Reviewerly/exHarmony huggingface dataset:
Lifelong Learning of Task-Parameter Relationships for Knowledge Transfer

The ability to acquire new skills and knowledge continually is one of the defining qualities of the human brain, which is critically missing in most modern machine vision systems. In this work, we focus on knowledge transfer in the lifelong learning setting. We propose a lifelong learner that models the similarities between the optimal weight spaces of tasks and exploits this in order to enable knowledge transfer across tasks in a continual learning setting. To characterize the "task-parameter relationships", we propose a metric called adaptation rate integral (ARI), which measures the expected rate of adaptation over a finite number of steps for a (task, parameter) pair. These task-parameter relationships are learned using an auxiliary network trained on guided explorations of parameter space. The learned auxiliary network is then used to heuristically select the best parameter sets on seen tasks, which are consolidated using a hypernetwork. Given a new (unseen) task, knowledge transfer occurs through the selection of the most suitable parameter set from the hypernetwork that can be rapidly finetuned. We show that the proposed approach can improve knowledge transfer between tasks across standard benchmarks without any increase in overall model capacity, while naturally mitigating catastrophic forgetting.
Multi-Wavelength Diffractive Photonic Neural Network for Multi-Task Learning

Photonic neural networks use photons instead of electrons to perform artificial intelligence (AI) tasks, with the advantage of high-speed, low-power information processing. However, existing architectures are designed for a single task, which cannot reuse multiple tasks in parallel in a single system because the competition among different tasks will degrade the model performance. In this paper, a novel optical multi-task learning system is proposed by designing a multi-wavelength diffractive photonic neural network (DPNN) using a joint optimization method. By encoding the input of multiple tasks into multi-wavelength channels, the system can significantly reduce the competition to execute multi-tasks in parallel with high precision. We design the two-task and four-task DPNNs with two and four spectral channels respectively, for classifying different inputs from the EMNIST, KMNIST, FMNIST, and MNIST databases. Numerical evaluations show that for multitask learning, multi-wavelength DPNNs achieve significantly higher classification accuracies than single-wavelength DPNNs under the same network size. Moreover, as the network size increases, the classification accuracy of multi-wavelength DPNNs is comparable to that of individually training multiple single-wavelength DPNNs to perform multiple tasks separately. Our work provides a proven technical solution for developing high-throughput neuromorphic photonic computing and more general artificial intelligence systems to perform multiple tasks in parallel.
Serial Document Layout Analysis Method Based on Improved Mask R-CNN Architecture

Document Layout Analysis plays an important part in understanding the content of the document and extracting OCR text. Existing methods rely primarily on parallel analytical and identification techniques. Based on the analysis of the advantages and disadvantages of parallel methods, this article proposes a Serial Classification Recognition Method based on the improved architecture of the Mask R-CNN. To be specific, an improved feature pyramid model SCBAM-FPN is proposed, which utilizes Spatial Attention Module and Channel Attention Module as well as Atrous Convolution to reduce the loss of local information in feature maps and expand the receptive field, thereby increasing the performance of FPN express ability. Experiments show that using SCBAM-FPN to replace the original feature pyramid network in Mask R-CNN improves the detection performance of Mask R-CNN network for multi-scale targets. In addition, in order to improve the accuracy of segmentation, a Mask edge correction algorithm is proposed, which combines the FCN semantic segmentation results with edge superpixel information and uses the Mask-IoU mechanism to perform edge corrections on Masks with scores of less than 90%. The serial classification recognition method utilizes the improved Mask R-CNN architecture to perform Document Layout Analysis. Firstly, the document image is recognized and the table is segmented. Secondly, the image after the split table is used to identify and segment the illustrations. Finally, identify the title and paragraph of the image after removing the table and the illustration. Although the serial classification and recognition method has a certain degree of decline in speed, this method solves the problem of competition among multiple attributes and improves the accuracy of recognition compared with existing methods. In this article, experiments that were conducted on three datasets show that the proposed method surpasses existing methods in terms of accuracy and efficiency.
Forecasting YouTube QoE over SATCOM

