nils-herrmann/clean_scirepeval huggingface dataset:
A Communication System for Deaf and Dumb People

One of the important problems that our society faces is that people with disabilities are finding it hard to cope up with the fast growing technology. The access to communication technologies has become essential for the handicapped people. Generally deaf and dumb people use sign language for communication but they find difficulty in communicating with others who don’t understand sign language. Sign language is an expressive and natural way for communication between normal and dumb people (information majorly conveyed through the hand gesture). So, we need a translator to understand what they speak and communicate with us. The sign language translation system translates the normal sign language to speech and hence makes the communication between normal person and dumb people easier. So, the whole idea is to build a communication system that enables communications between speech-hearing impaired and a normal person.
Device-to-Device Communications for National Security and Public Safety

Device-to-device (D2D) communications have been proposed as an underlay to long-term evolution (LTE) networks as a means of harvesting the proximity, reuse, and hop gains. However, D2D communications can also serve as a technology component for providing public protection and disaster relief (PPDR) and national security and public safety (NSPS) services. In the United States, for example, spectrum has been reserved in the 700-MHz band for an LTE-based public safety network. The key requirement for the evolving broadband PPDR and NSPS services capable systems is to provide access to cellular services when the infrastructure is available and to efficiently support local services even if a subset or all of the network nodes become dysfunctional due to public disaster or emergency situations. This paper reviews some of the key requirements, technology challenges, and solution approaches that must be in place in order to enable LTE networks and, in particular, D2D communications, to meet PPDR and NSPS-related requirements. In particular, we propose a clustering-procedure-based approach to the design of a system that integrates cellular and ad hoc operation modes depending on the availability of infrastructure nodes. System simulations demonstrate the viability of the proposed design. The proposed scheme is currently considered as a technology component of the evolving 5G concept developed by the European 5G research project METIS.
The state of artificial intelligence

Artificial intelligence has been an active branch of research for computer scientists and psychologists for 50 years. The concept of mimicking human intelligence in a computer fuels the public imagination and has led to countless academic papers, news articles and fictional works. However, public expectations remain largely unfulfilled, owing to the incredible complexity of everyday human behavior. A wide range of tools and techniques have emerged from the field of artificial intelligence, many of which are reviewed here. They include rules, frames, model-based reasoning, case-based reasoning, Bayesian updating, fuzzy logic, multiagent systems, swarm intelligence, genetic algorithms, neural networks, and hybrids such as blackboard systems. These are all ingenious, practical, and useful in various contexts. Some approaches are pre-specified and structured, while others specify only low-level behavior, leaving the intelligence to emerge through complex interactions. Some approaches are based on the use of knowledge expressed in words and symbols, whereas others use only mathematical and numerical constructions. It is proposed that there exists a spectrum of intelligent behaviors from low-level reactive systems through to high-level systems that encapsulate specialist expertise. Separate branches of research have made strides at both ends of the spectrum, but difficulties remain in devising a system that spans the full spectrum of intelligent behavior, including the difficult areas in the middle that include common sense and perception. Artificial intelligence is increasingly appearing in situated systems that interact with their physical environment. As these systems become more compact they are likely to become embedded into everyday equipment. As the 50th anniversary approaches of the Dartmouth conference where the term ‘artificial intelligence’ was first published, it is concluded that the field is in good shape and has delivered some great results. Yet human thought processes are incredibly complex, and mimicking them convincingly remains an elusive challenge. ADVANCES IN COMPUTERS, VOL. 65 1 2458/DOI 10.1016/S0065-2458(05)65001-2 All rights reserved.
On conceptualizing self-control as more than the effortful inhibition of impulses.

