposed or improved a technique, were implemented as case studies analyzing one case in details and validated their findings using evaluation. Method engineering was the base for tailoring, the approaches are independent of agile method and the main criteria used are internal environment and objectives variables.
Hearing lips and seeing voices

MOST verbal communication occurs in contexts where the listener can see the speaker as well as hear him. However, speech perception is normally regarded as a purely auditory process. The study reported here demonstrates a previously unrecognised influence of vision upon speech perception. It stems from an observation that, on being shown a film of a young woman's talking head, in which repeated utterances of the syllable [ba] had been dubbed on to lip movements for [ga], normal adults reported hearing [da]. With the reverse dubbing process, a majority reported hearing [bagba] or [gaba]. When these subjects listened to the soundtrack from the film, without visual input, or when they watched untreated film, they reported the syllables accurately as repetitions of [ba] or [ga]. Subsequent replications confirm the reliability of these findings; they have important implications for the understanding of speech perception.
Multi-Document Summarization via Discriminative Summary Reranking

Existing multi-document summarization systems usually rely on a specific summarization model (i.e., a summarization method with a specific parameter setting) to extract summaries for different document sets with different topics. However, according to our quantitative analysis, none of the existing summarization models can always produce high-quality summaries for different document sets, and even a summarization model with good overall performance may produce low-quality summaries for some document sets. On the contrary, a baseline summarization model may produce high-quality summaries for some document sets. Based on the above observations, we treat the summaries produced by different summarization models as candidate summaries, and then explore discriminative reranking techniques to identify high-quality summaries from the candidates for difference document sets. We propose to extract a set of candidate summaries for each document set based on an ILP framework, and then leverage Ranking SVM for summary reranking. Various useful features have been developed for the reranking process, including word-level features, sentence-level features and summary-level features. Evaluation results on the benchmark DUC datasets validate the efficacy and robustness of our proposed approach.
Loop gain analysis and development of high-speed high-accuracy current sensors for switching converters

Loop gain analysis for performance evaluation of current sensors for switching converters is presented. The MOS transistor scaling technique is reviewed and employed in developing high-speed and high-accuracy current-sensors with offset-current cancellation. Using a standard 0.35/spl mu/m CMOS process, and integrated full-range inductor current sensor for a boost converter is designed. It operated at a supply voltage of 1.5 V with a DC loop gain of 38 dB, and a unity gain frequency of 10 MHz. The sensor worked properly at a converter switching frequency of 500 kHz.
Navigating car-like robots in unstructured environments using an obstacle sensitive cost function

We propose a method for navigating a car-like vehicle within an unstructured environment. Path planning is posed as a graph search problem. The search graph is set up in a way that implies derivation of a feed forward term for a downstream closed loop controller. An informed search algorithm is used that is guided by a heuristic cost function that accounts for both kinematic constraints of the vehicle and the topology of the vehiclepsilas free space. Configuration space obstacles are computed from an obstacle map acquired from a high definition laser range scanner and search is restricted to the collision free subset of the configuration space. The algorithm allows for solving all of the following problems: Precise parking maneuvers, narrow turns, long distance navigation. The system has been used successfully on board the autonomous car ANNIEWAY in the DARPA urban challenge competition of 2007.
On Motion Planning in Changing, Partially Predictable Environments

We present a framework for analyzing and computing motion plans for a robot that operates in an environment that both varies over time and is not completely predictable. We rst classify sources of uncertainty in motion planning into four categories, and argue that the problems addressed in this paper belong to a fundamental category that has received little attention. We treat the changing environment in a exible manner by combining traditional connguration space concepts with a Markov process that models the environment. For this context, we then propose the use of a motion strategy, which provides a motion command for the robot for each contingency that it could be confronted with. We allow the speciication of a desired performance criterion, such as time or distance, and determine a motion strategy that is optimal with respect to that criterion. We demonstrate the breadth of our framework by applying it to a variety of motion planning problems. Examples are computed for problems that involve a changing conng-uration space, hazardous regions and shelters, and processing of random service requests. To achieve this, we have exploited the powerful principle of optimality, which leads to a dynamic programming-based algorithm for determining optimal strategies. In addition, we present several extensions to the basic framework that incorporate additional concerns, such as sensing issues or changes in the geometry of the robot.
DPSNET: END-TO-END DEEP PLANE SWEEP STEREO

