Reviewerly/exHarmony huggingface dataset:
Blockchain-assisted D2D Data Sharing in Fog Computing

In fog network, device-to-device (D2D) sharing is an important way to obtain data. However, due to an untrusted environment, it is difficult for a device to assess the reliability of the received data. What’s more, devices may be reluctant to share data because of selfish, resulting in data supply and demand imbalances. In this regard, a data sharing scheme assisted by blockchain and matching algorithm is proposed. In order to ensure the authenticity of the data, the Bayesian inference model is employed to predict quality of the data, and a multi-factor data evaluation method is presented to make accurate judgments. Furthermore, different utility functions for data requesters and providers are defined, and a two-way matching game is introduced to balance of data supply and demand. To reduce the blockchain consensus delay and ensure the activeness of fog nodes, a practical byzantine fault tolerates (PBFT) consensus mechanism based on the frequency of interaction is investigated. The simulation results verify the effectiveness of the algorithm. The proposed data sharing scheme promotes the interaction of information in the fog computing network.
What more can empirical contextual data tell about the real usage of words and collocations?

Abstract This article presents a corpus-based distributional analysis of the usage patterns of a cluster of words and compounds containing the morpheme qià ( 恰 ) ‘just, exactly’, by the aid of an extended concordancer to retrieve representative collocations from their adjacent contexts in Chinese Gigaword. Upon a survey of the historical evolution of the qià cluster with exemplar data and an overview of existing proposals to account for their usages in terms of expectational match, our distributional analysis is conducted to identify the salient collocational or contextual features that lead to a number of interesting findings. Substantial evidences are provided for clarifying the non-word status of qià rú ( 恰如 ) and qià sì ( 恰似 ) and their similarities, the exchangeability of qiàhǎo ( 恰好 ) and qiàqiǎo ( 恰巧 ), distinct collocational preferences of the adverbs qià ( 恰 ), qiàqià ( 恰恰 ) and the others with different subsets of verbs, the prosodic requirement of an even number of syllables for a qià -adverb and its main verb, and the contrastive popularity of qiàqià ( 恰恰 ) vs qiàdàng ( 恰当 ) to reveal different usage tendencies between speakers in Taiwan and the Mainland. All these novel findings and insights about the subtle (dis)similarities in the usage and meanings of the qià ( 恰 ) cluster suggest that distributional analysis of contextual collocations using large-scale language data remains a powerful tool that can complement other analytical approaches for the advancement of lexical semantic research.
U-Shaped Xception-Residual Network for Polyps Region Segmentation

Segmenting the region of interest helps gastroenterologists for removing polyps during the surgery in the gastrointestinal tract. We propose a segmentation system to segment the area of the polyp from the informative frames. Informative frames are the frames that contain at least a polyp in colonoscopy still frames. Our proposed U-shaped convolution neural network model utilizes the concept of residual connection and Xception as a backbone structure. We consider colonoscopy still frames as input to the segmentation model and achieved a Dice and Jaccard score of 86.3 and 79, respectively. It outperformed the conventional U-net with a 3.7% performance gain with respect to Dice score. This proposed method can be used as a reliable alternative system to identify a region of polyps during colonoscopy analysis.
Hand Gesture Based Computer Vision Mouse

The popularity of the Human-Computer Interaction (HCI) technology exponentially grew with the advancement of artificial intelligences. This work with the help of computer vision will enable the user to interact with the computer at their fingertips. The user would essentially be able to control the cursor and do associative functionalities such as left-click, right-click, move, and almost everything a mouse can do. A cross-platform application is developed which is the underlining vision of replacing a mouse.
BUS-Net: A Fusion-based Lesion Segmentation Model for Breast Ultrasound (BUS) Images

Breast cancer is the most common cancer(s) among women worldwide. The survival rate decreases if the cancer is not detected at an early stage. Breast ultrasound (BUS) is emerging as a popular modality for breast cancer detection owing to its several advantages over other modalities. We proposed a novel deep learning framework named BUS-Net for automated lesion segmentation in BUS images in this work. However, every deep learning framework has disadvantages of its own; however, the drawbacks associated with individual models can be overcome when combined. Our proposed BUS-Net is an ensemble of three popular deep learning frameworks, namely attention U-net, U-Net and SegNet. The final segmentation map generated by BUS-Net is a pixel-level fusion on the outputs of each of the individual frameworks. The potentiality of BUS-Net was tested on a publicly available dataset named BUSI dataset. This dataset consists of 647 tumor images collected from 600 different female patients. To prevent biased results, the training and test set were separate. BUS-Net framework achieved an accuracy—93.19%, precision—93.18%, recall—88.75%, dice—90.77%, and volume similarity—95.55% for lesion segmentation in the test set. The degree of correlation between the lesion region segmented by the medical experts and that segmented by BUS-Net was high ( $$R^2 = 0.9131$$ ). Further, the performance of BUS-Net was also compared with the state-of-the-art techniques. This comparison showed that BUS-Net maintains a tradeoff between precision and recall, proving the robustness, efficiency, and reliability of the framework.
Analyzing Behavior to Detect Cervical Cancer

