nils-herrmann/clean_scirepeval huggingface dataset:
A Brain Controlled Wheelchair to Navigate in Familiar Environments

While brain-computer interfaces (BCIs) can provide communication to people who are locked-in, they suffer from a very low information transfer rate. Further, using a BCI requires a concentration effort and using it continuously can be tiring. The brain controlled wheelchair (BCW) described in this paper aims at providing mobility to BCI users despite these limitations, in a safe and efficient way. Using a slow but reliable P300 based BCI, the user selects a destination amongst a list of predefined locations. While the wheelchair moves on virtual guiding paths ensuring smooth, safe, and predictable trajectories, the user can stop the wheelchair by using a faster BCI. Experiments with nondisabled subjects demonstrated the efficiency of this strategy. Brain control was not affected when the wheelchair was in motion, and the BCW enabled the users to move to various locations in less time and with significantly less control effort than other control strategies proposed in the literature.
Biomedical question answering: A survey

In this survey, we reviewed the current state of the art in biomedical QA (Question Answering), within a broader framework of semantic knowledge-based QA approaches, and projected directions for the future research development in this critical area of intersection between Artificial Intelligence, Information Retrieval, and Biomedical Informatics.



We devised a conceptual framework within which to categorize current QA approaches. In particular, we used "semantic knowledge-based QA" as a category under which to subsume QA techniques and approaches, both corpus-based and knowledge base (KB)-based, that utilize semantic knowledge-informed techniques in the QA process, and we further classified those approaches into three subcategories: (1) semantics-based, (2) inference-based, and (3) logic-based. Based on the framework, we first conducted a survey of open-domain or non-biomedical-domain QA approaches that belong to each of the three subcategories. We then conducted an in-depth review of biomedical QA, by first noting the characteristics of, and resources available for, biomedical QA and then reviewing medical QA approaches and biological QA approaches, in turn. The research articles reviewed in this paper were found and selected through online searches.



Our review suggested the following tasks ahead for the future research development in this area: (1) Construction of domain-specific typology and taxonomy of questions (biological QA), (2) Development of more sophisticated techniques for natural language (NL) question analysis and classification, (3) Development of effective methods for answer generation from potentially conflicting evidences, (4) More extensive and integrated utilization of semantic knowledge throughout the QA process, and (5) Incorporation of logic and reasoning mechanisms for answer inference.



Corresponding to the growth of biomedical information, there is a growing need for QA systems that can help users better utilize the ever-accumulating information. Continued research toward development of more sophisticated techniques for processing NL text, for utilizing semantic knowledge, and for incorporating logic and reasoning mechanisms, will lead to more useful QA systems.
Analysis and Simulation of a Digital Mobile Channel Using Orthogonal Frequency Division Multiplexing

This paper discusses the analysis and simulation of a technique for combating the effects of multipath propagation and cochannel interference on a narrow-band digital mobile channel. This system uses the discrete Fourier transform to orthogonally frequency multiplex many narrow subchannels, each signaling at a very low rate, into one high-rate channel. When this technique is used with pilot-based correction, the effects of flat Rayleigh fading can be reduced significantly. An improvement in signal-to-interference ratio of 6 dB can be obtained over the bursty Rayleigh channel. In additim, with each subchannel signaling at a low rate, this technique can provide added protection against delay spread. To enhance the behavior of the technique in a heavily frequency-selective environment, interpolated pilots are used. A frequency offset reference scheme is employed for the pilots to improve protection against cochannel interference.
Referral Web: Combining Social Networks and Collaborative Filtering

Part of the success of social networks can be attributed to the “six degrees of separation’’ phenomena that means the distance between any two individuals in terms of direct personal relationships is relatively small. An equally important factor is there are limits to the amount and kinds of information a person is able or willing to make available to the public at large. For example, an expert in a particular field is almost certainly unable to write down all he knows about the topic, and is likely to be unwilling to make letters of recommendation he or she has written for various people publicly available. Thus, searching for a piece of information in this situation becomes a matter of searching the social network for an expert on the topic together with a chain of personal referrals from the searcher to the expert. The referral chain serves two key functions: It provides a reason for the expert to agree to respond to the requester by making their relationship explicit (for example, they have a mutual collaborator), and it provides a criteria for the searcher to use in evaluating the trustworthiness of the expert. Nonetheless, manually searching for a referral chain can be a frustrating and time-consuming task. One is faced with the trade-off of contacting a large number of individuals at each step, and thus straining both the time and goodwill of the possible respondents, or of contacting a smaller, more focused set, and being more likely to fail to locate an appropriate expert. In response to these problems we are building ReferralWeb, an interactive system for reconstructing, visualizing, and searching social networks on the World-Wide Web. Simulation experiments we ran before we began construction of ReferralWeb showed that automatically generated referrals can be highly
Risk Taking Under the Influence: A Fuzzy-Trace Theory of Emotion in Adolescence.

