Reviewerly/exHarmony huggingface dataset:
The Depths of Cost-per-Use: Historical Context and Applications

Practicing librarians, library science researchers, operations researchers, and economists have documented their extensive efforts to ensure that libraries serve users effectively and efficiently. The measures and methods used have, in some ways, grown substantially more complex and sophisticated. Such tools include development of statistical models describing the use, and predicting future use, of resources (largely books and journals), as well as incorporating more variables to better explain the use. The cost-per-use metric, however, has become ubiquitous and nearly universal for evaluating resources, especially renewable resources such as journals and databases. Composed of only two factors, cost and use (and, by implication, time), this measure provides context missing from either metric alone, yet is simple enough for most practicing librarians and the library stakeholders to instantly comprehend. This article provides a background to the forces that led to the development, use, and gradual acceptance of this metric, and concludes with a case study of its application in different collection development decisions at the author's institution.
Underwater Image Processing and Target Detection Algorithm Based on Deep Learning

With the growth of the earth’s population, people’s demand for space and resources continues to grow, making marine environmental research and exploration a new field that human development needs. The composition of the marine environment is unique to humans, and the application of intelligent underwater vehicle equipment with the ability to autonomously detect and recognize the environment can expand the scope of human research in the ocean. This article focuses on the research of underwater image processing and target detection algorithms based onDL, and understands the relevant theories of underwater image processing and target detection based on the literature, and then the underwater image processing and target detection algorithms based on DL In the design, in order to improve the accuracy of target detection, the DL model is optimized, and the optimized model is tested. The test result shows that if the Atrous convolution operation is added, the calculation amount of the network model will increase, resulting in detection.The speed drops to 50FPS. But after adding Atrous processing, the detection accuracy will be improved accordingly.
Comparing Explanations from Glass-Box and Black-Box Machine-Learning Models

Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into the decision-making process of AI systems. In recent years, most efforts were made to build XAI algorithms that are able to explain black-box models. However, in many cases, including medical and industrial applications, the explanation of a decision may be worth equally or even more than the decision itself. This imposes a question about the quality of explanations. In this work, we aim at investigating how the explanations derived from black-box models combined with XAI algorithms differ from those obtained from inherently interpretable glass-box models. We also aim at answering the question whether there are justified cases to use less accurate glass-box models instead of complex black-box approaches. We perform our study on publicly available datasets.
A Workload and Machine Categorization-Based Resource Allocation Framework for Load Balancing and Balanced Resource Utilization in the Cloud

This paper proposes a workload and machine categorization based resource allocation framework for balancing the load across active physical machines as well as utilizing their different resource capacities in a balanced manner. The workload, essentially independent and non-preemptive tasks are allocated resources on the physical machines whose resource availability complements the resource requirement of tasks. Simulation based experiments are performed using CloudSim simulator to execute three different set of tasks comprising 10000, 20000, and 30000 number of tasks. The metric of load imbalance across active physical machines and the metric of utilization imbalance among their considered resource capacities (i.e., CPU and RAM) are measured in different scheduling cycles of a simulation run. Simulation results show that the proposed resource allocation method outperforms the compared methods in terms of balancing the load across active physical machines and utilizing their different resource capacities in a balanced manner.
Implementation of Blockchain-Based Cryptocurrency Prototype Using a PoW Consensus Mechanism

The term “blockchain” has arisen as a dynamic technological force in a variety of government and private-sector processes. The technology creates a data structure with built-in security features. It is based on encryption, decentralization, and consensus principles, which maintain transaction trustworthiness. The fundamental target of this blockchain-based work is to give data with respect to digital currency advancement utilizing PoW, a productive agreement component. The information and ideas presented are based on the blockchain's features, which provide changeable and unchangeable advanced records created without the use of a central repository and, in most cases, without the use of a central authority. They enable a local area of clients to see and write exchanges in a common record in that local area, with the purpose that no exchange may be changed after distribution under normal circumstances of the blockchain network. This article provides a study of high-level specialist overview of blockchain technology.
Quality Evaluation of Machine Translation Based on BP Neural Network

From the perspective of adaptation theory, this paper discusses the translation defects and improvement measures of Jiangxi tourism publicity materials. Relevant means are used to reflect the original culture as faithfully as possible, and to conform to the cognitive ability and aesthetic expectation of tourists. Such as transliteration + interpretation. Also, the improvement measures based on BP neural network is shown in the paper with a semantic mapping model for eliminating errors in English translation of tourism publicity materials, which can supplement the quantity, history, culture and aesthetic information of the landscape, and help the development of tourism in Jiangxi Province.
Multi-document Text Summarization Based on Genetic Algorithm and the Relevance of Sentence Features

