nils-herrmann/clean_scirepeval huggingface dataset:
Prediction of students performance using Educational Data Mining

Data mining plays an important role in the business world and it helps to the educational institution to predict and make decisions related to the students' academic status. With a higher education, now a days dropping out of students' has been increasing, it affects not only the students' career but also on the reputation of the institute. The existing system is a system which maintains the student information in the form of numerical values and it just stores and retrieve the information what it contains. So the system has no intelligence to analyze the data. The proposed system is a web based application which makes use of the Naive Bayesian mining technique for the extraction of useful information. The experiment is conducted on 700 students' with 19 attributes in Amrita Vishwa Vidyapeetham, Mysuru. Result proves that Naive Bayesian algorithm provides more accuracy over other methods like Regression, Decision Tree, Neural networks etc., for comparison and prediction. The system aims at increasing the success graph of students using Naive Bayesian and the system which maintains all student admission details, course details, subject details, student marks details, attendance details, etc. It takes student's academic history as input and gives students' upcoming performances on the basis of semester.
Cross-Sector Partnerships to Address Social Issues : Challenges to Theory and Practice †

Project-based cross-sector partnerships to address social issues (CSSPs) occur in four “arenas”: business-nonprofit, business-government, government-nonprofit, and trisector. Research on CSSPs is multidisciplinary, and different conceptual “platforms” are used: resource dependence, social issues, and societal sector platforms. This article consolidates recent literature on CSSPs to improve the potential for cross-disciplinary fertilization and especially to highlight developments in various disciplines for organizational researchers. A number of possible directions for future research on the theory, process, practice, method, and critique of CSSPs are highlighted. The societal sector platform is identified as a particularly promising framework for future research.
Recent Advances in Neural Program Synthesis

In recent years, deep learning has made tremendous progress in a number of fields that were previously out of reach for artificial intelligence. The successes in these problems has led researchers to consider the possibilities for intelligent systems to tackle a problem that humans have only recently themselves considered: program synthesis. This challenge is unlike others such as object recognition and speech translation, since its abstract nature and demand for rigor make it difficult even for human minds to attempt. While it is still far from being solved or even competitive with most existing methods, neural program synthesis is a rapidly growing discipline which holds great promise if completely realized. In this paper, we start with exploring the problem statement and challenges of program synthesis. Then, we examine the fascinating evolution of program induction models, along with how they have succeeded, failed and been reimagined since. Finally, we conclude with a contrastive look at program synthesis and future research recommendations for the field.
Shape-programmable magnetic soft matter.

Shape-programmable matter is a class of active materials whose geometry can be controlled to potentially achieve mechanical functionalities beyond those of traditional machines. Among these materials, magnetically actuated matter is particularly promising for achieving complex time-varying shapes at small scale (overall dimensions smaller than 1 cm). However, previous work can only program these materials for limited applications, as they rely solely on human intuition to approximate the required magnetization profile and actuating magnetic fields for their materials. Here, we propose a universal programming methodology that can automatically generate the required magnetization profile and actuating fields for soft matter to achieve new time-varying shapes. The universality of the proposed method can therefore inspire a vast number of miniature soft devices that are critical in robotics, smart engineering surfaces and materials, and biomedical devices. Our proposed method includes theoretical formulations, computational strategies, and fabrication procedures for programming magnetic soft matter. The presented theory and computational method are universal for programming 2D or 3D time-varying shapes, whereas the fabrication technique is generic only for creating planar beams. Based on the proposed programming method, we created a jellyfish-like robot, a spermatozoid-like undulating swimmer, and an artificial cilium that could mimic the complex beating patterns of its biological counterpart.
Botanical insecticides: for richer, for poorer.

Botanical insecticides presently play only a minor role in insect pest management and crop protection; increasingly stringent regulatory requirements in many jurisdictions have prevented all but a handful of botanical products from reaching the marketplace in North America and Europe in the past 20 years. Nonetheless, the regulatory environment and public health needs are creating opportunities for the use of botanicals in industrialized countries in situations where human and animal health are foremost--for pest control in and around homes and gardens, in commercial kitchens and food storage facilities and on companion animals. Botanicals may also find favour in organic food production, both in the field and in controlled environments. In this review it is argued that the greatest benefits from botanicals might be achieved in developing countries, where human pesticide poisonings are most prevalent. Recent studies in Africa suggest that extracts of locally available plants can be effective as crop protectants, either used alone or in mixtures with conventional insecticides at reduced rates. These studies suggest that indigenous knowledge and traditional practice can make valuable contributions to domestic food production in countries where strict enforcement of pesticide regulations is impractical.
Privacy in the Digital Age: A Review of Information Privacy Research in Information Systems

