Reviewerly/exHarmony huggingface dataset:
Adaptive Lossless Image Data Compression Method Inferring Data Entropy by Applying Deep Neural Network

When we compress a large amount of data, we face the problem of the time it takes to compress it. Moreover, we cannot predict how effective the compression performance will be. Therefore, we are not able to choose the best algorithm to compress the data to its minimum size. According to the Kolmogorov complexity, the compression performances of the algorithms implemented in the available compression programs in the system differ. Thus, it is impossible to deliberately select the best compression program before we try the compression operation. From this background, this paper proposes a method with a principal component analysis (PCA) and a deep neural network (DNN) to predict the entropy of data to be compressed. The method infers an appropriate compression program in the system for each data block of the input data and achieves a good compression ratio without trying to compress the entire amount of data at once. This paper especially focuses on lossless compression for image data, focusing on the image blocks. Through experimental evaluation, this paper shows the reasonable compression performance when the proposed method is applied rather than when a compression program randomly selected is applied to the entire dataset.
Large margin projection-based multi-metric learning for classification

Metric learning has been a promising technology to improve classification performance, which aims to learn a data-dependent distance metric such that the similarity between samples can be more effectively evaluated. Metric plays a significant role in the description of similarity between samples, however, learning a single distance metric is usually inadequate, especially when dealing with the heterogeneously distributed data. Traditional metric learning only considers a global metric, while the local metric, which is critical for heterogeneous data, is ignored. In this paper, we formulate a novel large margin projection-based multi-metric learning (LMML) for the binary classification of heterogeneous data, which constructs a unified framework based on global metric and local metrics, where two local distance metrics are learned, one for each class, so that the covariance of samples is as small as possible, and the sample of another class is as far away as possible from the mean of the sample. Moreover, a global distance metric is introduced to capture the common structure between the two classes, which requires that the distance metric in each class should be as close as possible to the global one. An efficient iterative algorithm is designed to optimize the LMML. We also conduct some insightful analyses on the computational complexity and the convergence of the proposed algorithm. Experiments are conducted on artificial datasets, UCI benchmark datasets and handwritten digit datasets to evaluate the proposed method. Compared with the state-of-the-art approaches, the experiment results demonstrate the feasibility and effectiveness of the proposed method.
Cyberhate against academics

Hate speech is endemic in digital space, and it does not spare academia. Especially scholars working in fields prone to political debate - from migration to climate change, from gender to refugee integration, and many more topics - find themselves increasingly attacked. With this chapter, we hope to raise awareness for the increasingly prevalent phenomenon of cyberhate targeting academics. Our intention is to shed light on some of its harmful effects, and, by providing some conceptual analysis, to contribute to individual and organisational prevention and coping strategies. We conclude that guarding against cyberhate is now part of academics’ and their institutions’ responsibility.
Investigating the role of swear words in abusive language detection tasks

Abstract Swearing plays an ubiquitous role in everyday conversations among humans, both in oral and textual communication, and occurs frequently in social media texts, typically featured by informal language and spontaneous writing. Such occurrences can be linked to an abusive context, when they contribute to the expression of hatred and to the abusive effect, causing harm and offense. However, swearing is multifaceted and is often used in casual contexts, also with positive social functions. In this study, we explore the phenomenon of swearing in Twitter conversations, by automatically predicting the abusiveness of a swear word in a tweet as the main investigation perspective. We developed the Twitter English corpus SWAD (Swear Words Abusiveness Dataset), where abusive swearing is manually annotated at the word level. Our collection consists of 2577 instances in total from two phases of manual annotation. We developed models to automatically predict abusive swearing, to provide an intrinsic evaluation of SWAD and confirm the robustness of the resource. We model this prediction task as three different tasks, namely sequence labeling, text classification, and target-based swear word abusiveness prediction. We experimentally found that our intention to model the task similarly to aspect-based sentiment analysis leads to promising results. Subsequently, we employ the classifier to improve the prediction of abusive language in several standard benchmarks. The results of our experiments show that additional abusiveness feature of the swear words is able to improve the performance of abusive language detection models in several benchmark datasets.
An Analysis of a Meta Heuristic Optimization Algorithms for Cloud Computing

