Reviewerly/exHarmony huggingface dataset:
Content-Based Image Retrieval (CBIR): Using Combined Color and Texture Features (TriCLR and HistLBP)

Content-Based Image Retrieval (CBIR) is a broad research field in the current digital world. This paper focuses on content-based image retrieval based on visual properties, consisting of high-level semantic information. The variation between low-level and high-level features is identified as a semantic gap. The semantic gap is the biggest problem in CBIR. The visual characteristics are extracted from low-level features such as color, texture and shape. The low-level feature increases CBIRs performance level. The paper mainly focuses on an image retrieval system called combined color (TriCLR) (RGB, YCbCr, and [Formula: see text]) with the histogram of texture features in LBP (HistLBP), which, is known as a hybrid of three colors (TriCLR) with Histogram of LBP (TriCLR and HistLBP). The study also discusses the hybrid method in light of low-level features. Finally, the hybrid approach uses the (TriCLR and HistLBP) algorithm, which provides a new solution to the CBIR system that is better than the existing methods.
Long-Run and Short-Run Relationship Between Remittances and GDP Per Capita Growth, Evidence from Balkans Countries

This research study aims to explore the link between remittances as percentage of GDP and GDP per capita growth in six Balkans countries and Kosovo for the period 2007-2020. Panel cointegration based on dynamic ordinary least squares (DOLS) model was applied to check the magnitude of the potential long-run coefficients between the remittances as percentage of GDP and GDP per capita growth. In addition, the pairwise Granger causality tests were performed to capture the causal link among these two variables. The findings from the DOLS model indicate a positive association between remittance’s inflows and GDP per capita growth in the long-run. DOLS short–run relationships showed that the value of variable-remittances as percent of GDP has predictable properties to be a leading and lagging factor for these countries in the coming years. Results from the Granger causality confirm causality in unilateral direction running from remittance’s inflow to GDP per capita growth.
Automatic Detection of Modulation Scheme Using Convolutional Neural Networks

Automatic Modulation Classification (AMC) assumes a pivotal role in wireless communication systems. This paper presents an innovative intelligent modulation scheme detection approach utilizing a Convolutional Neural Network (CNN). The primary objective is to accurately identify the modulation schemes in the incoming signals without the need for selective feature extraction. The methodology involves transforming raw modulated signals into a 2D format and training a specialized CNN architecture. This novel approach eliminates the manual feature extraction process, simplifying the overall procedure and reducing computational complexity. The CNN learns intricate patterns and variations within the signals, enabling precise classification. Rigorous testing and validation demonstrate the high effectiveness of the CNN, achieving a remarkable prediction accuracy of 87.39%. The simulation results unequivocally substantiate the exceptional performance of the proposed. Furthermore, the system's robustness against noise is extensively evaluated and modelled, ensuring its reliability in real-world scenarios where signals are frequently corrupted by various forms of interference. The unique training methodology, well-designed CNN architecture and comprehensive evaluation of noise performance contribute to the novelty and efficacy of the proposed system.
Integration of RFID-based APDS and IoT

The integration of Radio Frequency Identification (RFID) technology with the Internet of Things (IoT) has resulted in significant advancements in various domains, including supply chain management. This study explores the potential of integrating RFID-based Automated Public Distribution Systems (APDS) with IoT technologies to enhance the efficiency of sensor data analytics. By leveraging the capabilities of IoT sensors and RFID tags, this integration enables a comprehensive view of the distribution process, facilitates proactive decision making, and optimizes resource allocation. In this study, the key components and benefits of this integration are discussed, and practical use cases are presented, along with the challenges and opportunities of implementation. Furthermore, discussions were conducted on the implications for stakeholders, including government agencies, distributors, and end-consumers, emphasizing the positive impact on overall distribution efficiency and effectiveness.
Smart Contract based Skill Verification System for Recruitment

Every recruitment process usually starts with shortlisting candidates based on the skills mentioned in their resumes. Candidates may try to boost their level of expertise on their resume to appear more qualified to employers, and in order to confirm their skills and experience, the company’s HR staff tries to contact the candidate’s former employers. However, this process is laborious and not very thorough. As a result, the candidates’ exaggerated remarks about their skills on resumes may result in poor hires, ultimately leading to a financial and reputational loss for the company. Therefore, in order to revamp the existing workflow, an automated system is required for skill verification in the recruitment process, which guarantees an effective hiring process in the company. In this research work, a novel system is devised and implemented for automatically verifying employees’ skills with the help of Smart Contracts based on the Ethereum blockchain. The major contribution of the proposed system is that it allows the registered companies to monitor their employees and their skills. Furthermore, the proposed system will benefit the employees registering into the system to get their skills endorsed and experiences approved. Due to the need for achieving consensus in Blockchain technology, it is harder to make fraudulent profiles. By leveraging the security and decentralization offered by blockchain technology, the companies can evaluate the skills as well as experiences of the candidates before hiring them and be sure about the information verified by the candidate’s former employers.
Fault Tolerance using Reinforcement Learning for Cloud Resource Management

