nils-herrmann/clean_scirepeval huggingface dataset:
Fully Lexicalising CCGbank with Hat Categories

We introduce an extension to CCG that allows form and function to be represented simultaneously, reducing the proliferation of modifier categories seen in standard CCG analyses. We can then remove the non-combinatory rules CCGbank uses to address this problem, producing a grammar that is fully lexicalised and far less ambiguous. There are intrinsic benefits to full lexicalisation, such as semantic transparency and simpler domain adaptation. The clearest advantage is a 52-88% improvement in parse speeds, which comes with only a small reduction in accuracy.
The Cetus Source-to-Source Compiler Infrastructure: Overview and Evaluation

This paper provides an overview and an evaluation of the Cetus source-to-source compiler infrastructure. The original goal of the Cetus project was to create an easy-to-use compiler for research in automatic parallelization of C programs. In meantime, Cetus has been used for many additional program transformation tasks. It serves as a compiler infrastructure for many projects in the US and internationally. Recently, Cetus has been supported by the National Science Foundation to build a community resource. The compiler has gone through several iterations of benchmark studies and implementations of those techniques that could improve the parallel performance of these programs. These efforts have resulted in a system that favorably compares with state-of-the-art parallelizers, such as Intel’s ICC. A key limitation of advanced optimizing compilers is their lack of runtime information, such as the program input data. We will discuss and evaluate several techniques that support dynamic optimization decisions. Finally, as there is an extensive body of proposed compiler analyses and transformations for parallelization, the question of the importance of the techniques arises. This paper evaluates the impact of the individual Cetus techniques on overall program performance.
Structured Dialogue Policy with Graph Neural Networks

Recently, deep reinforcement learning (DRL) has been used for dialogue policy optimization. However, many DRL-based policies are not sample-efficient. Most recent advances focus on improving DRL optimization algorithms to address this issue. Here, we take an alternative route of designing neural network structure that is better suited for DRL-based dialogue management. The proposed structured deep reinforcement learning is based on graph neural networks (GNN), which consists of some sub-networks, each one for a node on a directed graph. The graph is defined according to the domain ontology and each node can be considered as a sub-agent. During decision making, these sub-agents have internal message exchange between neighbors on the graph. We also propose an approach to jointly optimize the graph structure as well as the parameters of GNN. Experiments show that structured DRL significantly outperforms previous state-of-the-art approaches in almost all of the 18 tasks of the PyDial benchmark.
Basal ganglia circuit loops, dopamine and motivation: A review and enquiry

Dopamine neurons located in the midbrain play a role in motivation that regulates approach behavior (approach motivation). In addition, activation and inactivation of dopamine neurons regulate mood and induce reward and aversion, respectively. Accumulating evidence suggests that such motivational role of dopamine neurons is not limited to those located in the ventral tegmental area, but also in the substantia nigra. The present paper reviews previous rodent work concerning dopamine's role in approach motivation and the connectivity of dopamine neurons, and proposes two working models: One concerns the relationship between extracellular dopamine concentration and approach motivation. High, moderate and low concentrations of extracellular dopamine induce euphoric, seeking and aversive states, respectively. The other concerns circuit loops involving the cerebral cortex, basal ganglia, thalamus, epithalamus, and midbrain through which dopaminergic activity alters approach motivation. These models should help to generate hypothesis-driven research and provide insights for understanding altered states associated with drugs of abuse and affective disorders.
Music and aggression: the impact of sexual-aggressive song lyrics on aggression-related thoughts, emotions, and behavior toward the same and the opposite sex.

Three studies examined the impact of sexual-aggressive song lyrics on aggressive thoughts, emotions, and behavior toward the same and the opposite sex. In Study 1, the authors directly manipulated whether male or female participants listened to misogynous or neutral song lyrics and measured actual aggressive behavior. Male participants who were exposed to misogynous song lyrics administered more hot chili sauce to a female than to a male confederate. Study 2 shed some light on the underlying psychological processes: Male participants who heard misogynous song lyrics recalled more negative attributes of women and reported more feelings of vengeance than when they heard neutral song lyrics. In addition, men-hating song lyrics had a similar effect on aggression-related responses of female participants toward men. Finally, Study 3 replicated the findings of the previous two studies with an alternative measure of aggressive behavior as well as a more subtle measure of aggressive cognitions. The results are discussed in the framework of the General Aggression Model.
Predicting intraindividual changes in teacher burnout : The role of perceived school environment and motivational factors

