Reviewerly/exHarmony huggingface dataset:
Analysis of Issues Affecting IoT, AI, and Blockchain Convergence

The purpose of this project was to appraise the integration or convergence issues influencing the mutual functioning of blockchain, AI, and IoT. The study argued that the recent developments in the field of IoT and blockchain prediction have involved the integration of innumerable classification schemes to establish a hybrid model. The introduction of the hybrid technique relies on the prediction performance that strives to override the limitations of any available architectural scheme. This study offers a comprehensive exploratory appraisal of the issues influencing the successful integration of IoT and blockchain in regards to functionality and effectiveness of security, trust, and flawless communication issues. The exploratory research methodology was used in analyzing the issues affecting the integration of blockchain, artificial intelligence (AI), and the internet of things (IoT). The findings indicated that the integration challenges influencing the effective operations of blockchain, AI, and IoT as a single system involve security, scalability, accountability, and trust of communications. The study recommends that successful and effective integration will enhance the development of new business models as well as the digital transformation of market corporations. Accordingly, new approaches to convergence should ensure that executives address the new technology demands to obtain significant gains in efficiency.
Exploring the Potentials of Smart Data Analytics in the Banking Industry

The Indian banking industry is undergoing significant changes due to advancements in technology and increasing adoption of smart data analytics. One of the key trends driving the industrys future is the increasing digitization of banking services, facilitated by the growth of digital payments and mobile banking. The use of digital payments has resulted in the collection of large volume of data on customer behavior and preferences, which is being used by banks to assess their credit worthiness and manage the risk. The Government has introduced initiatives to encourage digital payments and the Covid-19 pandemic has further accelerated the adoption of digital banking services and data explosion.
The NPU-ASLP System for The ISCSLP 2022 Magichub Code-Swiching ASR Challenge

This paper describes our NPU-ASLP system submitted to the ISCSLP 2022 Magichub Code-Switching ASR Challenge. In this challenge, we first explore several popular end-to-end multilingual ASR architectures and training strategies, including bi-encoder, language-aware encoder (LAE) and mixture of experts (MoE). To improve our system’s language modeling ability, we further attempt the internal language model as well as the long context language model. Given the limited training data in the challenge, we further investigate data augmentation strategies, including speed perturbation, pitch shifting, speech codec, SpecAugment and synthetic data from text-to-speech (TTS). Finally, we explore ROVER-based score fusion to make full use of complementary hypotheses from different models. Our submitted system achieves 16.87% on mix error rate (MER) on the test set and comes to the 2nd place in the challenge ranking.
Two-Branch Stacked Transformer for 2D Skeleton-based Action Recognition

Human Action Recognition (HAR) is a challenging computer vision task with various applications, ranging from smart surveillance to human-computer interaction. Recently, the human skeleton, a compact and intuitive data modality, has attracted increasing attention in many studies and has achieved good results in HAR. However, some challenges such as body occlusion and action similarity still need to be addressed. In this paper, to overcome these challenges, we propose a model combining short action-snippets for storing meaningful information about human body transition and a deep network configured by two parallel branches of Transformer for thoroughly learning the temporal correlation of skeletal representations in upper and lower body parts, hence concurrently enabling to handle of partial occlusions of skeleton data and boosting the HAR performance. In experiments, we validate the proposed approach's outperformance compared with the state-of-the-art methods on the JHMDB dataset in terms of both the size (i.e., number of learned parameters) and the accuracy.
Impact of Remote Work on Project Risks Management: Focus on Unknown Risks

Unidentified risks, also called unknown unknowns, have been outside the scope of organization risk management for a long time. Many leaders believe uncertainties are difficult to find or imagine in advance. For instance, when COVID-19 spread quickly to different parts of the world in early 2020, the sudden shift of tens of millions of workers from on-site to remote work locations was experienced across the globe. Many businesses sent their staff to work at home, and most enterprises adopted technology to maintain their operations. Remote work has been aided by new technological platforms, including virtual private networks (VPNs), voice over Internet protocols (VoIPs), virtual meetings, and other collaboration tools. During this COVID-19 pandemic, these technologies have reduced the spread of the virus, enabled employers to decrease overhead expenses, helped workers to gain flexibility, and abridged overall organization risks. This study gathers critical information about remote work amidst unknown risks, such as COVID-19 pandemic, by relying on qualitative and quantitative data. The study develops a model to characterize unidentified risks by gathering employees’ perspectives on remote working. Data collected will be compared in terms of the benefits and challenges of remote work. The findings and discussion will be used to suggest and recommend ways organizations can rely on remote work to improve performance and mitigate sudden, unknown risks.
Synthetic Voice Detection and Audio Splicing Detection using SE-Res2Net-Conformer Architecture