Fuzzy-trace theory explains risky decision making in children, adolescents, and adults, incorporating social and cultural factors as well as differences in impulsivity. Here, we provide an overview of the theory, including support for counterintuitive predictions (e.g., when adolescents "rationally" weigh costs and benefits, risk taking increases, but it decreases when the core gist of a decision is processed). Then, we delineate how emotion shapes adolescent risk taking-from encoding of representations of options, to retrieval of values/principles, to application of those values/principles to representations of options. Our review indicates that: (i) Gist representations often incorporate emotion including valence, arousal, feeling states, and discrete emotions; and (ii) Emotion determines whether gist or verbatim representations are processed. We recommend interventions to reduce unhealthy risk-taking that inculcate stable gist representations, enabling adolescents to identify quickly and automatically danger even when experiencing emotion, which differs sharply from traditional approaches emphasizing deliberation and precise analysis.
Tablets and humanoid robots as engaging platforms for teaching languages

Educational use of mobile robots is among the most explored subtopics of Human-Robot Interaction (HRI), given the potential attention calling possibilities drawn by its novelty aspect. In educational environments is expected that social robots enhance learning, such as virtual agents that have been used. However, the real effect of applying a physical robot has not been well exploited. This paper presents an attention span comparison between a humanoid robot and a tablet during an English lecture. Analysis of recorded videos of the interactive sessions was performed to gather objective measurements about the experiment, and a questionnaire was applied to evaluate subjective perceptions of the children. Results have shown that the tablet performed better regarding attention, comprehension and learning perception, while the robot drew more interest.
Automatic segmentation of the airway tree from thoracic CT scans using a multi-threshold approach

A method for automatic extraction of the airway tree from thoracic CT scans is presented that uses adaptive thresholds while growing the airways. The method is evaluated on 20 volumetric chest CT scans provided by the Extraction of Airways from CT 2009 (EXACT09) challenge. The scans were acquired at different sites, using several different scanners, scanning protocols, and reconstruction parameters. There are scans of clinical dose, low dose, and ultra-low dose data, in inspiration and expiration, from both relatively healthy and severely ill patients. The results show that the method is able to detect a large number of airway branches at the cost of relatively high leakage volume.
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep RL for image processing are still limited. Therefore, we extend deep RL to pixelRL for various image processing applications. In pixelRL, each pixel has an agent, and the agent changes the pixel value by taking an action. We also propose an effective learning method for pixelRL that significantly improves the performance by considering not only the future states of the own pixel but also those of the neighbor pixels. The proposed method can be applied to some image processing tasks that require pixel-wise manipulations, where deep RL has never been applied. We apply the proposed method to three image processing tasks: image denoising, image restoration, and local color enhancement. Our experimental results demonstrate that the proposed method achieves comparable or better performance, compared with the state-of-the-art methods based on supervised learning.
Attentive Tensor Product Learning for Language Generation and Grammar Parsing

This paper proposes a new architecture — Attentive Tensor Product Learning (ATPL) — to represent grammatical structures in deep learning models. ATPL is a new architecture to bridge this gap by exploiting Tensor Product Representations (TPR), a structured neural-symbolic model developed in cognitive science, aiming to integrate deep learning with explicit language structures and rules. The key ideas of ATPL are: 1) unsupervised learning of role-unbinding vectors of words via TPR-based deep neural network; 2) employing attention modules to compute TPR; and 3) integration of TPR with typical deep learning architectures including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FFNN). The novelty of our approach lies in its ability to extract the grammatical structure of a sentence by using role-unbinding vectors, which are obtained in an unsupervised manner. This ATPL approach is applied to 1) image captioning, 2) part of speech (POS) tagging, and 3) constituency parsing of a sentence. Experimental results demonstrate the effectiveness of the proposed approach.
Social Participation Ontology: community documentation, enhancements and use examples

