Reviewerly/exHarmony huggingface dataset:
Cybercrime Activities and the Emergence of Yahoo Boys in Nigeria

Yahoo-Yahoo, a form of cybercrime among the youth, is a major problem in modern Nigerian society. Numerous people have seen their fortunes eroded by this scourge, and the country’s standing among other nations continues to suffer from reputational damage. Addressing this problem requires unbundling its root causes. Therefore, explore youth’s motivations for cybercrime activities and the emergence of Yahoo-Boys in Nigeria. An online survey through an electronic questionnaire was administered to various respondents across Nigeria in gathering data for the study. A total of 110 questionnaire were returned completed from the online survey. Descriptive and inferential statistics were used in analysing the data. The results showed that unemployment (b = 0.153; p < 0.01) laziness (b = 0.158; p <.028), greed (b = 0.199; p < 0.11) and peer influence (b = 0.185; p < 0.34) are significant factors responsible for cybercrime activities among Nigerian youths. The paper recommends that relevant agencies of government, civil society organisations as well as parents and religious leaders should increase the level of sensitization on the implications of engaging in cybercrimes. Government should also cushion the impact of economic hardship and provide employment opportunities for the idle teeming youths.
Context aware human machine interface for decision support

Humans are often impacted by the presentation of choices in a digital environment. But they differ in their characteristics, behaviours, skills, limitations, and act differently in the same environment. Therefore, Influencing users choices through adaptive human-machine interfaces is crucial in context-aware technologies characterizing the industry 5.0. Our research efforts therefore focus on developing a reference framework of context-aware human-machine interface for decision support. We provide an overview of common components of frameworks based on the literature review, then we present context factors that influence decision-making and trigger HMI adaptation.
Decision Tree and Random Forest Models for Internal Audit Effectiveness Factors Evaluation

this paper investigates the impact of auditors’ competency, independency, member’s size, and digital transformation as factors that enhance the effectiveness of internal audit in Bahraini Context during and after Covid-19 outbreak. In this quantitative study, this paper used a survey questionnaire. The survey used to collect data for a sample of 50 respondents from both public and private organization in Bahrain to measure the study variables among auditors. Findings, there is a strong relationship between internal audit effectiveness and management support, digital transformation, and IA member’s size. While independency, competency, and auditors’ experience positively affect the internal audit effectiveness. The results of this paper will encourage decision makers of such organizations to focus on the issue of that enhance the effectiveness of internal audit after pandemic outbreaks and will strengthen the internal audit capacity of these organizations. This Paper offer academic contributions to existing research of internal audit effectiveness in developing context. The decision trees and Random Forest approaches used in this paper will allow future researchers to reclassify the factors that enhance the effectiveness of internal audit.
Joint Rate and Resource Allocation for Panoramic Videos over Future Network

With the popularity of panoramic video, it is urging research on the viewpoint-aware adaptive streaming methods to alleviate the resource shortage of wireless networks thus providing higher quality of experience (QoE) for the users. Therefore, in this paper, we focus on MEC-assisted 5G heterogeneous network system, and formulate a novel joint rate and resource allocation optimization problem with the aim of maximizing the overall QoE of all users, which comprehensively takes the characteristics of panoramic video in quality assessment, the delay requirements and the resource constraints into account. To solve this problem with low complexity, a heuristic algorithm based on Lagrange Relaxation and Greedy strategy is developed. The simulation results show that our algorithm can improve the overall performance of all users in the video quality, the quality fluctuation and the interruption time.
Stackelberg Game Based Resource Allocation Algorithm for Federated Learning in MEC Systems

Introducing Federated Learning (FL) into the mo- bile edge computing (MEC) system can effectively deal with delay-sensitive tasks and protect end devices (EDs) data privacy. In the process of participating in FL, the EDs will carry out a large number of local iterations and multiple rounds of communication with the MEC server to achieve a target model accuracy. These will bring delay and energy cost which may reduce EDs’ willingness to participate. In this paper, a resource allocation algorithm considering EDs incentives is proposed. We model the resource allocation of the MEC server and EDs as a two-layer Stackelberg game model and design two-layer utility functions. In EDs layer, we provide rewards to incentive EDs to contribute computing resource to achieve higher local model accuracy and weigh it against energy consumption of ED. In MEC server layer, the tradeoff between global model accuracy and system delay is conducted. We take utilities maximization as the optimization objective, and optimize the number of local iterations and bandwidth of EDs to achieve joint computing and communication resource allocation in the MEC system. Then, according to the solution of the optimization problems, we propose a resource allocation algorithm. Finally, the simulation results show that the proposed algorithm is superior to the benchmark schemes in reducing EDs’ energy consumption and system delay, which can achieve the purpose of encouraging EDs to participate.
Building an efficient convolution neural network from scratch: A case study on detecting and localizing slums

