Miracle-dz/UniFAR-FTUs huggingface dataset:
A TABTRANSFORMER BASED MODEL FOR DETECTING BOTNET-ATTACKS ON INTERNET OF THINGS USING DEEP LEARNING

The Internet-of-Things (IoT) has revolutionized with the increase in data and serves as a link for the ecosystem of various tools, actuators, and sensors, and this is one of the disruptive technologies. IoT enjoys numerous advantages by accelerating communication between various smart objects around us Hence, IoT has become not only an essential part by serving as a medium for growth in advancing technology; the most crucial part is protecting the data from malicious attacks. To resolve Intrusion Attacks ranging from cyber to rule-based, the new advancing AI plays a significant role. Hence, it is well established in the literature that such attacks can be solved by using Machine Learning Algorithms. We analysed various Machine Learning and Deep Learning frameworks for tackling these attacks on the standard dataset N-BaIoT. This extensive analysis has confirmed that the TabTransformer model with SGD, Adam, and Avg & Sub optimizers has exhibited excellent performance, achieving at least 92.33% accuracy. It was observed that deep learning approaches such as Conv-Net or LSTM-based methods have come close to achieving similar results. Furthermore, we have compared the accuracy of our classification method with that of other studies, and the TabTransformer has demonstrated superior performance. This is the first study to conduct a series of experiments on the N-BaIoT dataset using a range of traditional machine learning and deep learning techniques. Our study has achieved state-of-the-art accuracy of 92.330% for the Provision PT-737E device using the TabTransformer model with Adam optimizer.
VANET-assisted Cooperative Traffic Congestion Forecasting

Existing mechanisms to monitor vehicular traffic, such as the use of induction loops or cameras are expensive to deploy and maintain. Vehicular communications open a new world of optimization opportunities as each vehicle can be used as a sensor to measure the fundamental variables defining the traffic state (flow, density and speed). In this article we propose ABEONA, a distributed algorithm for traffic monitoring that captures current and recent-past traffic trends to forecast near future road conditions. Compared to existing monitoring approaches, ABEONA allows estimating the vehicular density and, reducing installation and maintenance costs. ABEONA's algorithm incurs in low overhead and enables drivers to use forecast traffic congestion events to re-plan their route accordingly.
DIGITIZING IN BUSINESS MODEL INNOVATION: PRELIMINARY RESEARCH OF COURIER, EXPRESS, AND PARCEL SECTORS

This study aims to identify digital business model innovations carried out by Indonesian courier service companies to meet customer needs. This research also uses three case studies and several references or previous research as the main data sources. This study uses a qualitative approach with the Historical method, with the sampling method used is a purposive sample with three case studies at DiDi Taxi, Cisco, Dell companies. This study found that e-commerce needs to make some innovations in adjusting to the needs of today's customers, especially in the era of digitalization. Some of the innovations that can be done consist of product terms, service features, customer satisfaction, involvement in the e-commerce marketplace, and in terms of delivery patterns. Based on this, this study designs several recommendations based on crucial issues that need to be studied further from the issue of digital business innovation models in Indonesian courier service companies. The results of this study are expected to be able to recommend several crucial issues that need to be studied further from the issue of digital business innovation models in Indonesian courier service companies. Therefore, this review is expected to initiate further research related to the scope of the research.
BUSINESS PROCESS VERIFICATION WITH INTEGRATED SIMULATION METHODS: FOCUS ON "CUSTOMER ENGAGEMENT»

Many business process-modeling techniques cover and address various aspects of the business process. A limited number of these process models allows further quantitative analysis, and only a few allow structuring the process. This article discusses and classifies the main methods of modeling business processes from the view of their analysis and the possibilities of optimizing the business process taking into account the client's attracting. There are three types of methods, on the basis of which the choice of representative methods of business process modeling is classified. A similar classification is presented for the approaches of analysis and optimization of business processes identified in the relevant literature. The main contribution of the article is that it determines which business process methods are suitable for analysis and optimization, and also it highlights the disadvantages of such approaches. This article presents a modern review in the field of modeling, analysis and optimization of business processes with the emphasis on the ways to attract customers, which have not received sufficient coverage and support in the literature of post-Soviet countries. Organizations can make a big profit for themselves by having a documented business process model available. This gives it’s activities greater transparency, which in turn is an additional stimulating factor of investment attractiveness for the organization. For developing companies seeking to ensure competition in the market, it is especially important to have a clear understanding of the role of business process modeling and its necessity, and its place in the organization. Today, most organizations are actively trying to create their own models business processes or use existing models, and there is a reason for this their reasons, which can be easily explained by various factors.
RECURRENT NEURAL NETWORK FOR THE CLASSIFICATION OF AUDIO SIGNAL COMING FROM OUD INSTRUMENT

