Reviewerly/exHarmony huggingface dataset:
Recommendations of Cash Social Assistance (BST) Recipients for People Affected by Covid-19 Using AHP-TOPSIS

The coronavirus disease pandemic that began in 2019 had an extraordinary impact. Not only in the health aspect but also in various other aspects of people's lives. One of the most severe aspects is the economy of the community. Many people whose income is disturbed even must be laid off from work. Therefore, the government of the Republic of Indonesia adopted various policies to solve these problems. Cash Social Assistance (BST) is one of the government's policies to overcome the community's economic problems. However, in various media outlets, it was reported that there were indications that BST was not on target. There are many allegations that BST recipients are close to social service officials and even the head of the local neighborhood association. The purpose of this research is to propose analytical techniques as recommendations to the local government for prospective BST recipients. Thus, the determination of BST recipients can be more objective. This research uses the AHP method to analyze prospective BST recipients based on several established criteria. TOPSIS is used to determine prospective BST recipients based on the analysis results using AHP. The results of this study included 21 people who were recommended as BST recipients and are compared to the data owned by the Social Service office to see the accuracy.
Blockchain, NFT, Federated Learning and Model Cards enabled UAV Surveillance System for 5G/6G Network Sliced Environment

In recent years, the use of UAVs has expanded to various applications such as surveillance, disaster response, agriculture, and delivery. However, traditional UAV monitoring systems rely on direct communication between the UAV and the ground pilot, which has several limitations such as limited range, poor reliability, and susceptibility to interference. To overcome these limitations, there has been significant interest in integrating UAVs into cellular networks such as 5G/6G network slicing. The flexibility of network slicing allows UAVs to operate on different slices based on their communication needs, which can improve their performance and efficiency. However, integrating UAVs into network slicing also poses several challenges, such as managing communication and permissions of UAVs and base stations, access control of UAVs, and identity management of UAVs. To address these challenges, we propose a blockchain, Non-Fungible Token(NFT), Federated Learning(FL), and Zero-Trust(ZT) security-enabled UAV monitoring platform for 5G/6G network sliced environments. We propose a novel approach in which UAVs are represented as NFT tokens within the platform. This innovative representation allows for enhanced security and trust in the system, aligning with the principles of the Zero-Trust security model, which assumes no implicit trust in any network component or user. Furthermore, we propose a FL system that operates on top of the blockchain, which can analyze data from multiple UAVs across different network slices. Our proposed FL system uses coordinator-less models, which eliminates the attacks of a centralized coordinator. As a use case, we consider a scenario where our proposed system detects anomaly communications of UAVs and identifies attack surfaces via analyzing network traffic data of UAVs using FL. The 5G system testbed implemented with FreedomFi 5G gateway and Indoor Radio Cell.
Towards Efficient Video Object Detection on Embedded Devices

The challenge of adapting various object recognition techniques from still images to videos remains unsolved. When applied to videos, methods that are specifically designed for images do not perform well due to several complications. These include blurriness, shifting or ambiguous locations, subpar quality, and other similar concerns. In addition, a lack of effective long-term memory in video object detection has yet to be addressed. It is widely recognized that consecutive frames in a video tend to produce highly similar results in most cases. Therefore, this characteristic can be exploited to improve performance. Moreover, the information contained in a series of sequential or non-consecutive frames exceeds that of a single frame. In our research, we have introduced a novel recurrent cell for feature propagation and have determined the optimal layer placement to augment the memory span. This has resulted in superior precision compared to methods presented in previous research. Furthermore, hardware constraints may exacerbate this issue. Therefore, we have focused on implementing and improving the effectiveness of these techniques on embedded devices. Our approach has yielded impressive results, with a 67.5% mAP accuracy on the real-time ImageNet VID dataset for mobile devices at a rate of 62 fps.
An Exploratory Study of the Relationship between SATD and Other Software Development Activities