Multiview stereo aims to reconstruct scene depth from images acquired by a camera under arbitrary motion. Recent methods address this problem through deep learning, which can utilize semantic cues to deal with challenges such as textureless and reflective regions. In this paper, we present a convolutional neural network called DPSNet (Deep Plane Sweep Network) whose design is inspired by best practices of traditional geometry-based approaches. Rather than directly estimating depth and/or optical flow correspondence from image pairs as done in many previous deep learning methods, DPSNet takes a plane sweep approach that involves building a cost volume from deep features using the plane sweep algorithm, regularizing the cost volume via a context-aware cost aggregation, and regressing the depth map from the cost volume. The cost volume is constructed using a differentiable warping process that allows for end-to-end training of the network. Through the effective incorporation of conventional multiview stereo concepts within a deep learning framework, DPSNet achieves state-of-the-art reconstruction results on a variety of challenging datasets.
Intelligent System for Information Security Management: Architecture and Design Issues

The limitations of each security technology combined with the growth of cyber attacks impact the efficiency of information security management and increase the activities to be performed by network administrators and security staff. Therefore, there is a need for the increase of automated auditing and intelligent reporting mechanisms for the cyber trust. Intelligent systems are emerging computing systems based on intelligent techniques that support continuous monitoring and controlling plant activities. Intelligence improves an individual’s ability to make better decisions. This paper presents a proposed architecture of an Intelligent System for Information Security Management (ISISM). The objective of this system is to improve security management processes such as monitoring, controlling, and decision making with an effect size that is higher than an expert in security by providing mechanisms to enhance the active construction of knowledge about threats, policies, procedures, and risks. We focus on requirements and design issues for the basic components of the intelligent system.
D 3 : Data-Driven Documents

Data-Driven Documents (D3) is a novel representation-transparent approach to visualization for the web. Rather than hide the underlying scenegraph within a toolkit-specific abstraction, D3 enables direct inspection and manipulation of a native representation: the standard document object model (DOM). With D3, designers selectively bind input data to arbitrary document elements, applying dynamic transforms to both generate and modify content. We show how representational transparency improves expressiveness and better integrates with developer tools than prior approaches, while offering comparable notational efficiency and retaining powerful declarative components. Immediate evaluation of operators further simplifies debugging and allows iterative development. Additionally, we demonstrate how D3 transforms naturally enable animation and interaction with dramatic performance improvements over intermediate representations.
Troubling Trends in Machine Learning Scholarship

Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of the following goals, among others: to theoretically characterize what is learnable, to obtain understanding through empirically rigorous experiments, or to build a working system that has high predictive accuracy. While determining which knowledge warrants inquiry may be subjective, once the topic is fixed, papers are most valuable to the community when they act in service of the reader, creating foundational knowledge and communicating as clearly as possible. What sort of papers best serve their readers? We can enumerate desirable characteristics: these papers should (i) provide intuition to aid the reader’s understanding, but clearly distinguish it from stronger conclusions supported by evidence; (ii) describe empirical investigations that consider and rule out alternative hypotheses [62]; (iii) make clear the relationship between theoretical analysis and intuitive or empirical claims [64]; and (iv) use language to empower the reader, choosing terminology to avoid misleading or unproven connotations, collisions with other definitions, or conflation with other related but distinct concepts [56]. Recent progress in machine learning comes despite frequent departures from these ideals. In this paper, we focus on the following four patterns that appear to us to be trending in ML scholarship:
Ambiguity Detection: Towards a Tool Explaining Ambiguity Sources

