Reviewerly/exHarmony huggingface dataset:
On Determining the Position of Expert Confidence Concentration Points When Modifying Membership Functions

The apparatus of fuzzy sets membership functions is a powerful tool widely used to express expert preferences when solving problems in various subject areas. Algorithmic ways to modify membership functions without additional involvement of experts are common modern means of obtaining new functions on the basis of the existing ones. This paper investigates methods of possible resolution of ambiguity in determining the position of the middle point of expert confidence concentration of triangular membership functions in cases of their modification with the application of algorithms that use the reference functions. The obtained membership functions of fuzzy sets can be used in modeling the behavior of agents under conditions of substantial uncertainty caused by COVID–19.
Sentiment Analysis: An Approach for Analysing Tamil Movie Reviews Using Tamil Tweets

Indian languages are often used in social media messages. Tamil is one of the oldest languages which has been used often in tweets. Sentiment Analysis (SA) is a multidisciplinary unit which is a part of text mining as well as natural language processing.&nbsp; Sentiment analysis has gained incredible development in recent times mostly for English language. However very less work of sentiment analysis has done for Indian languages like Hindi, Tamil, Kannada etc., In this chapter, Tamil tweets are focused to find the sentiment of the Tamil movie reviews. It is essential to analyze the Tamil language content for tweets and get perception of opinion expressed by the tweets. The objective is to classify the sentiment of the Tamil movies based on Tamil tweets using Tamil SentiWordNet (TSWN). Term Frequency - Inverse Document Frequency (TF-IDF) method is proposed to find the sentiment polarity of the Tamil movie dataset. Domain specific ontology is applied to identify the primary sentiment categorization of the Tamil movies. In contextual semantic, the sentiment of a word may flip based on the neighbouring word. In this research, sentiment-bearing terms and its neighbouring terms in Tamil tweets are evaluated using contextual semantic sentiment analysis to get more accurate result for the movie sentimental classification.
Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering

Distantly supervised models are very popular for relation extraction since we can obtain a large amount of training data using the distant supervision method without human annotation.In distant supervision, a sentence is considered as a source of a tuple if the sentence contains both entities of the tuple.However, this condition is too permissive and does not guarantee the presence of relevant relation-specific information in the sentence.As such, distantly supervised training data contains much noise which adversely affects the performance of the models.In this paper, we propose a selfensemble filtering mechanism to filter out the noisy samples during the training process.We evaluate our proposed framework on the New York Times dataset which is obtained via distant supervision.Our experiments with multiple state-of-the-art neural relation extraction models show that our proposed filtering mechanism improves the robustness of the models and increases their F1 scores.
What happens if you treat ordinal ratings as interval data? Human evaluations in NLP are even more under-powered than you think

Previous work has shown that human evaluations in NLP are notoriously under-powered.Here, we argue that there are two common factors which make this problem even worse: NLP studies usually (a) treat ordinal data as interval data and (b) operate under high variance settings while the differences they are hoping to detect are often subtle.We demonstrate through simulation that ordinal mixed effects models are better able to detect small differences between models, especially in high variance settings common in evaluations of generated texts.We release tools for researchers to conduct their own power analysis and test their assumptions.We also make recommendations for improving statistical power.
uFTIR: An R package to process hyperspectral images of environmental samples captured with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e462" altimg="si2.svg"><mml:mi>μ</mml:mi></mml:math>FTIR microscopes

uFTIR is an R package that implements an automatic approach to analyze μFTIR hyperspectral images with a strong focus on microplastic recognition in environmental samples. The package performs image classification using a Spectral Angle Mapper algorithm in a library search approach. It interacts with other R packages used for spectral analysis. It exports its output as raster and vector files that can be post-processed in common Geographical Information Systems software. The package was designed around the principles of modular development, compatibility, and open-source software. We hope our contribution will serve researchers to size the occurrence of microplastics in ecosystems.
Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval

Software developers frequently reuse source code from repositories as it saves development time and effort. Code clones (similar code fragments) accumulated in these repositories represent often repeated functionalities and are candidates for reuse in an exploratory or rapid development. To facilitate code clone reuse, we previously presented DeepClone, a novel deep learning approach for modeling code clones along with non-cloned code to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The probabilistic nature of language modeling, however, can lead to code output with minor syntax or logic errors. To resolve this, we propose a novel approach called Clone-Advisor. We apply an information retrieval technique on top of DeepClone output to recommend real clone methods closely matching the predicted clone method, thus improving the original output by DeepClone. In this paper we have discussed and refined our previous work on DeepClone in much more detail. Moreover, we have quantitatively evaluated the performance and effectiveness of Clone-Advisor in clone method recommendation.
Key Object Classification for Action Recognition in Tennis Using Cognitive Mask RCNN