The notion that self-control entails effortful inhibition of impulses dominates prevailing psychological models of self-control. This article describes some of the conceptual and empirical limitations of defining self-control as the effortful inhibition of impulses. The present article instead advocates for a dual-motive conceptualization, which describes self-control as the process of advancing distal rather than proximal motivations when the two compete. Effortful impulse inhibition in this model represents only one of many means by which people promote their self-control efforts. Adopting a dual-motive approach offers new insight and proposes several new research directions. This article discusses these implications and calls for psychologists to reconsider the way self-control is currently understood.
Microgrid: a conceptual solution

Application of individual distributed generators can cause as many problems as it may solve. A better way to realize the emerging potential of distributed generation is to take a system approach which views generation and associated loads as a subsystem or a "microgrid". During disturbances, the generation and corresponding loads can separate from the distribution system to isolate the microgrid's load from the disturbance (providing UPS services) without harming the transmission grid's integrity. This ability to island generation and loads together has a potential to provide a higher local reliability than that provided by the power system as a whole. In this model it is also critical to be able to use the waste heat by placing the sources near the heat load. This implies that a unit can be placed at any point on the electrical system as required by the location of the heat load.
Stress Management through Yoga

Stress is a common condition, a response to a physical threat or psychological distress, that generates a host of chemical and hormonal reactions in the body. In essence, the body prepares to fight or fiee, pumping more blood to the heart and muscles and shutting down all nonessential functions. As a temporary state, this reaction serves the body well to defend itself When the stress reaction is prolonged, however, the normal physical functions that have in response either been exaggerated or shut down become dysfunctional. Many have noted the benefits of exercise in diminishing the stress response, and a host of studies points to these benefits. Yoga, too, has been recommended and studied in relationship to stress, although the studies are less scientifically replicable. Nonetheless, several researchers claim highly beneficial results from Yoga practice in alleviating stress and its effects. The practices recommended range from intense to moderate to relaxed asana sequences, along yNith.pranayama and meditation. In all these approaches to dealing with stress, one common element stands out: The process is as important as the activity undertaken. Because it fosters self-awareness. Yoga is a promising approach for dealing with the stress response. Yoga and the Stress Response Stress has become a common catchword in our society to indicate a host of difficulties, both as cause and effect.  Even a recent issue of Consumer Reports suggests Yoga for stress relief.̂ Many books and articles claim, as does Dr. Susan Lark, that practicing Yoga will "provide effective relief of anxiety and stress."^ But is this an accurate promise? What Is the Stress Response? A review of the current thinking on stress reveals that the process is both biochemical and psychological. A very good summary of research on the stress response is contained in Robert Sapolsky's Why Zebras Don't Get
Large-Scale Visual Relationship Understanding

Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of 〈subject, relation, object〉 triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved. We learn a visual and a semantic module that map features from the two modalities into a shared space, where matched pairs of features have to discriminate against those unmatched, but also maintain close distances to semantically similar ones. Benefiting from that, our model can achieve superior performance even when the visual entity categories scale up to more than 80, 000, with extremely skewed class distribution. We demonstrate the efficacy of our model on a large and imbalanced benchmark based of Visual Genome that comprises 53, 000+ objects and 29, 000+ relations, a scale at which no previous work has been evaluated at. We show superiority of our model over competitive baselines on the original Visual Genome dataset with 80, 000+ categories. We also show state-of-the-art performance on the VRD dataset and the scene graph dataset which is a subset of Visual Genome with 200 categories.
An Architecture for Fast and Accurate Control of Shape Memory Alloy Actuators

This paper presents a new control architecture for fast, accurate force control of antagonistic pairs of shape memory alloy wires. The main components are: a differential-mode controller, which controls the output force, an anti-slack mechanism, a rapid-heating mechanism and an anti-overload mechanism. The closed-loop response is fast and accurate, even in the presence of large external motion disturbances. There is no sign of limit cycles; and the performance is unaffected by large load inertias. This paper also presents an architecture for position control, in which a position feedback loop is added to the force control architecture. Experimental results show force control accuracies as high as 1mN in a ±3N range, force output rates as high as 50Ns−1, and highly accurate position control with steady-state errors below the resolution of the position encoder.
Research on TPB model for Participating Behavior in Sharing Economy

The sharing economy is a new economic model from practice, with the aid of the Information and Communications Technology, transfer the temporary use right of idle resources from the supplier to demander, improving the efficiency of the existing resource, promoting the sustainable development of social economy. Scale effect exists in sharing economy, the more resource providers and users participate in the sharing economy, the lower the cost, and the higher the efficiency. To encourage more people to participate in sharing economy, we should have a dearth understanding of why people would participate in it first. This research takes the meaning, classification, characteristics and essence of sharing economy as a premise, the Theory of Planned Behavior as a basic, purposes a TPB model for participating behavior in sharing economy, and analyzes the variables of different levels, attempting to study the factors that influence people's participating behavior in sharing economy. This research argues that the attitude toward the behavior, subjective norm and perceived behavior control are the general variables impacting the behavior intention of shared resources providers and users, and according to the essence of sharing economy, providers have their own special variables.
Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network