The cloud environment has become an essential platform due to its computing abilities and is being used in various fields and sectors all around the globe. Users from all over the globe use this computing platform to process their challenging tasks. The cloud computes these tasks on its Virtual Machines (VM) using the appropriate resource scheduling algorithms. While a particular task is being computed, there is always a chance that the cloud suffers damages due to the dynamically generated faults of the task. The cloud also needs better performance with proper resource scheduling, leading to increased costs. To focus on these problems and provide an intelligence mechanism to the cloud, an algorithm named Reinforcement Learning – First Come, First Serve (RL – FCFS) has been designed and implemented by combining the Reinforcement Learning (RL) technique with the existing resource scheduling algorithm First Come First Serve (FCFS) to handle the dynamic faults and provide better cost by improving the resource scheduling at its end. This RL – FCFS algorithm provides a fault-tolerance mechanism at the cloud's end by computing 55.5 % of tasks aggregately compared to an aggregate of 11.1 % for the FCFS. Also, it aggregately improves the cost by 18.50 % across all scenarios. With the RL – FCFS algorithm, the cloud will be in a learning phase at the beginning. With RL rewards and feedback, the cloud will adapt and begin to handle these dynamic faults over time and improve its resource scheduling process, ultimately providing the best Quality of Service (QoS).
Symbolic Sequence Optimization Approach for Task and Motion Planning of Robot Manipulators

In recent years, there has been a growing trend toward the use of robots to perform various tasks, such as setting out products in retail stores. Many new methods have been proposed for motion planning and object placement planning, however, there are few methods that optimize both of them simultaneously. In addition, if these methods are to be used in actual retail stores, users need to be able to easily give tasks to the robot. In this paper, we propose an optimization algorithm that allows a robot manipulator can optimize both object placement planning and motion planning at the same time from a simple input represented by a symbol sequence. We compare three types of search methods: random search, heuristic search with target state, combined heuristic and random search and UCT (Upper Confidence Tree). The results show that the combination of heuristic and random search is more efficient than other methods for searching state transitions, and is particularly effective for complex problems. In addition, comparing the conventional TAMP method with the proposed method, we found that the proposed method is able to search state transitions with a shorter robot operation time.
Assessment of Basic Computer Skills of the Alternative Learning System (ALS) Learners

Lack of instructional materials like computers was determined as one of the problems the ALS itself has faced therefore, this study objectified the basic computer skills among Alternative Learning System (ALS) Learners in San Jose, Dinagat Islands. 51 ALS learners comprised the respondents’ group and the assessment was conducted at Sta. Cruz, San Jose, Dinagat Islands. Results revealed that majority of the ALS learners had good knowledge of operating Windows, while a large percentage of them cannot effectively operate the Microsoft Word software with little to no knowledge of creating and editing documents. On the other hand, learners were determined to be beginners whose knowledge of operating emails and the internet were insufficient and were less exposed to learning Excel as this has found more complicated to use. However, ALS learners showed positive personal interest in learning computers despite their inability to own one due to socioeconomic or situational. Thus, it may recommend that the government must support the ALS program by providing an adequate number of computer facilities to cater to all the ALS learners and better enhance their level of basic computer skills, particularly in areas of General Windows, Microsoft Word, MS Excel or Spreadsheet and Internet/E-mail. Significantly, the DepEd needs to closely monitor the ALS program as its parallel learning system, so that out-of-school youth are provided with improved learning opportunities in all areas, especially in ICT.
Determination of Oncourological Pathologies Based on the Analysis of Medical Images Using Machine Learning Methods

The chapter presents a method for diagnosing oncourological diseases based on machine learning algorithms. MobileNet50, ResNet50 convolutional neural networks are used to solve the problem of classifying patient biopsy image segments according to the Gleason scale. Augmentation technologies were applied to the existing data set for better performance of the neural network. The accuracy of the algorithm was estimated by the total error and the Cohen's Kappa coefficient. The results of the algorithm in software show a good level of accuracy: in 65% of cases, the algorithm accurately determined the Gleason index, and the rest of the data had a slight deviation of the confusion matrix.
Hardware-efficient large-scale reconfigurable optical neural network (ONN) using complex media