Previous research studies in sports content analysis have focused based on the spatiotemporal instead of a content-based viewpoint. Currently the view for classification of sports is done using object and activities present in videos meaningfully. As it can have achieved through various means, one among them is through knowledge-based approach, which choose the path of continuous training along with some intelligence induced to the system. Hence, in the proposed work, our major focus is toward meaningfully masking the key functional object that acts as base for sports scene detection. In order to achieve that, improvised mask RCNN technique is used which have knowledge base at classification level of label prediction. As our approach results with 91% of accuracy, it shows the improvised stage in the concept of meaningful segmentation of functional sports objects.
Exploring Hong Kong Youth Culture via a Virtual Reality Tour

The advantages of employing virtual reality tours in teaching are attributed to the virtual reality experience it provides to the students. In the case of teaching popular culture, benefits from the potential of VR tour are amplified by the empirical significance that would lead to the students’ imagination and reflection. In addition, an online VR tour suggests a flexibility that allows students to learn anyplace anytime, satisfying the need for blended learning and distance learning, which is a very critical mode of teaching and learning during the COVID-19 pandemic. This article discusses the advantages and challenges of blending “virtual reality” into the teaching of popular culture, and, furthermore, the implications of VR in tertiary education are discussed by examining the research that is conducted through the application of a VR tour in the course: Hong Kong Popular Culture. Sixty-eight students participated in the course. After implementing the VR tour, a questionnaire survey and interviews were conducted. In addition, students wrote essays to reflect on the youth culture of contemporary Hong Kong after the explanation of the tour, and these were also examined. We observed the positive responses from the students and the way in which the VR tour could enhance the learning qualities in the course on cultural studies.
Reinforcing SLA Consensus on Blockchain

Cloud Infrastructure as a Service (IaaS) Service Level Agreements (SLAs) assessment constitutes the de facto area of interest and applications in the public cloud infrastructure. However, the domination of colossal corporations tends to monopolize the way metrics and Key Performance Indicators (KPIs) are measured and determined, leading to governed environments where the clientele is unable to obtain accurate and unbiased assessment of SLAs. Leaning toward SLA self-assessment, this paper provides a fair SLA consensus approach with innate transparency and privacy by leveraging permissioned blockchains that are equipped with Trusted Execution Environments (TEEs). The SLA assessment intelligence is performed inside enclaved smart contracts isolated from the on-chain entities views. The result constitutes a permissioned blockchain ecosystem where the IaaS and their clientele commonly agree on all the respective SLA monitoring and computation rules beforehand, as defined in any SLA assessment process, while the SLA consensus scheme constantly audits the SLA metrics based on these pre-approved regulations.
Application of Modified Lowa Model Evidence Based Practice on Trauma Care Nurses Regarding Head Injury

Background: Head injuries are a regular occurrence in emergency departments around the world, with more than 2 million annual visits in North American EDs and more than 400 000 in the United Kingdom alone. Despite the fact that the mechanism of injury is consistent,, Head injuries are a regular occurrence in emergency departments around the world, with over 2 million visits in North American EDs and over 400 000 in the European Union alone. Regardless of how consistent the injury mechanism is.&#x0D; Objectives: Holds data what nurses already know about the modified LOWA model. 2. Develop and test a protocol using a IOWA model that was adjusted. 3. Assess the updated LOWA model's effectiveness 4.To connect the knowledge score to demographic data.&#x0D; Research Approach: Interventional approach Research design: - One group pre test and post test. Setting of the study: - The study will be conducted in AVBRH Hospital. Sample: - Staff Nurse Sample Size is 50Sampling Technique is Purposive sampling. Setting of the study is The study will be conducted in AVBRH Sample: - Staff Nurse Sampling Technique: - convenient sampling Data Collection: - Field data Will be collected by the use of standardised questionnaires with three key sections: Section A (Standard standards), Section B (Socio-demographics and work history of staff) used the modified LOWA model and check list).&#x0D; Expected Results: Oriented it toward the application of the LOWA model. Those characteristics are what evidence-based practise on trauma care nurses concerning head injury entails, but they may be able to address the issues that Traumatic Brain Injury Nursing faces. Adopting this paradigm into traumatic brain injury nursing units is worth a shot.With the assistance of a specific case, this article will discuss the clinical application of the Lowa Model in traumatic brain injury nursing care.&#x0D; Conclusion: In the light of the study findings, this study shows that, the implementation of LOWA Model evidence based practice has a positive effect on nurse’s knowledge and practices regarding trauma care nurses regarding head injury. There was a significant improvement in the nurses ‘knowledge and practice regarding LOWA Model evidence-based practice implementation compared with that before it. There was positive significant correlation between nurses’ knowledge and their practice before and after program. Nurses’ knowledge and practice about LOWA Model improved after application of this program.
Programming a quantum phase of matter