Cloud computing enables users to access pooled services via the internet on demand through a cloud platform that is extremely flexible and provides consumers with a pay-per-use model with variable SLA. The majority of cloud services operate on virtual machines, and in this scenario, job scheduling is critical. Because of the restricted number of optimum parameters evaluated in task scheduling, current meta heuristic optimization methods in cloud computing are insufficient for finding optimal solutions. Existing studies, such as load balancing, energy cost per unit, and VM migration, lack dependability and availability factors, which are required to create an effective task scheduling algorithm. Finally We did a proportional analysis of prior algorithms and identified scope to research in the field of cloud scheduling algorithms.
Ubiquitous learning: the effect of LingAR application on EFL learners’ language achievement and the realization of their motivation towards mobile learning

With the rapid development of mobile learning, a growing body of research has been carried out to assess the efficacy of this education method. To resolve the existing gap in the literature regarding new ways of mobile learning, the researcher aimed to study 120 Vietnamese students’ achievements once they employed two models of learning (LingAR Application model and Augmented Reality model). About the students’ achievement in the two kinds of individual learning, those instructed by the Application model outperformed those who learned English by the Augmented Reality model. Finally, to get some information about the students’ motivation towards using augmented reality technology in their language learning, the researcher conducted a real-time focus group interview, and then, they extracted some themes from students’ answers, and participants indicated different merits and drawbacks of the Augmented Reality model of this application. Keller’s motivational model (2010) was adopted to analyze this interview. These results offered valuable insights into the successful implementation of LingAR applications in the field of education. Also, they highlighted possible factors that could establish the basis for future experimental research. The author suggested further study with more subjects from more cultural contexts with broader sample size.
Machine Learning-Based Models in the Diagnosis, Prognosis and Effective Cancer Therapeutics: Current State-of-the-Art

Currently, the advancement of computational intelligence (CI) and machine learning (ML) leading to the integration of methods. Cancer, one of the major challenges in organisms, is considered a heterogeneous disease that has been classified into various subtypes. Therefore, the classification of cancer subtypes led the researchers from the bioinformatics and biomedical field, to focus on the major applications of ML methods. From the last decades, various research has been done for the prediction, treatment, and prognosis of cancer, yet there is a challenge of morbidity and mortality of cancer patients. In order to limit these challenges, effective prediction and diagnosis methods are essential. Many researchers in this field have implemented ML-based models with high-performance such as Support Vector Machines, Decision Trees, Artificial Neural Networks, and Bayesian Networks, and their ensembling for the prediction and early diagnosis, prognosis, and effective cancer therapeutics. This chapter presents recent advancements in ML-based models in the prediction, prognosis, and effective cancer therapeutic research. We also focus on ML-based models in various types of cancers like lung, breast, head, brain, liver, neck, gynecological, esophagus, and prostate cancers.
Analysis and Comparison of Symmetric Key Cryptographic Algorithms on FPGA

Data security is considered as one of the prime concern for every communication systems. Cryptography has come up as a resolution which plays a dynamic role in data security against various data attacks. The cryptography is most significant feature of communications security and becoming an vital building block for network security. This security mechanism uses some algorithms to convert readable data into unreadable data text which can be only accessed by intended receiver. In this paper the implementation of some algorithms is done such as Triple Data Encryption Standard (TDES), International Data Encryption Algorithm (IDEA), Data Encryption Standard (DES) and Blowfish algorithm block ciphers for encryption as well as decryption process using Xilinx 14.5 tool in Verilog as programming language and comparison of algorithms is carried out with performance parameters such as area utilization, power consumption, encryption and decryption time using Spartan6 FPGA device.
Evaluation System of Distribution Network Admission to Roof Distributed Photovoltaic Based on AHP-EW-TOPSIS

In recent years, roof distributed photovoltaic has gradually become the most widely used distributed power generation device, resulting in great changes in the development form of distribution network. The key to reliable operation of distribution network is to accurately evaluate the acceptance capacity of distributed generation. This paper constructs a photovoltaic optimization model based on AHP-EW-TOPSIS method. Firstly, an evaluation system including 8 evaluation indicators is constructed from the three dimensions of economy, reliability and stability. Secondly, use the entropy weight method (EW) and the analytic hierarchy process (AHP) to obtain the comprehensive weight of each indicator, and build a differentiated weight system; finally, based on the weight and the specific scores of each indicator, the power grid acceptance capacities of three regions are obtained, and can accurately find the specific reasons for the weaker power grid acceptance level. This paper provides a scientific basis for the planning and operation of a high-proportion distributed power distribution network.
Sentiment Analysis for COVID Vaccinations Using Twitter: Text Clustering of Positive and Negative Sentiments