Synthetic voice and splicing audio clips have been generated to spoof Intemet users and artificial intelligence (AI) technologies such as voice authentication. Existing research work treats spoofing countermeasures as a binary classification problem: bonafide vs. spoof. This paper extends the existing Res2Net by involving the recent Conformer block to further exploit the local patterns on acoustic features. Experimental results on ASVspoof 2019 database show that the proposed SE-Res2Net-Conformer architecture is able to improve the spoofing countermeasures performance for the logical access scenario. In addition, this paper also proposes to re-formulate the existing audio splicing detection problem. Instead of identifying the complete splicing segments, it is more useful to detect the boundaries of the spliced segments. Moreover, a deep learning approach can be used to solve the problem, which is different from the previous signal processing techniques.
Employee Empowerment and Intention to Quit: The Mediating Role of Work Engagement: Evidence from the Information Technology Sector in Jordan

The objective of this study to understands the role of work engagement as a mediator in the relationship between employee empowerment and intention to quit. A quantitative approach was adopted in order to achieve this study aim: 318 responses were collected via an e-questionnaire from the respondents belonging to IT sector employees in Jordan, one of the most significant Jordanian sectors. The Statistical Analysis Package for Social Science (SPSS) was used for data analysis, and the Sobel test was further used to assess the mediating impact of work engagement on the relationship between employee empowerment and employees’ intentions to quit. Findings indicated that that work engagement has a mediating effect between employee empowerment and employees’ intention to quit. In light of the findings of this research, the most important recommendations include that information technology companies should focus on enhancing employee empowerment within organizations so that the employee becomes more affiliated and engaged in the organization in which they work. More future studies on empowerment, intention to quit and work engagement could be conducted in other organizations.
Identifying Driving Factors of Arsenic Distribution at the Mining Area Scale: Methods and Practice

Identifying driving factors is of great significance for understanding the mechanisms of soil pollution. In this study, a data processing method for driving factors was analyzed to explore the genesis of As pollution in mining areas. The wind field that affects the atmospheric diffusion of pollutants was simulated using the standard model. Machine learning and GeoDetector methods were used to identify the primary driving factors. The results showed that the prediction performances of the three machine learning models were improved after data processing. The R2 values of random forest (RF), support vector machine, and artificial neural network increased from 0.45, 0.69, and 0.24 to 0.55, 0.76, and 0.52, respectively. The importance of wind increased from 20.85% to 26.22%. The importance of distance to the smelter plant decreased from 43.26% to 33.19% in the RF model. The wind's driving force (q value) increased from 0.057 to 0.235 in GeoDetector. The average value of historical atmospheric dust reached 534.98 mg/kg, indicating that atmospheric deposition was an important pathway for As pollution. The outcome of this study can provide a direction to clarify the mechanisms responsible for soil pollution at the mining area scale.
End-To-End Training Of Object Segmentation Task And Video Question-Answering Task

For complicated Visual Question Answering(VQA) tasks that incorporates multiple objects, to train the VQA model using segmented objects data as inputs is proved to be effective for various downstream tasks. In this work, we tried to train the VQA task model and object segmentation model in end-to-end fashion instead of training independently. We employed CLEVRER as a target VQA task. We first trained MONet(Multiple Object Network), an object segmentation network, with the dataset, and trained Aloe, a VQA model, using the output of the trained MONet. Finally we connect MONet and Aloe to fine-tune them in end-to-end setting and confirmed that the performance of VOA task has been greatly improved.
The Effectiveness of Mobile Phones Applications in Learning English Vocabularies

There is an increasing trend of moving away from traditional technology in the learning process and toward mobile technologies. Today students are well-equipped with this technology, which may facilitate this transition. The purpose of this study is to determine how frequently Jordanian students use their smartphones to learn vocabulary, as well as which application is the most and least preferred for learning vocabulary, and whether they believe that utilizing smartphones will help them enhance their vocabulary. The quantitative research approach was used in this research. The researchers adopted the questionnaire to collect the research data. (135) fourth-year students from Zarqa University (ZU), who studies English language and Translation studies, participated in this research. The findings showed that the majority of Jordanian bachelor students at ZU use their Smartphones for learning vocabulary. IOS and Android were the most used applications among students at ZU. While the least preferred applications were Skype, Viber, Imo, and Hi. The majority of the students believe that using Smartphone help them to improve their vocabularies while searching for the meaning, spelling, pronunciation of the words, listening to English videos through YouTube, English channels, and English music, and using social media. Based on the findings, the researchers provide a number of recommendations and more studies that may aid academics in understanding the innovations needs in the area.
Picuit - An Application of Pictogram Content Creation using State Transition Diagram -