We investigate the feasibility of using machine learning methods for predicting the Quality of Experience (QoE) of end users in the context of video streaming over satellite networks. To achieve this, we analyzed QoE and traffic data from 2,400 YouTube video sessions over emulated geosynchronous (GSO) satellite links. The objective is to determine whether existing learning methods, originally developed for wired or mobile networks, can be adapted to accurately predict key QoE factors over SATCOM. We particularly investigate a specific existing framework, which achieves outstanding performance in predicting resolution and initial delay. However, we point out some discrepancies in their hypothesis, leading to optimistic forecasting results. We then refine their methodology to ensure a complete independence between training and test datasets, leading to a fairer QoE video streaming forecast over satellite networks.
Content-Based Image Retrieval using Hard Voting Ensemble Method of Inception, Xception, and Mobilenet Architectures

Advancements in internet accessibility and the affordability of digital picture sensors have led to the proliferation of extensive image databases utilized across a multitude of applications. Addressing the semantic gap between low-level attributes and human visual perception has become pivotal in refining Content-Based Image Retrieval (CBIR) methodologies, especially within this context. As this field is intensely researched, numerous efficient algorithms for CBIR systems have surfaced, precipitating significant progress in the artificial intelligence field. In this study, we propose employing a hard voting ensemble approach on features derived from three robust deep learning architectures: Inception, Exception, and Mobilenet. This is aimed at bridging the divide between low-level image features and human visual perception. The Euclidean method is adopted to determine the similarity metric between the query image and the features database. The outcome was a noticeable improvement in image retrieval accuracy. We applied our approach to a practical dataset named CBIR 50, which encompasses categories such as mobile phones, cars, cameras, and cats. The effectiveness of our method was thereby validated. Our approach outshone existing CBIR algorithms with superior accuracy (ACC), precision (PREC), recall (REC), and F1-score (F1-S), proving to be a noteworthy addition to the field of CBIR. Our proposed methodology could be potentially extended to various other sectors, including medical imaging and surveillance systems, where image retrieval accuracy is of paramount importance.
Q8VaxStance: Dataset Labeling System for Stance Detection Towards Vaccines in Kuwaiti Dialect

The Kuwaiti dialect is a particular dialect of Arabic spoken in Kuwait; it differs significantly from standard Arabic and the dialects of neighboring countries in the same region. Few research papers with a focus on the Kuwaiti dialect have been published in the field of NLP. In this study, we created Kuwaiti dialect language resources using Q8VaxStance, a vaccine stance labeling system for a large dataset of tweets. This dataset will fill this gap and provide a valuable resource for researchers studying vaccine hesitancy in Kuwait. Furthermore, it will contribute to the Arabic natural language processing field by providing a dataset for developing and evaluating machine learning models for stance detection in the Kuwaiti dialect. The proposed vaccine stance labeling system combines the benefits of weak supervised learning and zero-shot learning; for this purpose, we implemented 52 experiments on 42815 unlabeled tweets extracted between December 2020 and July 2022. The results of the experiments show that using keyword detection in conjunction with zero-shot model labeling functions is significantly better than using only keyword detection labeling functions or just zero-shot model labeling functions. Furthermore, using the Arabic language in both the labels and prompt or a mix of Arabic labels and an English prompt is statistically significant compared to using English in both the labels and prompt for the total number of generated labels evaluation metric. Finally, the best accuracy for Macro-F1 values were found in the experiments KHZSLF-EE4 and KHZSLF-EA1, with values of 0.83 and 0.83, respectively. And, for the total automatically labeled data evaluation metric, experiment KHZSLF-EE4 labeled 42,270 tweets, while experiment KHZSLF-EA1 was able to generate 42,764 labels.
Tree Algorithm Model on Size Classification Data Mining

