nils-herrmann/clean_scirepeval huggingface dataset:
Development of the radiographic union score for tibial fractures for the assessment of tibial fracture healing after intramedullary fixation.

The objective was to evaluate the newly developed Radiographic Union Score for Tibial fractures (RUST). Because there is no "gold standard," it was hypothesized that the RUST score would provide substantial improvements compared with previous scores presented in the literature.



: Forty-five sets of X-rays of tibial shaft fractures treated with intramedullary fixation were selected. Seven orthopedic reviewers independently scored bony union using RUST. Radiographs were reassessed at 9 weeks. Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI) measured agreement.



: Overall agreement was substantial (ICC, 0.86; 95% CI, 0.79-0.91). There was improved reliability among traumatologists compared with others (ICC = 0.86, 0.81, and 0.83, respectively). Overall intraobserver reliability was also substantial (ICC, 0.88; 95% CI, 0.80-0.96).



: The RUST score exhibits substantial improvements in reliability from previously published scores and produces equally reproducible results among a variety of orthopedic specialties and experience levels. Because no "gold standards" currently exist against which RUST can be compared, this study provides only the initial step in the score's full validation for use in a clinical context.
The Neglect of Power in Recent Framing Research By

This article provides a critique of recent developments in research examining media frames and their influence. We contend that a number of trends in framing research have neglected the relationship between media frames and broader issues of political and social power. This neglect is a product of a number of factors, including conceptual problems in the definition of frames, the inattention to frames sponsorship, the failure to examine framing contests within wider political and social contexts, and the reduction of framing to a form of media effects. We conclude that framing research needs to be linked to the political and social questions regarding power central to the media hegemony thesis, and illustrate this focus by exploring how framing research can contribute to an understanding of the interaction between social movements and the news media.
Commutation Torque Ripple Reduction of Brushless DC Motor in Braking Operation

In this paper, the commutation torque ripple is studied in the brushless DC motor (BLDCM) braking operation. As the phase currents flow in the reversed directions in the braking operation, the current circuits in the braking operation are different from those in the motor operation. For this reason, it is found that the commutation torque ripple can be reduced in the speed range from the rated speed to zero just by the common braking pulse width modulation strategies, where the three-phase modulation is not needed. However, it is impossible in motor operation. Under the common braking pulse width modulation strategies, the speed ranges where commutation torque ripple can be reduced are derived and compared. Based on the analysis of these speed ranges, a simple braking method is proposed. With this method, the BLDCM can be slowed down without commutation torque ripple in the whole braking process from the rated speed to zero. Experiment results verify the effectiveness of the method and the correctness of the theory.
Word Sense Induction for Novel Sense Detection

We apply topic modelling to automatically induce word senses of a target word, and demonstrate that our word sense induction method can be used to automatically detect words with emergent novel senses, as well as token occurrences of those senses. We start by exploring the utility of standard topic models for word sense induction (WSI), with a pre-determined number of topics (=senses). We next demonstrate that a non-parametric formulation that learns an appropriate number of senses per word actually performs better at the WSI task. We go on to establish state-of-the-art results over two WSI datasets, and apply the proposed model to a novel sense detection task.
Predicting information seeker satisfaction in community question answering

Question answering communities such as Naver and Yahoo! Answers have emerged as popular, and often effective, means of information seeking on the web. By posting questions for other participants to answer, information seekers can obtain specific answers to their questions. Users of popular portals such as Yahoo! Answers already have submitted millions of questions and received hundreds of millions of answers from other participants. However, it may also take hours --and sometime days-- until a satisfactory answer is posted. In this paper we introduce the problem of predicting information seeker satisfaction in collaborative question answering communities, where we attempt to predict whether a question author will be satisfied with the answers submitted by the community participants. We present a general prediction model, and develop a variety of content, structure, and community-focused features for this task. Our experimental results, obtained from a largescale evaluation over thousands of real questions and user ratings, demonstrate the feasibility of modeling and predicting asker satisfaction. We complement our results with a thorough investigation of the interactions and information seeking patterns in question answering communities that correlate with information seeker satisfaction. Our models and predictions could be useful for a variety of applications such as user intent inference, answer ranking, interface design, and query suggestion and routing.
Question Answering in the Context of Stories Generated by Computers

