Reviewerly/exHarmony huggingface dataset:
An International Collaboration Strategy Based on the Scientific Strengths and Weaknesses in Security Domain

For security, no matter how high some countermeasures are, if there is a weakness the attacker will break the defense at least focusing on the weakness. Observing the security domain with scientometric approach, there are strengths and weaknesses in each country. Exporting the strengths will enhance the defenses of the others, and gaining support for our own weaknesses will be enhancing our defenses. This research extracts the strengths and weaknesses of each country, and identify the science and technology that should be exported and that should be incorporated. The data retrieved from Web of Science are used to identify the strengths and weaknesses of each country in security.
Using Visualization to Facilitate Knowledge Acquisition and Dissemination for M&amp;A

Acquisition researchers pointed out that to understand real world complexity it is necessary to look at mergers and acquisitions (M&A) from different perspectives. Thus, we investigated the presentation of M&A research in a dynamic and illustrative way that provides rare insights into the potential outcomes of acquisitions. Through an iterative process and much trial and error, it was found that large amounts of information can be combined in a single page to facilitate overall understanding and enable instantaneous comprehension of complex data. In the context of this research, using such illustrations enhanced both the seller’s perspective and that of the potential buyers in the M&A process. Visualization techniques have been accepted as a key tool to manage knowledge for strategic purposes and clearly the addition of the visualization diagrams enhanced this case-based acquisition research, in addition to being found useful and relevant in the decision-making process for those players in industry considering entering into the M&A process.
ISUC: IoT-Based Services for the User’s Comfort

Emotions are alluded to as characteristic intuitive perspectives from certain conditions or temperaments. IoT applications can help in routine tasks and businesses. Most advances have not been taken advantage of regarding emotions. Emotions could be detected via the data gathered through IoT. Our investigation of related works revealed an absence of strategic methodologies in planning IoT frameworks according to feelings and shrewd alteration rules; thus, we present a philosophy that can rapidly assist in planning an IoT framework in this situation, where the identification of users is significant. We applied the proposed phases to test an IoT recommender framework named ISUC. The framework involves anticipating a user’s future emotions by utilizing boundaries gathered from IoT gadgets. It suggests new exercises for the user to obtain the ‘last’ state. Experimental results confirm our recommended framework has achieved over 85% exactness in anticipating users’ emotions in the future. The examination results presumed that an IoT-based framework could be created to detect positive emotions (e.g., peace, concretism, patience, enjoyment, and comfort) and negative emotions (e.g., irritation, abstraction, impatience, displeasure, and discomfort) to incite good emotions.
Software Education for Pre-service Early Childhood Teachers: Focusing on Computational Thinking and Collaborative Problem Solving Competency

Objectives The purpose of this study was to specify pre-service early childhood teachers could improve their computational thinking and cooperative problem-solving skills through the subject which provides software education to be used in the field of early childhood education.&#x0D; Methods For this purpose, the ‘AI Multimedia’ subject class was applied to 63 first-year students in the department of early childhood education and pre-post surveys were conducted in the first and last weeks of class to measure computational thinking and collaborative problem-solving competency. The collected data was subjected to paired t-test to compare and analyze the results of pre-post surveys on the computational thinking and cooperative problem-solving competency of pre-service early childhood teachers.&#x0D; Results After participation in the software education, the average score of the pre-service early childhood teachers' computational thinking was 4.189 and the standard deviation was .604. The average score of the cooperative problem-solving competency was 4.315 and the standard deviation was .664, confirming the overall improvement.&#x0D; Conclusions The results of this study showed that the computational thinking program tailored for pre-service early childhood teachers had a significant effect on the competency improvement of them. Plus, instructors should consider the instructional design that can enhance the collaboration between the students who have difference in collaborative problem solving competency.
Construction and Application of the Talent Training System in Colleges and Universities Based on the Fuzzy Analytic Hierarchy Process