Cervical cancer is preventable if one can detect it at an early stage. Due to cervical cancers, the human body imparts various behaviors and seven different determinants of this behavior. Behaviors can be conscious and also subconscious. And also, all expressing behaviors might not be completely correlated. In this work, a strategy has been proposed to exclude non-correlated features to maintain or enhance the detection accuracy of cervical cancer at an early stage. Experimental results show a maximum of 94.4% accuracy, and thus, it proves the efficacy of the proposed work.
Convexity Defects-Based Fingertip Detection and Hand Gesture Recognition

Vision-based hand gesture recognition is perhaps the most widely used technique for human–computer interaction technology with special application toward sign languages used by differently abled people. Perhaps the most important step in such a gesture recognition system is feature extraction. In this paper, we use a novel fingertip detection mechanism based on convexity defects and use nine geometrical features that are translation and rotation invariant. We use seven different classifiers on two different public hand digit datasets (NTU hand digit dataset and SP-EMD color-depth hand gesture dataset) and find that for both the datasets, the random forest classifier gives the best classification accuracy (94.2% and 90.1%, respectively).
Speech Recognition System of Spoken Isolated Digit in Standard Khasi Dialect

This paper aims to analyze the performance of a speaker independent spoken isolated digit recognition system in standard (Sohra) dialect of Khasi Language. Initially, we have prepared an ideal set of word-based digit pronunciations of 15 digits for the said dialect. The standard approach was used to collect speech data in an open room using Zoom H4N handy portable digital recorder from native language speakers of diverse age groups and gender. Each spoken isolated data has been processed and segmented using a WaveSurfer software tool. Sampling frequency was set at 16 kHz and bit resolution at 16 bits per sample. We extracted the relevant speech feature vectors using Mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) that are compatible with the hybrid acoustic models of conventional (Gaussian mixture model-hidden Markov model)-based models (monophone and triphone) and hidden Markov model-deep neural network (HMM-DNN). Using MFCC and PLP features, we found that the HMM-DNN model performs better than GMM-HMM-based models with a word error rate (WER) of 6.67% and 7.40%, respectively.
VLSI Implementation of Artificial Neural Network

In this work, VLSI circuit-based artificial neural network has been implemented. The artificial neuron consists of three components such as multiplier, adder and neuron active function circuit, which perform arithmetic operations for realizing neural network. The focus on this work is linearity investigation in nonlinear artificial neural network as well as learning efficiency. A multiplier design has been proposed for reducing nonlinearity in an artificial neuron. Sigmoid circuit has been used for activation function. New weight update technique with both retrieving and on chip learning function has been proposed for better accuracy and improved learning ability. In this neural network, pulse width modulation technique has been used to compute the output signals using both multiplication and summation operations. This improves the linearity of the neural network. The learning operation of the neural network has been verified through simulation results by adopting digital function like “NAND”. A high-speed and accurate error detection block has also been used for this purpose. Cadence Virtuoso has been used to perform circuit level simulation. Design has been done in TSMC 180 nm CMOS technology. In the proposed design, an error calculation time of 0–100 ps has been achieved, thereby making the overall operation fast and the learning efficiency 99%. Supply voltage is 1.8 V, and total power dissipation has been measured to be is 8 mW.
An Acoustic/Prosodic Feature-Based Audio Dataset for Assamese Speech Summarization

The spoken form is the most predominant form of communication laden with rich information than its textual counterpart. Development of summarization techniques for spoken data in a resource-poor language is a challenging task, especially due to the unavailability of appropriate datasets. In this paper, we focus on creating a dataset for speech summarization in Assamese, a low-resource language of Northeast India, the genre of speech being broadcast news. The contribution of this paper is twofolds: Firstly, a dataset of the audio samples of the broadcast news comprising of acoustic/prosodic features is developed, where each tuple is the manually segmented utterance representing a sentence. Each utterance is labeled as either summary-worthy or not summary-worthy, in accordance to the handcrafted summaries. Secondly, the relation of the prosodic features in indicating the informativeness of an utterance is evaluated. Binary classification is performed with the help of various classifiers, and a statistical analysis of the performance of the classifiers is presented. This approach for speech summarization is highly relevant for resource-poor language which does not have adequate resources to generate the textual representation of the audio. To the best of our knowledge, this is the first effort to develop a dataset in Assamese language for speech summarization.
Swift Sort: A New Divide and Conquer Approach-Based Sorting Algorithm