Natural language is the main representation means of industrial requirements documents, which implies that requirements documents are inherently ambiguous. There exist guidelines for ambiguity detection, such as the Ambiguity Handbook . In order to detect ambiguities according to the existing guidelines, it is necessary to train analysts.  Although ambiguity detection guidelines were extensively discussed in literature, ambiguity detection has not been automated yet. Automation of ambiguity detection is one of the goals of the presented paper. More precisely, the approach and tool presented in this paper have three goals: (1) to automate ambiguity detection, (2) to make plausible for the analyst that ambiguities detected by the tool represent genuine problems of the analyzed document, and (3) to educate the analyst by explaining the sources of the detected ambiguities.  The presented tool provides reliable ambiguity detection, in the sense that it detects four times as many genuine ambiguities as than an average human analyst. Furthermore, the tool offers high precision ambiguity detection and does not present too many false positives to the human analyst.  The presented tool is able both to detect the ambiguities and to explain ambiguity sources. Thus, besides pure ambiguity detection, it can be used to educate analysts, too. Furthermore, it provides a significant potential for considerable time and cost savings and at the same time quality improvements in the industrial requirements engineering.
A Novel Continuum Trunk Robot Based on Contractor Muscles

We describe the design, construction, and operation of a novel continuous backbone “continuum” robot. Inspired by biological trunks and tentacles, the robot is actuated by the pneumatic muscles which form its structure. In contrast to previous designs, the actuators are contractor muscles, which decrease in length as pressure is increased. This choice of actuator results in novel and improved performance with respect to previous pneumatically actuated trunk robots, particularly in use as an active hook. We detail the design process, discuss construction issues, and describe the results of initial experiments using the robot.  Robotics, continuum, trunk, tentacle, pneumatic, McKibben muscles
Characteristics of sexual homicides committed by psychopathic and nonpsychopathic offenders.

In this study, the relationship between psychopathy and the prepetration of sexual homicide was investigated. The official file descriptions of sexual homicides committed by 18 psychopathic and 20 nonpsychopathic Canadian offenders were coded (by coders unaware of Psychopathy Checklist--Revised [PCL--R] scores) for characteristics of the victim, victim/perpetrator relationship, and evidence of gratuitous and sadistic violent behavior. Results indicated that most (84.7%) of the sexual murderers scored in the moderate to high range on the PCL--R. The majority of victims (66.67%) were female strangers, with no apparent influence of psychopathy on victim choice. Homicides committed by psychopathic offenders (using a PCL--R cut-off of 30) contained a significantly higher level of both gratuitous and sadistic violence than nonpsychopathic offenders. Most (82.4%) of the psychopaths exhibited some degree of sadistic behavior in their homicides compared to 52.6% of the nonpsychopaths. Implications for homicide investigations are discussed.
Artificial bee colony algorithm, its variants and applications: A survey.

Artificial Bee Colony Algorithm (ABC) is nature-inspired metaheuristic, which imitates the foraging behavior of bees. ABC as a stochastic technique is easy to implement, has fewer control parameters, and could easily be modify and hybridized with other metaheuristic algorithms. Due to its successful implementation, several researchers in the optimization and artificial intelligence domains have adopted it to be the main focus of their research work. Since 2005, several related works have appeared to enhance the performance of the standard ABC in the literature, to meet up with challenges of recent research problems being encountered. Interestingly, ABC has been tailored successfully, to solve a wide variety of discrete and continuous optimization problems. Some other works have modified and hybridized ABC to other algorithms, to further enhance the structure of its framework. In this review paper, we provide a thorough and extensive overview of most research work focusing on the application of ABC, with the expectation that it would serve as a reference material to both old and new, incoming researchers to the field, to support their understanding of current trends and assist their future research prospects and directions. The advantages, applications and drawbacks of the newly developed ABC hybrids are highlighted, critically analyzed and discussed accordingly.