At present, the competition among talents to seize jobs is becoming more and fiercer. How to stand out in the fierce competition for talents and seize market resources is a problem that every graduate must think deeply. Domestic research on the composition of the implementation elements of talent training in colleges and universities from the perspective of excellent school talent training has achieved fruitful results. Their measures and implementation ideas mainly include the following aspects: first, analyze the system of talent training in colleges and universities; second, analyze the teaching methods of college teachers; third, analyze the future development direction of talents. Taking a university as a pilot unit, this paper constructs a talent training system based on the combination method of fuzzy hierarchy, determines the training goal according to students' own ability, formulates the talent training scheme according to the goal, determines the rectangular array relationship between the training system and students' own ability requirements and attaches importance to practical teaching. The algorithm used in this paper firstly establishes a hierarchical structure model and then combines the relevant measures of AHP to sort the weights of indicators and finally calculates the entropy value by quoting entropy weight. We use the fuzzy method combined with hierarchical analysis to evaluate the five indexes of students' own influencing factors, such as learning attitude, basic knowledge, cooperation ability, development ability, and professional ability. It can be seen that the algorithm model is more accurate and the error value is the smallest. Compared with their combination method, the fuzzy evaluation method and the hierarchical analysis method are practical, and the fuzzy method combined with the hierarchical analysis combination method has more practical significance. Finally, the employment situation of graduates is compared and analyzed to further highlight the effectiveness of the model.
An Approach to Synthesis of the Neuromorphic Functional Models for Analog Components and Blocks

Numerical simulation of analog circuits and functional blocks (FB) is an important design stage of analog and mixed-signal integrated circuits as well as the state-of-the-art embedded systems. The application of adequate mathematical models for components and FB defines the quality of simulation and influences the time and cost characteristics of the up-to-date microelectronic devices development process. An approach to the automated synthesis of functional models for analog components and blocks using machine learning methods—neuromorphic functional models (NFM)—is proposed in the paper. The approach implements the possibility to use either analytically defined dependencies (model-based) or dependencies obtained during natural experimental measurements (data-driven) as the raw data for the NFM synthesis. The design flow including the description of the mathematical models for an analog circuit (MMC) applying the NFMs and further numerical simulation in accordance with the assigned type of the circuit analysis is presented. The results of experimental research for a model of the semiconductor diode D1N4934 and circuits of the voltage rectifiers on its base are showed. The obtained results demonstrate the high precision of the synthesized NFM and the high quality of simulation. The comparison of obtained results with results of simulation in the Cadence CAD tools based on a structural model of the diode is performed. The simulation errors consist of less than 1% of the input signals’ amplitude.
Learnings from a Pilot Hybrid Question Answering System: CQAS

The Singapore Government first released their digital government blueprint in 2018 with the key message for all their agencies to be "digital to the core and served with heart". With this push, agencies are moving towards human-centric digital services, especially for individual citizens. During COVID-19, Singapore government agencies introduced many COVID-19 digital initiatives resulting in more incoming inquiries from citizens to respective agencies. This surge in inquiries created the challenge on the agencies' end to meet service level agreements. One widely adopted solution is the use of chatbot technology that directly interfaces with the customer. However, several organisations have faced backlash from the citizens or customers when such chatbots cannot answer or give inappropriate answers to the questions. Hence this research takes a different approach to address this challenge using a question answering (QA) system that supports the CSOs to help answer the citizen inquiries more efficiently.
Research for making smart city cybersecurity policy according to communication theory: Focus on the communication structure

With the development of IoT and various data processing technologies, Smart City, a city that provides various services to citizens based on data, is emerging. However, IoT, big data, artificial intelligence, and platform technologies applied to these smart cities pose various risks related to data inherently. Moreover, it exists throughout the data life cycle, from collecting data to processing and managing data. Therefore, a policy that can manage cybersecurity risks implied in smart cities is essential. In addition, smart city cybersecurity policies need to be created through communication by stakeholders, including citizens, rather than making it top-down for effective implementation of the policies. So, through Habermas' communication theory, this paper focuses on the communication structure required for making smart city cybersecurity policies, analyzes cases, and presents a smart city cybersecurity policy-making framework that can be applied.
Design of the Best Linear Classifier for Box-Constrained Data Sets

We construct a binary linear classifier for n-dimensional data sets with the special box-constrained structure. Data sets with this structure arise naturally in many real-world areas. We apply a linear separability criterion proposed in J. Optim. Theory Appl. (2012, https://doi.org/10.1007/s10957-012-0155-x ). The Minkowski difference of the two data sets allows us to reduce a two-class classification problem to the problem in more easy to solve form. The greatest benefit of this reduction is that it allows to compute the parameters of a linear binary classifier by way of exact formulas. For this reason, a proposed framework has low computational costs. We rigorously prove that the developed linear classification model provides the possibility of constructing the data separator (or pseudo-separator) which really has the best estimate of its thickness. There are studied both regular and singular cases of separability arising in the theory and practice of linear classification of data sets.
The Impact of Data Governance on OGD Publication – An Ethnographic Odyssey