Participatory democracy advances in virtually all governments. South America presents a prominent context with mixed culture and social predisposition. In 2012, civil, academic and governmental parties started elaborating the “Common Vocabulary of Social Participation” (vcps from the Brazilian name Vocabulário Comum de Participação Social), as a public and online process. By May, 2013, first reference documents were publicized, together with a preliminary owl code, logos, and a diagram of a general “public consultation”. The corais platform kept online records of the process, like discussions and preparation of texts. This article exposes this material and proposes an elementary unfolding: the “Social Participation Ontology” (ops from the Brazilian name Ontologia de Participação Social). To exhibit this new ontology, these steps were considered: correction of ontological contradictions and owl protocol use errors; completion of vcps owl code into a preliminary version of the ops; translations and standardizations; enhancements of class names and labels in Portuguese, Spanish and English; a toy expansion of the ontology by further specifying classes; linked data examples regarding dereferencing, a sparql endpoint and participatory instances; use cases by researchers and public managers. Ongoing work involves further adoption of ops by the official Brazilian federal portal of social participation, further adoption by civil participatory organizations, and linkage to other participatory ontologies. ops is being used as an upper ontology, and all classes linked further to foaf and bfo as higher upper ontologies.
Modeling and Analyzing Millimeter Wave Cellular Systems

We provide a comprehensive overview of mathematical models and analytical techniques for millimeter wave (mmWave) cellular systems. The two fundamental physical differences from conventional sub-6-GHz cellular systems are: 1) vulnerability to blocking and 2) the need for significant directionality at the transmitter and/or receiver, which is achieved through the use of large antenna arrays of small individual elements. We overview and compare models for both of these factors, and present a baseline analytical approach based on stochastic geometry that allows the computation of the statistical distributions of the downlink signal-to-interference-plus-noise ratio (SINR) and also the per link data rate, which depends on the SINR as well as the average load. There are many implications of the models and analysis: 1) mmWave systems are significantly more noise-limited than at sub-6 GHz for most parameter configurations; 2) initial access is much more difficult in mmWave; 3) self-backhauling is more viable than in sub-6-GHz systems, which makes ultra-dense deployments more viable, but this leads to increasingly interference-limited behavior; and 4) in sharp contrast to sub-6-GHz systems cellular operators can mutually benefit by sharing their spectrum licenses despite the uncontrolled interference that results from doing so. We conclude by outlining several important extensions of the baseline model, many of which are promising avenues for future research.
The Vertica Analytic Database: C-Store 7 Years Later

This paper describes the system architecture of the Vertica Analytic Database (Vertica), a commercialization of the design of the C-Store research prototype. Vertica demonstrates a modern commercial RDBMS system that presents a classical relational interface while at the same time achieving the high performance expected from modern “web scale” analytic systems by making appropriate architectural choices. Vertica is also an instructive lesson in how academic systems research can be directly commercialized into a successful product.
Docker Cluster Management for the Cloud - Survey Results and Own Solution

Docker provides a good basis to run composite applications in the cloud, especially if those are not cloud-aware, or cloud-native. However, Docker concentrates on managing containers on one host, but SaaS providers need a container management solution for multiple hosts. Therefore, a number of tools emerged that claim to solve the problem. This paper classifies the solutions, maps them to requirements from a case study and identifies gaps and integration requirements. We close some of these gaps with our own integration components and tool enhancements, resulting in the currently most complete management suite.
MRI BRAIN IMAGE SEGMENTATION TECHNIQUES - A REVIEW

Brain tumour is one of the most dangerous disease occurring commonly among human beings. The chances of survival can be increased if the tumour is detected correctly at its early stage. MRI brain imaging technique is widely used to visualize the anatomy and structure of the brain. The images produced by MRI are high in tissue contrast and have fewer artifacts. It has several advantages over other imaging techniques, providing high contrast between soft tissues. However, the amount of data is far too much for manual analysis, which has been one of the biggest obstacles in the effective use of MRI. The detection of tumour requires several processes on MRI images which includes image preprocessing, feature extraction, image enhancement and classification. The final classification process concludes that a person is diseased or not. Although numerous efforts and promising results are obtained in medical imaging area, reproducible segmentation and classification of abnormalities are still a challenging task because of the different shapes, locations and image intensities of different types of tumours. In this paper, various approaches of MRI brain image segmentation algorithms are reviewed and their advantages, disadvantages are discussed.
How do teachers perceive educational robots in formal education ? A study based on the Thymio robot *