We proposed a pictogram content creation environment called “Pictogramming.” This application includes functions such as the transformation of human-shaped pictograms, drawing objects, and setting safety signs. The combination of these three types of functions enables users to quickly create various works based on pictogram design guidelines and learn some basic concepts in programming languages. Pictogramming is classified as a procedural programming language. This time, we constructed “Picuit,” to allow users to create pictograms that previously could only be made by writing code directly, by adopting state transition diagrams. This paper discusses the implementation method, availability of the application.
Evaluation of Cyber Threat Intelligence Maturity Models: A Comparison Based on Maturity Model Design Principles

Cyber Threat Intelligence (CTI) has become a trending alternative for organizations to improve their security posture in the last few years. In 2022, a survey conducted by SANS showed a significant increase in organizations that are newly developing their CTI capabilities. Three of four survey participants feel the significant impacts of CTI on their ability to detect, respond, and prevent cyber threats. However, 21% are still determining the impact of CTI on their companies, meaning the effectiveness of CTI is questioned in this case. To help organizations to measure the effectiveness of a process systematically, researchers are developing maturity models. CTI maturity model is the model that tries to meet this objective, specifically in the CTI domain. In this paper, we compare several publicly accessible CTI maturity models against the maturity model design principles framework. This research aims to give insights to organizations on which model is the most comprehensive based on its design and as a reference for researchers to know what must be improved in the current models. In this study, we found that the CTIM model developed by TUDelft University and Cyber Threat Intelligence Lab is the model that completes most of the design principles’ points compared to the other analyzed models. Therefore, we conclude that organizations can consider the model for assessing their CTI maturity.
Server load balancing using availability information based on fuzzy logic decision

Load balancing has been distributed server load to increase service performances as well as service distribution. The algorithms have been long discussions, experiments, and implementations. This work assesses server availability by measuring central processing unit (CPU) and memory loads and feeding these data to fuzzy set to decide which server is available for incoming requests. Experiments were conducted, results were presented, and patterns were obtained.
Impact of Manufacturing Flexibility on Response to Customer Requirements of Manufacturing Companies in King Abdullah II Ibn Al Hussein Industrial City in Jordan

This study aims to examine the impact of manufacturing flexibility on the response to customer requirements which is the dependent variable. This study was applied on 210 random samples of 458 manufacturing companies in King Abdullah II Ibn Al Hussein Industrial City. The researcher used the questionnaire tool to collect the primary data from the units of observation. The data was analyzed by using SPSS program. The study findings showed the impact of manufacturing flexibility on response to customer requirements. The study recommends increasing of job rotations of employees among company’s activities to amend labor flexibility.
Reading Books While Listening Music: Case Study To Students Of Universitas Negeri Semarang

Researchers conducted a case study on reading while listening music in Universitas Negeri Semarang students. The purpose of this study is to find out and explain what types of music students listen to while reading and to find out and explain how the functions of music that students listen to while reading. This study used Mix Method Research (MMR), which is a research method that combines qualitative and quantitative methods with a phenomenological approach. Data collection techniques used in this study were questionnaires and interviews. Data validity uses source triangulation. The results of this study indicate that the types of music that can be used as a medium to increase students' reading concentration are popular types of music, and also types of music with the same theme as the reading books they are reading.&#x0D; From the percentage, it can be concluded that music has an influence when it is listened to along with reading activities. The influence felt by respondents when listening to music together with reading activities is that it can make them comfortable, focus, and support the story or book they are reading.
Person Segmentation in Human Height Measurement Using Bodypix

Image processing can be used to measure height. Most research in this measurement uses a place adjusted to the green screen background to facilitate image segmentation. Using a green screen background can facilitate the image segmentation process, but using the space in question is inefficient. In this paper, a height measurement experiment is carried out using the body pix module to segment images of human height. The body pix module is expected to make the image processing process more efficient and inexpensive. From the study results, the best height calculation accuracy using the body pix module is 99.927059%. This experiment does not require any additional devices on the background of the human object, so it is cheaper and more efficient.
Exploratory Model of Technology Acceptance in The Covid-19 Era