Document Text Summarization aims to create a short and condensed version from the original document, which transmits the main idea of the document in a few words. We formulated extractive multi-document text summarization as a combinatorial optimization problem. In which we used sentence features to select the most important content. We conduct experiments on Document Understanding Conference (DUC01) dataset using the ROUGE toolkit. Our experiments demonstrate that the proposed method contributes significant improvements over the state-of-the-art methods and heuristics.
Modular and Productive Deep Learning Code

In the previous chapter, I explored the idea that most data scientists often come from a background that is quite far removed from traditional computer science/software engineering. Consequently, they produce code that is perfectly suitable for great exploratory data analysis, statistical modeling, or innovative ML experiments, but not robust enough for the production phase of a large business platform. Data scientists often think in terms of the next analysis script but not along the lines of the next software module that integrates into a larger system. In this chapter, we will explore how similar principles of modularized coding can help you write better code for deep learning tasks with some hands-on examples using Keras/TensorFlow.
Designing for Future Professional Activity – Examples from Ship Bridge Concept Design

This paper proposes a design approach for generating radical design concepts for professional activity. The approach includes analysis of domain-specific work activity, user-experience goals setting as well as work domain and technology trend foresight. The intention of the approach is that the concept solutions reflect the existing means and professional recourses applied in work activity, the potential benefits of future technology, and the existing and future challenges particular to a certain work domain. This approach is exemplified with a ship command bridge design case in which future bridge concepts were generated for three ship types: tugboats, platform supply vessels, and cargo ships. Several subject-matter expert interviews and observations were conducted for gathering understanding required to generate the concept ideas. From the existing user-centred design approaches, our approach combines elements of experience design, co-design, and contextual design, but includes certain features for the purpose of creating radical instead of incremental design solutions, the latter typically being the result of user-centred design. These features include, futurology, generation of broad systems usability design goals with Core-Task Analysis method, reformulation of user-experience goals into themes, stories, or personas, and co-design in the end of the concept design process.
Soft Actor–Critic-Based Multilevel Cooperative Perception for Connected Autonomous Vehicles

Cooperative perception is an effective way for connected autonomous vehicles to extend sensing range, improve detection precision, and thus enhance perception ability by combining their own sensing information with that of other vehicles. The existing cooperation perception schemes share only raw-, feature-, or object-level data, thus lacking the flexibility to adapt to highly dynamic vehicular network conditions, which leads to either bandwidth saturation or bandwidth underutilization, degrading the detection precision in the long run. In this article, we propose ML-Cooper, a multilevel cooperative perception framework, to fully utilize the bandwidth and hence improve detection precision. The key idea of ML-Cooper is to divide each frame of sensing data of the sender vehicle into three parts, and the corresponding raw data, feature data, and object data are transmitted to and fused at the receiver vehicle. We also develop a soft actor–critic (SAC) deep reinforcement learning algorithm to adaptively adjust the proportion of the three parts according to the channel state information of the Vehicle-to-Vehicle (V2V) link. The experimental results on KITTI and our collected data sets on two real vehicles show that ML-Cooper can achieve the highest average detection precision compared to existing single-level cooperative perception schemes.
Password-Authenticated Proofs of Retrievability for Multiple Devices Checking Cloud Data

Traditional privately-verifiable proofs of retrievability (PoR) require users to securely generate and store secret keys for data storage and retrieval. This incurs significant inconveniences for users who would like to store and retrieve their data on multiple different devices, as secret keys need to be installed on all of these devices. In this paper, we propose a cryptographic primitive called password-authenticated proofs of retrievability (PAPoR), where users are able to usememorable passwords to outsource their data to a cloud and retrieve their data for integrity check. In a PAPoR scheme, users are required to register their passwords with the cloud server before issuing storage and retrieval requests. During the storage and retrieval phases, users communicate with the cloud server to recover secrets tied to the passwords. These secrets are used to complete the storage and retrieval tasks. After describing a formal security definition for the PAPoR framework, we give a concrete construction and the security analysis, and discuss several variants to improve its security. In addition, we implement the given PAPoR construction to evaluate its performance.
The Differences of Interpretation of the Term 'Cyber Threat' in the USA, China and Russia and Their Relation with the Countries’ Policies