Based on self-determination theory, this study proposes and tests a motivational model of intraindividual changes in teacher burnout (emotional exhaustion, depersonalization, and reduced personal accomplishment). Participants were 806 French-Canadian teachers in public elementary and high schools. Results show that changes in teachers’ perceptions of classroom overload and students’ disruptive behavior are negatively related to changes in autonomous motivation, which in turn negatively predict changes in emotional exhaustion. Results also indicate that changes in teachers’ perceptions of students’ disruptive behaviors and school principal’s leadership behaviors are related to changes in self-efficacy, which in turn negatively predict changes in three burnout components.
HIV-AIDS, is not a Viral Disease; It is a Metabolic Syndrome

In the past 3 decades, we have been living with the hypothesis that, “HIV causes AIDS.” HIV, Human Immunodeficiency Virus is considered as the causative agent of AIDS, Acquired Immunodeficiency Syndrome; wherein the body's immune system gets damaged opening doors for major infections. However, the 3 Noble prize winner prestigious scientists including Luc Montagnier (discovered HIV), Kary Mullis (invented PCR test for HIV detection), and Wangari Maathai, (renowned African environmentalist), along with thousands of other scientists and intellects worked on the other side of the coin to prove back and again that the above said theory is a misconception. Last 35 years have provided immense literature and investigation evidences to firmly conclude that HIV is not the real cause of AIDS. On the other hand, malnutrition and metabolic syndrome due to drug abuse have been pointed as the real convicts. This review article revolves around the same theory for better understanding the HIV-AIDS hypothesis to be fallacious and search for valid causes.
An experimental kinestatic comparison between continuum manipulators with structural variations

Continuum manipulators attract a lot of interests due to their advantageous properties, such as distal dexterity, design compactness, intrinsic compliance for safe interaction with unstructured environments. However, these manipulators sometimes suffer from the lack of enough stiffness while applied in surgical robotic systems. This paper presents an experimental kinestatic comparison between three continuum manipulators, aiming at revealing how structural variations could alter the manipulators' stiffness properties. These variations not only include modifying the arrangements of elastic components, but also include integrating a passive rigid kinematic chain to form a hybrid continuum-rigid manipulator. Results of this paper could contribute to the development of design guidelines for realizing desired stiffness properties of a continuum or hybrid manipulator.
ON-THE-FLY NETWORK PRUNING

Object detection with deep neural networks is often performed by passing a few thousand candidate bounding boxes through a deep neural network for each image. These bounding boxes are highly correlated since they originate from the same image. In this paper we investigate how to exploit feature occurrence at the image scale to prune the neural network which is subsequently applied to all bounding boxes. We show that removing units which have near-zero activation in the image allows us to significantly reduce the number of parameters in the network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that up to 40% of units in some fully-connected layers can be entirely eliminated with little change in the detection result.
A voice-controlled personal assistant robot

Personal robotic assistants help reducing the manual efforts being put by humans in their day-to-day tasks. In this paper, we develop a voice-controlled personal assistant robot. The human voice commands are given to the robotic assistant remotely, by using a smart mobile phone. The robot can perform different movements, turns, start/stop operations and relocate an object from one place to another. The voice commands are processed in real-time, using an online cloud server. The speech signal commands converted to text form are communicated to the robot over a Bluetooth network. The personal assistant robot is developed on a micro-controller based platform and can be aware of its current location. The effectiveness of the voice control communicated over a distance is measured through several experiments. Performance evaluation is carried out with encouraging results of the initial experiments. Possible improvements are also discussed towards potential applications in home, hospitals and industries.
Traffic Accident Analysis Using Machine Learning Paradigms

Engineers and researchers in the automobile industry have tried to design and build safer automobiles, but traffic accidents are unavoidable. Patterns involved in dangerous crashes could be detected if we develop accurate prediction models capable of automatic classification of type of injury severity of various traffic accidents. These behavioral and roadway accident patterns can be useful to develop traffic safety control policies. We believe that to obtain the greatest possible accident reduction effects with limited budgetary resources, it is important that measures be based on scientific and objective surveys of the causes of accidents and severity of injuries. This paper summarizes the performance of four machine learning paradigms applied to modeling the severity of injury that occurred during traffic accidents. We considered neural networks trained using hybrid learning approaches, support vector machines, decision trees and a concurrent hybrid model involving decision trees and neural networks. Experiment results reveal that among the machine learning paradigms considered the hybrid decision tree-neural network approach outperformed the individual approaches.
Blockchain: A game changer for securing IoT data