Robots have generated interest in schools since Seymour Papert’s works; however when his Logo turtles were introduced into schools in the 1980s, they proved to be unreliable, expensive, and limited . Since then, we have seen various affordable, reliable, and polyvalent platforms such as Lego Mindstorms  or the Bee-Bot . Robotics has become more appealing, and it is an established fact that educational robots can improve children’s motivation , . Robotics also embodies a wide range of disciplines, which allows its use in a broad educational area and in interdisciplinary studies. Its use in compulsory schools could bring technology to a larger audience, including both genders. Although there has been an increasing number of extracurricular robotics activities such as robotic contests or festivals , which show the widespread adoption of robotics in informal education, several authors are struggling to understand why robots are still underused in schools, for formal education. Some argue, without clear evidence, that this is due to the lack of material available for teachers [6], missing functionalities , a paucity of flexibility and dynamism in schools [7], or a dearth of evidence regarding the educational benefits of this approach . Although there is no agreement over the exact reasons for this situation, it seems clear, from these and other studies , , that teachers play a key role in the introduction of technology in schools. Despite this obvious observation, there is a severe lack of studies analyzing this key factor, and, in particular, the attitude of teachers toward educational robots. Lee et al.  examined the perception of such robots in Korea, by teachers, students, and parents. Their results showed that, while the teachers’ opinions of robots were worse than those of the students and parents, none of them wanted robots to replace teachers. Fridin and Belokopytov  studied the first-time acceptance by teachers of a socially assistive humanoid robot, showing that teachers’ desire to use robots is mainly linked to their perceived utility as tools. A limiting factor in this study is that the teachers were interacting with a robot for the first time. Kim et al.  performed a short survey of 116 Korean teachers who had had an initial experience of using robots in
Food-related illness and death in the United States.

Letters Letters Letters Letters Letters correctly identified E. faecium and E. faecalis to the species level, most (4 of 5) did not correctly identify E. gallinarum (three misidentified it as E. casseliflavus and one as E. faecalis). The results of this study are consistent with those of previous studies in the United States (4,5), South America (6), Spain (7), and Mexico (8). Although in countries like Chile, disk diffusion is practical and reliable for most susceptibility testing, detecting low-level vanco-mycin resistance in enterocci is difficult without supplementary testing. In Chile, as in other countries, strategies should be implemented to improve detection of these strains, including improvement of phenotypical and genotypical methods for VRE detection and species identification. Documentation of proficiency in detecting VRE is important for improving laboratory performance, detecting clinical isolates, and accurate and reliable reporting to local, national, and international surveillance systems. A, Febre N, et al. Epidemiologic analysis for acquisition of vancomycin-resistant enterococcus (VRE) in an intensive care unit in Brazil. Altschuler M, et al. Study to determine the ability of clinical laboratories to detect antimicrobial-resistant  Dr. Mead and colleagues should be commended for attempting to estimate the prevalence of foodborne disease in the United States (1). Their study provides more complete estimates than previous studies in terms of the number of foodborne pathogens included; for example, it includes the first realistic estimate of the number of cases of disease due to Norwalk-like caliciviruses. However, the publication of these estimates raises some important issues. Even though " accurate estimates of disease burden are the foundation of sound public health policy " (2), most of these estimates (in particular, the assumption that unknown agents are transmitted by food in the same proportion as known agents) were derived from assumptions rather than data. Known foodborne agents clearly cannot account for most gastrointestinal illnesses (1). However, illnesses from unknown agents may be as likely to have the transmission characteristics of rotavirus (1% foodborne) or Cryptosporidium (10% foodborne) as those of the Norwalk-like viruses (40% foodborne). Furthermore , it was assumed that detecting outbreaks or cases of toxin-mediated illnesses (e.g., due to Bacillus cereus, Staphylococcus aureus, or Clostridium perfringens) follows the model of Salmonella. In the authors' entire list of known foodborne agents, data are presented for cases identified both from outbreaks and active surveillance for only three agents: Salmonella, Shigella, and Campylobacter. Salmonella is clearly the most …
Meme-tracking and the dynamics of the news cycle

Tracking new topics, ideas, and "memes" across the Web has been an issue of considerable interest. Recent work has developed methods for tracking topic shifts over long time scales, as well as abrupt spikes in the appearance of particular named entities. However, these approaches are less well suited to the identification of content that spreads widely and then fades over time scales on the order of days - the time scale at which we perceive news and events.
 We develop a framework for tracking short, distinctive phrases that travel relatively intact through on-line text; developing scalable algorithms for clustering textual variants of such phrases, we identify a broad class of memes that exhibit wide spread and rich variation on a daily basis. As our principal domain of study, we show how such a meme-tracking approach can provide a coherent representation of the news cycle - the daily rhythms in the news media that have long been the subject of qualitative interpretation but have never been captured accurately enough to permit actual quantitative analysis. We tracked 1.6 million mainstream media sites and blogs over a period of three months with the total of 90 million articles and we find a set of novel and persistent temporal patterns in the news cycle. In particular, we observe a typical lag of 2.5 hours between the peaks of attention to a phrase in the news media and in blogs respectively, with divergent behavior around the overall peak and a "heartbeat"-like pattern in the handoff between news and blogs. We also develop and analyze a mathematical model for the kinds of temporal variation that the system exhibits.