Reviewerly/exHarmony huggingface dataset:
A multilayer affective computing model with evolutionary strategies reflecting decision-makers’ preferences in process control

Many industrial control problems related to multi-objective optimization, such as controller parameters tuning, often require operators to perform multiple-step interactions without considering the changes of decision-makers' affective states and quantitative description of decision-makers' preferences during the interactive decision. Regarding this problem, we developed a multilayer affective computing model (MACM), including three factors: human personality, emotional space, and affective states, to demonstrate the iterative affective computing during the interactions. First, a concise model of affective computing-driven interactive decision-making was built before three submodules involved were described in detail. (1) An affective state recognition method based on facial expressions was presented, providing the basis for obtaining expert affective states during decision-making. (2) An identification method of affective parameters was given, providing an approach to describing personalized affective state-changing rules of different persons. (3) A definition of decision-makers' preferences in interactive decision-making was specified. In addition, a decision-makers' preferences mining method was developed by the MACM and an iterative learning control (ILC) strategy. Thus, we proposed affective computing-driven interactive decision-making method, which provided a simplified approach to converting the interactive decision problems based on decision-makers' preferences to decision issues based on incremental decision vector, along with assisting computers to learn from human experts and perform decision-making automatically in a general sense. Then, two typical process control cases-PI controller tuning for decoupling problem and manipulate vector optimization for batch processes were used to show the correctness and effectiveness of the contributions. Compared with other traditional optimization algorithms without affective state tracking and recognition (fuzzy control, ILC, reinforcement learning, and so on), experimental results indicated that the proposed method could achieve good performance. Finally, this study presented the efficiency and limitations of using this technique for a specific application.
Big Data Analytics in Cloud Computing

In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.
Exploration of the Influence on Training Deep Learning Models by Watermarked Image Dataset

Deep learning has achieved great success in various applications with the help of a large scale of datasets. As a result, sharing the valuable big data that can be applied to training deep learning models is of essential importance currently. However, how to claim ownership and protect the copyright of image big data during the sharing process is still a vital issue that should be addressed. The application of digital watermarks can protect the copyright of image data, at the same time, it also degrades the image quality at the same time. As for invisible digital watermarks, the higher the watermark embedding intensity causes the greater the host image changes. Therefore, the performance of deep learning models may decline due to using the watermarked training set. In this paper, we evaluate the influences of different embedding intensities of various watermarking algorithms on several mainstream models and conclude how the watermarking intensity affects the model training. Besides, referring to watermarking algorithms that have been proposed, we proposed a novel discrete Fourier transform-based watermarking algorithm that can achieve image copyright protection yet maintain the utility of models.
Enhancing the Performance of 3D Rotation Perturbation in Privacy Preserving Data Mining Using Correlation Based Feature Selection

A large amount of valuable data is being produced every day with the development of technologies. To retrieve knowledge and information from these data, mining and analysis are mandatory. But, the data may contain sensitive information of the individuals like medical diagnostic reports which they do not want to expose. Privacy preserving data mining, i.e., PPDM can help in this issue keeping the sensitive information private as well as preserving the data utility. Rotation-based perturbation technique contributes to satisfying both aspects of PPDM, i.e., individuals’ privacy and data utility besides other PPDM techniques. In this work, we proposed a way for generating the triplet (set of three features) for 3D rotation perturbation technique using correlation among the features. This triplet generation is a fundamental step in 3D rotation perturbation technique. The analysis of information entropy, privacy protection and utility analysis elucidates that correlation-based triplet generation provides better data privacy and utility than existing triplet generation for 3D rotation perturbation technique.
An oblique-enhanced interpolation algorithm for color image restoration

Most current digital imaging technologies are based on Bayer color filter array and interpolation algorithms to achieve color image restoration. Common interpolation algorithms include bilinear interpolation algorithm, edge-directed image interpolation algorithm, and so on. Hibbard algorithm, a kind of edge-directed image interpolation algorithm, significantly improves zipper effect and blurred edges through a gradient-based method and constant chromatic aberration ideas compared with the bilinear interpolation algorithm. However, it only considers the possibility of horizontal and vertical edges, ignoring further judgement for edges in other directions. For solving the severe problems, we propose an oblique interpolation algorithm with the addition of oblique direction gradient factors. In this way, we consider the edges in both main diagonal and sub-diagonal directions. The adjacent pixel information in these directions is used to restore the red and blue channels. The experimental results show that images reconstructed by our algorithm are closer to original images. The reconstructed images have a certain improvement on the sharpness and coherence of oblique edges and arc edges. And the problem of false color in edges with an obvious color difference on both sides is weakened.
Two-to-one functions from Galois extensions