Sorting implies the task of presenting a specific type of data in a specific order. These tasks are getting accomplished by different algorithms proposed by researchers. Researchers are trying to achieve this task in minimal space and time complexity with improved stability, correctness, finiteness and effectiveness. Sorting is used in wide range of fields namely, in Operating systems, Data Base Management systems, in searching and in various other data science related areas. In this paper, a divide and conquer approach-based algorithm is proposed to sort the data in a specific order using min–max searching. The time complexity of the proposed Swift Sort algorithm is O(nlogn) and O(n2) in the average and worst cases, respectively. Moreover, time complexity of the proposed algorithm is comparable to Quick Sort, Merge Sort, Heap Sort and TimSort but at the same time Randomised Quick Sort, Merge Sort and Heap Sort produces a better Time Complexity in their worst cases than Swift Sort. The experimental results prove the correctness of the proposed algorithm.
Moving Object Detection in Dynamic Environment via Weighted Low-Rank Structured Sparse RPCA and Kalman Filtering

As a classical problem for computer vision, moving object detection (MOD) can be efficiently achieved by foreground and background separation. The Robust Principal Component Analysis (RPCA)-based method has been potentially utilized to solve the problem. However, the detection accuracy for RPCA-based method is limited for complex scenes with slow-motion. Besides, it is time consuming for the way to seek for background modeling based on solving a low-rank minimization problem, for which multiple frames of the videos are required as the input. Therefore, a real-time MOD framework (LSRPCA_KF) is proposed for the dynamic background, where a weighted low-rank and structured sparse RPCA algorithm is used to achieve background modeling for history data, while the online MOD is achieved by the background subtraction method and updated by the Kalman filter for every real time frame. Specifically, for the background model, a newly designed weight is incorporated to distinguish the significance of different singular values, and a structured sparse prior is added to penalty the spatial connection property of the moving object. Besides, the weighted low-rank and structured sparse RPCA model is efficiently solved by the Alternating Direction Method of Multipliers (ADMM) optimization algorithm. Experimental results demonstrated that better performance of our method with significantly reduced delay in processing and better detect the moving object has been achieved, especially for the dynamic background.
CORE APPROACHES TO TEACHING A FOREIGN LANGUAGE AT UNIVERSITIES IN THE ERA OF DIGITALIZATION

The article highlights core approaches to teaching a foreign language that would help develop students' abilities and skills for future successful cooperation in a multidisciplinary environment.The authors present such approaches as multidisciplinary, interdisciplinary and cross-or transdisciplinary.It is mentioned that old approaches to teaching specific topics or developing four language competencies as speaking, writing, listening and reading are no more in the focus from pedagogical point of view as before.Today the development of technologies and the creation of new jobs require to educate such a professional who would work in a multidisciplinary and multicultural environment effectively.Thus, the implementation of multidisciplinary and interdisciplinary approaches to teaching a foreign language require the combination of knowledge and skills from different disciplines in order to create an innovative product or a service.Interdisciplinary teaching of a foreign language has been defined in the article as a process by which students integrate insights and modes of thinking and processing of information from two or more disciplines to advance their practical understanding of a discipline that stands beyond the scope of one discipline.The authors identified the ways of interdisciplinary approach to teaching a foreign language at universities such as socio-economic articles / blogs analysis, case studies, boosting research activity, reallife interdisciplinary role-plays and project-based learning.The analysis of scientific literature has shown that Intradisciplinary means working within a single discipline, Crossdisciplinary implies viewing one discipline from the perspective of another; Multidisciplinary occurs when students from different disciplines working together,
Bidirectional recurrent neural network with multi-head attention for automatic scene generation using sentiment analysis