The goal of this research is to use a tree algorithm to categorize student clothing in order to acquire an accurate size. This research is qualitative through descriptive analysis, while the analysis used C.45 tree algorithm classification. Manual calculations utilizing the tree algorithm formula revealed that most students require XL-sized clothing. On the characteristic of X5 (length of the shoulder), the maximum entropy and information gain values were obtained at 0.212642462. According to the forecast, the shoulder length attribute is the first calculation in developing a decision tree scheme since it has the largest entropy and the value of information gain. Lastly, the findings of this study analysis can be used as a mapping prediction to make decisions on the size of the student group's clothing.&#x0D;
PSYCHOLOGICAL AND PEDAGOGICAL CONDITIONS OF FUTURE TEACHERS’ MOTIVATIONAL READINESS FORMATION IN UKRAINIAN AND EUROPEAN EDUCATIONAL INSTITUTIONS

The article is dedicated to the problem comparative analysis the motivational readiness formation of future teachers for professional activity in Ukrainian and European educational institutions. The analysis has shown similarities in theory of Ukrainian and Swedish scientific understanding are in the approaches: educational and professional dialogue stimulated independent thinking, created a space for communication, and activated the application of knowledge already acquired by students. The investigation demonstrated, that work in small groups, as well as the "brainstorming" method, which allowed to significantly increase the creative activity of students in a short time, proved to be effective. At the same time, it was noticed that it is not easy for students to depart from existing stereotypes in solving any problem and to offer a new, original solution that goes beyond the possible. In general, the “brainstorming” that was used in seminars, laboratory-practical classes included the following stages: preparatory – defining the conditions of group work (group rules and time budget), creating several groups of “idea generators” and groups of “experts”, formulating the problem to be solved; basic (“brainstorming”) – problem solving, maximum manifestation of creative possibilities, free expression of ideas, recording of all expressed ideas; revision - on the basis of defined criteria, "experts" chose the best ideas (up to 10 minutes); final – discussion of the work, substantiation and presentation of the best ideas, recommendation for their practical implementation. The participants of the "brainstorming" were placed in a circle in the auditorium. "Experts" were located outside the circle, monitored the work and recorded all statements based on the need to receive the largest number of ideas. The teacher managed the course of work, but did not exert any pressure on the participants. It was also distingiushe that the principle differences are in the practice of the motivational readiness formation of future teachers for professional activity in Ukrainian and European educational institutions: in European universities, these practices are more case oriented, investigations practices usage; on the contemporary in Ukraine – more pedagogical and theoretical practices are included.
Coverage Hole Elimination System in Industrial Environment

The paper proposes a framework to identify and avoid the coverage hole in an indoor industry environment. We assume an edge cloud co-located controller that followers the Automated Guided Vehicle (AGV) movement on a factory floor over a wireless channel. The coverage holes are caused due to blockage, path-loss, and fading effects. An AGV in the coverage hole may lose connectivity to the edge-cloud and become unstable. To avoid connectivity loss, we proposed a framework that identifies the position of coverage hole using a Support-Vector Machine (SVM) classifier model and constructs a binary coverage hole map incorporating the AGV trajectory re-planning to avoid the identified coverage hole. The AGV’s re-planned trajectory is optimized and selected to avoid coverage hole the shortest coverage-hole-free trajectory. We further investigated the look-ahead time’s impact on the AGV’s re-planned trajectory performance. The results reveal that an AGV’s re-planned trajectory can be shorter and further optimized if the coverage hole position is known ahead of time.
Deep Dual-Resolution Road Scene Segmentation Networks Based on Decoupled Dynamic Filter and Squeeze–Excitation Module