Internet of Things (IoT) is now in its initial stage but very soon, it is going to influence almost every day-to-day items we use. The more it will be included in our lifestyle, more will be the threat of it being misused. There is an urgent need to make IoT devices secure from getting cracked. Very soon IoT is going to expand the area for the cyber-attacks on homes and businesses by transforming objects that were used to be offline into online systems. Existing security technologies are just not enough to deal with this problem. Blockchain has emerged as the possible solution for creating more secure IoT systems in the time to come. In this paper, first an overview of the blockchain technology and its implementation has been explained; then we have discussed the infrastructure of IoT which is based on Blockchain network and at last a model has been provided for the security of internet of things using blockchain.
CURVE FITTING FOR COARSE DATA USING ARTIFICIAL NEURAL NETWORK

This paper demonstrates the capability of curve fitting using Artificial Neural Network (ANN), not only for a moderate set of input data but also for a coarse set of input. When appropriate number of neurons is chosen for the training purpose, accurate graphs can be obtained, despite having a coarse data. The effect of number of neurons used for curve fitting and the accuracy obtained is also studied. This aspect of ANN has been illustrated through 2 examples, Weibull distribution and another complex sinusoidal system. This curve fitting technique has been applied to a real world problem i.e. mechanism of a deep drawing press, for both slider displacement and slider velocity.  curve fitting, ANN, accurate, coarse data, best fit, neurons
A Thesis for the Degree of Master Consumer Attitudes toward Location-based Advertising via Mobile Devices : An Empirical Study

Mobile advertising is evolving rapidly and becoming the key mobile data and revenue drivers of the mobile contents market. More powerful mobile devices have made possible the creation of better and richer mobile advertising. Moreover, the integration of location-aware technologies such as Cell Identification and GPS (Global Positioning Systems) into mobile devices has inspired the development of location-based advertising (LBA). As location-based services (LBS) have the potential to become the first realizable example of ubiquitous computing, business opportunities from these appear quite feasible. LBA can provide relevant, targeted, and timely advertising information to consumers at the point of need. The purpose of this study is to investigate consumer attitudes toward LBA, and the
The influence of Afrocentric facial features in criminal sentencing.

Prior research has shown that within a racial category, people with more Afrocentric facial features are presumed more likely to have traits that are stereotypic of Black Americans compared with people with less Afrocentric features. The present study investigated whether this form of feature-based stereotyping might be observed in criminal-sentencing decisions. Analysis of a random sample of inmate records showed that Black and White inmates, given equivalent criminal histories, received roughly equivalent sentences. However, within each race, inmates with more Afrocentric features received harsher sentences than those with less Afrocentric features. These results are consistent with laboratory findings, and they suggest that although racial stereotyping as a function of racial category has been successfully removed from sentencing decisions, racial stereotyping based on the facial features of the offender is a form of bias that is largely overlooked.
Multimodal Named Entity Disambiguation for Noisy Social Media Posts

We introduce the new Multimodal Named Entity Disambiguation (MNED) task for multimodal social media posts such as Snapchat or Instagram captions, which are composed of short captions with accompanying images. Social media posts bring significant challenges for disambiguation tasks because 1) ambiguity not only comes from polysemous entities, but also from inconsistent or incomplete notations, 2) very limited context is provided with surrounding words, and 3) there are many emerging entities often unseen during training. To this end, we build a new dataset called SnapCaptionsKB, a collection of Snapchat image captions submitted to public and crowd-sourced stories, with named entity mentions fully annotated and linked to entities in an external knowledge base. We then build a deep zeroshot multimodal network for MNED that 1) extracts contexts from both text and image, and 2) predicts correct entity in the knowledge graph embeddings space, allowing for zeroshot disambiguation of entities unseen in training set as well. The proposed model significantly outperforms the stateof-the-art text-only NED models, showing efficacy and potentials of the MNED task.
A Greedy Algorithm for Aligning DNA Sequences