Numerous applications in science and technology nowadays utilize deep learning to tackle challenging computational tasks. With the increasing demand for deep learning, high-speed and energy-efficient accelerators are urgently needed. Although electronic accelerators are flexible, optical computers holds great promise due to their potential for massive parallelism and low power consumption. However, optical computing platforms demonstrated so far have mostly been limited to relatively small-scale computing tasks, despite the potential for scalability. Here, we propose and demonstrate a hardware-efficient design that allows deployment of a reconfigurable deep neural network (DNN) architecture without a direct isomorphism to standard DNN designs. Our proposed system is scalable and supports larger-scale computing. Our system realizes an optical neural network (ONN) using a digital micromirror device (DMD) for encoding data and trainable parameters, a complex medium for random complex weight mixing, and a camera for nonlinear activation and optical readout. A straight-through estimator enables backpropagation, even with a DMD as a binary encoding device. With this ONN as an elementary building block and automating the search for neural architectures, we can build complex and deep ONNs for a range of large-scale computing tasks, such as 3D medical image classification. The architecture-optimized deep ONNs are deployed by time-multiplexing data streams in one system. Our system enables large-scale training and inference in situ. Furthermore, we demonstrate that our system is capable of achieving task accuracies close to that of state-of-the-art benchmarks with more complex architectures implemented in silico.
EMO-MoviNet: Enhancing Action Recognition in Videos with EvoNorm, Mish Activation, and Optimal Frame Selection for Efficient Mobile Deployment

The primary goal of this study is to develop a deep neural network for action recognition that enhances accuracy and minimizes computational costs. In this regard, we propose a modified EMO-MoviNet-A2* architecture that integrates Evolving Normalization (EvoNorm), Mish activation, and optimal frame selection to improve the accuracy and efficiency of action recognition tasks in videos. The asterisk notation indicates that this model also incorporates the stream buffer concept. The Mobile Video Network (MoviNet) is a member of the memory-efficient architectures discovered through Neural Architecture Search (NAS), which balances accuracy and efficiency by integrating spatial, temporal, and spatio-temporal operations. Our research implements the MoviNet model on the UCF101 and HMDB51 datasets, pre-trained on the kinetics dataset. Upon implementation on the UCF101 dataset, a generalization gap was observed, with the model performing better on the training set than on the testing set. To address this issue, we replaced batch normalization with EvoNorm, which unifies normalization and activation functions. Another area that required improvement was key-frame selection. We also developed a novel technique called Optimal Frame Selection (OFS) to identify key-frames within videos more effectively than random or densely frame selection methods. Combining OFS with Mish nonlinearity resulted in a 0.8-1% improvement in accuracy in our UCF101 20-classes experiment. The EMO-MoviNet-A2* model consumes 86% fewer FLOPs and approximately 90% fewer parameters on the UCF101 dataset, with a trade-off of 1-2% accuracy. Additionally, it achieves 5-7% higher accuracy on the HMDB51 dataset while requiring seven times fewer FLOPs and ten times fewer parameters compared to the reference model, Motion-Augmented RGB Stream (MARS).
Trends and Issues of Ethnoscience Research from 2008 to 2023: A Bibliometric Analysis

This paper aims to analyze research trends on ethnoscience using bibliometric analysis from 2008-2023. The research sample consisted of 153 documents obtained from the Scopus database. The results of the study show that the distribution of publication frequency reaches its peak in 2021 with 32 articles identified. The distribution of research themes consists of 4 primary clusters and 35 secondary clusters. The ethnoscience research area is dominated by social science research (30.2%). The country with the best documents shows that Indonesia is ranked first as the most productive country in publishing on ethnoscience with 74 identified documents. The United States released second place with 28 documents, third Brazil with 10 documents, fourth Canada with 9 documents, and fifth France, Germany, Italy and the Russian Federation with 5 documents each. Institutions that contributed the most came from Indonesia, Universitas Negeri Semarang 22 papers 33.66%, University of Alberta 9 papers 13.77%, Universitas Negeri Surabaya 7 papers 10.71, Universitas Negeri Padang 7 papers 7.65%. The best author with the highest number of citations is Dahdouh. Meanwhile, if we look at the number of documents published by the author, Sudarmin has 10 documents with a contribution of 15.3%.
Welcome to the Tenth International Workshop on Evolving Security and Privacy Requirements Engineering (ESPRE'23)