Text generation is one of the complex tasks associated with natural language processing. For efficient text generation, syntax and semantics of the language have to be considered to assign context to key phrases. The main objective of the proposed work is to perform text generation specifically for movie scripts. The training data consist of a self-annotated corpus of movie scripts depicting scenes, specific to certain genre where the annotation mainly focuses on a specific director’s movie scripts. The scene generation is set forth by word embedding with sentiment classification where the emotionally analyzed words are vectorized using the EmoVec algorithm performing sentiment analysis. Based on the sentiment and location associated with each scene, context for the phrases are identified and proceeded to build a well-defined script. Bidirectional Long Short-Term Memory BLSTM with multi-head Attention is used to capture the information processed in both forward and backward propagation in order to understand future context. The vocabulary is built using Stanford’s Internet Movie Database IMDB datasets to perform word based encoding for which requirement of an extensive vocabulary is imminent.
Designing Policy Mixes to Address the World’s Worst Devastation of a Rural Landscape Caused by Xylella Epidemic

The socio-economic consequences of the Xylella fastidiosa epidemic represent a global problem that can only be addressed through tailored, local solutions. The selection of public interventions is not a trivial task for policy makers, as they must weigh many different interests (e.g., private profit, ecosystem services, usability, preservation and growth of real estate value, amenities, and land protection). The present paper addresses this challenge by building participatory scenarios based on “fuzzy cognitive maps,” with the aim of identifying effective, acceptable, and efficient policy mixes to address the Xylella epidemic. The work investigates the case of southern Salento (Italy)–an olive production area at the epicentre of the global Xylella outbreak–to identify the most suitable actions for regenerating the landscape. To this end, the most efficient policy mixes are determined according to three possible policy perspectives, which provide different weights for effectiveness and acceptability. The results show that the proposed methodological approach may assist policy makers in coping with multifaceted policy challenges.
The Suitability and Characteristics Analysis of Key Science Inquiry Activities in the 2015 National Science Curriculum in Korea

Abstract The suitability and characteristics of middle school inquiry activities in the 2015 National Curriculum of Science in Korea were studied from the perspective of eight science teachers with rich experience in teaching science inquiry activities. In-depth interviews revealed problems teachers experienced while conducting inquiry classes. Problems were classified into five categories: inquiry level, method, tool, inquiry result, difficulty in guidance, and safety. Based on this analysis, strategies for an inquiry improvement plan are suggested, including that quantitative inquiry requiring numerical interpretation of data obtained from the process of inquiry should be emphasized. Concrete guidance of inquiries should be provided for non-major science teachers to help them instruct inquiries more easily. Institutional improvement is needed to develop curriculum activities and the improvement of science lab and classroom environments are necessary for conducting inquiry activities utilizing technology. The results of this study can contribute to the development of science inquiry activity programs.
Inverse design in quantum nanophotonics

Traditional design methods (those in which a designer proposes and refines a solution) are rapidly being superseded by algorithmic alternatives. There, a computer is given only a design goal and a set of manufacturing constraints and is allowed to create any permissible solution that does the job. In nanophotonics, this is now routinely applied to photonic structures such as waveguides and demultiplexers, where the design goal could be, for example, maximised transmission at a particular wavelength. A step towards extending this to the quantum realm of single-photon interactions with atomic systems was recently taken, in which the ideas of macroscopic quantum electrodynamics are mixed with those of adjoint optimisation. In this talk, I will introduce the formalism of quantum electrodynamical inverse design, before showing a few recent proof-of-principle applications to processes such as resonant energy transfer.
The Core Cluster-Based Subspace Weighted Clustering Ensemble

In recent years, the Internet of Things (IoT) technology has developed rapidly and is widely used in various fields. It is of great research significance to uncover underlying patterns and insights from the high-dimensional data of IoT, to excavate valuable information to guide people’s production and life. Clustering can explore the natural cluster structure of the data, which is conducive to further understanding of the data, and is an essential preprocessing step for data analysis. However, clustering is highly dependent on the data. In order to reduce the complexity of the model, reduce the computational cost, and obtain a more robust clustering solution, we combine subspace clustering and ensemble learning to propose a novel subspace weighted clustering ensemble framework for high-dimensional data. The proposed framework first combines random feature selection and unsupervised feature selection to generate a set of base subspaces. Clustering is performed on each base subspace to achieve a set of subspace clustering solutions that generate a set of adaptive core clusters. The size of the core cluster is between the sample and the cluster. In the ensemble process, the core clusters are viewed as the basic unit, and the stability of the cluster is evaluated by measuring the distance between the core cluster pairs, and the similarity between the core clusters and the clusters in the base subspace, and then weighting the subspace clustering solution. Under this framework, we propose four subspace ensemble approaches based on core cluster to improve the accuracy of consensus clustering solutions. Extensive experiments are conducted on multiple real-world high-dimensional datasets, demonstrating that the proposed framework can process high-dimensional data for the IoT, and the proposed subspace clustering ensemble approaches are superior to the state-of-the-art clustering approaches.