The objective of the chapter is to analyze Twitter data to extract sentiments and opinions in unstructured text. The team attempted to use contextual text analytics to categorize Twitter data to understand the positive or negative sentiments for COVID vaccinations and wish to highlight key concerns. Text clustering has been performed on positive and negative sentiments to understand the key themes behind them. We followed a two-step process. In the first step, we identified positive and negative sentiments from Twitter feeds. In the second step, we aggregated all sentiments into categories to deduce what the Twitterati is thinking about COVID-19 vaccinations. The whole analysis was performed using Python, including TextBlob and Vader libraries. TextBlob library uses the Naïve-Bayes (probabilistic algorithms using Bayes's Theorem to predict the category of a text) classifier to assess the polarity of a sentence and generates a score ranging between −1 (strongly negative) and +1 (strongly positive). The Naïve Bayes classifier categorizes based on probabilities of events. Although it is a simple algorithm, it performs well in many text classification problems. On the other hand, the Vader library uses a lexical approach that uses preassigned scores labeled positive and negative for different words found in a text. These scores are based on pre-trained models classified as positive/negative by actual human beings. We then performed the topic extraction that discovers the keywords in sentiments that capture the recurring theme of a text and is widely used to analyze large sets of sentiments to identify the most common topics easily and efficiently. We found a large segment as neutral (53%) followed by a positive sentiment segment that contributed 36% of tweets. However, at the same time, many people (10%+) remain on the fence regarding the potential repercussions of COVID vaccines as they are relatively new and yet untested over longer periods of time. It is reasonable to expect that people are a bit skeptical about vaccinations. Text clustering of negative sentiments identified late vaccinations and side effects being the key concerns. Positive sentiments mainly were driven by the readiness of other vaccines and weak reactions following vaccinations. The study contributes to text mining literature by providing a framework for analyzing public sentiments. This can help to understand the key themes in negative sentiments related to COVID vaccinations and can help in adjusting policies.
Online Microservice Orchestration for IoT via Multiobjective Deep Reinforcement Learning

By providing loosely coupled, lightweight, and independent services, the microservice architecture is promising for large-scale and complex service provision requirements in the Internet of Things (IoT). However, it requires more fine-grained resource management and orchestration for service provision. Most of the existing microservice orchestration solutions are based on those designed for the traditional cloud. They can only provide coarse-grained resource allocation using possibly conflicting weighted objectives. In this article, we present a fine-grained microservice orchestration approach to provide services online for dynamic requests of IoT applications. By using a fine-grained resource model of energy cost and service end-to-end response time of orchestrated microservices, we formulate the microservice orchestration problem as a multiobjective Markov decision process. We then propose a multiobjective optimization solution based on deep reinforcement learning (DRL) to simultaneously reduce energy consumption and response time. Through extensive experiments, our proposed algorithm presents significant performance results than the state of the art. To the best of our knowledge, this is the first work that addresses microservice orchestration using DRL for multiple conflicting objectives.
Finding AI’s Faults with AAR/AI: An Empirical Study

Would you allow an AI agent to make decisions on your behalf? If the answer is “not always,” the next question becomes “in what circumstances”? Answering this question requires human users to be able to assess an AI agent—and not just with overall pass/fail assessments or statistics. Here users need to be able to localize an agent’s bugs so that they can determine when they are willing to rely on the agent and when they are not. After-Action Review for AI (AAR/AI), a new AI assessment process for integration with Explainable AI systems, aims to support human users in this endeavor, and in this article we empirically investigate AAR/AI’s effectiveness with domain-knowledgeable users. Our results show that AAR/AI participants not only located significantly more bugs than non-AAR/AI participants did (i.e., showed greater recall) but also located them more precisely (i.e., with greater precision). In fact, AAR/AI participants outperformed non-AAR/AI participants on every bug and were, on average, almost six times as likely as non-AAR/AI participants to find any particular bug. Finally, evidence suggests that incorporating labeling into the AAR/AI process may encourage domain-knowledgeable users to abstract above individual instances of bugs; we hypothesize that doing so may have contributed further to AAR/AI participants’ effectiveness.
Parameter-free surrounding neighborhood based regression methods

In machine learning, nearest neighbor (NN) regression is one of the most prominent methods for numeric prediction. It estimates the output variable of a new data point by averaging the output variables of the neighboring points. The selection of the neighborhood and its parameter(s) is crucial for the performance of NN regression, however this is still an open issue. This study contributes to the literature by adopting the parameter-free surrounding neighborhood (PSN) concept for NN regression. PSNs are based on proximity graphs, i.e. minimum spanning tree, relative neighborhood graph, and Gabriel graph. They yield a unique neighborhood for each point by combining proximity, connectivity and spatial distribution. The performances of the PSN regression methods are compared with k-nearest neighbors, k-nearest centroid neighbors, and support vector regression using real-world data sets. The statistical tests show that the PSN regression methods perform significantly better than most of the competing approaches. Also, the proposed approaches do not have any parameters to be set.
CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing

The text-based speech editor allows the editing of speech through intuitive cutting, copying, and pasting operations to speed up the process of editing speech. However, the major drawback of current systems is that edited speech often sounds unnatural due to cut-copy-paste operation. In addition, it is not obvious how to synthesize records according to a new word not appearing in the transcript, which often needs the help of text-to-speech (TTS) and voice conversion (VC) technology at the same time. This paper first proposes a novel end-to-end text-based speech editing method called context-aware mask prediction network (CampNet). The model can simulate the text-based speech editing process by randomly masking part of speech and then predicting the masked region by sensing the speech context. It can solve unnatural prosody in the edited region and synthesize the speech corresponding to the unseen words in the transcript. Secondly, for the possible operation of text-based speech editing, we design three text-based operations based on CampNet: deletion, insertion, and replacement. These operations can cover various situations of speech editing. Thirdly, to synthesize the speech corresponding to long text in insertion and replacement operations, a word-level autoregressive generation method is proposed, which can synthesize the speech of arbitrary length text. Fourthly, we propose a speaker adaptation method using only one sentence for CampNet and explore the ability of few-shot learning based on CampNet, which provides a new idea for speech forgery tasks. The subjective and objective experiments<xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label> Examples of generated speech can be found at <uri>https://hairuo55.github.io/CampNet</uri>. </fn> on VCTK and LibriTTS datasets show that the speech editing results based on CampNet are better than TTS technology, manual editing, and VoCo method (the combination of TTS and VC). We also conduct detailed ablation experiments to explore the effect of the CampNet structure on its performance. Finally, the experiment shows that speaker adaptation with only one sentence can further improve the naturalness of speech editing for one-shot learning.
Scalability of Blockchain based E-voting system using Multiobjective Genetic Algorithm with Sharding

The blockchain specifications distributed ledger, traceability, immutability provide a secure high-speed voting system. Data stored on the blockchain cannot be traced back, edit or deleted due to which transparent and high-performance capabilities are inherent to the network. But as the information of the e-voting system increases storage space and processing cost also increases due to an increase in delay. This delay increases as the comparison of hash value with the existing hash value for uniqueness. Apart from the uniqueness of hash designer also required initial 8 bytes of hash to be zeros. In this paper, we propose the multiobjective genetic algorithm-based creation of a side-chain to enhance the scalability and performance of the blockchain-based e-voting system. We compare the storage cost and the processing cost of the state of the art models with the proposed model. our experiments yielded observations on the scalability and the performance of the blockchain-based e-voting system.
Thermodynamic Constraints on Quantum Information Gain and Error Correction: A Triple Trade-Off

Trade-off relations linking quantum error correction--viewed as a heat engine--and quantum measurements used for error identification are established, helping to understand how thermodynamics can inform, or limit, our ability to reach fault-tolerance.
TP-VIT: A Two-Pathway Vision Transformer for Video Action Recognition

Recently, inspired by the success of Transformer in natural language processing tasks, a number of works have attempted to apply Transformer-based models to video action recognition. Existing works only use one RGB stream as the input for Transformer. How to use multiple pathways and multiple streams with Transformer for action recognition has not been studied. To address this issue, we present a novel structure namely Two-Pathway Vision Transformer (TP-ViT). Two parallel spatial Transformer encoders are used as two pathways with different framerates and resolutions of the input video. The high-resolution pathway contains more spatial information, while the high-framerate pathway contains more temporal information. The two outputs are fused and fed into a temporal Transformer encoder for action recognition. Furthermore, we also fuse skeleton features into our model to get better results. Our experiments demonstrate that our proposed models achieve the state-of-the-art results on both the coarse-grained dataset Kinetics and the fine-grained dataset FineGym.
Smart City Intelligent Traffic Control for Connected Road Junction Congestion Awareness with Deep Extreme Learning Machine

Congestion-free traffic management has been a top priority for Machine Learning (ML) in the smart city sector for the past decade. Machine learning Algorithms are superfluous although working with the increased amount of data but these improve the capability and intelligence at a level cost. In this research, we propose a model based on a deep learning framework with a multi-layer Extreme Learning Machine (ELM) is proposed considering congestion information at all possible connection points to smooth a signal working over that recorded information. A more desirable outcome will be achieved by the proposed method, and traffic flow and congestion will improve.