Reviewerly/exHarmony huggingface dataset:
Graph convolutional networks with hierarchical multi-head attention for aspect-level sentiment classification

Aspect-level sentiment classification has been widely used by researchers as a fine-grained sentiment classification task to predict the sentiment polarity of specific aspect words in a given sentence. Previous studies have shown relatively good experimental results using graph convolutional networks, so more and more approaches are beginning to exploit sentence structure information for this task. However, these methods do not link aspect word and context well. To address this problem, we propose a method that utilizes a hierarchical multi-head attention mechanism and a graph convolutional network (MHAGCN). It fully considers syntactic dependencies and combines semantic information to achieve interaction between aspect words and context. To fully validate the effectiveness of the method proposed in this paper, we conduct extensive experiments on three benchmark datasets, which, according to the experimental results, show that the method outperforms current methods.
Considering the role of material gerontology in reimagining technology design for ageing populations

The promise of technology to provide solutions to the global concern of ageing populations has largely been unfulfilled. We argue that this is, in part, related to design processes that fail to take account of the rich material lives of older people, and that often adopt stereotypical views of older people as frail, vulnerable and unskilled. We draw on empirical data from two co-design projects, to suggest the contributions that material gerontologists could make to design teams creating technologies for ageing populations. We suggest material gerontologists bring three key elements to interdisciplinary design teams: (1) making visible the intra-action of humans and non-humans in co-design processes; (2) reconfiguring co-design response-ably with older adults; and (3) reimagining possible outcomes of technology design. We believe that this approach can result in the design of products, services and innovations that respond better to the heteroge­neous needs and life-worlds of older adults.
Adaptive initialization LSHADE algorithm enhanced with gradient-based repair for real-world constrained optimization

A straightforward and effective scheme for adaptive initialization is proposed and coupled with the Linearly reduced population size Success History based Adaptive Differential Evolution (LSHADE) for solving constrained optimization problems. The novel contributions are in the initialization phase: (i) exploiting the information of problem dimensions and the corresponding computational budgets to determine the suitable initial population size and hence the use of linear size reduction. Based on the initial population size, the next two inputs in the constraint handling scheme are appropriately initialized: (ii) the use of tolerance for inequality constraints and (iii) the choice between the original and a proposed setting of a crucial parameter controlling the tolerance, in order to balance exploration–exploitation orientation. Additionally, a gradient-based repair technique is incorporated into the method to significantly improve solution feasibility in high-dimensional problems. The proposed algorithm, referred to as En(L)SHADE, is validated on the CEC2020 competition challenging test-suite of 57 real-world constrained problems in various fields. Beside the advantage of simplicity, En(L)SHADE also shows a superior performance over the benchmark challengers.
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision

Automatic extraction of product attributes from their textual descriptions is essential for online shopper experience. One inherent challenge of this task is the emerging nature of e-commerce products — we see new types of products with their unique set of new attributes constantly. Most prior works on this matter mine new values for a set of known attributes but cannot handle new attributes that arose from constantly changing data. In this work, we study the attribute mining problem in an open-world setting to extract novel attributes and their values. Instead of providing comprehensive training data, the user only needs to provide a few examples for a few known attribute types as weak supervision. We propose a principled framework that first generates attribute value candidates and then groups them into clusters of attributes. The candidate generation step probes a pre-trained language model to extract phrases from product titles. Then, an attribute-aware fine-tuning method optimizes a multitask objective and shapes the language model representation to be attribute-discriminative. Finally, we discover new attributes and values through the self-ensemble of our framework, which handles the open-world challenge. We run extensive experiments on a large distantly annotated development set and a gold standard human-annotated test set that we collected. Our model significantly outperforms strong baselines and can generalize to unseen attributes and product types.
AN EMPIRICAL ANALYSIS OF POTENTIAL CENTRAL BANK DIGITAL CURRENCY ADOPTION PROCEDURE BY INDIAN GOVERNMENT AND ITS IMPLICATIONS