Over the past decade, Open Government Data (OGD) strategies have become a continuing concern in administrative services. This is even truer than at any time. Given the current situation, data management, specifically consistent data publication, has been central to public institutions. The Covid-19 pandemic has shown that data collected by public administrations could make valuable contributions. However, in Switzerland, the pandemic has highlighted the limitations of public organizations' capability to lead the publication of their data. Based on an ethnography and a literature review, this paper explores how data governance components impact OGD publication process and presents a model of OGD governance. For this purpose, we identify key data governance components necessary to OGD publication - structural, procedural, and relational - and illustrate how OGD challenges rarely arise from the publication of OGD or the open nature of data itself, but a lack of data governance.
Align and Conquer: An Ensemble Approach to Classify Aggressive Texts from Social Media

The phenomenal proliferation of social media platforms has facilitated the spontaneous sharing of expressions, opinions, and emotions in public spaces. Unfortunately, with this rapid rise, these mediums have been repeatedly used to spread propaganda, excite religious and political violence, jeopardize social harmony and disseminate other aggressive content. The pernicious societal effects of such undesired content have become a severe concern for tech giants and government bodies. Studies revealed that the majority of such activities are performed via texts written in regional languages. Developing an automatic system to identify and classify aggressive texts in resource constraint languages like Bengali is monumental. This work proposes an ensemble-based classifier model to compensate for this deficiency. A corpus of 10095 annotated texts (5095 for non-aggressive and 5000 for aggressive) is developed to train the system. Moreover, aggressive texts are classified into four fine-grained categories: political, gendered, verbal and religious using hierarchical annotation schema. This work also investigates 21 standard classifier models developed based on Convolutional Neural Network (CNN), Bidirectional Long Short Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) with different embedding models (i.e., Word2Vec, GloVe, FastText) and ensemble strategies. All the models are trained, tuned and tested on the developed dataset (EATxtC-Extended Aggressive Text Corpus). The experimental result exhibits that the ensemble of CNN and GRU (i.e. CNN+GRU) outperformed the other baseline models with acquiring the highest weighted f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score of 89.55% (coarse-grained) and 83.77% (fine-grained).
A Study on Privacy Protection of Mobile Evidence in Relation to Criminal Investigative Procedures

The growth of mobile devices used in our daily lives can mean more than one type of digital forensic targets may exist. Unlike desktop computers and file servers, mobile devices are more often used in daily life and contain precise and detailed personal information. Accordingly, if a mobile device is confiscated and digital forensic procedures are conducted, various private information may be disclosed during the investigation process. Moreover, privacy exposure from the investigative agency is not the entire risk. For example, privacy exposure may occur when evidence is released outside the investigation agency or possibly when other case managers arbitrarily read the evidence. Additional risks may arise during storage process as well. Privacy is a fundamental right of the people protected by the Constitution. Thus, if under the confines of the law, infringement or privacy limitations are necessary then it should be done to the minimum extent possible. In this paper, we will study 1) the laws relating to digital forensic procedures in Korea, the United States, the E.U., 2) examine the types of private information that can be exposed through mobile devices, and 3) suggest passcode (which is a first-generation cryptography, based on authentication) and other encryption methods, more specifically public-key and isomorphic encryption to protect the privacy of mobile evidence.
Deep Transfer Learning Using Class Augmentation for Sensor-Based Human Activity Recognition

Transfer learning (TL) is an essential technique for human activity recognition (HAR) since collecting a large amount of labeled sensor data is cost-intensive. In the image recognition field, parameter-based TL using ImageNet has become the defacto standard; however, no multipurpose TL method has been established in HAR thus far. Therefore, we proposed a simple and novel technique, i.e., class augmentation (CA), which augments the diversity of the class categories of source domains based on the hypothesis that HAR datasets used as source domains have low diversity in class categories. Data augmentation (DA) enhances the diversity of input data, whereas CA enhances the diversity of output labels. We evaluated three types of CA using DA and HAR-specific auxiliary information (gender and sensor position). Our experiments using public HAR datasets revealed that using CA seems to enhance the transfer performance. Moreover, we confirmed that CA closes the interdomain distance between the source and target domains.
Knowledge Sharing in AI Services: A Market-Based Approach