In this paper, we propose a model for recognizing the playing style of the oud player according to the grandmasters, and we identify the influence rates of each oud player concerning a way to play, this solution is based on the extraction of technical characteristics of the audio signals through the signal processing mechanism. Standardization, data reduction via mathematical tools as well as a selection procedure of the optimal characteristics are also started in this work in order to allow the proposed classification model to generate a better result. After having modelled and tested several classification models of deep learning, we evoke in these papers the most adequate model answering perfectly this problem of classification of an audio signal coming from the musical instrument Oud. Practical cases have also been developed in this work to test the relevance and efficiency of our model.
Parking and Traffic at a Local University

Students arriving on campus for the academic year often bring their personal automobiles. This then leads to an increase in traffic, which has many health implications. To understand students’ driving and parking behaviors on campus, traffic and parking lot counts were performed and a parking survey was administered to randomly selected undergraduate students (N = 1120). Results of these methods revealed primarily single occupancy vehicles in use, almost 70% of participants made multiple trips to campus and drove alone, and parking lots were not equally utilized. Convenience and safety were reasons given for driving. Efforts to improve traffic and parking are underway. INTRODUCTION AND REVIEW OF LITERATURE College campuses impact the communities in which they reside through ebb and flow of students and changing patterns of traffic and congestion. As students arrive on campus for the beginning of the school year, they often bring their personal automobiles as well. In a community of less than 50,000 and with a university of over 18,000, this increase in automobile traffic can be up to an additional 10,600 cars, which can have many health implications. The purpose of this article is to describe how students commute to campus and their decisionmaking processes surrounding their commute. Health implications of these behaviors are discussed.
ADAPTIVE SCALING IN DEEP CONVOLUTIONAL NEURAL NETWORKS FOR COVID-19 IDENTIFICATION IN X-RAY IMAGES

For X-ray image COVID-19 identification, researchers turn to deep convolutional neural networks (CNNs). Deep convolutional neural networks (CNNs) may struggle with detecting objects in noisy X-ray pictures. To enhance COVID-19 identification in noisy X-ray pictures without the need for any pre-processing for noise reduction, we offer a unique CNN technique using adaptive convolution to increase the resilience of a deep CNN against impulsive noise. Based on the standard CNN architecture, this method adds an impulsive an adaptive convolution layer, adaptive scaling layer, and a noise-map layer. Furthermore, we enhanced the deep CNN's generalization by using a learning-to-augment technique on noisy X-ray pictures. Twenty-nine hundred and three chest X-ray photos have been gathered, including those showing Healthy and COVID 19. GoogleNet, SqueezeNet, ResNet18, MobileNetv2, ShuffleNet, ResNet50 and EfficientNetb0 are just some of the pre-trained networks that have had their design tweaked to improve their resilience against impulsive noise. Verification using a distorted X-ray ResNet50 trained with the suggested noise-robust layers and learning-to-augment technique outperformed the state-of-the-art method by 2%.
USING MACHINE LEARNING MODELS TO COMPARE VARIOUS RESAMPLING METHODS IN PREDICTING INSURANCE FRAUD

One of the most common types of fraudulent is insurance fraud. And in particular fraud in automobile insurance, the cost of automobile insurance fraud is substantial for property insurance companies and has a long-term impact on insurance firms' pricing strategies. And In order to minimize insurance rates, car insurance fraud detection has become necessary. Although predictive models for the detection of insurance fraud are in active use in practice, there are relatively few documented studies on the use of machine learning approaches to detect insurance fraud, likely due to the lack of available data. In this paper, by using real-life data, we evaluate 13 machine learning approaches. And Because of the imbalanced datasets in this area, predicting insurance fraud has become a significant challenge. Due to our data consist mostly of a "non-fraud claims " class with a small percentage of "fraud claims. " Thus that the prediction of fraud appears weakly with classification models; therefore, the present study seeks to suggest an approach that enhances machine learning algorithms' results by using resampling techniques, such as Random Over Sampler, Random under Sampler, and hybrid methods, to address the issue of unbalanced data. And we compare between them. This paper shows that after using resampling techniques, the efficiency of all ML classifiers is enhanced. Furthermore, the findings confirm that there is no one resampling method that overall outperforms. Besides, among all the other models, the Stochastic Gradient Boosting classifier obtained the best result when using the hybrid resampling technique.
Fast ice characteristics, with special reference to the eastern Canadian Arctic