The following paper discusses the introduction to Central Bank Digital Currency (CBDC) and how the adoption of such a measure will result in a developing economy like India looking at past references. The paper aims to reach new measures of adoption procedure and why it becomes essential to bring out such a concept at an early age. Various documents and research on technologies have proved the significance of virtual currencies. The social media forums which are abuzz with such investment options as provided by cryptocurrency market leads to a talk on potential regulation of the market for fiscal benefits. The committee developing such a digital currency has many options at hand, like hash graph or blockchain, POW or POS framework, adopting ABFT or not and others. The paper concludes determining the best measures a committee can adopt to frame CBDCs, discussing the pros and cons while timing the launch based on certain indices available yet lesser known in general.
Efficient Training of Neural Transducer for Speech Recognition

As one of the most popular sequence-to-sequence modeling approaches for speech recognition, the RNN-Transducer has achieved evolving performance with more and more sophisticated neural network models of growing size and increasing training epochs. While strong computation resources seem to be the prerequisite of training superior models, we try to overcome it by carefully designing a more efficient training pipeline. In this work, we propose an efficient 3-stage progressive training pipeline to build highly-performing neural transducer models from scratch with very limited computation resources in a reasonable short time period. The effectiveness of each stage is experimentally verified on both Librispeech and Switchboard corpora. The proposed pipeline is able to train transducer models approaching state-of-the-art performance with a single GPU in just 2-3 weeks. Our best conformer transducer achieves 4.1% WER on Librispeech test-other with only 35 epochs of training.
Challenges to agile project management during COVID-19 pandemic: an emerging economy perspective

Globally, COVID-19 pandemic has affected more than 214 countries across the world, creating uncertainty and affecting every institution and individual. The organisations recognise the need of agile projects that may offer several benefits including faster deployments, adaptability and best fit alignment to fulfill the customer needs. Implementing agile projects is the key to survive in the post pandemic situation, but emerging economies have limited scope for implementation. The present study determines the critical factors that restricts implementation of agile projects in emerging economies. The critical factors are identified from literature and validated by experts. The validated critical factors are further assessed to identify the cause-and-effect relationship using Fuzzy Decision-Making Trial and Laboratory (F-DEMATEL) method. The results of the study posit ‘Skepticism towards the new way of working’ is the most significant causal factor affecting other factors. This study is an attempt to help project managers to consider the significant factors for agile project implementation in post pandemic situation. The project managers may be benefitted from this study by considering these factors to manage challenges for agile project implementation in emerging economies. This study contributes to assess the influencing and the influenced challenging factors for agile project implementation.
Belief Entropy Tree and Random Forest: Learning from Data with Continuous Attributes and Evidential Labels

As well-known machine learning methods, decision trees are widely applied in classification and recognition areas. In this paper, with the uncertainty of labels handled by belief functions, a new decision tree method based on belief entropy is proposed and then extended to random forest. With the Gaussian mixture model, this tree method is able to deal with continuous attribute values directly, without pretreatment of discretization. Specifically, the tree method adopts belief entropy, a kind of uncertainty measurement based on the basic belief assignment, as a new attribute selection tool. To improve the classification performance, we constructed a random forest based on the basic trees and discuss different prediction combination strategies. Some numerical experiments on UCI machine learning data set were conducted, which indicate the good classification accuracy of the proposed method in different situations, especially on data with huge uncertainty.
Towards Complete Icon Labeling in Mobile Applications

Accurately recognizing icon types in mobile applications is integral to many tasks, including accessibility improvement, UI design search, and conversational agents. Existing research focuses on recognizing the most frequent icon types, but these technologies fail when encountering an unrecognized low-frequency icon. In this paper, we work towards complete coverage of icons in the wild. After annotating a large-scale icon dataset (327,879 icons) from iPhone apps, we found a highly uneven distribution: 98 common icon types covered 92.8% of icons, while 7.2% of icons were covered by more than 331 long-tail icon types. In order to label icons with widely varying occurrences in apps, our system uses an image classification model to recognize common icon types with an average of 3,000 examples each (96.3% accuracy) and applies a few-shot learning model to classify long-tail icon types with an average of 67 examples each (78.6% accuracy). Our system also detects contextual information that helps characterize icon semantics, including nearby text (95.3% accuracy) and modifier symbols added to the icon (87.4% accuracy). In a validation study with workers (n = 23), we verified the usefulness of our generated icon labels. The icon types supported by our work cover 99.5% of collected icons, improving on the previously highest 78% coverage in icon classification work.
Non-equilibrium dynamics of a dissipative two-site Hubbard model simulated on IBM quantum computers