Technical Debt is a common issue that arises when short-term gains are prioritized over long-term costs, leading to a degradation in the quality of the code. Self-Admitted Technical Debt (SATD) is a specific type of Technical Debt that involves documenting code to remind developers of its debt. Previous research has explored various aspects of SATD, including detection methods, distribution, and its impact on software quality. To better understand SATD, one comprehension technique is to examine its co-occurrence with other activities, such as refactoring and bug fixing. This study investigates the relationship between removing and adding SATD and activities such as refactoring, bug fixing, adding new features, and testing. To do so, we analyzed 77 open-source Java projects using TODO/FIXME/XXX removal or addition in inline comments as indicators of SATD. We examined the co-occurrence of SATD with each activity in each project through chi-square and odds ratio evaluations. Our results show that SATD removal occurs simultaneously with refactoring in 95% of projects, while its addition occurs in 89% of projects. Furthermore, we found that three types of refactoring - "move class", "remove method", and "move attribute" - occur more frequently in the presence of SATD. However, their distribution is similar in projects with and without SATD.
A Novel Independent Monitoring Model for Efficient Out-of-Distribution Detection in CNNs: Utilizing The Forward-Forward Algorithm

Out-of-Distribution (OOD) detection is critical for identifying inputs incongruent with the trained classes of a model. Incorrect classifications, notably in sectors like healthcare, autonomous driving, and industrial inspection, can be catastrophic. Addressing this, our paper introduces a new method for OOD detection essential in these fields. Our approach employs a standalone monitoring model, not connected by weights to the image classification model, thus avoiding retraining needs. The Forward-Forward (FF) Algorithm helps in understanding the connection between the final feature maps of the classification model and its results, leading to a Positive or Negative classification. This strategy enhances OOD detection efficiency and integrates well with existing deep learning models.
Pareto Frontier Optimization of Discontinuous Areas Based on Sewage Treatment

Practical engineering wastewater treatment procedures are susceptible to influence from outside sources and have other features because of their complexity and unpredictability. Its multi-objective optimization problem's pareto frontier may have discontinuity and unsmooth problems, making it difficult for the method to successfully search close to the discontinuity area. This paper proposes an optimization strategy based on subspace evolution, which uses information from the population evolution process to determine the discontinuous region. The target space is divided into multiple subspaces for optimization. At the same time, cross mutation is performed on individuals near the discontinuous region to predict the shape of the pareto frontier one by one in the subspaces. Finally, the various optimized subsets are combined to output the optimized solution set. The experimental findings demonstrate that the suggested strategy produces better distribution of Pareto borders in the target space and better optimization outcomes.
Understanding Money Distribution in Closed Economic Systems: A Graph Theoretical Approach with Quantum Random Walks

In this article, we explore money distribution in closed economic systems, where the number of economic agents is constant and the total amount of money is conserved. We simulate these systems through graph theory, representing economic agents as vertices connected by edges that signify possible transactions. In our model, these transactions follow a quantum random walk. We apply different attributes of the graph, such as vertex connectivity, vertex degree, and global clustering coefficient, to understand their impact on the Gini coefficient, a measure of wealth inequality.
Information Theoretic Learning-based Deep Embedded Clustering (ITL-DEC)

Clustering as the best grouping algorithm for the datasets is a main problem in various data-driven scientific and real-world applications. There are several methods based on some similarity measures for clustering, mostly suffering from high computational complexity on large-scale datasets. The performance of clustering methods highly depends on the quality of data representation; thus, in the literature, various linear and nonlinear representation methods and deep learning-based clustering algorithms have been exploited. This paper presents a novel fully unsupervised deep clustering method, namely information theoretic learning-based deep embedded clustering (ITL-DEC), with end-to-end training capable of simultaneously learning both feature representations and cluster assignments using deep neural networks (DNN). We use autoencoder as our powerful feature extraction deep neural network and two information-theoretic divergence measures, Cauchy-Schwarz divergence and Jensen-Shannon divergence, as cost functions to train the network parameter and appropriate clustering feature space. Experiments performed on three different benchmark datasets validate the effectiveness of the proposed method.
Transforming Semantic Segmentation into Instance Segmentation with a Guided U-Net

Traditional approaches to instance segmentation typically rely on the utilization of object detectors in the initial phase. In contrast, our approach leverages a semantic segmentation model as the foundational stage for instance segmentation. We introduce a novel “Guided U-Net” to extract instance segmentation masks from the output of the semantic segmentation phase. Inspired by educational strategies, we incorporate a “guide” mechanism akin to a teacher directing a student’s attention to specific regions of an image and prompting inquiries about the boundaries of the indicated instance.The Guided U-Net is trained using a triad of inputs: the original image, the semantic segmentation output from the primary stage, and the guide—a matrix containing a single white dot placed randomly within an instance. Through a comparison between the predicted mask and the corresponding ground truth mask, a loss value is computed. Remarkably, our approach capitalizes on a straightforward loss function, in contrast to contemporary models that rely on intricate loss functions requiring weight tuning. The proposed method shows promise, particularly in scenarios where a well-trained semantic segmentation model is already available and the need for instance-level detail arises.
Information technology for movie recommendations using neural network methods