Designing a convolution neural network from scratch is one of the biggest challenges facing the creation of reproducible models. Despite feeding the model with an adequate amount of labeled data to mitigate fluctuations during training, the model still suffers from high variance in the final overall accuracy and loss among identical training runs. Many of the reasons behind this are the randomness in data shuffling and augmentation, the behavior of gradient decent function, and the non-determinism in convnet layers and floating-point computation of the GPU. The method used to address the aforementioned issues, specifically in the case of negative transfer learning, is divided into three steps: First, designing an efficient lightweight convnet architecture with respect to available resources. Second, mitigating oscillations during training. Third, after setting the random seed across the training, select the appropriate weight initialization. Our extensive experiments in the use case of binary slum localization and detection show that our method improves the reproducibility of our model from scratch with an accuracy of 98.88±1.15%, a loss of 0.03±0.05 for a confidence level of 99.73%. These results make this model a strong competitor to the pre-trained models using transfer learning.
Combining Blockchain and Image Processing to Counterfeit Goods Detection

stract: Fake products are a big problem since it's difficult to tell the difference between real and fake goods in today's environment. However, some people create these false goods for financial gain alone, without considering how they will affect customers or how they will influence a company's reputation, earnings, or sales. With blockchain technology, it is possible to verify the validity of a product and determine if it is real or false. Blockchain is a popular technology that is used in many applications. Information is maintained in the form of blocks in numerous databases that are connected by chains using blockchain technology, and no permission from outside users is needed. The immutability and security of blockchain technology are advantages.
Hand Gesture Recognition System for Deaf and Dumb People

Deaf and dumb people communicate using language known as Sign language. There are various sign languages but comparing to other sign languages, Indian Sign Language interpretation has got least mindfulness by researchers. Alphabets in English sign language and various phrases can be recognized using the application. It deals with images of bare hands. Hand-Gesture recognition is the ability to recognize hand-gesture. The hand gesture recognition system is solution to this problem which uses the image of hand gesture and recognize the sign present in the image. These features based on gesture analysis of the hand on which image of sign is shown and it extract slant and slope information. System provides an opportunity for deaf and dumb people to communicate with normal people effortlessly. Our research focuses on recognition of English sign language for vast reach of audience. There have been several upgrades in technology and a lot of research has been done to aid the deaf and dumb people. Aiding the cause, Deep learning along with computer vision is being used to make an impact on this cause. It can help the deaf and dumb people in communicating with others as many of us don’t know what sign language is. In this sign language recognition project, we create an kotlin application, which detects numbers from 1 to 10 and also the English alphabets. The initial step of this system is to build a database of English Sign Language. Hand gesture detection is the most determining step in each hand gesture recognition system since if we get better segmented output, better recognition rates can be achieved. The proposed system also includes efficient and robust hand segmentation and tracking algorithm to achieve finer recognition rates. Various isolated words from the Standard English sign language have been recognized using a large set of samples. In proposed system, we intend to recognize some very basic elements of sign language and to translate them to text .
Offline Signature Verification based on Geometric Features using Filter Method

This paper presents an offline signature verification using Geometric features.In this approach the acquired signatures samples undergoes pre-processing operation which includes resize, filtering, cropping, and thinning.Then Geometric features are extracted from each signature image.The extracted features are from normalized signature area.Experiments are conducted on publically available benchmark datasets namely CEDAR and GPDS.The best feature subsets of the data sets were selected using filter and wrapper methods.Based on the feature vector, the proposed approach will detect the forgery or genuine signature using filter matching method.Experimental results shows the performance of our proposed approach.
Bribery and Corruption in Matthew 28:11-15: A Lesson for the Church in Africa