Abstract Many-body physics is one very well suited field for testing quantum algorithms and for finding working heuristics on present quantum computers. We have investigated the non-equilibrium dynamics of one- and two-electron systems, which are coupled to an environment that introduces decoherence and dissipation. In our approach, the electronic system is represented in the framework of a two-site Hubbard model while the environment is modelled by a spin bath. To simulate the non-equilibrium population probabilities of the different states on a quantum computer we have encoded the electronic states and environmental degrees of freedom into qubits and ancilla qubits (bath), respectively. The total evolution time was divided into short time intervals, during which the system evolves. After each of these time steps, the system interacts with ancilla qubits representing the bath in thermal equilibrium. We have specifically studied spin baths leading to both, unital and non-unital dynamics of the electronic system and have found that electron correlations clearly enhance the electron transfer rates in the latter case. For short time periods, the simulation on the quantum computer is found to be in very good agreement with the exact results if error mitigation methods are applied. Our method to simulate also non-unitary time-evolution on a quantum computer can be well extended to simulate electronic systems in correlated spin baths as well as in bosonic and fermionic baths.
Research on improvement of DPoS consensus mechanism in collaborative governance of network public opinion

With the increasingly complex social situation, the problems of traditional online public opinion governance are increasingly serious. Especially the problem of transmission efficiency, public opinion data management and user information security of Internet users is urgently needed. Here, we design a functional infrastructure framework of the network public opinion collaborative governance model based on the blockchain with strong practicality and comprehensiveness. In order to reach the consensus mechanism requirements under the framework, the algorithm is improved on the basis of the defects of the traditional DPoS consensus algorithm. Considering time dynamic factors in the process of reaching consensus, the paper proposes a reputation-based voting model. Furthermore, the paper purposes a rewards and punishments incentive mechanism, and also designs a new method of counting votes. From the simulation results, it was found that after the improvement of the algorithm, the enthusiasm of node participation was significantly increased, the proportion of error nodes was significantly reduced, and the operating efficiency was significantly improved. It shows that the improved consensus algorithm we propose applies to public opinion governance can not only improve the security of the system with the reduce of false public opinion spreading, but also improve the efficiency of information processing, so it can be well applied to information sharing and public opinion governance scenarios.
TriBYOL: Triplet BYOL for Self-Supervised Representation Learning

This paper proposes a novel self-supervised learning method for learning better representations with small batch sizes. Many self-supervised learning methods based on certain forms of the siamese network have emerged and received significant attention. However, these methods need to use large batch sizes to learn good representations and require heavy computational resources. We present a new triplet network combined with a triple-view loss to improve the performance of self-supervised representation learning with small batch sizes. Experimental results show that our method can drastically outperform state-of-the-art self-supervised learning methods on several datasets in small-batch cases. Our method provides a feasible solution for self-supervised learning with real-world high-resolution images that uses small batch sizes.
A combination of eye-gaze and head-gaze interactions improves efficiency and user experience in an object positioning task in virtual environments

Eye-gaze and head-gaze are two hands-free interaction modes in virtual reality, each of which has demonstrated different strengths. Selecting suitable interaction modes in different scenarios is important to achieve efficient interaction in virtual scenes. This study compared the movement time in an object positioning task by examining eye-gaze interaction and head-gaze interaction in various conditions. In turn, it identified the superior zones for each mode, respectively. Based on this information, we designed a combination mode – utilizing eye-gaze interaction at the acceleration phase and deceleration phase and head-gaze interaction at the correction phase – to achieve the optimal interaction mode, which has allowed us to obtain higher efficiency and subjective satisfaction. This study provides a comprehensive analysis of the characteristics of the eye-gaze and head-gaze interaction modes and provides valuable insights into selecting the appropriate interaction modes for virtual reality applications.
Google Trends and cryptocurrencies: a nonparametric causality-in-quantiles analysis