The QUEST model of question answering is a computational-cognitive model designed to describe a person’s mental narrative comprehension process, which predicts behavioral responses to openended questions about the narrative. The model is based around a QUEST knowledge structure, a directed graph that captures the story’s events and the causal and goal-oriented relationships between them. We present a family of computational methods to transform an automated planning narrative knowledge representation that has been successfully used for computational narrative generation, to a QUEST knowledge structure. We then report an experiment to test the hypothesis that these plan-driven knowledge structures have a meaningful relationship with a story consumer’s comprehension of a narrative. The results help advance a research agenda that uses cognitive states as the target of computationally generated narratives.
Questioning the role of sparse coding in the brain

Coding principles are central to understanding the organization of brain circuitry. Sparse coding offers several advantages, but a near-consensus has developed that it only has beneficial properties, and these are partially unique to sparse coding. We find that these advantages come at the cost of several trade-offs, with the lower capacity for generalization being especially problematic, and the value of sparse coding as a measure and its experimental support are both questionable. Furthermore, silent synapses and inhibitory interneurons can permit learning speed and memory capacity that was previously ascribed to sparse coding only. Combining these properties without exaggerated sparse coding improves the capacity for generalization and facilitates learning of models of a complex and high-dimensional reality.
Difficulty Rating of Sudoku Puzzles: An Overview and Evaluation

How can we predict the difficulty of a Sudoku puzzle? We give an overview of difficulty rating metrics and evaluate them on extensive dataset on human problem solving (more then 1700 Sudoku puzzles, hundreds of solvers). The best results are obtained using a computational model of human solving activity. Using the model we show that there are two sources of the problem difficulty: complexity of individual steps (logic operations) and structure of dependency among steps. We also describe metrics based on analysis of solutions under relaxed constraints – a novel approach inspired by phase transition phenomenon in the graph coloring problem. In our discussion we focus not just on the performance of individual metrics on the Sudoku puzzle, but also on their generalizability and applicability to other problems.
Antioxidant defense responses: physiological plasticity in higher plants under abiotic constraints

Environmental stresses (salinity, drought, heat/cold, light and other hostile conditions) may trigger in plants oxidative stress, generating the formation of reactive oxygen species (ROS). These species are partially reduced or activated derivatives of oxygen, comprising both free radical $$ ( {\text{O}}_{2}^{\cdot - } ,} \cdot , }_{ 2} \cdot ) $$ and non-radical (H2O2) forms, leading to cellular damage, metabolic disorders and senescence processes. In order to overcome oxidative stress, plants have developed two main antioxidants defense mechanisms that can be classified as non-enzymatic and enzymatic systems. The first class (non-enzymatic) consists of small molecules such as vitamin (A, C and E), glutathione, carotenoids and phenolics that can react directly with the ROS by scavenging them. Second class is represented by enzymes among them superoxide dismutase, peroxidase and catalase which have the capacity to eliminate superoxide and hydrogen peroxide. In this review, we have tried to explore the related works, which have revealed the changes in the basic antioxidant metabolism of plants under various abiotic constraints.
Dynamic encoding of binaural beats for brainwave entrainment

This paper shows the development of the dynamic binaural beat encoding using JAVA program. Binaural beat is an auditory phenomenon that has been used worldwide to alter our brain state, to get to the desired state, e.g. Relax. However, most of the Binaural Beat system designed is a static binaural beat on which the user is not able to alter the frequency of the audio. The binaural beat frequency listened by the user might not be suitable to the user's current brain state. Such incompatibility may cause the user to feel uncomfortable and may also cause dizziness while listening to the audio being played. Hence, dynamic encoding of binaural beats is designed and implemented to replace the existing static encoding of binaural beats. The GUI of the system has two options, Manual Configuration and Automatic Configuration on which the user able to choose the base frequency and the offset frequency as desired. The developed system has been tested and validated in the lab to show its ability to generate the binaural beat frequency.
A Deep Learning Approach to IoT Authentication

At its peak, the Internet-of-Things will largely be composed of low-power devices with wireless radios attached. Yet, secure authentication of these devices amidst adversaries with much higher power and computational capability remains a challenge, even for advanced cryptographic and wireless security protocols. For instance, a high-power software radio could simply replay chunks of signals from a low-power device to emulate it. This paper presents a deep-learning classifier that learns hardware imperfections of low-power radios that are challenging to emulate, even for high- power adversaries. We build an LSTM framework, specifically sensitive to signal imperfections that persist over long durations. Experimental results from a testbed of 30 low-power nodes demonstrate high resilience to advanced software radio adversaries.
Exploring instructors’ technology readiness, attitudes and behavioral intentions towards e-learning technologies in Egypt and United Arab Emirates