The ability to measure long-range entanglement may enable robust quantum memory.
Iterative Gaussian mixture model and multi‐channel attributes for arrival picking in extremely noisy environments

ABSTRACT Arrival picking is a traditional and important topic in the field of seismic exploration. Depending on the quality of seismic data, the picking results are prone to large errors. As commonly used arrival‐picking strategies, conventional clustering methods, such as K‐means, suffer from insufficient flexibility, for example the clustering boundaries are fixed given a set of attribute vectors. In order to overcome this drawback, we propose a new strategy which can provide flexible clustering results (i.e. the variable clustering boundaries) based on an iterative Gaussian mixture model and utilize the local correlation among adjacent traces to enhance the anti‐noise ability. First, we use local principal component analysis to roughly distinguish the areas of signal waveforms. Then, two multi‐channel attributes are calculated and input to the iterative Gaussian mixture model. These two attributes can correctly identify the arrivals and avoid redundant computation caused by the high‐dimensional attribute vectors. The final step is to establish an optimized Gaussian mixture model and to iteratively select the proper boundaries for each trace. Although the iterative Gaussian mixture model takes a longer calculation time, the synthetic and real data tests have shown superior results over conventional methods even in situations of extremely low signal‐to‐noise ratios.
A Cloud Data Integrity Verification Scheme Based on Blockchain

Cloud storage saves a lot of storage overheads for users to manage data. However, it also brings data integrity challenges. Relying only on message authentication codes (MAC) and hash functions introduces serious communication overheads. Existing integrity verification schemes usually rely on third-party auditors (TPAs) to reduce users’ audit burden. However, malicious TPAs not only extract raw data from repeated audit processes but also collude with a cloud server to launch collusive attacks. Recently, some integrity verification schemes based on blockchain are presented to solve challenges of raw data extraction and collusive attacks. These schemes cannot realize large-scale data processing on a blockchain as well as dynamic data operations. To address the above issues, we propose a blockchain-based cloud data integrity verification scheme (BCD-IV) that removes TPAs by using chain-code that solves collusive attacks. Furthermore, we construct a new homomorphic verification tag (HVT) based on the signature proposed by Zhang, Safavi, and Susilo (ZSS), which supports blockless verification. The proposed scheme realizes full dynamic operations with auxiliary information that stores on a blockchain. Mathematical analysis shows that our scheme has lower complexity in terms of communication and computation overhead, which are both O(c). Finally, we conduct experiments on Hyperledger Fabric V2.2 to demonstrate that the computation efficiency of audit is the highest compared to the state-of-the-art and the storage overhead is negligible.
Can Home Use of Speech-Enabled Artificial Intelligence Mitigate Foreign Language Anxiety – Investigation of a Concept

The harmful effects of speech-related anxiety among learners and users of foreign language is well documented. Aiming to investigate the potential of reducing anxiety through ad libitum interaction with Artificial Intelligence chatbots, a controlled pilot study was conducted on East Asian staff of a large financial institution over a 4-week period, the sample of 40 divided between a test group using AI and the non-exposed subjects. Variety of measures were employed to gain as comprehensive an insight as warranted given the study limitations of a small sample, brief study period and relatively unsophisticated but freely available AI chatbot. The measures ranged from surveys of anxiety, attitudes toward daily chatbot usage, oral interviews and IELTS testing of English speaking ability. The results were cautiously encouraging: nearly universal endorsement of AI as a non-threatening interlocutor positively impacting subjects’ confidence and to the extent limited by time constraint, enhancing individual components of the IELTS paradigm. Moreover, significant linear relationship between individual anxiety survey items and overall IELTS scores at baseline was observed, lending a measure of validity to the construct developed for the occasion. In summary, it is felt that subject to further development and refinement, conversationally enhanced AI chatbots hold a significant promise toward reducing speech-related anxieties and learning inhibitions of English as a foreign language and thus merit further investigation toward this objective.
The Distributed and Centralized Fusion Filtering Problems of Tessarine Signals from Multi-Sensor Randomly Delayed and Missing Observations under Tk-Properness Conditions