Celebrating our tenth year, ESPRE continues to bring together international researchers and practitioners with a common interest towards evolving security and privacy requirements. The workshop probes the interfaces between Security, Privacy, and Requirements Engineering, exploring innovative approaches enhancing the practice of Security and Privacy Requirements Engineering, whilst accounting for the ever-changing needs of consumers and organisations.
Future of Learning and Education

Abstract According to “A Scorecard for Humanity,” a report from the Copenhagen Consensus Center, our world gains every time an individual is educated, and our world pays a price when people go illiterate. In the future, those regions and nations of the world that manage to educate its people would gain, whereas those that do not pay much attention to this crucial aspect would continue to suffer losses, hidden in plain sight. Hence, by 2050, we suggest that though the world would have made tremendous progress in basic education, illiteracy would persist with almost 500 million uneducated in the world. Illiteracy shall be most concentrated in the regions of Africa and South Asia, whereas most other nations would move toward a total eradication. However, there would also be a systemic change in the higher education sector across the globe, as the age cohort of 18 to 24 years in Europe would shrink, but in South Asia and Africa, this would continue to grow. There would also be a disruption of the current pedagogic and regimental learning systems toward flatter, flexible, and open systems based on research in learning sciences. Technology would also come by to play a larger role, where education would be driven by artificial intelligence (AI) and big data—resulting in complete personalization of curriculum and methodology focusing on each individual's minute needs.
Self-Censorship, Self-Restraint, and the Ethics of Conversation

Abstract “Self-Censorship, Self-Restraint, and the Ethics of Conversation” distinguishes between self-censorship and the kind of self-restraint that responsible speakers exercise. It then lays out several categories of bad speech—speech from which responsible speakers refrain and argues that failures in self-restraint give others defeasible reasons to engage in social censorship. If people do not exercise their liberties responsibly, sanctions aimed at motivating people to do better (attempts to censor bad speech) are one fitting response. But since censorship is goal-directed activity, its rationality is subject to an effectiveness constraint. The chapter concludes with an analysis of peer-to-peer censorship dynamics, arguing that though bad speech generates pro tanto grounds for social interference with speakers, we are often not well positioned to intervene effectively.
Eye State Detection Based on MTCNN and ESP

Eye state detection is an essential task in computer vision with diverse applications including emotion recognition, fatigue detection in high-risk areas, and computer interaction. This paper introduces a classification model called ERNet, which leverages the Efficient Spatial Pyramid (ESP) technique. The ERNet model is combined with the MTCNN to create a novel eye state detection model called MTED. The ERNet proposed in this paper has better performance than the existing model on the MRL Eyes 2018 dataset. Our ERNet model is more than 99.06% accurate on the MRL Eyes 2018 dataset while maintaining a high processing speed of 87 frames per second. Furthermore, the MTED model achieves a commendable accuracy of 95.41% on the ABD dataset.
LSTM Based Container Scheduling In Kubernetes

Container scheduling is a critical task in cloud computing environments, where resource demands of containers need to be predicted and allocated efficiently. This paper proposes a novel approach for container scheduling using LSTM, which is a type of recurrent neural network that can effectively model multi-dimensional time series data and capture temporal patterns. In this approach, historical resource usage of containers is used as input to the LSTM model, which predicts their future resource demands. To deploy the trained model, we use Kubernetes, an open-source container orchestration platform that provides flexible resource management and scalability features. We demonstrate the effectiveness of our approach by conducting experiments using real-world container traces and evaluating the scheduling performance. The results show our approach performs more efficiently compared to currently available container scheduling algorithms in cloud computing in achieving high resource utilization and minimizing the number of unscheduled containers. Our study highlights the importance of choosing the right orchestration platform, Kubernetes which is a suitable platform for container scheduling using LSTM due to its scalability, flexibility, and fault-tolerance features. Our work provides a promising direction for improving container scheduling in cloud computing.
An Adversarial Learning with Sum of Top-K Loss Framework for Credit Card Fraud Detection

Credit card fraud detection is a challenging problem due to imbalanced data and adversarial attacks. We propose an adversarial learning with sum of top-K loss (AST) framework for this task. Our approach integrates adversarial learning into logistic regression to mitigate the blind spots of traditional machine learning methods, such that fraudsters manipulate features to evade detection. To further improve the robustness of classification under imbalanced data, we introduce a sum of top-K loss, replacing the plain empirical loss. We have designed a gradient descent approach to optimize the adversarial logistic regression model with the sum of top-K loss. Experiments validate the effectiveness of our AST framework over the prevalent machine learning techniques and existed adversarial learning algorithm.