Recently, two-to-one mappings have received a lot of attention due to their applications to cryptography and to the construction of classes of cryptographic functions. In this paper, we propose a new method to obtain m-to-1 functions based on Galois groups of rational functions. We feel that such an approach might be the starting point of a new line of research in the area of m-to-1 mappings.
A matching method for low SNR SAR images

Aiming at the requirement of high accuracy of synthetic aperture radar (SAR) image matching in scene matching guidance technology, and the problem that traditional matching method is not suitable for low signal to noise ratio (SNR) SAR image, a new SAR image matching method based on Scale-invariant feature transform (SIFT) feature is proposed. Firstly, the enhanced Lee filter is used to pre-process the image, and then the ROEWA is used to assign the main direction and construct the feature vector for SIFT feature points. Finally, the matching result is obtained according to the bidirectional Euclidean distance. The reliability and robustness of the proposed method are verified by comparative simulation.
Heuristic-based Blockchain Assignment: An Empirical Study

The power of blockchain is compromised with the rapid growth of blockchain size, which puts a heavy storage burden on each node in the blockchain system due to the limited capacity. A mainstream technology to overcome the scalability limitations is to combine sharding with the multiple-replication mechanism to split the storage overhead among nodes. However, the computation complexity for generating block allocation strategy will be exponentially growing when both the sizes of blocks, nodes, and replications boom. In this paper, we abstract the assignment problem on blockchain as an Energy-aware Block Allocation Minimum Problem (EBAMP) and implement several heuristic algorithms, including genetic algorithms, discrete particle swarm algorithms and greedy algorithms. In order to identify similarities and differences of these algorithms, we compare and analyze different methods based on common principles.
Message Digest 5 (MD-5) Decryption Application using Python-Based Dictionary Attack Technique

The growth of information in this digital era is growing very rapidly throughout the world. All forms of information needed can be easily and quickly obtained, especially this is obtained by using a technology called the internet. Of its various types, the web has become one of the most widely used technologies known to everyone in the world. With all the functions provided, the web can accommodate and present various kinds of information needed by users anyone. A web contains a database that is used to store various data that can be seen by internet users as well as sensitive data, such as usernames and passwords. Therefore, the website really needs data security related to the database so that it cannot be accessed by the public other than the owner of the data. The popular technique used is message digest 5 (MD5) which can be applied to encrypt databases on a website, but message digest 5 (MD5) has a weakness. To test the weakness of MD5, in this research an application is made that can decrypt MD5 very quickly accompanied by a dictionary attack technique. The dictionary attack technique uses rockyou.txt which contains about 14,344,392 unique passwords, the application made will test how fast MD5 decryption is. The results of this study indicate that the application made can decrypt MD5 from the wordlist used very quickly. However, not all words can be decrypted because they are not in the word list and the dictionary attack technique is different from the brute force method which tests password decryption, i.e. word by word.
Older Victims of Mass Marketing Scams: An Analysis of Data Seized From Scammers

Abstract Mass marketing scams are some of the most common frauds in America, and include scams perpetrated through the mail. A growing body of research indicates that older adults face a greater risk of victimization due to age-related changes in cognitive functioning and social isolation, and may be more likely to fall victim repeatedly. The aim of this study is to determine the frequency of repeat mass marketing fraud (revictimization) among older adults and patterns of victimization associated with age, scam type, seasonality, and geography. We use two decades of non-public administrative data from the United States Postal Inspection Service (USPIS). These databases were seized during law enforcement investigations of mass mailing scam organizations and contain more than 2 million unique U.S. victims and their transactions with four different fraud organizations. Victims were matched across datasets using name, address, and a change of address file. We find that revictimization rates increase with age in psychic scams. The 10,000 victims who responded the most times (between 82 and 562 times) were 78 years old on average and suffered $4,700 in total losses per person. Other significant trends emerged for lottery and sweepstakes scams. Unlike prior fraud victimization studies, inferences on victim characteristics are based on actual victim experiences with fraud rather than hypothetical scenarios or surveys where victims must self-report fraud. Findings provide valuable policy-relevant information regarding older victims and the patterns of chronic victimization.
Shifting of Language Teaching Methodology from Traditional Classroom to Virtual Classroom