Image semantic segmentation is an important part of automatic driving assistance technology. The complexity of road scenes and the real-time requirements of application scenes for segmentation algorithm are the challenges facing segmentation algorithms. In order to meet the above challenges, Deep Dual-resolution Road Scene Segmentation Networks based on Decoupled Dynamic Filter and Squeeze–Excitation (DDF&amp;SE-DDRNet) are proposed in this paper. The proposed DDF&amp;SE-DDRNet uses decoupled dynamic filter in each module to reduce the number of network parameters and enable the network to dynamically adjust the weight of each convolution kernel. We add the Squeeze-and-Excitation module to each module of DDF&amp;SE-DDRNet so that the local feature map in the network can obtain global features to reduce the impact of image local interference on the segmentation result. The experimental results on the Cityscapes dataset show that the segmentation accuracy of DDF&amp;SE-DDRNet is at least 2% higher than that of existing algorithms. Moreover, DDF&amp;SE-DDRNet also has satisfactory inferring speed.
SSLJBA: Joint Backdoor Attack on Both Robustness and Fairness of Self-Supervised Learning

&lt;p&gt;Self-supervised learning (SSL) aims to learn more general and rich representations from large amounts of unlabeled data. Despite the great success, SSL methods may suffer from the class bias, which inevitably leads to unfair decisions for some groups in the application. Moreover, SSL are vulnerable to backdoor attacks, through which malicious users can degrade the model predication performance. Recent studies have shown that the fairness of machine learning models is also vulnerable and can be the target of attackers. Nevertheless, attacks to the fairness of SSL have not been addressed, and jointly attacking the robustness and fairness of SSL are still unexplored. In this paper, we analyze the limitations of existing backdoor attacks, and propose the first joint backdoor attack (SSLJBA) approach targeting both the robustness and fairness of SSL.&lt;/p&gt;
Developing English Learning Media Using Edpuzzle for Understanding Language Expressions in a Listening and Speaking Class

This research aimed to develop an English learning medium using Edpuzzle to understand language expressions in listening and speaking classes. A Research and Development (RD) design was employed in this study, using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) as the development model. Data were collected through an online questionnaire consisting of a set of written statements for respondents to answer. Descriptive analysis was utilized as the technique for data analysis, dealing with quantitative data. Four Edpuzzle-based videos, featuring explanations and quizzes, were developed and validated by media and language experts. The validation results revealed that the video 'Invitation' achieved an average score of 4.25 (valid), the video 'Apologizing, Expressing Anger and Resolving Conflict' scored an average of 4.30 (valid), the video 'Giving Compliments and Replying to Compliments' received an average score of 3.81 (quite valid), and the 'Expressing Opinions' video garnered an average score of 3.91 (quite valid). Overall, the average score was 4.10, categorized as valid. Therefore, the Edpuzzle-based video, as a developed English learning medium, was deemed suitable and appropriate for application in listening and speaking classes in Indonesia.
Using Virtual Reality as Learning Tools on Chemistry: Advantages and Challenges

This research aims to elucidate the potential benefits and inherent challenges of employing Virtual Reality (VR) technology as an instructional tool in contemporary educational settings. With an emphasis on understanding teachers' perceptions, the study investigates the difficulties faced during traditional oral teaching and the potential advantages that VR-based pedagogy may bring to the educational environment. The participants of this empirical investigation included 20 educators (11 males and 9 females), who provided their insight on a set of 15 questions specifically designed and later analyzed via Rasch measurement. The Rasch model's assessment provided comprehensive insight into the teaching difficulties experienced by educators and the potential benefits accrued from the application of VR as a teaching aid. The findings indicate that the majority of educators face challenges while instructing through oral methodologies without appropriate pedagogical tools. Conversely, the integration of VR in teaching methods was found to foster students' knowledge acquisition, comprehension, and application in novel situations, augmenting their analytical and evaluative capabilities. VR application was also found to bolster student engagement, motivation, and attention in the learning process. However, despite these promising findings, several obstacles were identified, most notably the cost of VR technology and the requisite technical skills needed for its implementation by educators. The results of this study bear significant implications for future research, particularly as a teaching tool in chemistry. It also prompts a discussion on educators' capacity-building to seamlessly integrate VR technology into existing teaching methodologies.
Advancing Intelligence: Unveiling the Power of Advanced Machine Learning Algorithms for Real-World Applications