In the following article the concepts of cybersecurity and cyber-threat in different countries, such as the USA, China, Russia, are analyzed. The national peculiarities of interpretation of these concepts are given. A comparison of interpretation of these terms is provided. The author shows the way in which the concept of cyber-threat is tied with the current foreign policy course of the states. The author comes to the conclusion that in the majority of cases the concepts of cybersecurity coincide with the provisions of the country’s foreign policy, however, in the cases of China and Russia there are some minor differences.
High Quality and Efficiency Operation and Maintenance

As an information system develops, it needs more continuous and orderly operation and maintenance. Traditional operation and maintenance support focuses on predicting, discovering and dealing with various breakdowns in time to ensure that each system is in a normal state, which we call “intact operation and maintenance”. In line with the core mission and construction content of the smart court, we propose that we should not only ensure that the information system is in good condition but also pay close attention to, collect, display and analyze the operation quality and efficiency of various systems through operation and maintenance, that is, “high quality and efficiency operation and maintenance”. Its significance is reflected in the following aspects.
Prediction of Fake Images through Image Noise Pattern using Multitask Cascaded CNN Approach

Information age has generated spurious content to predominately influence the views and opinions of the people in large scale and images, videos are not exception to them. Deepfake techniques which apply AI-based images to replace the real anchors projecting as legitimate presenters bringing in significant impact to the information presented online. Eventually such content could harness the need to influence the views, effect the public discourse, and jeopardize the safety of individuals through maliciously distributed misinformation, cyber bullying, harassment, and visual perplexity issues. Technological developments in AI, deep learning are still evolving to address these issues, and our current study brings in one of those facets of research which applies image noise obtained from images generated from random video datasets taken from deepfake detection challenge to predict the fake images from real images using multitask cascading CNN architecture. Results of our study show 81% accuracy of identifying fake images from real ones. We hope to extend the existing study further with improved computer vision framework to improve the accuracy of prediction and labeled objects in the imagery.
A Novel Method to Evaluate the Privacy Protection in Speaker Anonymization

The technique to hide the real identity of speakers is called speaker anonymization. Aiming at deceiving automatic speaker verification (ASV) systems, speaker anonymization is usually conducted by modifying the temporal or spectral properties of original voices, e.g., by pitch scaling, by vocal tract length normalization (VTLN) or by voice conversion (VC). However, the real identity of anonymized speech can be recovered with a careful re-training of ASVs, e.g., data augmentation by anonymizing voices of the same speaker. In order to evaluate the effectiveness of speaker anonymization, a pre-restoration method for both enrollment and testing data is proposed, investigated and compared for the de-anonymization of anonymized voices. Experimental results show that the pre-restoration method is effective to speaker de-anonymization. Moreover, it is also found that the pre-restoration for testing data performs better than that for enrollment data, which would also be useful to other decision-making tasks involving enrollment and testing stages.
Humanizing Chatbots: The Effect of Fear from the COVID-19 Pandemic

Does fear of COVID-19 shape consumers’ tendency to anthropomorphize chatbots? To answer this question, this paper analyzed data from a survey of 377 US participants. Grounded in the three-factor theory of anthropomorphism, our results showed that fear of COVID-19 increases consumers’ tendency to imbue chatbots with human-like characteristics, which in turn leads to increased intention to use chatbot-powered mobile apps. In addition, our results also showed that consumers’ sensation-seeking (versus pragmatism-seeking) tendency toward using mobile apps moderated the indirect effect of fear on intention to use via perceived anthropomorphism. Our findings provide both theoretical and practical implications, especially with the recent call for utilizing chatbots in customer service due to the COVID-19 pandemic.
Ethical Considerations for the Application of Virtual Reality in Education and Training

Virtual Reality (VR) refers to a visual experience that creates a fully immersive sensation surrounded by three-dimensional (3D) stereoscopic images of a virtually artificial or pre-recorded environment. Differing new aspects of VR usage in different domains (e.g., aviation, healthcare, military, education etc.) have sprung up in the past few decades. However, ethical problems in the use of VR along with its physical and psychological harm calls for a critical evaluation of the technology before it can be utilized for everyday use. The goal of this paper is to inspect the ethical implications involving both personal as well as the societal impact of deploying VR in education and training purposes. In addition, a few recommendations for ensuring the ethical use of VR in the future is presented.
Adaptive Generation-based Approaches of Oversampling using Different Sets of Base and Nearest Neighbor’s Instances