A new paradigm in the field of education has been noticed since last few years. The educators have accepted that they have to indulge themselves in technology based education with innovative methodology to challenge the current need and to coexist in the modified world. The instructors and the learners shift from traditional method to virtual method of teaching learning by adapting the conditional role of staying at home for the pandemic of corona virus all over the world and subsequently have understood the flexibility nature of teaching learning on this way. However, this is a challenging phase to shift educational environment from contemporary traditional classroom to a technology oriented learner centred classroom. Here, the teachers need to expertise on technological use, methodology of teaching, instructional strategy, and assessment and evaluation system and so on. This paper mainly aims to focus on methodological transformation in language teaching. For this study, information is collected from internet sources, books, newspaper article, lectures, and workshops.
Centralized Critic per Knowledge for Cooperative Multi-Agent Game Environments

Cooperative multiplayer games are based on rules where players must collaborate to solve certain tasks. These games bring specific challenges when using Multi-Agent Reinforcement Learning (MARL), since they present requirements related to the training of these collaborative behaviors, such as partial observations, non-stationary, and the problem of credit assignment. One of the approaches used in MARL to solve these challenges is centralized training with decentralized execution. The idea is to use the available knowledge about the full state and information of the environment in the training phase, but policy learning takes place in a decentralized way, not depending on this knowledge. In this work, we study the approach of centralized training with decentralized execution. We seek to validate whether the division of knowledge about the environment (e.g. observations, perception of objects, enemies, obstacles) by different groups (different centralized critics) improves learning performance in multi-agent environments. Our results show that specifying a centralized critic per knowledge improves the training, but it also increases the time of the training process.
Recognition and calculation of objects in images using YOLOv3 architecture

Program that searches for five types of fruits in the images of fruit trees, classifies them and counts their quantity is presented. Its creation took into account the requirement to be able to work both in the background and in real time and to identify the desired objects at a sufficiently high speed. The program should also be able to learn from available computers (including laptops) and within a reasonable time. In carrying out this task, the possibilities of several existing approaches to the recognition and identification of visual objects based on the use of convolutional neural networks were analyzed. Among the considered network archi-tectures were R-CNN, Fast R-CNN, Faster R-CNN, SSD, YOLO and some modifications based on them. Based on the analysis of the peculiarities of their work, the YOLO architecture was used to perform the task, which allows the analy-sis of visual objects in real time with high speed and reliability. The software product was implemented by modifying the YOLOv3 architecture implemented in TensorFlow 2.1. Object recognition in this architecture is performed using a trained Darknet-53 network, the parameters of which are freely available. The modification of the network was to replace its original classification layer. The training of the network modified in this way was carried out on the basis of Transfer learning technology using the Agrilfruit Dataset. There was also a study of the peculiarities of the learning process of the network under the use of different types of gradient descent (stochastic and with the value of the batch 4 and 8), as a result of which the optimal version of the trained network weights was selected for further use. Tests of the modified and trained network have shown that the system based on it with high reliability distin-guishes objects of the corresponding classes of different sizes in the image (even with their significant masking) and counts their number. The ability of the program to distinguish and count the number of individual fruits in the analyzed image can be used to visually assess the yield of fruit trees
Weibo Sentiment Analysis Based on Advanced Capsule Network

Sentiment analysis is to discover and prove the individual's emotions for a partic-ular content, mainly to analyze the emotions and opinions in the text. Social media contains a large amount of sentiment data, and sentiment analysis of these large amounts of generated data can reflect the opinions of the public. Traditional text sentiment analysis methods mainly study extended texts, such as news reports and complete text files. However, sentiment analysis on Douban film reviews and Weibo is more difficult than general sentiment analysis, because his language is not standardized and mostly spoken. Accompanied by typos, acronyms, Internet language, and correct recognition of the emotion of each word is very important. Previous sentiment classification methods have poor classification results when processing short texts like Weibo, because they often fail to extract obvious features. In our model, the newly proposed BERT model and the capsule network model are combined. The word vector generated by the input layer in the BERT model and the word vector obtained through the attention mechanism are con-nected, and at the same time with the feature extraction based on the capsule net-work In combination, it is used for the latest reviews of movies in the Weibo top-ic, which is called an advanced capsule network. This model is used to divide reviews into positive reviews and negative reviews, and judge the accuracy of their classification based on a series of evaluation criteria. The experimental results show that the use of our advanced capsule network model is more accurate in Weibo texts, and can well reflect the reputation of movies.
A feature augmentation-based method for constructing generative-discriminative hybrid models