Today’s deep neural networks (DNNs) are very accurate when trained on a large amount of data. However, suitable input might not be available or may require extensive data collection. Data sharing is one option to address these issues, but it is generally impractical because of privacy concerns or due to the problematic process of finding a sharing agreement. Instead, this work considers knowledge sharing by first exchanging the weights of pretrained DNNs and then applying transfer learning (TL). Specifically, it addresses the economics of knowledge sharing in AI services by taking a market-based approach. In detail, a model based on Fisher’s market is devised for optimal knowledge sharing, defined as the gain in inference accuracy from exchanging DNN weights. The proposed approach is shown to reach a market equilibrium and to satisfy important economic properties, including Pareto optimality. A technique for weight fusion is also introduced to merge acquired knowledge with the existing one. Finally, an extensive evaluation is conducted in a distributed intelligence scenario. The obtained results show that the proposed solution is efficient and that weight fusion with TL significantly increases inference accuracy compared to the original DNN, without the overhead of federated learning.
Dynamic Scheduling Stochastic Multiworkflows With Deadline Constraints in Clouds

Nowadays, more and more workflows with different computing requirements are migrated to clouds and executed with cloud resources. In this work, we study the problem of stochastic multi-workflows scheduling in clouds and formalize this problem as an optimization problem that is NP-hard. To solve this problem, an efficient stochastic multi-workflows dynamic scheduling algorithm called SMWDSA is designed to schedule multi-workflows with deadline constraints for optimizing multi-workflows scheduling cost. The proposed SMWDSA consists of three stages including multi-workflows preprocessing, multi-workflow scheduling and scheduling feedback. In SMWDSA, a novel task sub-deadlines assignment stretagy is design to assign the task sub-deadlines to each task of multi-workflows for meeting workflow deadline constraints. Then, we propose a task scheduling method based on the minimal time slot availability to execution task for minimizing workflow scheduling cost while meetingt workflow deadlines. Finally, a scheduling feedback strategy is adopted to update the priorities and sub-deadlines of unscheduled tasks, for further minimizing workflow scheduling cost. We conduct the experiments using both synthetic data and real-world data to evaluate SMWDSA. The results demonstrate the superiority of SMWDSA as compared with the state-of-the-art algorithms. Note to Practitioners—Workflow scheduling in clouds is significantly challenging due to not only the large scale of workflows but also the elasticity and heterogeneity of cloud resources. Moreover, minimizing workflow scheduling cost and satisfying workflow deadlines are two critical issues in scheduling with cloud resources, especially the uncertainty of workflow arrive time and task execution time are considered. To meet workflow deadlines, it is an effective strategy to decompose workflow deadline constraints into task sub-deadline constraints. To minimize the workflow scheduling cost, each task in a workflow needs to be assigned to their most suitable VMs for execution. This article presents a novel workflow scheduling algorithm to schedule stochastic multi-workflows in clouds for optimizing multi-workflows scheduling cost and meeting workflows deadlines. This algorithm obtains the task sub-deadline constraints based on the characteristics of workflows for meeting the worklfow deadline constraint. Under the premise of meeting task deadlines, it schedules tasks to a VM with minimum the slot time, for minimizing the cost. Case studies based on well-known real-world workflows data sets suggest that it outperforms traditional ones in terms of success and cost of multi-workflows scheduling. It can thus aid the design and optimization of multi-workflows scheduling in a cloud environment. It can help practitioners better manage the scheduling cost and performance of real-world applications built upon cloud services.
Machine Learning-Based Rowhammer Mitigation

Rowhammer is a security vulnerability that arises due to the undesirable electrical interaction between physically adjacent rows in DRAMs. Bit flips caused by Rowhammer can be exploited to craft many types of attacks in platforms ranging from edge devices to datacenter servers. Existing DRAM protections using error-correction codes and targeted row refresh are not adequate for defending against Rowhammer attacks. In this work, we propose a Rowhammer mitigation solution using machine learning (ML). We show that the ML-based technique can reliably detect and prevent bit flips for all the different types of Rowhammer attacks (including the recently proposed Half-double and Blacksmith attacks) considered in this work. Moreover, the ML model is associated with lower power and area overhead compared to recently proposed Rowhammer mitigation techniques, namely, Graphene and Blockhammer, for 40 different applications from the Parsec, Pampar, Splash-2, SPEC2006, and SPEC 2017 benchmark suites.