Standard classification algorithms often face a challenge of learning from imbalanced datasets.While several approaches have been employed in addressing this problem, methods that involve oversampling of minority samples remain more widely used in comparison to algorithmic modifications.Most variants of oversampling are derived from Synthetic Minority Oversampling Technique (SMOTE), which involves generation of synthetic minority samples along a point in the feature space between two minority class instances.The main reasons these variants produce different results lies in (1) the samples they use as initial selection / base samples and the nearest neighbors.(2) Variation in how they handle minority noises.Therefore, this paper presented different combinations of base and nearest neighbor's samples which never used before to monitor their effect in comparison to the standard oversampling techniques.Six methods; three combinations of Only Danger Oversampling (ODO) techniques, and three combinations of Danger Noise Oversampling (DNO) techniques are proposed.The ODO's and DNO's methods use different groups of samples as base and nearest neighbors.While the three ODO's methods do not consider the minority noises, the three DNO's include the minority noises in both the base and neighbor samples.The performances of the proposed methods are compared to that of several standard oversampling algorithms.We present experimental results demonstrating a significant improvement in the recall metric.
Evolution of the Evolutionary Software Development Methods

In the early days of software development model, researchers expressed his views on handling various software projects of many years of experience. What they felt like handling it difficult for large software projects. So, they proposed a method that was considered as the base and standard till late 90s for handling various types of software projects. As software projects were moving towards more commercial utilities and more customer centric, there were demands that were concerned with lower or no downtime, rapid updates implementation and highly available software projects, so it was evident that changes in the methodologies were required and various development groups were creating and naming their own standards as per their own need of aspirations. Clearly, it was the era of evolutionary life cycle methods. Then almost all these evangelists met and created a common manifesto that was considered as Agile methodologies. In this document, we have tried travelling from Waterfall model to DevOps via Agile. In this study, we have collaborated the various evolution strategies and their need.
Information Fusion Based on Gaussian Process Latent Variable Model

Gaussian Process Latent Variable Model (GPLVM) is capable of representing the data without a determined function, which is a generative and non-parametric model. Compared with sparse/collaborative representation, GPLVM enjoys the non-linearity, which does exist in real-world datasets. This chapter proposes three GPLVM based information fusion methods, contributing to the classification performance improvement. After reading this chapter people can have preliminary knowledge on GPLVM based fusion algorithms.
Identification of Food Packaging Activity Using MoCap Sensor Data

The automation system has brought a revolutionary change in our lives. Food packaging activity recognition can add a new dimension to industrial automation systems. However, it is challenging to identify the packaging activities using only skeleton data of the upper body due to the similarities between the activities and subject-dependent results. Bento Packaging Activity Recognition Challenge 2021 provides us with a dataset of ten different activities performed during Bento box packaging in a laboratory using MoCap (motion capture) sensors. Bento box is a single-serving packed meal that is very popular in Japanese cuisine. In this paper, we develop methods using the classical machine learning approach, as the given dataset is small compared to other skeleton datasets. After preprocessing, we extract different hand-crafted features and train different models like extremely randomized trees, random forest, and XGBoost classifiers and select the best model based on cross-validation score. Then, we explore different combinations of features and use the best combination of features for prediction. By applying our methodology, we achieve 64% accuracy and 53.66% average accuracy in tenfold cross-validation and leave-one-subject-out cross-validation, respectively.
Automated Voice Assistant

For blind and motion-impaired users, we implement Automatic Voice Assistant, which offers more powerful and natural interaction, greatly enhancing the experience of graphic interface interaction. We humans have therefore created applications such as Personal Voice Assistant with the ability to communicate with the world only through one of the materialistic modes of human interaction. Our proposed system has the potential to operate with and without access to the Internet. It is an automatic assistant, which receives input from the user as voice or text, processes it, and then sends the results back to the end user in various forms, such as actions to be taken or order search results.
Application of Digital Image Correlation Technique to Study Strain Localization in Fe–Ni–W Alloy

In this paper, strain induced by uniaxial deformation under a scanning electron microscope (SEM) of Fe–Ni–W alloy has been studied by digital image correlation (DIC) technique using open-source software, Ncorr. In this study, samples of Fe–Ni–W alloy were subjected to in situ deformation under SEM, and intermittently, images were recorded at different stages of deformation. Using the DIC technique, changes in the microstructure were identified and displacement was computed. Using these displacements, normal strains and shear strains were computed up to subpixel accuracy. The analysis of displacements and strain has shown that during the initial stage’s deformation was mainly confined to the large grain area, whereas small grain areas remain unaffected. Detailed statistical analysis of the strains has shown that the correlation between strains changed from medium to weak with increasing deformation.