Movie recommendation systems are software programs that suggest movies to users based on their viewing history, ratings, and preferences. These systems use algorithms and machine learning techniques to analyze data and generate personalized recommendations for users. With the increasing amount of content available on streaming platforms, movie recommendation systems have become essential in helping users discover movies that they are likely to enjoy. In this article, I will discuss the existing limitations of the movie recommendation system and explore a new approach that may overcome some of them.
Compressing Deep Neural Networks Using Explainable AI

Deep neural networks (DNNs) have demonstrated remarkable performance in many tasks but it often comes at a high computational cost and memory usage. Methods to reduce data size, such as pruning and quantization are applied to reduce the memory footprint of DNNs and make it possible to accommodate them on resource-constrained edge devices. Recently, explainable artificial intelligence (XAI) methods have been introduced with the purpose of understanding and explaining AI methods. XAI can be applied to understand the internal operations of Deep Neural Networks (DNNs), including the significance of various neurons and features in shaping the overall performance of DNNs. In this paper, a novel DNN compression approach using XAI is proposed to efficiently reduce the DNN model size with negligible accuracy loss. In the proposed approach, the importance score of DNN parameters (i.e. weights) are computed using a gradient-based XAI technique called Layer-wise Relevance Propagation (LRP). Then, the scores are used to compress the DNN as follows: 1) the parameters with the negative or zero importance scores are pruned and removed from the model, 2) mixed-precision quantization is applied to quantize the weights with higher/lower score with higher/lower number of bits. The experimental results show that, the proposed compression approach reduces the model size by 64% while the accuracy is improved by 42% compared to the state-of-the-art XAI-based compression method.
A learning-based method using data augmentation for light field salient object detection

In this paper, a new method is proposed for light field SOD by using convolutional neural networks. First, the light field dataset is extended by geometric transformations such as stretching, cropping, flipping, rotating, etc. The augmented data are then weighted with natural data to train the light field SOD. We propose a mutual attention approach in this process, extracting and fusing features from RGB images as well as depth maps. Therefore, our network can generate an accurate saliency map from the input light field images after training. The obtained saliency map can provide reliable a priori information for tasks such as semantic segmentation, target recognition, and visual tracking.
Concurrent Path Planning for the End Effector and the Mobile Platform of an Autonomous Mobile Manipulator

The autonomous mobile manipulator (AMM) that is able to navigate complicated environments and handle objects plays a crucial role in production lines of unmanned factories and warehouses automation. Path planning is a crucial function for an AMM to perform various tasks in a complicated environment. Efficiency, obstacle avoidance, (nonholonomic) kinematic constraints of AMMs and smoothness of the path are important factors that should be considered in path planning. Moreover, we consider to plan the paths of the manipulator and the mobile platform concurrently such that they can move simultaneously to reduce the execution time of a task. This paper proposes RRT-Connect-based path sampling and midpoint smoothing methods for this purpose. By sampling the paths of the end effector and the mobile platform separately, the path sampling can be accomplished efficiently. Then, the simple and effective midpoint smoothing algorithm is applied to generate a smooth and collision-free path based on the sampled points in the previous step. Simulations are conducted to verify the effectiveness of the proposed method in complicated environments like a warehouse with obstacles.
SUT: a new multi-purpose synthetic dataset for Farsi document image analysis

This paper introduces a new large-scale dataset for Farsi document images, named SUT, which aims to tackle the challenges associated with obtaining diverse and substantial ground-truth data for supervised models in document image analysis (DIA) tasks, like document image classification, text detection and recognition, and information retrieval. The dataset comprises 62,453 images that have been categorized into 21 distinct classes, including identity documents featuring synthetically generated personal information superimposed on various backgrounds. The dataset also includes corresponding files with labeling information for the images. The ground-truth data is organized in CSV files containing image file paths and associated information about the embedded data. To demonstrate the efficacy of the SUT dataset in DIA tasks, it was utilized for document classification (achieving an accuracy of 86% using a convolutional neural network) and OCR (achieving a CER of 0.083 and 0.072 using Tesseract and EasyOCR engines, respectively). The SUT dataset serves as an esteemed asset for scholars engaged in the development and assessment of supervised models in Farsi document image analysis.
Temporal Squeeze-and-Excitation Networks for Skeleton-Based Action Recognition

