nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge Based Approach for Word Sense Disambiguation using Hindi Wordnet

------------------------------------------------------------Abstract-------------------------------------------------------------Word sense disambiguation (WSD) is an open research area in natural language processing and artificial intelligence. and it is also based on computational linguistics. It is of considerable theoretical and practical interest. WSD is to analyze word tokens in context and specify exactly,which sense of several word is being used.It can be declare as theortically motivated,one or more methods and techniques. WSD is a big problem of natural language processing (NLP) and artificial intelligence. But here, the problem is to search the sense for a word in given a context and lexicon relation. It is a technique of natural language processing in which requires queries and documents in NLP or texts from Machine Translation (MT). MT is an automatic translation. It is involving some languages such as Marathi, Urdu, Bengali, Punjabi, Hindi, and English etc. Most of the work has been completed in English, and now the point of convergence has shifted to others languages. The applications of WSD are disambiguation of content in information retrieval (IR), machine translation (MT), speech processing, lexicography, and text processing. In our paper, we are using knowledge based approach to WSD with Hindi language . A knowledge based approach use of external lexical resources suach as dictionary and thesauri . It involve incorporation of word knowledge to disambiguate words. We have developed a WSD tool using knowledge base d approach with Hindi wordnet. Wordnet is built from cooccurrence and collocation and it includes synset or synonyms which belong to either noun, verb, adjective, or adverb, these are called as part-ofspeech (POS) tagger. In this paper we shall introduce the implementation of our tool and its evaluation.
Online Discussion and the Moral Pathway to Identity Politicization and Collective Action

Research on the mobilizing potential of the Internet has produced some controversy between optimistic vs. skeptical perspectives. Although some attention has been paid to the effects of online discussions on collective participation, very little is known about how people's experience of online interactions affects the key psychosocial predictors of collective action. The present research investigated whether use of the Internet as a channel for deliberation influenced the moral pathway to collective mobilization by shaping users' politicized identity, thereby indirectly influencing collective action. Results showed that when people perceived online discussions as a constructive communication context, their politicized identity was imbued with the meaning of responding to a moral obligation, and willingness to participate in collective action was sustained. However, when participants perceived that online discussions were not constructive, their identification with the movement did not refer to moral obligation, and intention to participate in collective action was not sustained. Our discussion focuses on the need to deepen investigation of how people experience the particularities of interacting online, and on how this can affect psychosocial processes leading to collective action.
Accurate multi-view reconstruction using robust binocular stereo and surface meshing

This paper presents a new algorithm for multi-view reconstruction that demonstrates both accuracy and efficiency. Our method is based on robust binocular stereo matching, followed by adaptive point-based filtering of the merged point clouds, and efficient, high-quality mesh generation. All aspects of our method are designed to be highly scalable with the number of views. Our technique produces the most accurate results among current algorithms for a sparse number of viewpoints according to the Middlebury datasets. Additionally, we prove to be the most efficient method among non-GPU algorithms for the same datasets. Finally, our scaled-window matching technique also excels at reconstructing deformable objects with high-curvature surfaces, which we demonstrate with a number of examples.
Standards for smart education – towards a development framework

Smart learning environments (SLEs) utilize a range of digital technologies in supporting learning, education and training; they also provide a prominent signpost for how future learning environments might be shaped. Thus, while innovation proceeds, SLEs are receiving growing attention from the research community, outputs from which are discussed in this paper. Likewise, this broad application of educational digital technologies is also the remit of standardization in an ISO committee, also discussed in this paper. These two communities share a common interest in, conceptualizing this emerging domain with the aim to identifying direction to further development. In doing so, terminology issues arise along with key questions such as, ‘how is smart learning different from traditional learning?’ Presenting a bigger challenge is the question, ‘how can standardization work be best scoped in today's innovation-rich, networked, cloud-based and data-driven learning environments?’ In responding, this conceptual paper seeks to identify candidate constructs and approaches that might lead to stable, coherent and exhaustive understanding of smart learning environments, thereby providing standards development for learning, education and training a needed direction. Based on reviews of pioneering work within smart learning, smart education and smart learning environments we highlight two models, a cognitive smart learning model and a smartness level model. These models are evaluated against current standardization challenges in the field of learning, education and training to form the basis for a development platform for new standards in this area.
NFC-Based Traceability in the Food Chain