The intensive use of technologies, devices and electronic networks not only own differences between users and non - users, but also even among those who use one or the other same technology that eventually perfects and involves continuous learning abilities and knowledge processing and dissemination of information. The objective of the present study was to establish the reliability and validity of an instrument that measures the intention to search for information. From a non - experimental, exploratory and cross - sectional study with a non - probabilistic selection of 100 students at a public university, it is planned eight dimensions among navojoa.uson.mx which explain the information selectivity of 21% at full. Regarding the design limits of the study sample and the testing of the dimensions recommended in an investigation of major components in the analysis confirmatory factor.
The Impact of Strategic Vigilance on Crisis Management in the Jordanian Dairy Companies: The Mediating Role of Organizational Learning

The major aim of the study was to examine the impact of strategic vigilance on crisis management through the mediating role of organizational learning. Therefore, it focused on Jordanian dairy companies. Data were primarily gathered through self-reported questionnaires creating by Google Forms which were distributed to a sample of (285) managers via email. In total, (262) responses were received including (16) invalid to statistical analysis due to uncompleted or inaccurate. Hence, the final sample contained (246) responses suitable to analysis requirements. Structural equation modeling (SEM) was conducted to test hypotheses. The results showed that organizational learning plays a partial mediation effect on the relationship between strategic vigilance and crisis management. Based on the results, this study presents a set of recommendations to decision makers in order to better manage the crises facing their organizations.
A New Technique of Blackboard Architecture in Decision-Making for Timber Harvesting

A new technique of Blackboard architecture is presented herein, the implementation of a system that dynamically decides its action based on certain reasons that correspond to the problem-solving plan and strategies. We describe the principal elements of the blackboard; the design of domain and control knowledge sources (KSs). The mechanism of problem-solving uses a blackboard on how domain and control KSs integrate to solve the problem. The concept being used and the problem that they applied is supported by the trigger and feasible conditions as well as an adaptive scheduler that decides its own actions corresponding to the current interest of problem-solving at that particular time. The implementation of these new concepts on the blackboard is presented in timber harvesting decision-making for sustainable forest management.
The Affect of Solar Panel Tilt Angle with Reflector on The Output Power Using Calculation and Experimental Methods

The performance of solar panels is affected by several things, one of which is the intensity of solar radiation. In this study, the solar panel will be paired with a mirror as a reflector and calculation methods, and experimental methods will determine the tilt angle. This study aims to determine the optimal angle of the panel and reflector, the output power and the efficiency produced by solar panels. From this study, the most optimal panel performance is obtained at a panel tilt angle of 25.7° and reflector of 41.5° with an output power of 59.021 W and an efficiency of 12.94%. The panel with the experimental method with a slope of15o and a reflector of 70° has the highest output power among the panels with other experimental angles of 4S.654 Watt while the highest efficiency is obtained at a panel angle of 10° and a reflector of 70° by 9.911%. And for a solar panel without a mirror, the average output power is 56.161 Watt and the efficiency of 12.3%.
An Improved Reverse Distillation Model for Unsupervised Anomaly Detection

Using knowledge distillation for unsupervised anomaly detection problems is more efficient. Recently, a reverse distillation (RD) model has been presented a novel teacher-student (T-S) model for the problem [7]. In the model, the student network uses the one-class embedding from the teacher model as input with the goal of restoring the teacher's rep-resentations. The knowledge distillation starts with high-level abstract presentations and moves down to low-level aspects using a model called one-class bottleneck embedding (OCBE). Although its performance is expressive, it still leverages the power of transforming input images before applying this architecture. Instead of only using raw images, in this paper, we transform them using augmentation techniques. The teacher will encode raw and transformed inputs to get raw representation (encoded from raw inputs) and transformed representation (encoded from transformed inputs). The student must restore the transformed representation from the bottleneck to the raw representation. Testing results obtained on benchmarks for AD and one-class novelty detection showed that our proposed model outperforms the SOTA ones, proving the utility and applicability of the suggested strategy.
Breast Cancer Prediction Using Machine Learning and Image Processing Optimization

In this study, it is wanted to implement Naïve Bayes data mining techniques on a dataset gained from digitized Image of fine needle aspirate (or FNA) belonged to benign and malignant type of breast tumors. One of the most important factors causing cancers in the females, which can be measured from sample after maintaining in several hours’ laboratory sample preparation at certain condition. As an alternative, Image processing can be substituted by measuring the geometrical features of FNA are well adopted economically and efficiently.