Reviewerly/exHarmony huggingface dataset:
How to Enlighten Novice Users on Behavior of Machine Learning Models?

Background: Machine learning models are sometimes embedded in software to implement the required functions. As a result, non-experts in machine learning are becoming familiar with the models. However, the interpretability of the built models is often low in machine learning, such as deep learning, and the recognition process of such models is very different from that of humans. Therefore, it is not easy for novice users, such as end-users and beginners, to anticipate the behavior of models that they will use or build. Aim: We assist novice users to realize an aspect of the behavior of machine learning models relating to robustness intuitively. Method: We formalized and evaluated quiz-based analysis, which is often applied by practitioners to test the robustness of machine learning models arbitrarily. To generate test cases of the models, the analysis converts images towards the boundary of classification for both machine learning and humans. It can be regarded as a type of boundary value analysis of software development. Results: In the experiment, we evaluated whether the analysis quantitatively clarified the aspects of the models. The analysis clarified the robustness of the model for image conversion and misclassification quantitatively. Conclusion: The analysis is expected to enlighten novice users on the behavior of machine learning models. This may promote behavioral changes in the evaluation of models for novice users.
The ‘digital transnational solidarity’ and protection of health: Commentary to Principle no. 7 of the Rome Declaration

Point 7 of the Rome Declaration is aimed at improving data sharing and technological and knowledge transfer as instruments to implement health policies.The objective of the current paper is to understand if the legal framework (especially at a transnational level) may actually help to achieve the objectives laid down by Principle 7 or if some interpretative or legislative actions will be necessary.The paper analyses the legal sources at international and EU levels.The applicable rules are based on the idea that the information and material are forms of 'property' of the individuals or of the states (i.e., the sovereign). According to the traditional idea of property, the owner has a sort of absolute power over the res and is entitled to exclude others from any rights over the thing. The 'property paradigm' may be useful to protect some relevant interests, but it can also affect collective interests, such as those concerning health during the COVID-19 pandemic.In order to build a global system for the exchange of data and materials as provided for by Principle 7 of the Rome Declaration, a new legal approach should be elaborated which takes into consideration the international corpus of fundamental rights, including research freedom, the right to health and the principle of transnational 'digital' solidarity.
Introduction

Over the last two decades, information systems have revolutionized with more computer networking, Internets, World Wide Web (www), and Internet of Things (IoT). This resulted in a voluminous increase in both static and dynamic data size, offering a high chance of having potential threats to the global information infrastructure.
Modified SHA-512 Algorithm for Secured Password Hashing

The secure hash algorithm (SHA) is a widely used function for password hashing. This study introduced a modified SHA-512 algorithm by modifying the message scheduling, hash construction, and compression function, and reducing the iterations, as well as examined the modified algorithm&#x0027;s computational efficiency according to the avalanche effect and attack resistance capacity by performing brute-force, rainbow table, dictionary, and online cracking attacks via the Cain and Abel password-cracking tool and CrackStation. The modified SHA-512&#x0027;s avalanche percentage reached 50.84 percent, which is 0.84 percent higher than the target of 50 percent. During experimentation, password cracking attacks failed to crack the hashes generated by the modified algorithm. In addition, the modified algorithm&#x0027;s runtime speed reached an average elapsed time of 3,485 ms, while the original SHA-512 attained 5,384 ms. Hence, the modified SHA-512 algorithm is an efficient and secure method of hashing passwords.
A Recommendation Model Based on Heterogeneous Information Network Interaction

Most traditional recommendation systems are based on homogeneous networks. However, a large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relationship types. Although the existing recommendation approaches based on heterogeneous information network have achieved performance improvement, while they still face the following problems. On one hand, existing models usually define multiple meta-paths in heterogeneous graphs to capture the composite relations and guide neighbor selection. However, these models usually discard intermediate nodes along the meta-path and cannot fully mine the interaction information of heterogeneous graphs. On the one hand, they do not take into account the time sequence between user and items. To address these two limitations, we propose a new model named Time-aware Interaction networks for Recommendation based on Heterogeneous information network (TIH4Rec) to improve performance. Specifically, TIH4Rec employs two major components, i.e., an interaction module that aggregates semantic information inter-meta-path and a time-aware module that incorporates time information. Experiments on two publicly heterogeneous graph datasets demonstrate the performance gains of TIH4Rec comparing with baselines.
Multi-Turn Response Selection Using Business Sequential Relations in Traffic Field