In recent years, many events related to food and public health, such as bovine spongiform encephalopathy (BSE, commonly known as mad cow disease), dioxin, bird flu and swine flu, brought the issue of food security to the center of international attention. Many countries established strict rules to ensure the food traceability “from farm to fork” to meet the demand of consumer safety and to ensure public health. Consequently, the concepts of controlled supply chain, brand of quality and traceability of food products have had a strong evolution. Companies in the food production sector must evolve and change their organizational and management chain to satisfy increasingly stringent government rules and to respond to the requirements of the market. From this point of view, NFC (Near Field Communication) has all the requirements to become the main technology for traceability in the various food sectors. This paper proposes a solution to gather information throughout the entire food supply chain and bring it directly to the consumer. The architecture consists of a complex identification system based on NFC tags, which cumulates data during all phases of the production process. The aim of this work is to keep track of the information generated during food processing, not only for traceability purposes, but also for enhancing and optimizing production. The data generated by the traceability system are collected in a cloud database through apps on Android smartphones. The information is accessible by the consumers through a public Android application. The pork supply chain has been used as an application example of the proposed methodology.
Distributed Segment-Based Anomaly Detection With Kullback–Leibler Divergence in Wireless Sensor Networks

In this paper, we focus on detecting a special type of anomaly in wireless sensor network (WSN), which appears simultaneously in a collection of neighboring nodes and lasts for a significant period of time. Existing point-based techniques, in this context, are not very effective and efficient. With the proposed distributed segment-based recursive kernel density estimation, a global probability density function can be tracked and its difference between every two periods of time is continuously measured for decision making. Kullback-Leibler (KL) divergence is employed as the measure and, in order to implement distributed in-network estimation at a lower communication cost, several types of approximated KL divergence are proposed. In the meantime, an entropic graph-based algorithm that operates in the manner of centralized computing is realized, in comparison with the proposed KL divergence-based algorithms. Finally, the algorithms are evaluated using a real-world data set, which demonstrates that they are able to achieve a comparable performance at a much lower communication cost.
A Probabilistic Model for Traditional Chinese Architecture

Increasing numbers of 3D models provide a greatopportunity for data-driven shape modeling, analysis andsynthesis. The most critical core technique is to aggregateinformation from model collections to improve reasoningtheir properties and relationships. In this paper, we proposea probabilistic model for traditional Chinese architectures, which encodes the semantic type and hierarchicalrelationships for their basic components. Firstly aprobabilistic hierarchical graph is designed to represent thetypical component structure of Chinese ancient buildings. Secondly, a Bayesian Network is trained from a collection of3D models with consistent labels. Finally, the BayesianNetwork with structure and parameters learned from datacan be used to synthesis and recommend components inapplications. Experimental results show the effective of theproposed method.
Application of Social Media Analytics in the Banking Sector to Drive Growth and Sustainability: A Proposed Integrated Framework

Large amounts of data sets being generated from social media platforms have led to surge in demand for social media analytics (SMA) use in business operations. Strategic operation of SMA yields a positive impact on marketing activities, customer engagement, risk analysis and assessment product or service design, credit rating of customer profile, customer education and competitive analysis. Banks have started tapping into advanced prescriptive and predictive analytics into a bid to develop insights, managing high costs of compliance and non-compliance such as financial risks and reputational risks thereby generating a significant impact in business operations. Optimisation modelling of business portfolios, products and services offerings are contributing significantly towards achieving a sustainable and profitable growth. This is however, against the background of high volatility and weakening demand for traditional products in the banking industry. Systematic literature review was used in examining how banks are applying social media analytics to improve their business operations. Findings reveals that social media analytics provide concrete solutions which helps to improve revenue streams of banks, providing a guarantee for compliance, survival, sustainability and growth objectives. As a result, a integrated framework is proposed to address the gap existing in literature which lacks a focus on an integrated framework that can assist decision makers on the social media analytics to employ in the banking operations.
Systems Development in Information Systems Research

In this paper, the use of systems development as a methodology in information systems (is) research is described and defended. A framework to explain the nature of systems development as a research methodology in is research is proposed. Use of this methodology in the engineering field in general is compared with its use specifically in computer science and computer engineering. An integrated program for conducting IS research that incorporates theory building, systems development, experimentation, and observation is proposed. Progress in several application domains is reviewed to provide a basis upon which to argue that systems development is a valid research methodology. A systems development research process is presented from a methodological perspective. Software engineering, which is the basic method of applying the systems development research methodology, is then discussed. It is the authors' belief that systems development and other research methodologies are complementary and that an integrated multi-dimensional and multimethodological approach will generate fruitful is research results. The premise is that research contributions can result from systems development, experimentation, observation, and performance testing of the systems under development and that all of these research approaches are needed to investigate different aspects of the research question.
A critical review of Bitcoins usage by cybercriminals