In this paper we present a novel instance segmentation algorithm that extends a fully convolutional network to learn to label objects separately without prediction of regions of interest. We trained the new algorithm on a challenging CCTV recording of beef cattle, as well as benchmark MS COCO and Pascal VOC datasets. Extensive experimentation showed that our approach outperforms the state-of-the-art solutions by up to 8% on our data.
Perceived Self-Efficacy in Cognitive Development and Functioning Albert Bandura

In this article, I review the diverse ways in which perceived self-efficacy contributes to cognitive development and functioning. Perceived self-efficacy exerts its influence through four major processes. They include cognitive, motivational, affective, and selection processes. There are three different levels at which perceived self-efficacy operates as an important contributor to academic devellopment. Students' beliefs in their efficacy to regulate their own learning and to master academic activities determine their aspirations, level of motivation, and academic accomplishments. Teachers' beliefs in their personal efficacy to motivate and promote learning affect the types of learning environments tlhey create and the level of academic progress their students achieve. Faculti~es' beliefs in their collective instructional efficacy contribute significantly to their schools' level of academic achievement. Student body characteristics influence school-level achievement more strongly by altering faculties' beliefs in their collective efficacy than through direct affects on school achievement.
Prepaid Energy Meter for Billing System Using Microcontroller and Recharge Card

In this paper, the idea of a Prepaid energy meter using an AT89S52 microcontroller has been introduced. This concept provides a cost efficient manner of electricity billing. The present energy billing systems are discrete, inaccurate, costly and slow. They are also time and labour consuming. The major drawback of traditional billing system is power and energy theft. This drawback is reduced by using a prepaid energy meter which is based on the concept “Pay first and then use it”. Prepaid energy meter also reduces the error made by humans while taking readings to a large extent and there is no need to take reading in it. The prepaid energy meter uses a recharge card which is available in various ranges (i.e. Rs. 50, Rs. 100, Rs. 200, etc.). The recharge is done by using a keypad and the meter is charged with the amount. According to the power consumption, the amount will be reduced. An LDR (light Dependant Resistor) circuit counts the amount of energy consumed and displays the remaining amount of energy on the LCD. A relay system has been used which shut down or disconnect the energy meter and load through supply mains when the recharge amount is depleted. A buzzer is used as an alarm which starts before the recharge amount reaches a minimum value.
A Qualitative Review on 3D Coarse Registration Methods

3D registration or matching is a crucial step in 3D model reconstruction. Registration applications span along a variety of research fields, including computational geometry, computer vision, and geometric modeling. This variety of applications produces many diverse approaches to the problem but at the same time yields divergent notations and a lack of standardized algorithms and guidelines to classify existing methods. In this article, we review the state of the art of the 3D rigid registration topic (focused on Coarse Matching) and offer qualitative comparison between the most relevant approaches. Furthermore, we propose a pipeline to classify the existing methods and define a standard formal notation, offering a global point of view of the literature.
 Our discussion, based on the results presented in the analyzed papers, shows how, although certain aspects of the registration process still need to be tested further in real application situations, the registration pipeline as a whole has progressed steadily. As a result of this progress in all registration aspects, it is now possible to put together algorithms that are able to tackle new and challenging problems with unprecedented data sizes and meeting strict precision criteria.
Modeling language and cognition with deep unsupervised learning: a tutorial overview

Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.
A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies.