BERT-based models play an essential role and achieve significant results in many tasks of natural language processing (NLP), including dialog tasks. However, there is a limitation of BERT to handle long dialogs. The paper studies how pre-trained model handles long conversations as the input in the multi-turn response selection task. Given the lack of data with long conversations and current dialog tasks are based on engineering applications, it is necessary to collect large dataset with long text sequences. Meanwhile, collecting dialog dataset is a time-consuming work, practical industrial settings usually need more accurate. In this paper, we cite the Chinese Multi-Intention Dialogue (The CMID-Transportation) dataset of transportation customer service, and change it to adapt to response selection task in the dialog systems. To this end, we propose a business-level strategy and use truncation methods to address this problem on the corpus. The experimental results on this corpus show that our proposed approach is fit for the BERT-based model and brings better performance.
Research on Medical Image Registration Comparison Based on Algorithm Stitching Optimization

With the development of medical technology, the problem of medical image registration is becoming more and more prominent. Two CT images were performed on the same patient. During the time difference between the two images, different degrees of lesions may occur in the detected part. However, due to the softness of human tissue and the automatic zooming of the image when the equipment finally outputs, the image size and rotation angle of the two outputs lose contrast.
IOTDMS Blockchain Framework for Secure Data Exchange Between IoT Devices

In the current era of digital communication and digital transaction, most companies and industries are using IoT devices to manage and store day-to-day transaction data. Most of these organizations use a biometric-based attendance system to spot the attendance of employees. Few organizations use RFID cards or tags embedded in the identity card of the employee for attendance marking. Smartphone has become a handy tool for people for communication and transferring money from bank accounts and digital wallets using QR codes as a means for the transaction. However, the user, device authentication, and secure data transmission are the primary concerns in IoT networks. In recent years, blockchain arose as a decentralized data security mechanism. It has been successfully implemented as a secured mechanism for exchanging several applications, including cryptocurrencies and other areas. There are several blockchain frameworks available and discussed in the literature. The proposed work discusses the IOTDMS Blockchain framework called the Internet of Things Data Management System Chain (IOTDMS chain) built-in Python. The proposed approach considered the use case of an attendance management system, and this paper presents the current work in progress of how blockchain can potentially improve the IoT.
Multi-View Clustering Analysis via Density Peak Clustering with Novel Co-Training Strategy

Multi-view clustering has emerged as an advanced technique for multi-view learning. In this paper, we propose a multi-view clustering algorithm based on density peak clustering. Combined with a novel co-training strategy, the proposed method adjusts the distances of intra-cluster and inter-cluster simultaneously by using the consistency and inconsistency of each view. Subsequently, the final clustering result is obtained by integrating the clustering result of each view while reducing the interference of low-value views. Experimental results on 8 real multi-view data sets validate that the proposed algorithm can make full use of the information of different views and obtain good clustering accuracy.
Gordon Tucker, Guowen Huang, Lise Fontaine &amp; Edward McDonald: Approaches to Systemic Functional Grammar: Convergence and divergence

Article Gordon Tucker, Guowen Huang, Lise Fontaine & Edward McDonald: Approaches to Systemic Functional Grammar: Convergence and divergence was published on April 1, 2022 in the journal Folia Linguistica (volume 56, issue 1).
Workshops in TEI: Development, Evaluation, Exploration, and Implementation

Workshops are frequently used in human-computer interaction research, in a diverse range of research projects. However, the COVID-19 pandemic has made this research activity difficult to conduct since they often involve group work, physical interaction with tangibles and/or bodily activity. Motivated by this, the authors conducted a review of papers from the International Conference on Tangible, Embedded and Embodied Interaction (TEI) to develop a better understanding of workshops as a research method in TEI. The meta-review led to the development of a preliminary classification for workshops in research. Four categories of workshops were identified: Design development, Evaluation, Exploration, and Implementation. This work is intended to spark discussion and further research around the value and the challenges of conducting research workshops.
Semantic Inpainting of Images using Deep Learning

Computer Vision enables computers to retrieve information from digital images and use the inferred data to perform the required task. Image inpainting, a computer vision technique, helps to reconstruct damaged images by refilling the missing pixels, called holes, using the relevant and known pixels, so that repaired image looks very natural and realistic. Traditional inpainting methods generally fill the holes by matching the most similar pixels in the surrounding known regions, focusing to reconstruct the exact ground truth image, leaving behind the texture and quality. Currently, many deep learning methods produced drastic improvements in visual quality and texture and also look for the semantic context of the image. However, achieving success on high resolution images with complex structures remains challenging. This paper imparts an intensive vision of the existing Inpainting methods by providing a comprehensive description of the methods used, datasets and evaluation metrics for all the analyzed techniques.
Development of a window positioning method considering network characteristics for a mobile tiled display