Bitcoin is a new form of global digital currency based on peer-to-peer network, enabling a new payment system, and also a completely decentralised cryptocurrency. The P2P network consists of a digital file listing transactions like a ledger, a copy of which is also maintained on every computer on the network, and the transactions are also broadcasted in the public ledger of the Bitcoin network. Anonymity is the core feature that makes Bitcoin popular among people around the world. This feature is attained through the Bitcoin addresses in the public ledger, which represents the users in the Bitcoin network. Because of the illicit use of Bitcoins, the level of anonymity has reduced even though the users are still using the anonymizers like TOR to keep the anonymity stronger to connect to the Bitcoin network. In this paper, we analyse the complete process of transaction of Bitcoins and the anonymity thatlies in that process. The study also focuses on finding the forensic artefacts and the investigative way of approach towards the Bitcoin using forensic tools. The forensic tools are used to analyse the web browser activities, local drive, hard disk image, cookies, downloads and session data related to Bitcoins. The attacker can relate the transaction of the users and can control the Bitcoin blocks by even delaying the transactions. This research will focus on the methods in which the memory and even mobile devices involved in the transaction could be captured and analysed.
"yeah Right": Sarcasm Recognition for Spoken Dialogue Systems

The robust understanding of sarcasm in a spoken dialogue system requires a reformulation of the dialogue manager’s basic assumptions behind, for example, user behavior and grounding strategies. But automatically detecting a sarcastic tone of voice is not a simple matter. This paper presents some experiments toward sarcasm recognition using prosodic, spectral, and contextual cues. Our results demonstrate that spectral and contextual features can be used to detect sarcasm as well as a human annotator would, and confirm a long-held claim in the field of psychology – that prosody alone is not sufficient to discern whether a speaker is being sarcastic.
Knowledge distillation across ensembles of multilingual models for low-resource languages

This paper investigates the effectiveness of knowledge distillation in the context of multilingual models. We show that with knowledge distillation, Long Short-Term Memory(LSTM) models can be used to train standard feed-forward Deep Neural Network (DNN) models for a variety of low-resource languages. We then examine how the agreement between the teacher's best labels and the original labels affects the student model's performance. Next, we show that knowledge distillation can be easily applied to semi-supervised learning to improve model performance. We also propose a promising data selection method to filter un-transcribed data. Then we focus on knowledge transfer among DNN models with multilingual features derived from CNN+DNN, LSTM, VGG, CTC and attention models. We show that a student model equipped with better input features not only learns better from the teacher's labels, but also outperforms the teacher. Further experiments suggest that by learning from each other, the original ensemble of various models is able to evolve into a new ensemble with even better combined performance.
Advanced LSTM: A Study About Better Time Dependency Modeling in Emotion Recognition

Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However, conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of LSTM, advanced LSTM (A-LSTM), for better temporal context modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based weighted pooling RNN can also complement the state-of-the-art emotion classification framework. This shows the advantage of A-LSTM.
Experience in System Design for Human-Robot Teaming in Urban Search and Rescue

The paper describes experience with applying a user-centric design methodology in developing systems for human-robot teaming in Urban Search & Rescue. A human-robot team consists of several semi-autonomous robots (rovers/UGVs, microcopter/UAVs), several humans at an off-site command post (mission commander, UGV operators) and one on-site human (UAV operator). This system has been developed in close cooperation with several rescue organizations, and has been deployed in a real-life tunnel accident use case. The human-robot team jointly explores an accident site, communicating using a multi-modal team interface, and spoken dialogue. The paper describes the development of this complex socio-technical system per se, as well as recent experience in evaluating the performance of this system.
A Survey on Semi-Supervised Learning Techniques

Semi-supervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semi–supervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semi-supervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semi-supervised learning is a noble indication to shrink human labor and improve accuracy. There has been a large spectrum of ideas on semi-supervised learning. In this paper we bring out some of the key approaches for semi-supervised learning.
The Interplay of Cross-Situational Word Learning and Sentence-Level Constraints