This paper addresses the fusion estimation problem in tessarine systems with multi-sensor observations affected by mixed uncertainties when under Tk-properness conditions. Observations from each sensor can be updated, delayed, or contain only noise, and a correlation is assumed between the state and the observation noises. Recursive algorithms for the optimal local linear filter at each sensor as well as both centralized and distributed linear fusion estimators are derived using an innovation approach. The Tk-properness assumption implies a reduction in the dimension of the augmented system, which yields computational savings in the previously mentioned algorithms compared to their counterparts, which are derived from real or widely linear processing. A numerical simulation example illustrates the obtained theoretical results and allows us to visualize, among other aspects, the insignificant difference in the accuracy of both fusion filters, which means that the distributed filter, although suboptimal, is preferable in practice as it implies a lower computational cost.
Further cryptographic properties of the multiplicative inverse function

Differential analysis is an important cryptanalytic technique on block ciphers. In one form, this measures the probability of occurrence of the differences between certain input vectors and the corresponding output vectors. For this analysis, the constituent S-boxes of a block cipher need to be studied carefully. In this direction, we derive further cryptographic properties of the multiplicative inverse function, especially the ones related to higher order differentials. This improves some theoretical results of Boukerrou et al. [ToSC 2020(1)]. Further, we prove that the multiplicative inverse function defined over F2n has an error (bias) in its second order differential spectrum with probability 12n−2, and that error occurs in more than one place. Next, we analyze the Gowers uniformity norm of S-boxes, which is also a measure connected to higher order approximations. Finally, the bounds related to the nonlinearity profile of the multiplicative inverse function are derived using both Gowers U3 norm and Walsh–Hadamard spectrum. Some of our findings here provide slightly improved bounds over the work of Carlet [IEEE-IT, 2008]. These theoretical insights might have implications towards non-randomness of a block cipher, where the multiplicative inverse function is used as a primitive.
Auto-tagging of Short Conversational Sentences using Transformer Methods

The problem of categorizing short speech sentences according to their semantic features with high accuracy is a subject in natural language processing. In this study, a data set created with samples classified in 46 different categories was used. Examples consist of sentences taken from chat conversations between a company's customer representatives and the company's website visitors. The purpose is to automatically tag questions and requests from visitors in the most accurate way for 46 predetermined categories for use in a chat application to generate meaningful answers to the questions asked by the visitors. For this, different BERT models and one GPT-2 model, pre-trained in Turkish, were preferred. The classification performances of the relevant models were analyzed in detail and reported.
Hybrid Dynamic Fixed Point Quantization Methodology for AI Accelerators

Data quantization is an important task for the design of AI accelerators, because it has a great impact on both circuit area and inference accuracy. Dynamic fixed point (DFX) representation can save circuit area with accuracy loss, while dynamic dual-fixed-point (DDFX) representation can improve inference accuracy with area overhead. In this paper, we propose a hybrid dynamic fixed point (HDFX) quantization methodology: since the range of weight values is often limited, we use DFX to represent weight values; since the range of activation values is often large, we use DDFX to represent activation values. Based on the proposed HDFX quantization methodology, we develop the corresponding circuit for convolution operation. Experiment results show that the proposed methodology achieves good results in terms of both circuit area and inference accuracy.
Information diagrams in the study of entanglement in symmetric multi-quDit systems and applications to quantum phase transitions in Lipkin–Meshkov–Glick D-level atom models

Abstract In this paper we pursue the use of information measures (in particular, information diagrams) for the study of entanglement in symmetric multi-quDit systems. We use generalizations to $${U}(D)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>U</mml:mi> <mml:mo>(</mml:mo> <mml:mi>D</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> of spin $${U}(2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>U</mml:mi> <mml:mo>(</mml:mo> <mml:mn>2</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> coherent states and their adaptation to parity (multicomponent Schrödinger cats), and we analyse one- and two-quDit reduced density matrices. We use these correlation measures to characterize quantum phase transitions occurring in Lipkin–Meshkov–Glick models of $$D=3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>D</mml:mi> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> -level identical atoms, and we propose the rank of the corresponding reduced density matrix as a discrete order parameter.