A Mobile Tiled Display (MTD) is a technology to realize a large virtual display using multiple mobile terminals. Since a MTD which composed of a group of terminals with different characteristics, differences in performance among terminals and differences in the network environment affect the video playback. In this study, we propose a MTD configuration method that controls the window composition of terminals in consideration of terminal performance and network characteristics. In addition, we develop a novel MTD system that incorporates our proposed method. The developed system aims to reduce the impact of differences in terminal characteristics by placing terminals with good communication quality in the center of the screen. As an evaluation of the developed system, we investigated the time over-head of the initial window positioning configuration. The result shows that there is no problem in displaying the video in MTD, although it takes about 4.5 seconds in the case of 16 terminals. Moreover, we prepare several MTD videos using the developed system, and conduct questionnaires for subjective evaluation of the viewing experience from 60 people. and we clarified the screen composition method we developed was effective.
A Report on Voice Recognition System: Techniques, Methodologies and Challenges using Deep Neural Network

Voice recognition has been advancing at a fast rate. Many cases involving edited audio clips and incorrect identity claims are reported on a daily basis. Due to the growing importance of information processing technology, it becomes easier and easier to identify people by their voices. Voice recognition consists of detecting a user&#x0027;s identity based on characteristics of their voice. It is a widely applied form of biometric recognition in the world, particularly in fields where security has a high priority. The deep neural networks were used as feature extractor alongside classifiers, but they haven&#x0027;t been completely trained due to the success of deep learning. While such methods are extremely efficient, they still require manual attention. Especially in DNN, interactivity between people and machines is essential. This is where the art of voice recognition comes from. In addition to their application in speech recognition, deep neural networks have demonstrated their potential to be used for voice recognition as well. They provide an efficient implementation of complex nonlinear models for learning unique and invariant data structures. The main contribution of this work is to provide a brief overview of the field of deep neural networks and voice recognition, describing its system, underlying approaches, and challenges.
An Image Encryption Method for Object Detection Based on Chaotic System and DNA Sequence

Existing image encryption algorithms for object detection have shortcomings such as small key space, poor anti-attack ability. Therefore, this paper proposes an encryption algorithm based on Logistic map, Chen system, and DNA sequence for object detection image encryption. The main idea is to dynamically generate a prediction frame based on the object detection network, encrypt the image block in the prediction frame for the first time, then encrypt the whole image. The key is employed to drive the Logistic map and Chen system to generate the chaotic sequences, which is used in DNA computing, scrambling, and diffusion operations. This paper describes the design of the encryption algorithm in detail and conducts security analysis, including histogram statistics, adjacent element correlation analysis, and information entropy analysis. The results show that the algorithm has good cryptographic characteristics and strong anti-attack, and can be used for object detection image encryption.
Histogram Equalization based on Binary Optimization Model with Subjective and Objective Consistency

Histogram equalization (HE) is the most famous method for image enhancement due to its low computation complexity and wide application scope. However, existing HE-based methods often lead to over-enhancement, under-enhancement and unnatural visual perception. To overcome these defects, an adaptive HE algorithm is proposed in this paper. The core idea is to adjust the histogram with the optimal gamma correction parameter. Firstly, a sequence of images with gradually changing quality is obtained by traversing the gamma parameters, and two measurement values are calculated for each image in the sequence. Then a binary optimization model is proposed to search for the optimal parameter. Importantly, in order to compensate for the difference between the objective model and the subjective perception, a novel correction factor is designed to adjust the optimal parameter from the model. Finally, secondary gamma correction is performed by inverting the final parameter to preserve image details and prevent under enhancement. Experimental results show that the proposed algorithm outperforms those state-of-the-art HE-based algorithms.
Secure and Efficient White-box Encryption Scheme for Data Protection against Shared Cache Attacks in Cloud Computing

In cloud computing, since virtual machines (VMs) running on the same physical server share CPU caches, adversaries can exploit CPU's vulnerabilities to launch shared cache attacks (e.g., Spectre vulnerability) for illegally accessing sensitive data (e.g., key of symmetric encryption) on other VMs. Since it is difficult to fix such vulnerabilities, in this paper, we propose a novel solution that leverages two salient features of white-box encryption to protect data against such attacks: white-box encryption turns the keys and code into unintelligible programs; it is provably secure even if part of its critical data is accessed by adversaries. Although there are many white-box schemes, they cannot be used in our solution due to their limitations. Therefore, we propose a new white-box encryption scheme with highly efficient instances. These instances are parameterized, and can be configured according to the tradeoff between security margin and storage cost. Moreover, our scheme is provably secure in the space-hardness model. The evaluation shows that our solution works well in public clouds and outperforms other methods.
Global Voices in Hacking (Multinational Views)