The narrative of Jesus’ resurrection in Matthew 28:11-15 implies an incident of bribery and corruption and its effects on injustice, misinformation, and suffering in society. Literature abounds on bribery and corruption based on Matthew 28:11-15. Studies have also been done on bribery and corruption in Africa. However, few studies comparatively have emphasized the bribery and corruption mentioned in Matthew 28:11-15 and its relations to the church in Africa. Therefore, a review of the implication of bribery and corruption in Matthew 28:11-15 for African religious leaders is in order. The church in Africa may take some precautions from the implications of the narrative. The following implications can be drawn from the analysis of the bribery and corruption narrative of Matthew 28:11-15:(1)working for God does not exclude a person from evil. (2) the dare influence of bribery and corruption can destroy lots of good in society. The study adopted the textual-syntactical approach to biblical interpretation. The main objective of the study was to determine how a textual study of Matthew 28:11-15 can serve as a warning to religious leaders in Africa. To ensure that the church champions the fight against bribery and corruption in Africa, the study recommended that: (1) the church should demand accountability from its leadership as an example to the broader society;(2) the church exposes and fires corrupt leaders, and (3) church leaders should lead bribery and corruption-free life as examples to members and the wider society. This research will be of benefit to the clergy and other church leaders who would perceive the dire consequences of bribery and corruption in the church, especially as it relates to the African context. Second, it has also added to the existing literature on the interpretation of Matthew 28:11-15 as it relates to bribery and corruption in the Bible. Keywords: Bribery, Corruption, Africa, the Church, Jesus Christ.
Content-based image retrieval via transfer learning

In the past few years, due to the increased usage of internet, smartphones, sensors and digital cameras, more than a million images are generated and uploaded daily on social media platforms. The massive generation of such multimedia contents has resulted in an exponential growth in the stored and shared data. Certain ever-growing image repositories, consisting of medical images, satellites images, surveillance footages, military reconnaissance, fingerprints and scientific data etc., has increased the motivation for developing robust and efficient search methods for image retrieval as per user requirements. Hence, it is need of the hour to search and retrieve relevant images efficiently and with good accuracy. The current research focuses on Content-based Image Retrieval (CBIR) and explores well-known transfer learning-based classifiers such as VGG16, VGG19, EfficientNetB0, ResNet50 and their variants. These deep transfer leaners are trained on three benchmark image datasets i.e., CIFAR-10, CIFAR-100 and CINIC-10 containing 10, 100, and 10 classes respectively. In total 16 customized models are evaluated on these benchmark datasets and 96% accuracy is achieved for CIFAR-10 while 83% accuracy is achieved for CIFAR-100.
A parallel CF tree clustering algorithm for mixed-type datasets

Many traditional clustering algorithms are incapable of processing mixed-type datasets in parallel, limiting their applications in big data. In this paper, we propose a CF tree clustering algorithm based on MapReduce to handle mixed-type datasets. Mapper phase and reducer phase are the two primary phases of MR-CF. In the mapper phase, the original CF tree algorithm is modified to collect intermediate CF entries, and in the reducer phase, k-prototypes is extended to cluster CF entries. To avoid the high costs associated with I/O overheads and data serialization, MR-CF loads a dataset from HDFS only once. We first analyze the time complexity, space complexity, and I/O complexity of MR-CF. We also compare it with sklearn BIRCH, Apache Mahout k-means, k-prototypes, and mrk-prototypes on several real-world datasets and synthetic datasets. Experiments on two mixed-type big datasets reveal that MR-CF reduces execution time by 45.4% and 61.3% when compared to k-prototypes, and it reduces execution time by 73.8% and 55.0% when compared to mrk-prototypes.
PROFESSIONAL DEVELOPMENT AND PERFORMANCE ASSESSMENT SYSTEM FOCUSED ON STRENGTHENING TEACHERS’ COLLABORATION: THE CASE OF GEORGIA

Studies confirm that professional development at the school base through the teachers’ collaboration is the most result-oriented outcome among the alternative ways of teachers’ professional development. One of the declared goals of the centralized system of teachers’ professional development and performance assessment operating in Georgia is to facilitate teachers’ collaboration. However, the contribution of the mentioned system in strengthening the teachers’ collaboration has not been studied so far. The purpose of the present research was to study the impact of the centralized system of teachers’ professional development and performance assessment on teachers’ collaboration at general educational institutions. The issue was studied using the qualitative methods - focus group, interview, and content analysis of documents. Eight schools were selected for the study. Two focus groups were conducted with teachers and one interview was recorded with a school leader at each school. Furthermore, Interviews with six experts in the field were recorded. It was demonstrated that the centralized system of professional development and assessment focused on developmental assessment helps to strengthen the teachers’ professional relations. Activities closely related to the teacher's work, the correct attitude of the school leaders, teachers’ involvement in making decisions, the existence of preset goals, working schedule and space are important for collaborative relations. Existence of unified mechanisms for professional development, mistrust among colleagues, changes being constantly introduced to the education system, shortage of time were revealed as hindering factors for teacher collaboration. Keywords: qualitative research, school-based teachers’ professional development, teachers’ assessment, teachers’ collaboration
WATSON WAVELET TRANSFORM: CONVOLUTION PRODUCT AND TWO-WAVELET MULTIPLIERS