Near-infrared spectroscopy (NIRS) is a fast and non-destructive analytical method. Associated with chemometrics, it becomes a powerful tool for the pharmaceutical industry. Indeed, NIRS is suitable for analysis of solid, liquid and biotechnological pharmaceutical forms. Moreover, NIRS can be implemented during pharmaceutical development, in production for process monitoring or in quality control laboratories. This review focuses on chemometric techniques and pharmaceutical NIRS applications. The following topics are covered: qualitative analyses, quantitative methods and on-line applications. Theoretical and practical aspects are described with pharmaceutical examples of NIRS applications.
Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning

Learning analytics is the analysis of electronic learning data which allows teachers, course designers and administrators of virtual learning environments to search for unobserved patterns and underlying information in learning processes. The main aim of learning analytics is to improve learning outcomes and the overall learning process in electronic learning virtual classrooms and computer-supported education. The most basic unit of learning data in virtual learning environments for learning analytics is the interaction, but there is no consensus yet on which interactions are relevant for effective learning. Drawing upon extant literature, this research defines three system-independent classifications of interactions and evaluates the relation of their components with academic performance across two different learning modalities: virtual learning environment (VLE) supported face-to-face (F2F) and online learning. In order to do so, we performed an empirical study with data from six online and two VLE-supported F2F courses. Data extraction and analysis required the development of an ad hoc tool based on the proposed interaction classification. The main finding from this research is that, for each classification, there is a relation between some type of interactions and academic performance in online courses, whereas this relation is non-significant in the case of VLE-supported F2F courses. Implications for theory and practice are dis-
A Semantic Approach to Person Profile Extraction from Farsi Documents

Entity profiling (EP) as an important task of Web mining and information extraction (IE) is the process of extracting entities in question and their related information from given text resources. From computational viewpoint, the Farsi language is one of the less-studied and less-resourced languages, and suffers from the lack of high quality language processing tools. This problem emphasizes the necessity of developing Farsi text processing systems. As an element of EP research, we present a semantic approach to extract profile of person entities from Farsi Web documents. Our approach includes three major components: (i) pre-processing, (ii) semantic analysis and (iii) attribute extraction. First, our system takes as input the raw text, and annotates the text using existing pre-processing tools. In semantic analysis stage, we analyze the pre-processed text syntactically and semantically and enrich the local processed information with semantic information obtained from a distant knowledge base. We then use a semantic rule-based approach to extract the related information of the persons in question. We show the effectiveness of our approach by testing it on a small Farsi corpus. The experimental results are encouraging and show that the proposed method outperforms baseline methods.
Informed Representations for Linear-Time Discourse Parsing

Recent advances in RST discourse parsing have focused on two modeling paradigms: (a) high order parsers which jointly predict the tree structure of the discourse and the relations it encodes; or (b) lineartime parsers which are efficient but mostly based on local features. In this work, we propose a linear-time parser with a novel way of representing discourse constituents based on neural networks which takes into account global contextual information and is able to capture long-distance dependencies. Experimental results show that our parser obtains state-of-the art performance on benchmark datasets, while being efficient (with time complexity linear in the number of sentences in the document) and requiring minimal feature engineering.
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model

Predicting the price correlation of two assets for future time periods is important in portfolio optimization. We apply LSTM recurrent neural networks (RNN) in predicting the stock price correlation coefficient of two individual stocks. RNN’s are competent in understanding temporal dependencies. The use of LSTM cells further enhances its long term predictive properties. To encompass both linearity and nonlinearity in the model, we adopt the ARIMA model as well. The ARIMA model filters linear tendencies in the data and passes on the residual value to the LSTM model. The ARIMA-LSTM hybrid model is tested against other traditional predictive financial models such as the full historical model, constant correlation model, single-index model and the multi-group model. In our empirical study, the predictive ability of the ARIMA-LSTM model turned out superior to all other financial models by a significant scale. Our work implies that it is worth considering the ARIMA-LSTM model to forecast correlation coefficient for portfolio optimization.
Algorithmic challenges in big data analytics

This session studies specific challenges that Machine Learning (ML) algorithms have to tackle when faced with Big Data problems. These challenges can arise when any of the dimensions in a ML problem grows significantly: a) size of training set, b) size of test set or c) dimensionality. The studies included in this edition explore the extension of previous ML algorithms and practices to Big Data scenarios. Namely, specific algorithms for recurrent neural network training, ensemble learning, anomaly detection and clustering are proposed. The results obtained show that this new trend of ML problems presents both a challenge and an opportunity to obtain results which could allow ML to be integrated in many new applications in years to come.