Arctic sea ice is currently receiving increasing attention, both in relation to technological problems associated with resources development and shipping (Walker and Penney, 1973), and to basic research questions. The polar pack ice in the Beaufort Sea, for example, is the focus of the Arctic Ice Dynamics Joint Experiment (AIDJEX) (Untersteiner, 1974), while an analysis of physical links between the characteristics of polar surfaces and climate is to be the crux of the United States contribution to the Polar Experiment (POLEX) (Weller and Bierly, 1973; National Academy of Sciences, 1974). A general discussion of sea ice, with emphasis on pack ice, has been presented recently by Wittman and Burkhart (1973), but another aspect of the sea ice regime deserving separate attention, particularly in the light of Arctic offshore oil developments, is the landfast or fast ice, ie that part of the sea ice which remains attached to the shore (see “Sea ice terminology”). This paper attempts to provide a broad picture of fast ice characteristics in the context of our field experience in the eastern Canadian Arctic.
WHOIS Data Redaction and its Impact on Unsolicited Emails: A Field Experiment

The Internet Corporation for Assigned Names and Numbers (ICANN) mandates the disclosure of certain information for registered domain names. However, the European Union General Data Protection Regulation (GDPR) curtails what information can be publicly accessible, including personally identi�able information (PII). It has been suggested that public disclosure of registration data leads to email spam, but no systematic study of this effect has been conducted. This study �lls this gap by empirically assessing the impact of public PII disclosure on spam email volume. Over a year-long �eld experiment, we registered 66 domain names with disclosed and undisclosed PII and analyzed incoming unsolicited email advertising. Our results revealed that domains with publicly disclosed contact information received a mean of 19.7 spam emails per domain, more than quadrupling the mean of 4.2 for domains with undisclosed information. This signi�cant increase in spam emails linked to public PII disclosure indicates a clear negative impact. ICANN stakeholders should consider these �ndings, recognizing the potential privacy implications and spam risks of disclosing domain contact information.
REQUIREMENTS PRIORITIZATION IN AGILE PROJECTS: FROM EXPERTS’ PERSPECTIVES

Software becomes an essential part of our lives because of the required automation in every field. A software requirement plays an important role in its development. In Requirement Engineering (RE), requirement prioritization (RP) is the crucial activity to successfully deliver the software system. Recently, Agile Software Development (ASD) methods have become a widespread approach used by the software industry. ASD stresses the importance of providing the customer with a product of a maximized business value. To achieve that, RP is used. The aim of this study is to investigate the current practice related to RP process, including its timing, participants, criteria used and prioritization techniques applied. An online questionnaire (based on literature review) has been designed and a survey has been conducted with the focus group which mainly involving some practitioners or experts from industry (domain experts) together with academicians (knowledge experts) in few parts of Malaysia. The researchers received 20 valid responses indicating RP practices in agile projects. The researchers found out that despite the fact that business value is the most common criterion used to prioritize requirements; other criteria like important, complexity and cost are considered as well. Other findings indicate that consideration of such multiple criteria requires different viewpoints, thus making RP a process that has to involve many participants of different roles in prioritizing the requirements. While the most popular technique used by the practitioners in this study is MoSCoW technique. Besides, the survey study also asking on any special attention given to the non-functional requirements (NFRs) or user stories in prioritization process in agile projects, since commonly known, due to the nature of the agile environment itself, the NFRs are nearly always neglected during the RP process. The results shown that over 85% of respondents giving attention to NFRs during prioritization in agile projects and less than 15% stated that there are a few reasons why their team did not pay much attention on NFRs during the prioritization process.
Market Orientation And The Sustainable Growth Of Firms: The Moderating Role Of Access To External Finance: A Study Of Manufacturing Smes In Kpk, Pakistan

Studies show that SMEs survival rate at the early stage of their start-up is very low as compared to bigger firms. This is particularly more prominent in developing countries and is also a huge problem for Pakistani SMEs, where the business environment is very unstable and hard to compete. This study examined using the Resource Based View (RBV) of firms, the effect of market orientation for the improvement in SMEs performance and achievement of sustainable growth (SG). We have taken Access to External Finance (AEF) as a moderator in the proposed relationship. The focus of the study was Manufacturing SMEs of KPK, Pakistan. A questionnaire was developed using the Likert Scale for the collection of data. The Partial Least Square Analysis (PLS-SEM) is used to examine the research model as it is most appropriate for the sample size. The response rate for the questionnaire was 33 percent
DVRSF: A Deep Reinforcement Learning Method Based on Dynamic Velocity Reward & Stagnation Fine