This paper explores the association between technology readiness, (a meta-construct consisting of optimism, innovativeness, discomfort, and insecurity), attitude, and behavioral intention towards e-learning technologies adoption within an education institution context. The empirical study data is collected at two private universities located in Egypt and UAE. The research explores the role of instructors’ technology readiness level, in shaping their attitudes, preference to human interaction and ultimately behavioral intentions towards adopting e-learning technologies. Analysis of the data (Mann-Whitney U non-parametric test) shows no significant differences between instructors at the two universities in terms of technology readiness, attitudes, behavioral intentions, and preference to human interaction. The exploratory results provide evidence for the relationship between instructors’ technology, attitude, and behavioral intentions to adopt e-learning technologies. The study finds that preference to human interaction is equally important in Egypt and UAE with a strong potential to affect instructor’s behavioral intentions for adopting e-learning technologies. The research results provide initial insights to education managers on the nature and mechanisms of the relationship among the research variables, which would improve the ability of educational institutions to introduce and adopt e-learning technologies. An additional contribution is the validity and reliability tests for Technology Readiness (TR) scale, which shows its viability as a meaningful measurement instrument for use in an educational setting.
Quo Vadis, SLD? Reasoning About the Trends and Challenges of System Level Design

System-level design (SLD) is considered by many as the next frontier in electronic design automation (EDA). SLD means many things to different people since there is no wide agreement on a definition of the term. Academia, designers, and EDA experts have taken different avenues to attack the problem, for the most part springing from the basis of traditional EDA and trying to raise the level of abstraction at which integrated circuit designs are captured, analyzed, and synthesized from. However, my opinion is that this is just the tip of the iceberg of a much bigger problem that is common to all system industry. In particular, I believe that notwithstanding the obvious differences in the vertical industrial segments (for example, consumer, automotive, computing, and communication), there is a common underlying basis that can be explored. This basis may yield a novel EDA industry and even a novel engineering field that could bring substantial productivity gains not only to the semiconductor industry but to all system industries including industrial and automotive, communication and computing, avionics and building automation, space and agriculture, and health and security, in short, a real technical renaissance. In this paper, I present the challenges faced by industry in system level design. Then, I propose a design methodology, platform-based design (PBD), that has the potential of addressing these challenges in a unified way. Further, I place methodology and tools available today in the PBD framework and present a tool environment, Metropolis, that supports PBD and that can be used to integrate available tools and methods together with two examples of its application to separate industrial domains
Individual differences in non-verbal number acuity correlate with maths achievement

Human mathematical competence emerges from two representational systems. Competence in some domains of mathematics, such as calculus, relies on symbolic representations that are unique to humans who have undergone explicit teaching. More basic numerical intuitions are supported by an evolutionarily ancient approximate number system that is shared by adults, infants and non-human animals—these groups can all represent the approximate number of items in visual or auditory arrays without verbally counting, and use this capacity to guide everyday behaviour such as foraging. Despite the widespread nature of the approximate number system both across species and across development, it is not known whether some individuals have a more precise non-verbal ‘number sense’ than others. Furthermore, the extent to which this system interfaces with the formal, symbolic maths abilities that humans acquire by explicit instruction remains unknown. Here we show that there are large individual differences in the non-verbal approximation abilities of 14-year-old children, and that these individual differences in the present correlate with children’s past scores on standardized maths achievement tests, extending all the way back to kindergarten. Moreover, this correlation remains significant when controlling for individual differences in other cognitive and performance factors. Our results show that individual differences in achievement in school mathematics are related to individual differences in the acuity of an evolutionarily ancient, unlearned approximate number sense. Further research will determine whether early differences in number sense acuity affect later maths learning, whether maths education enhances number sense acuity, and the extent to which tertiary factors can affect both.
Facial recognition with PCA and machine learning methods