Purpose This article aims to uncover the impact of Google Trends on cryptocurrency markets beyond Bitcoin during the time of increased attention to altcoins, especially during the COVID-19 pandemic. Design/methodology/approach This paper analyses the nexus among the Google Trends and six cryptocurrencies, namely Bitcoin, New Economy Movement (NEM), Dash, Ethereum, Ripple and Litecoin by utilizing the causality-in-quantiles technique on data comprised of the years January 2016–March 2021. Findings The findings show that Google Trends cause the Litecoin, Bitcoin, Ripple, Ethereum and NEM prices at majority of the quantiles except for Dash. Originality/value The findings will help investors to develop more in-depth understanding of impact of Google Trends on cryptocurrency prices and build successful trading strategies in a more matured digital assets ecosystem.
Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation

The recent advance in deep generative models outlines a promising perspective in the realm of Zero-Shot Learning (ZSL). Most generative ZSL methods use category semantic attributes plus a Gaussian noise to generate visual features. After generating unseen samples, this family of approaches effectively transforms the ZSL problem into a supervised classification scheme. However, the existing models use a single semantic attribute, which contains the complete attribute information of the category. The generated data also carry the complete attribute information, but in reality, visual samples usually have limited attributes. Therefore, the generated data from attribute could have incomplete semantics. Based on this fact, we propose a novel framework to boost ZSL by synthesizing diverse features. This method uses augmented semantic attributes to train the generative model, so as to simulate the real distribution of visual features. We evaluate the proposed model on four benchmark datasets, observing significant performance improvement against the state-of-the-art.
Text and Code Embeddings by Contrastive Pre-Training

Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality vector representations of text and code. The same unsupervised text embeddings that achieve new state-of-the-art results in linear-probe classification also display impressive semantic search capabilities and sometimes even perform competitively with fine-tuned models. On linear-probe classification accuracy averaging over 7 tasks, our best unsupervised model achieves a relative improvement of 4% and 1.8% over previous best unsupervised and supervised text embedding models respectively. The same text embeddings when evaluated on large-scale semantic search attains a relative improvement of 23.4%, 14.7%, and 10.6% over previous best unsupervised methods on MSMARCO, Natural Questions and TriviaQA benchmarks, respectively. Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20.8% relative improvement over prior best work on code search.
PhishSim: Aiding Phishing Website Detection With a Feature-Free Tool

In this paper, we propose a feature-free method for detecting phishing websites using the Normalized Compression Distance (NCD), a parameter-free similarity measure which computes the similarity of two websites by compressing them, thus eliminating the need to perform any feature extraction. It also removes any dependence on a specific set of website features. This method examines the HTML of webpages and computes their similarity with known phishing websites, in order to classify them. We use the Furthest Point First algorithm to perform phishing prototype extractions, in order to select instances that are representative of a cluster of phishing webpages. We also introduce the use of an incremental learning algorithm as a framework for continuous and adaptive detection without extracting new features when concept drift occurs. On a large dataset, our proposed method significantly outperforms previous methods in detecting phishing websites, with an AUC score of 98.68%, a high true positive rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of 0.58%. Our approach uses prototypes, eliminating the need to retain long term data in the future, and is feasible to deploy in real systems with a processing time of roughly 0.3 seconds.
Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Breast cancer must be addressed by a multidisciplinary team aiming at the patient’s comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.
Intelligent Sign Language Recognition System for E-Learning Context

In this research work, an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and lines.This feature set has the ability to increase the overall performance of machine learning algorithms in an efficient way.The hand gesture recognition based on these features has been implemented for usage in real-time.The feature set used hand landmarks, which were generated using media-pipe (MediaPipe) and open computer vision (openCV) on each frame of the incoming video.The overall algorithm has been tested on two well-known ASLalphabet (American Sign Language) and ISL-HS (Irish Sign Language) sign language datasets.Different machine learning classifiers including random forest, decision tree, and naïve Bayesian have been used to classify hand gestures using this unique feature set and their respective results have been compared.Since the random forest classifier performed better, it has been selected as the base classifier for the proposed system.It showed 96.7% accuracy with ISL-HS and 93.7% accuracy with ASL-alphabet dataset using the extracted features.