After searching for the keywords “information privacy” in ABI/Informs focusing on scholarly articles, we obtained a listing of 340 papers. We first eliminated papers that were anonymous, table of contents, interviews with experts, or short opinion pieces. We also removed articles not related to our focus on information privacy research in IS literature. A total of 218 articles were removed as explained in Table A1.
Computing Linear Discriminants for Idiomatic Sentence Detection

In this paper, we describe the binary classification of sentences into idiomatic and non-idiomatic. Our idiom detection algorithm is based on linear discriminant analysis (LDA). To obtain a discriminant subspace, we train our model on a small number of randomly selected idiomatic and non-idiomatic sentences. We then project both the training and the test data on the chosen subspace and use the three nearest neighbor (3NN) classifier to obtain accuracy. The proposed approach is more general than the previous algorithms for idiom detection — neither does it rely on target idiom types, lexicons, or large manually annotated corpora, nor does it limit the search space by a particular linguistic con-
DETERMINING LYAPUNOV EXPONENTS FROM A TIME SERIES

We present the first algorithms that allow the estimation of non-negative Lyapunov exponents from an experimental time series. Lyapunov exponents, which provide a qualitative and quantitative characterization of dynamical behavior, are related to the exponentially fast divergence or convergence of nearby orbits in phase space. A system with one or more positive Lyapunov exponents is defined to be chaotic. Our method is rooted conceptually in a previously developed technique that could only be applied to analytically defined model systems: we monitor the long-term growth rate of small volume elements in an attractor. The method is tested on model systems with known Lyapunov spectra, and applied to data for the Belousov-Zhabotinskii reaction and Couette-Taylor flow.
Measuring Semantic Similarity of Words Using Concept Networks

We present a state-of-the-art algorithm for measuring the semantic similarity of word pairs using novel combinations of word embeddings, WordNet, and the concept dictionary 4lang. We evaluate our system on the SimLex-999 benchmark data. Our top score of 0.76 is higher than any published system that we are aware of, well beyond the average inter-annotator agreement of 0.67, and close to the 0.78 average correlation between a human rater and the average of all other ratings, suggesting that our system has achieved nearhuman performance on this benchmark.
The KUKA-DLR Lightweight Robot arm - a new reference platform for robotics research and manufacturing

Transforming research results into marketable products requires considerable endurance and a strong sense of entrepreneurship. The KUKA Lightweight Robot (LWR) is the latest outcome of a bilateral research collaboration between KUKA Roboter, Augsburg, and the Institute of Robotics and Mechatronics at the German Aerospace Center (DLR), Wessling. The LWR has unique characteristics including a low mass-payload ratio and a programmable, active compliance which enables researchers and engineers to develop new industrial and service robotics applications with unprecedented performance, making it a unique reference platform for robotics research and future manufacturing. The stages of product genesis, the most innovative features and first application examples are presented.
Learning question classifiers: the role of semantic information

In order to respond correctly to a free form factual question given a large collection of text data, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. This work presents the first work on a machine learning approach to question classification. Guided by a layered semantic hierarchy of answer types, we develop a hierarchical classifier that classifies questions into fine-grained classes. This work also performs a systematic study of the use of semantic information sources in natural language classification tasks. It is shown that, in the context of question classification, augmenting the input of the classifier with appropriate semantic category information results in significant improvements to classification accuracy. We show accurate results on a large collection of free-form questions used in TREC 10 and 11.
The HTM Spatial Pooler—A Neocortical Algorithm for Online Sparse Distributed Coding

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.
SLDR-DL: A Framework for SLD-Resolution with Deep Learning

This paper introduces an SLD-resolution technique based on deep learning. This technique enables neural networks to learn from old and successful resolution processes and to use learnt experiences to guide new resolution processes. An implementation of this technique is named SLDR-DL. It includes a Prolog library of deep feedforward neural networks and some essential functions of resolution. In the SLDR-DL framework, users can define logical rules in the form of definite clauses and teach neural networks to use the rules in reasoning processes.
Automated attendance management system based on face recognition algorithms