Human action recognition is a research hotspot in industry, such as intelligent safeguard and surveillance of abnormal worker behavior. Skeleton-based action recognition has drawn a lot of attention in recent years, as the skeleton is robust to background and directly related to actions. Most studies only exploit 3DCNN to handle multiple frames or introduce optical flow to represent temporal information, without considering the distinctions of temporal features contained in different frames. In this paper, we extend the well-known attention mechanism, the Squeeze-and-Excitation (SE) block, to the temporal dimension, which we termed Temporal Squeeze-and-Excitation (TSE) block. Instead of using the channel reduction in the vanilla SE block, we adopt a channel augmentation to accommodate fewer frames, which gives TSE block sufficient model capacity to capture motion information. Our model achieves good results on the NTU RGB+D dataset by embedding it into existing backbones. Ablation experiments on HMDB51 dataset are conducted to explore characteristics of TSE block.
More Optimized Stochastic Configuration Networks (MOSCNs) Enabled Smart Sensing for Soil Carbon Content Quantification

Stochastic Configuration Networks (SCNs) have the ability to maintain the universal approximation property by employing a constructive scheme for entire neural networks and applying a supervisory mechanism by randomly assigning input weights and biases. A few studies investigated the optimal parameters of random distribution for assigning weights and biases precisely. However, they still lack further illustration of more optimal random distributions and generalization exploration on the real-world datasets. Therefore, in this paper, we present more optimal stochastic configuration networks (MOSCNs) by applying fractional terms to randomly assign input weights and biases for fine-grained SCN construction. Furthermore, we studied optimal parameters for up to seven common random distributions using microwave reflectance signals from different soil carbon content levels to validate the MOSCNs’ generalization. Results revealed that using fractional SCNs achieved around 4% classification accuracy improvement compared with general SCNs and around 20% classification accuracy benefits compared to general classifiers.
Using LMS Moodle and Youtube Tools to Create Video Courses With Educational Expert System Features for Informatics e-learning

Online video courses using the LMS Moodle tools to teach students of economics specialties who are studying informatics as a basic discipline in their first year have been created in the university’s virtual learning environment. A feature of such video courses is the presence of a significant number of author’s video resources for all topics of the academic discipline and some elements of educational expert system. Video resources are available on the YouTube video hosting channel. They include video lectures, where the theoretical information of the course is presented, and video materials with examples of solving laboratory practice problems. According to the results of the approbation of the video course, it is claimed that it creates an effective basis for students to study the discipline independently, as it provides both online delivery of educational text and video materials and methodical support both to the lecture hall, to the computer laboratory, and to the student’s home workplace. The technical and methodical aspects of the implementation of face-to-face, blended and distance learning using the developed video courses were investigated. Some elements of the educational expert system that automate the learning process have been implemented. Systems of automated control and self-control of knowledge, initial and final control, and programmed electronic journal with teachers’ evaluations and feedback have been developed. Taking into account the force majeure circumstances caused by the state of war, which disrupt the continuity of the educational process, the described experience of blended and distance learning can be useful not only for teaching informatics, but also for organizing the education of other disciplines.
The Synthesis Method for Specifications and Requirements in the Process of IT Project Reengineering

This research aimed to improve the method of synthesizing specifications and requirements in IT project reengineering and apply it in practice. The method effectively analyzes archaic systems and considers the old system’s specifics and business needs. Parallel testing and results analysis ensure compliance of the new system and help correct requirements based on identified issues. The black-box model and parallel system testing algorithms facilitate requirements detection and adjustment. Implementing these enhancements ensures effective work organization, proper problem-solving, and high-quality results in IT project reengineering.
Quality of distance learning from the point of view of the assessment results of students’ knowledge

The article highlights the relevance and availability of quality education for Ukrainian students using Information and Communication Technologies. Advantages and disadvantages of computer-based testing are considered. The student survey results to study the quality of education, awareness, and competencies in safety disciplines are presented. An analysis of the distance learning effectiveness at the Lviv Polytechnic National University was carried out on the example of safety discipline.