In this paper, utilizing the theory of Watson transform and Watson convolution, we explore the Watson wavelet convolution product and its related properties. The relation between the Watson Wavelet convolution product and Watson convolution is also computed. Watson wavelet transform and its inversion formula are analyzed heuristically. Watson two-wavelet multipliers and its trace class are derived from Watson wavelet convolution product
Challenging deep learning models with image distortion based on the abutting grating illusion

Even state-of-the-art deep learning models lack fundamental abilities compared with humans. While many image distortions have been proposed to compare deep learning with humans, they depend on mathematical transformations instead of human cognitive functions. Here, we propose an image distortion based on the abutting grating illusion, which is a phenomenon discovered in humans and animals. The distortion generates illusory contour perception using line gratings abutting each other. We applied the method to MNIST, high-resolution MNIST, and "16-class-ImageNet" silhouettes. Many models, including models trained from scratch and 109 models pretrained with ImageNet or various data augmentation techniques, were tested. Our results show that abutting grating distortion is challenging even for state-of-the-art deep learning models. We discovered that DeepAugment models outperformed other pretrained models. Visualization of early layers indicates that better-performing models exhibit the endstopping property, which is consistent with neuroscience discoveries. Twenty-four human subjects classified distorted samples to validate the distortion.
View interpolation networks for reproducing the material appearance of specular objects

In this study, we propose view interpolation networks to reproduce changes in the brightness of an object's surface depending on the viewing direction, which is important in reproducing the material appearance of a real object. We use an original and a modified version of U-Net for image transformation. The networks were trained to generate images from intermediate viewpoints of four cameras placed at the corners of a square. We conducted an experiment with three different combinations of methods and training data formats. We found that it is best to input the coordinates of the viewpoints together with the four camera images and to use images from random viewpoints as the training data.
Improving the risk management process in the input control of raw materials used in the production of medicines

The article presents the rationale for the development of a set of measures that contribute to the improvement of the risk management procedure during the input control of raw materials and materials entering the pharmaceutical enterprise. The main methods and features of risk management during the input control of raw materials and materials entering the pharmaceutical enterprise are considered. The results of the risk analysis at the entrance control at the pharmaceutical enterprise are presented, the ranking of the identified risks according to the degree and probability of occurrence is carried out. Recommendations are formulated to improve the risk management process during the input control of raw materials used in the production of medicines.
IAACS: Image aesthetic assessment through color composition and space formation

Judging how an image is visually appealing is a complicated and subjective task. This highly motivates having a machine learning model to automatically evaluate image aesthetic by matching the aesthetics of general public. Although deep learning methods have been successfully learning good visual features from images, correctly assessing image aesthetic quality is still challenging for deep learning. To tackle this, we propose a novel multi-view convolutional neural network to assess image aesthetic by analyzing image color composition and space formation (IAACS). Specifically, from different views of an image, including its key color components with their contributions, the image space formation and the image itself, our network extracts their corresponding features through our proposed feature extraction module (FET) and the ImageNet weight-based classification model. By fusing the extracted features, our network produces an accurate prediction score distribution of image aesthetic. Experiment results have shown that we have achieved a superior performance.
Quantum Fisher Information of Three-Level Atom under the Influence of the Stark Effect and Intrinsic Dechorence

We study the dynamical evolution of quantum Fisher information (QFI) and von Neumann entropy (VNE) for a three-level atomic system interacting with the single-mode coherent field in the presence of the Stark effect and intrinsic decoherence (ID) with and without atomic motion. The effect of the ID is significant on the VNE and QFI for a three-level atom in the absence of atomic motion. It is observed that in the case of a three-level atomic system in the presence of ID, the decay of QFI and VNE is rapid and significant but no prominent effect of the Stark effect is observed. Hence, for a three-level atom, the decay of quantum entanglement (QE) with respect to time is very fast and rapid in the absence of atomic motion with an increasing value of ID. Moreover, ID is not suitable to maintain the QE for three-level atomic systems in the absence of atomic motion. The Stark effect has no significant effect on the QE. In the case of three-level atoms, ID and the Stark do not affect the periodic nature of QFI and VNE with time evolution in the presence of atomic motion. The periodic response of QFI and VNE is observed under the effect of the Stark effect and ID in the presence of a motion of a three-level atom. The QE sudden death and birth is observed in the presence of atomic motion. Therefore, the ID with the Stark effect is suitable to sustain and maintain the QE in the presence of atomic motion for three-level atomic systems. These results show the strong dependence of QFI and VNE on the Stark effect and ID.