This research paper explores advanced machine learning algorithms and their applications across various industries. It discusses different types of algorithms, such as classification analysis, regression, data clustering, feature engineering, association rule learning, reinforcement learning, and deep learning. The paper highlights their advantages and limitations, emphasizing the importance of selecting the right algorithm for specific use cases. Additionally, challenges in applying machine learning algorithms and potential solutions are discussed. The paper also provides insights into optimizing these algorithms for specific problems and best practices for their development and deployment in software applications.
Design of a Website-Based Point-of-Sale Information System at "Vapeboss" Vape Shop Using the Waterfall Method

Vapebos is a one-stop vape shop catering to vaping enthusiasts, offering a wide array of products essential for vapers or electronic cigarette users. Their inventory spans devices like Mods, Atomizers (RDA), a diverse range of flavored liquids, and various accessories. Presently, Vapebos employs a manual transaction system where each sale is noted in a physical ledger. However, this approach poses potential issues, such as the loss of transaction records or damage due to exposure to water. To tackle these challenges and ensure smoother operations, the store is transitioning to a modernized Point of Sale (POS) system. The chosen methodology for this transformation is the waterfall-based approach. By employing the waterfall model, the authors are developing a POS application that streamlines Vapebos' transaction processes. This system promises enhanced efficiency and accuracy, reducing the likelihood of data loss or errors that might occur with the manual method. This endeavor not only aims to simplify day-to-day transactions but also contributes to maintaining a secure and organized record-keeping system. The adoption of a POS system aligns with Vapebos' objective of providing seamless service to its customers and safeguarding its business operations against potential setbacks. Through this systematic upgrade, Vapebos is poised to offer a more reliable and efficient shopping experience for vape enthusiasts while ensuring the integrity of their transaction records.
SMART UNIVERSITY IMAGE: FROM BRANDING TO MARKETING STRATEGY IN PRIVATE UNIVERSITIES

The objective of the research is to analyze how Smart University Image can be a marketing strategy in private universities. The paper proposed a revised construct of brand image in smart university domain that is Smart University Image (SUI). The new construct of SUI will contribute to more appropriate university marketing strategy in highly competitive market in digital era. In addition, the research is aimed to discover SUI influence toward students’ satisfaction. This study employed a quantitative methodology, supported by study literature. Structural Equation Method is applied to analyse relationship between variables. The paper reveals that SUI able to increase student satisfaction by mediating it from Value Co Creation. It also means that SUI can be used as marketing strategy by promoting advanced technology toward Gen Z as the student’s candidate. Furthermore, the proposal of a new construct, namely SUI, becoming a more appropriate and integrated term for marketing strategy in the SU domain.
EVALUATING OF EFFICACY SEMANTIC SIMILARITY METHODS FOR COMPARISON OF ACADEMIC THESIS AND DISSERTATION TEXTS

Detecting semantic similarity between documents is vital in natural language processing applications. One widely used method for measuring the semantic similarity of text documents is embedding, which involves converting texts into numerical vectors using various NLP methods. This paper presents a comparative analysis of four embedding methods for detecting semantic similarity in theses and dissertations , namely Term Frequency–Inverse Document Frequency, Document to Vector, Sentence Bidirectional Encoder Representations from Transformers, and Bidirectional Encoder Representations from Transformers with cosine similarity. The study used two datasets consisting of 27 documents from Duhok Polytechnic University and 100 documents from ProQuest.com. The texts from these documents were pre-processed to make them suitable for semantic similarity analysis. The evaluation of the methods was based on several metrics, including accuracy, precision, Recall, F1 score, and processing time. The results showed that the traditional method, TF-IDF, outperformed modern methods in embedding and detecting actual semantic similarity between documents, with processing time not exceeding a few seconds.