： When dealing with the difficult exploration problems caused by sparse rewards, pre-designed environment reward leads to poor performance of most RL algorithms. The existing algorithms fail to resolve the contradiction between the difficulty exploration and exploration efficiency. In this situation, it is necessary to design rewards more carefully, make more accurate judgments and feedback on the exploration status of agents. Therefore, we designed a RL exploration method based on trajectory entropy, and extracted two features during the training process to participate in the construction of internal rewards. On this basis, we add a monitoring method based on exploration speed to improve the over divergence problem in the agent exploration process. This method constructs a potential feature based on exploration speed according to the information sampling obtained in real time during training, gives real-time feedback to the agent, and dynamically adds it to the reward of each MDP. These two methods are called stagnation fine and velocity reward. Stagnation fine is intended to encourage the agent to get out of the dilemma as soon as possible when the agent is falling in. Velocity reward is used to improve the agent's exploration speed in the right direction and improve the over divergence of exploration. The algorithm has good generality, can avoid the influence of network structure in different algorithms, and can be easily added to existing RL algorithms, such as A3C and PPO. We evaluated our approach in different types of environments in Super Mario Bros. The experimental results show that the learning speed and accuracy of agents using DVRSF are significantly better than those of baseline such as PPO and A3C in most test tasks.
DYNAMIC TIME WARPING AND HIDDEN MARKOV MODEL CLASSIFIER IN TEXT DEPENDENT SPEAKER VERIFICATION SYSTEM WITH DIFFERENT SPEAKING VARIANT

One of the main focus on IoT is how to make the facility more secure in order to increase the safety and convenience of the facility. While traditional authentication approach for verifying identity such as Personal Identification Number (PIN) or password is widely used, traditional authentication approach have major flaws that can make the verified user lost access to the protected resource or even pose a major security breach. Speaker verification is one of the many biometric system solutions to solve that problem. In speaker verification, a different speaking pattern and loudness of the voice may affect the performance for the system to verify an individual identity, which a change of speaker speaking pattern will occur very likely since a speaking pattern is highly affected by the speaker’s mental and physical condition, and loudness of the voice is affected by how far the speaker away from the microphone. In this paper, we compare two well research text-dependent speaker verification methods, dynamic time warping and hidden markov model, on verifying user’s identity on different voice variant (loud, normal, quiet, shout, and soft) to replicate the condition when the speaker is experiencing mental or physical conditions and the distance between the speaker and the microphone that affects the loudness of the voice and speaker speaking pattern. This paper uses 330 total train data and 1,600 total test voice data where every test voice data will be retested for every registered user. Research done in this paper shows hidden markov model achieved better accuracy on normal, shout, and soft voice variant by 2,6%, 0,5%, and 0.74% respectively, whereas dynamic time warping achieves better accuracy on loud and quiet voice variant by 2,79% and 2,3% respectively.
Analysing Trading Strategies and Forecasting Stock Prices Using Lstm

Algorithmic Stock Trading has been legalized in India since 2008, with SEBI (Securities and Exchange Board of India) regulating the norms governing the same. Earlier, regulations restricted third-party algorithms to be used in circulation with APIs (Application Programming Interface), as these were unregulated by registered brokerages. Owing to the COVID19 pandemic, SEBI relaxed these underlying norms, easing up both algorithm usage for an end-user, as well as the number of trade orders that can be placed per second (which went up from 20 to 120). This article aims to analyze optimal trading strategies for various stocks using both classic mathematical techniques and Recurrent Neural Networks (RNN). Stock prices will be forecasted using data analytics implementing indicators like supertrend and VWAP and machine learning models like LSTM. Upon fine-tuning parameters in both approaches, trend directions and triggers will be plotted (in the case of existing indicators and strategies) and predictions with trade triggers (in the case of LSTM). Furthermore, stocks will be categorized by trading techniques - growth, momentum, and value and further the best strategy with the expected profit percentage in each case will be identified. © 2022 Little Lion Scientific.
EFFECT OF TECHNOLOGY ORGANIZATION ENVIRONMENT AND INDIVIDUAL FACTORS TOWARDS ADOPTION INTENTION OF CLOUD-BASED ACCOUNTING SOFTWARE IN MSMES

The purpose of this research is to examine the adoption of technology, organization, environment, and individual factors towards the intention of cloud-based accounting software in MSMEs. The research method used is descriptive statistics with survey as data collection method which was distributed to owners/co-owners/manager of micro enterprises in Tangerang Regency. This research uses multiple regression analysis with SPSS version 25 as the data analysis tool. The result of the research shows that technology, organization, environment, and individual factors simultaneously affect the adoption intention of cloud-based accounting software in MSMEs, however technology and environment factors are the only factors that partially affect the adoption intention of cloud-based accounting software in MSMEs. The result contrasted results of the previous research but it is also in line with most research which stated that technology, organization, environment, and individual factors simultaneously affects the adoption intention of technology advancement.