This chapter will give an overview of research on hacking in the Netherlands and other non-English-language countries. Findings from these countries will be compared to general findings in the literature. The chapter will start with both qualitative and quantitative research based on judicial populations. These studies address topics like personal characteristics and situational risk factors of hackers, their social networks, their specialization and motives, labelling, the extent to which hackers can be seen as cyborg hackers, life-course research, and the role of hackers in organized cybercrime. Afterward, survey research on cyber-dependent offending among youth, based on general population samples, will be discussed. The chapter ends with a discussion on the need of international comparative research on hackers.
A comparative study of a combinatorial machine learning approach to face detection using a very small training dataset

In recent years, machine learning algorithms have improved the prediction rates in object detection task, but with high computational cost in the training stage. Haar cascade classifier is a cheap method widely used in object detection, but its resulting predictions contain a significant number of false positives when the model was not trained with a large dataset. In this work, we propose a method to detect faces in images by using combinatorial widely used machine learning algorithms. The main goal of our approach was to reach acceptable prediction rates with models trained with a very small training dataset. In this way, we present a practical implementation with a statistical comparison between different prediction models. The models were tested with a fixed dataset and then compared by using standard evaluation metrics. Furthermore, the challenging Face Detection Data Set and Benchmark (FDDB) was used for performance evaluation. The experimental results showed that our proposed method reach similar prediction rates than some the state-of-the-art methods, even with better false positive rate, once trained with a dataset 97.20% smaller.
A Numerical Investigation of Various Forms of Wavelet in Financial Time Series Analysis

In this work, seven forms of discrete wavelet transformation under the mother wavelet type are numerically studied for analyzing the financial time series data. They are Haar, Daubechies, Discrete FIR approximation of Meyer wavelet, Symlets, Coiflets, Biorthogonal, and Reverse biorthogonal. The data used consist of the Dow Jones Index (DJIA 30) from 17 July 2000 until 16 July 2020. Our numerical investigation shows that Haar performs best in solving the autocorrelation problem and denoising data.
Cloud Computing for Rural ICT Implementations

Half of the world's population live in rural areas and majority of them are in developing countries. The rural population face many challenges in their life compared to their urban counterparts. Some of these challenges include high unemployment rate, limited employment opportunities in their areas, high brain drain to more developed cities, lack of access to education and healthcare facilities. Information and communication technology has been identified as the enabling technology that can be used to overcome the present day problems. There are several ICT projects implemented across the world with the objective of helping these rural masses. But many of these projects face sustainability challenges due to lack of resources. In this chapter, the author takes an in depth look at how cloud computing can be leveraged successfully to address the sustainability problem of current rural ICT implementation.
Smart Learning and Fourth Industrial Age Effects on Higher Education

The shift toward e-learning is imminent. The world is heading to the fourth industrial age. What are the implications of the fourth industrial age on higher education? What can a trend such as artificial intelligence contribute to education? Can Blockchain technology play part in opening a new education model? Can credentials be awarded and granted online? Experts believe that market forces will push higher education institutes to expand online education. More hybrid learning spaces will be created and different degrees awarded, and curricula structures will be created in the few coming years. However, how will this affect the education, for the better or for the worse? We have investigated this major question through extensive research within available literature and through personal encounters with many stakeholders. What are the implications of the fourth industrial age on education? That would be a major question to answer.
Evaluating the Impact of the Use of Augmented Reality on Human Centered Design

A caution design of a workstation must consider all the factors of Human Centered Design, because it must include the Design of the environment along with the design of the workspace but without forgetting the compliance of international and local norms established for those topics. Evaluating an operator posture is an essential part of that design because it is the study of working conditions. For this specific task, there are various methods for assessing postures in a job: RULAS, Corlett, SWAT, VIRA, ARBAN, Keyserling, etc. In this paper we will present the result of the risk evaluation of various postures by different analysts using two different methods, the RULAS method and the use of an Augmented Reality application designed for the same purpose. The goal of the study is to define if there is an effect on the quality of the risk evaluation of the posture depending on the method utilized and the level of experience of the analyst.