A variety of mechanisms contribute to word learning. Learners can track co-occurring words and referents across situations in a bottom-up manner (cross-situational word learning, CSWL). Equally, they can exploit sentential contexts, relying on top-down information such as verb-argument relations and world knowledge, offering immediate constraints on meaning (word learning based on sentence-level constraints, SLCL). When combined, CSWL and SLCL potentially modulate each other's influence, revealing how word learners deal with multiple mechanisms simultaneously: Do they use all mechanisms? Prefer one? Is their strategy context dependent? Three experiments conducted with adult learners reveal that learners prioritize SLCL over CSWL. CSWL is applied in addition to SLCL only if SLCL is not perfectly disambiguating, thereby complementing or competing with it. These studies demonstrate the importance of investigating word-learning mechanisms simultaneously, revealing important characteristics of their interaction in more naturalistic learning environments.
Studying weak central coherence at low levels: children with autism do not succumb to visual illusions. A research note.

While anecdotal reports of abnormal perceptual experiences in autism abound, there have been to date no experimental studies showing fundamental perceptual peculiarities. The present paper reports results from a first study of low-level visual integration in autism. Twenty-five subjects with autism, 21 normal 7- and 8-year-olds, and 26 children with learning difficulties were asked to make simple judgements about six well-known visual illusions. Two conditions were used, in an attempt to explore group differences; standard two-dimensional black and white line drawings, and the same figures augmented with raised coloured lines. The subjects with autism were less likely to succumb to the two-dimensional illusions than were the other groups, and were less aided by the three-dimensional 'disembedded' condition. These striking results are discussed with reference to the 'central coherence' account of autism.
A new generation of microwave bandpass filters using multilayer ring resonators

A new generation of compact microwave bandpass filters (BPF) is proposed. The filters are based on multilayer ring resonators (MRR) technique which is applicable to both narrow band and ultra-wideband (UWB) bandpass filters. This class of filters has very compact circuit size and simple structure for fabrication. Multiple attenuation poles exist and can be simply placed to the left or right of the passband. The dimensions of the filters can be designed to around 2 mm × 2 mm × 1 mm. Out of the five filters presented, two of them are fabricated and measured. Good agreement between the simulation and measurements confirmed the design approach.
Crowdsourcing Literature Reviews in New Domains

A standard approach to exploring a domain is to conduct a literature review. However, conducting a literature review in a new domain presents unique challenges. Whereas in an existing domain, researchers can use established classifications of knowledge to guide their search for and interpretation of the literature, this is not the case for a new domain that lacks such classifications. In a new domain, the literature is typically broad, fragmented, and, at the same time, growing quickly. The task of the researcher is to make sense of evidence when it does not fit existing models and classifications. Encountering such evidence forces them to extend existing knowledge.
Some physical layer issues of wide-band cognitive radio systems

Recent study by FCC spectrum policy task force (SPTF) found that while the available spectrum becomes increasingly scarce, the assigned spectrum is significantly underutilized. Cognitive radio technology holds the key promise to solve such problems. In this paper, we investigate some physical layer issues of wide-band cognitive radio systems. Specifically, we discuss the physical layer signal structure to support flexible operations required by wide-band cognitive radios. We analyze the performances of generalized power sensing and waveform sensing. And we study the adjacent channel interference mechanisms focusing on the interference caused by time-domain signal truncation
Lightweight Cryptography for FPGAs

The advent of new low-power Field Programmable Gate Arrays (FPGA) for battery powered devices opens a host of new applications to FPGAs. In order to provide security on resource constrained devices lightweight cryptographic algorithms have been developed. However, there has not been much research on porting these algorithms to FPGAs. In this paper we propose lightweight cryptography for FPGAs by introducing block cipher independent optimization techniques for Xilinx Spartan3 FPGAs and applying them to the lightweight cryptographic algorithms HIGHT and Present. Our implementations are the first reported of these block ciphers on FPGAs. Furthermore, they are the smallest block cipher implementations on FPGAs using only 117 and 91 slices respectively, which makes them comparable in size to stream cipher implementations. Both are less than half the size of the AES implementation by Chodowiec and Gaj without using block RAMs. Present’s throughput over area ratio of 240 Kbps/slice is similar to that of AES, however, HIGHT outperforms them by far with 720 Kbps/slice.