From the perspective of probability framework, the generative model first learns the joint probability distribution from the data and then calculates the conditional probability distribution with a faster convergence speed. However, the discriminative model learns the conditional probability distribution directly from the data, thus often demonstrating higher accuracy. As an effective combination of the generative and discriminative models, the generative-discriminative hybrid model integrates their advantages. However, the existing methods must divide the original features into two independent feature spaces to train the two models. Feature division not only increases the time complexity of the model but also weakens the expression ability of the original feature space. To solve this problem, this paper proposes a feature augmentation-based method for constructing the generative-discriminative hybrid model. First, this novel method uses the generative model to learn the conditional probability distribution. Then, it augments the learned conditional probability distribution as new features into the original feature space. Finally, it trains the discriminative model in the augmented feature space and predicts the final classification result. The new method offers several advantages, including using feature augmentation to mix the models, not requiring feature division, exhibiting low time complexity, and enhancing the expression ability of the original feature space. The experimental results on 36 classical UCI benchmark datasets show that the new method is not only effective and universal but also follows bias-variance trade-off.
Some Remarks on Computer Terminology in Persian and Hindi on the Basis of the Localizations of the Kdelibs4 Package

Scientific and technological vocabulary, especially computer terminology, is a particularly interesting field in which to analyze/study the most recent trends in the development of vocabulary. The present article focuses on the Persian and Hindi translations of the Kdelibs4 software package. The author attempts to address a number of questions on the basis of the analyzed material, i.e., what are the origin and the proportion of loanwords within the analyzed vocabulary? Are the languages historically important as vocabulary donors (Arabic in the case of Persian and Persian in the case of Hindi) still prominent in this new sphere of vocabulary? What are the widespread syntactic and word-formational patterns among the discussed forms? The vocabulary in question is also juxtaposed with the official language policy in India and Iran, thus exhibiting various levels of deviations in both cases. The lexical items selected on the basis of objective criteria have been compared with the official vocabulary lists issued by the responsible/authoritative/ relevant governmental bodies. Additionally, in the case of Persian, an extensive Internet search has been performed to check their popularity among the users.
A Qualitative Insight of Success Factors of Information Security Management System

Information Security Management System (ISMS) is the vehicle that ensures correctness, completeness, and improvement of security controls in information security management. In the Malaysian context, the number of government agencies certified by ISMS is still low and efforts to retain the certification have been challenging in the recent year. The goal of this study is to describe the success factors identified from the literature review regarding the implementation of ISMS in the selected government agencies in Malaysia. This study applied a qualitative approach, which includes semistructured interviews of nine key informants among Malaysian government technologists. A deductive thematic analysis on interviews transcript and qualitative scoring were used to describe the findings of the study. Of seven identified factors, top management and implementer commitment were revealed as the most critical components in the ISMS implementation in the context of the Malaysian government agency.
A Multi-Factor Authentication-Based Framework for Identity Management in Cloud Applications

User's data is considered as a vital asset of several organizations. Migrating data to the cloud computing is not an easy decision for any organization due to the privacy and security concerns. Service providers must ensure that both data and applications that will be stored on the cloud should be protected in a secure environment. The data stored on the public cloud will be vulnerable to outside and inside attacks. This paper provides interactive multi-layer authentication frameworks for securing user identities on the cloud. Different access control policies are applied for verifying users on the cloud. A security mechanism is applied to the cloud application that includes user registration, granting user privileges, and generating user authentication factor. An intrusion detection system is embedded to the security mechanism to detect malicious users. The multi factor authentication, intrusion detection, and access control techniques can be used for ensuring the identity of the user. Finally, encryption techniques are used for protecting the data from being disclosed. Experimental results are carried out to verify the accuracy and efficiency of the proposed frameworks and mechanism. The results recorded high detection rate with low false positive alarms.