For aligning DNA sequences that differ only by sequencing errors, or by equivalent errors from other sources, a greedy algorithm can be much faster than traditional dynamic programming approaches and yet produce an alignment that is guaranteed to be theoretically optimal. We introduce a new greedy alignment algorithm with particularly good performance and show that it computes the same alignment as does a certain dynamic programming algorithm, while executing over 10 times faster on appropriate data. An implementation of this algorithm is currently used in a program that assembles the UniGene database at the National Center for Biotechnology Information.
Large Scale Implementations for Twitter Sentiment Classification

Sentiment Analysis on Twitter Data is indeed a challenging problem due to the nature, diversity and volume of the data. People tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide spectrum of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since no one can invest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample is not representative in order to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this work, we develop two systems: the first in the MapReduce and the second in the Apache Spark framework for programming with Big Data. The algorithm exploits all hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification method of diverse sentiment types in a parallel and distributed manner. Moreover, the sentiment analysis tool is based on Machine Learning methodologies alongside Natural Language Processing techniques and utilizes Apache Spark’s Machine learning library, MLlib. In order to address the nature of Big Data, we introduce some pre-processing steps for achieving better results in Sentiment Analysis as well as Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Finally, the proposed system was trained and validated with real data crawled by Twitter, and, through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable while confirming the quality of our sentiment identification.
Optimising Health Informatics Outcomes--Getting Good Evidence to Where it Matters.

This editorial is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Evidence-based Health informatics: How do we know what we know?", written by Elske Ammenwerth . Health informatics uses and applications have crept up on health systems over half a century, starting as simple automation of large-scale calculations, but now manifesting in many cases as rule- and algorithm-based creation of composite clinical analyses and 'black box' computation of clinical aspects, as well as enablement of increasingly complex care delivery modes and consumer health access. In this process health informatics has very largely bypassed the rules of precaution, proof of effectiveness, and assessment of safety applicable to all other health sciences and clinical support systems. Evaluation of informatics applications, compilation and recognition of the importance of evidence, and normalisation of Evidence Based Health Informatics, are now long overdue on grounds of efficiency and safety. Ammenwerth has now produced a rigorous analysis of the current position on evidence, and evaluation as its lifeblood, which demands careful study then active promulgation. Decisions based on political aspirations, 'modernisation' hopes, and unsupported commercial claims must cease - poor decisions are wasteful and bad systems can kill. Evidence Based Health Informatics should be promoted, and expected by users, as rigorously as Cochrane promoted Effectiveness and Efficiency, and Sackett promoted Evidence Based Medicine - both of which also were introduced retrospectively to challenge the less robust and partially unsafe traditional 'wisdom' in vogue. Ammenwerth's analysis gives the necessary material to promote that mission.
"What Parts of Your Apps are Loved by Users?" (T)

Recently, Begel et al. found that one of the most important questions software developers ask is "what parts of software are used/loved by users." User reviews provide an effective channel to address this question. However, most existing review summarization tools treat reviews as bags-of-words (i.e., mixed review categories) and are limited to extract software aspects and user preferences. We present a novel review summarization framework, SUR-Miner. Instead of a bags-of-words assumption, it classifies reviews into five categories and extracts aspects for sentences which include aspect evaluation using a pattern-based parser. Then, SUR-Miner visualizes the summaries using two interactive diagrams. Our evaluation on seventeen popular apps shows that SUR-Miner summarizes more accurate and clearer aspects than state-of-the-art techniques, with an F1-score of 0.81, significantly greater than that of ReviewSpotlight (0.56) and Guzmans' method (0.55). Feedback from developers shows that 88% developers agreed with the usefulness of the summaries from SUR-Miner.
Algorithms for Quantum Computers

Quantum computing is a new computational paradigm created by reformulating information and computation in a quantum mechanical framework [30, 27]. Since the laws of physics appear to be quantum mechanical, this is the most relevant framework to consider when considering the fundamental limitations of information processing. Furthermore, in recent decades we have seen a major shift from just observing quantum phenomena to actually controlling quantum mechanical systems. We have seen the communication of quantum information over long distances, the “teleportation” of quantum information, and the encoding and manipulation of quantum information in many different physical media. We still appear to be a long way from the implementation of a large-scale quantum computer, however it is a serious goal of many of the world’s leading physicists, and progress continues at a fast pace. In parallel with the broad and aggressive program to control quantum mechanical systems with increased precision, and to control and interact a larger number of subsystems, researchers have also been aggressively pushing the boundaries of what useful tasks one could perform with quantum mechanical devices. These in-