In this paper we propose an automated attendance management system. This system, which is based on face detection and recognition algorithms, automatically detects the student when he enters the class room and marks the attendance by recognizing him. The system architecture and algorithms used in each stage are described in this paper. Different real time scenarios are considered to evaluate the performance of various face recognition systems. This paper also proposes the techniques to be used in order to handle the threats like spoofing. When compared to traditional attendance marking this system saves the time and also helps to monitor the students.
Looking at People: Sensing for Ubiquitous and Wearable Computing

ÐThe research topic of looking at people, that is, giving machines the ability to detect, track, and identify people and more generally, to interpret human behavior, has become a central topic in machine vision research. Initially thought to be the research problem that would be hardest to solve, it has proven remarkably tractable and has even spawned several thriving commercial enterprises. The principle driving application for this technology is afourth generationo embedded computing: asmarto' environments and portable or wearable devices. The key technical goals are to determine the computer's context with respect to nearby humans (e.g., who, what, when, where, and why) so that the computer can act or respond appropriately without detailed instructions. This paper will examine the mathematical tools that have proven successful, provide a taxonomy of the problem domain, and then examine the stateof-the-art. Four areas will receive particular attention: person identification, surveillance/monitoring, 3D methods, and smart rooms/ perceptual user interfaces. Finally, the paper will discuss some of the research challenges and opportunities. Index TermsÐLooking at people, face recognition, gesture recognition, visual interface, appearance-based vision, wearable computing, ubiquitious.
Comorbidity of auditory processing, language, and reading disorders.

The authors assessed comorbidity of auditory processing disorder (APD), language impairment (LI), and reading disorder (RD) in school-age children.



Children (N = 68) with suspected APD and nonverbal IQ standard scores of 80 or more were assessed using auditory, language, reading, attention, and memory measures. Auditory processing tests included the Frequency Pattern Test (FPT; F. E. Musiek, 1994; D. Noffsinger, R. H. Wilson, & F. E. Musiek, 1994); the Dichotic Digit Test Version 2 (DDT; F. E. Musiek, 1983); the Random Gap Detection Test (R. W. Keith, 2000); the 500-Hz tone Masking Level Difference (V. Aithal, A. Yonovitz, & S. Aithal, 2006); and a monaural low-redundancy speech test (compressed and reverberant words; A. Boothroyd & S. Nittrouer, 1988). The Clinical Evaluation of Language Fundamentals, Fourth Edition (E. Semel, E. Wiig, & W. Secord, 2003) was used to assess language abilities (including auditory memory). Reading accuracy and fluency and phonological awareness abilities were assessed using the Wheldall Assessment of Reading Passages (A. Madelaine & K. Wheldall, 2002) and the Queensland University Inventory of Literacy (B. Dodd, A. Holm, M. Orelemans, & M. McCormick, 1996). Attention was measured using the Integrated Visual and Auditory Continuous Performance Test (J. A. Sandford & A. Turner, 1995).



Of the children, 72% had APD on the basis of these test results. Most of these children (25%) had difficulty with the FPT bilaterally. A further 22% had difficulty with the FPT bilaterally and had right ear deficits for the DDT. About half of the children (47%) had problems in all 3 areas (APD, LI, and RD); these children had the poorest FPT scores. More had APD-RD, or APD-LI, than APD, RD, or LI alone. There were modest correlations between FPT scores and attention and memory, and between DDT scores and memory.



LI and RD commonly co-occur with APD. Attention and memory are linked to performance on some auditory processing tasks but only explain a small amount of the variance in scores. Comprehensive assessment across a range of areas is required to characterize the difficulties experienced by children with APD.
Design, Simulation, and Experiments for an Improved Coaxial High-Voltage Vacuum Insulator in TPG700 for High-Power Microwave Generation

An improved coaxial high-voltage vacuum insulator applied in a Tesla-type generator, model TPG700, has been designed and tested for high-power microwave (HPM) generation. The design improvements include: changing the connection type of the insulator to the conductors from insertion to tangential, making the insulator thickness uniform, and using Nylon as the insulation material. Transient field simulation shows that the electric field (E-field) distribution within the improved insulator is much more uniform and that the average E-field on the two insulator surfaces is decreased by approximately 30% compared with the previous insulator at a voltage of 700 kV. Key structures such as the anode and the cathode shielding rings of the insulator have been optimized to significantly reduce E-field stresses. Aging experiments and experiments for HPM generation with this insulator were conducted based on a relativistic backward-wave oscillator. The preliminary test results show that the output voltage is larger than 700 kV and the HPM power is about 1 GW. Measurements show that the insulator is well within allowable E-field stresses on both the vacuum insulator surface and the cathode shielding ring.
RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement

We present RoarNet, a new approach for 3D object detection from 2D image and 3D Lidar point clouds. Based on two stage object detection framework (, ) with PointNet  as our backbone network, we suggest several novel ideas to improve 3D object detection performance. The first part of our method, RoarNet 2D, estimates the 3D poses of objects from a monocular image, which approximates where to examine further, and derives multiple candidates that are geometrically feasible. This step significantly narrows down feasible 3D regions, which otherwise requires demanding processing of 3D point clouds in a huge search space. Then the second part, RoarNet 3D, takes the candidate regions and conducts in-depth inferences to conclude final poses in a recursive manner. Inspired by PointNet, RoarNet 3D processes 3D point clouds directly without any loss of data, leading to precise detection. We evaluate our method in KITTI, a 3D object detection benchmark. Our result shows that RoarNet has superior performance to state-of-the-art methods that are publicly available. Remarkably, RoarNet also outperforms state-of-the-art methods even in settings where Lidar and camera are not time synchronized, which is practically important for actual driving environment. RoarNet is implemented in Tensorflow  and publicly available with pretrained models.
Control de una planta no lineal de temperatura con redes neuronales

Control de una planta no lineal de temperatura con redes neuronales Control of a no lineal temperature plant with neural networks Abstra ct— In t h is wor k a comp a r ison a p p ea r s b et ween a convention al control and an intelligent control, the controller s developed are a control PID and a control with neural networks for a thermal system, inside the project " Didactic Control Module ­ MODICO". In the first part of the article, the exposition of the mathema tical model of the plant is showed, and the model by means of parametr ic identifica tion of the temper ature plant is realized with least mean square, these models are used for the controller' s characterization. The identification is realized taken information of the plant by means of the card NI ­6259 card and the LabVIEW interface. For the conventional control, a control PID is designed by means of the method root locus. The control neuronal was implemented with NARM A­L2 neur al networ ks; this net has four neurons, two in the secret layer, one in the layer of entry and one in the layer of exit. The network neuronal is trained with six different training algorithms, once the network trained, the best training method is selected and the system is validated with the control PID and the neural control. control que ha sido fuente de investigación desde 1970, .
Musical anhedonia: selective loss of emotional experience in listening to music.

Recent case studies have suggested that emotion perception and emotional experience of music have independent cognitive processing. We report a patient who showed selective impairment of emotional experience only in listening to music, that is musical anhednia. A 71-year-old right-handed man developed an infarction in the right parietal lobe. He found himself unable to experience emotion in listening to music, even to which he had listened pleasantly before the illness. In neuropsychological assessments, his intellectual, memory, and constructional abilities were normal. Speech audiometry and recognition of environmental sounds were within normal limits. Neuromusicological assessments revealed no abnormality in the perception of elementary components of music, expression and emotion perception of music. Brain MRI identified the infarct lesion in the right inferior parietal lobule. These findings suggest that emotional experience of music could be selectively impaired without any disturbance of other musical, neuropsychological abilities. The right parietal lobe might participate in emotional experience in listening to music.
End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning

We study active object tracking, where a tracker takes visual observations (i.e., frame sequences) as inputs and produces the corresponding camera control signals as outputs (e.g., move forward, turn left, etc.). Conventional methods tackle tracking and camera control tasks separately, and the resulting system is difficult to tune jointly. Such an approach also requires significant human efforts for image labeling and expensive trial-and-error system tuning in real-world. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning. A ConvNet-LSTM function approximator is adopted for the direct frame-to-action prediction. We further propose environment augmentation techniques and a customized reward function which are crucial for successful training. The tracker trained in simulators (ViZDoom, Unreal Engine) demonstrates good generalization behaviors in the case of unseen object moving paths, unseen object appearances, unseen backgrounds, and distracting objects. The system is robust and can restore tracking after occasional lost of the target being tracked. We also find that the tracking ability, obtained solely from simulators, can potentially transfer to real-world scenarios. We demonstrate successful examples of such transfer, via experiments over the VOT dataset and the deployment of a real-world robot using the proposed active tracker trained in simulation.