Facial recognition is a challenging problem in image processing and machine learning areas. Since widespread applications of facial recognition make it a valuable research topic, this work tries to develop some new facial recognition systems that have both high recognition accuracy and fast running speed. Efforts are made to design facial recognition systems by combining different algorithms. Comparisons and evaluations of recognition accuracy and running speed show that PCA + SVM achieves the best recognition result, which is over 95% for certain training data and eigenface sizes. Also, PCA + KNN achieves the balance between recognition accuracy and running speed.
A Survey of Process Migration Mechanisms

We define process migration as the transfer of a sufficient amount of a process's state from one machine to another for the process to execute on the target machine.This paper surveys proposed and implemented mechanisms for process migration. We pay particular attention to the designer's goals, such as performance, load-balancing, and reliability. The effect of operating system design upon the ease of implementation is discussed in some detail; we conclude that message-passing systems simplify designs for migration.
Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks

Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others. One approach to this problem is through controlled experiments/trials but controlled experiments are expensive. Hence it is important to choose the best policies on the basis of observational data. This presents two difficult challenges: (i) missing counterfactuals, and (ii) selection bias. This paper presents theoretical bounds on estimation errors of counterfactuals from observational data by making connections to domain adaptation theory. It also presents a principled way of choosing optimal policies using domain adversarial neural networks. This illustrates the effectiveness of domain adversarial training together with various features of our algorithm on a semi-synthetic breast cancer dataset.
Survey of multiagents systems application in Microgrids

Microgrid will constitute the heart of the next generation of power systems. They are expected to incorporate more distributed generation (DG) mainly coming from renewable energy (RE). They are characterized by their ability to manage and control electricity by using the advance of information technology. In parallel, development of multi agent systems (MAS) and their tested proprieties increase researchers interest in applying them in Microgrid (MG) context. This paper presents a survey of MAS application in MG. It revisits the concept of MG and MAS. It introduces the agency domain and its characteristics. MAS design methodologies and implementation platform are also reported.
Compressed matching for feature vectors

The problem of compressing a large collection of feature vectors is investigated, so that object identification can be processed on the compressed form of the features. The idea is to perform matching of a query image against an image database, using directly the compressed form of the descriptor vectors, without decompression. Specifically, we concentrate on the Scale Invariant Feature Transform (SIFT), a known object detection method, as well as on Dense SIFT and PHOW features, that contain, for each image, about 300 times as many vectors as the original SIFT. Given two feature vectors, we suggest achieving our goal by compressing them using a lossless encoding by means of a Fibonacci code, for which the pairwise matching can be done directly on the compressed files. In our experiments, this approach improves the processing time and incurs only a small loss in compression efficiency relative to standard compressors requiring a decoding phase.
Experimental validation of a leg-wheel hybrid mobile robot Quattroped

This video submission presents the experimental validation and testing of a leg-wheel hybrid mobile robot Quattroped. By combining the smooth and efficient motion of wheels on the flat ground with the great mobility of legs on rough terrains, the design of the robot aims for agile and versatile yet efficient locomotion in both natural and artificial environments. Compared to most hybrid platforms, which have separate mechanisms of wheels and legs, this robot is implemented with a transformation mechanism that directly changes the morphology of wheels (i.e., a full circle) into half-circle legs, each with 2 active degrees-of-freedom (i.e., combining two half circles as a leg ). The experimental testing includes flat terrain driving and turning in the wheeled mode, leg-wheel mode switching, and step crossing, bar crossing, natural rough terrain walking, and stair climbing in the legged mode.
Real time health monitoring of industrial machine using multiclass support vector machine

Most of the failures in the industrial systems are due to motor faults which can be catastrophic and cause major downtimes. Hence, continuous health monitoring, precise fault detection and advance failure warning for motors are pivotal and cost-effective. The identification of motor faults requires sophisticated signal processing techniques for quick fault detection and isolation. This paper presents a real time health monitoring technique for induction motor using pattern recognition method. The proposed fault detection and isolation scheme comprises three stages: data acquisition, feature extraction and multiclass support vector machine classifier. This paper investigates single and multiple faults in single-phase induction motor including bearing fault, load fault and their combination. The testbed consists of 1/2 hp, 220V squirrel cage induction motor with load, vibration sensor, current sensor, data acquisition system and controller. Two features standard deviation and average value are computed for each sensor's data. Multiclass support vector machine classifier is implemented using a low-cost arduino controller for fault detection and isolation. The performance analysis of the classifier with real time sensor's data is presented which shows superior capabilities of developed method.