Reviewerly/exHarmony huggingface dataset:
A Practical Non-Profiled Deep-Learning-Based Power Analysis with Hybrid-Supervised Neural Networks

With the rapid advancement of deep learning, the neural network has become the primary approach for non-profiled side-channel attacks. Nevertheless, challenges arise in practical applications due to noise in collected power traces and the substantial amount of data required for training deep learning neural networks. Additionally, acquiring measuring equipment with exceptionally high sampling rates is difficult for average researchers, further obstructing the analysis process. To address these challenges, in this paper, we propose a novel architecture for non-profiled differential deep learning analysis, employing a hybrid-supervised neural network. The architecture incorporates a self-supervised autoencoder to enhance the features of power traces before they are utilized as training data for the supervised neural network. Experimental results demonstrate that the proposed architecture not only outperforms traditional differential deep learning networks by providing a more obvious distinction, but it also achieves key discrimination with reduced computational costs. Furthermore, the architecture is evaluated using small-scale and downsampled datasets, confirming its ability recover correct keys under such conditions. Moreover, the altered architecture designed for data resynchronization was proved to have the ability to distinguish the correct key from small-scale and desynchronized datasets.
Image Sampling Based on Dominant Color Component for Computer Vision

Image sampling is a fundamental technique for image compression, which greatly improves the efficiency of image storage, transmission, and applications. However, existing sampling algorithms primarily consider human visual perception and discard irrelevant information based on subjective preferences. Unfortunately, these methods may not adequately meet the demands of computer vision tasks and can even lead to redundancy because of the different preferences between human and computer. To tackle this issue, this paper investigates the key features of computer vision. Based on our findings, we propose an image sampling method based on the dominant color component (ISDCC). In this method, we utilize a grayscale image to preserve the essential structural information for computer vision. Then, we construct a concise color feature map based on the dominant channel of pixels. This approach provides relevant color information for computer vision tasks. We conducted experimental evaluations using well-known benchmark datasets. The results demonstrate that ISDCC adapts effectively to computer vision requirements, significantly reducing the amount of data needed. Furthermore, our method has a minimal impact on the performance of mainstream computer vision algorithms across various tasks. Compared to other sampling approaches, our proposed method exhibits clear advantages by achieving superior results with less data usage.
Software Product Line Maintenance Using Multi-Objective Optimization Techniques

Currently, software development is more associated with families of configurable software than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking. Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment’s time and financial constraints, these features are rendered unfeasible. Thus, testing subsets of configured products is one approach to solving this issue. To reduce the testing effort and obtain better results, alternative methods for testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately, the CIT method produces unscalable solutions for large SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, should also be considered. In this article, we proposed a search-based software engineering solution using multi-objective evolutionary algorithms (MOEAs). In particular, the research was applied to different types of MOEA method: the Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-dominant Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms were examined in the context of distinct objectives and two quality indicators. The results revealed how the feature model attributes, implementation context, and number of objectives affected the performances of the algorithms.
Improving Question Answering over Knowledge Graphs with a Chunked Learning Network

The objective of knowledge graph question answering is to assist users in answering questions by utilizing the information stored within the graph. Users are not required to comprehend the underlying data structure. This is a difficult task because, on the one hand, correctly understanding the semantics of a problem is difficult for machines. On the other hand, the growing knowledge graph will inevitably lead to information retrieval errors. Specifically, the question-answering task has three difficulties: word abbreviation, object complement, and entity ambiguity. An object complement means that different entities share the same predicate, and entity ambiguity means that words have different meanings in different contexts. To solve these problems, we propose a novel method named the Chunked Learning Network. It uses different models according to different scenarios to obtain a vector representation of the topic entity and relation in the question. The answer entity representation that yields the closest fact triplet, according to a joint distance metric, is returned as the answer. For sentences with an object complement, we use dependency parsing to construct dependency relationships between words to obtain more accurate vector representations. Experiments demonstrate the effectiveness of our method.
MENINGKATKAN KEMAMPUAN KOGNITIF MELALUI PEMANFAATAN MEDIA PEMBELAJARAN DIGITAL BAAMBOOZLE PADA KELOMPOK B DI TK ABA WASUR II

The purpose of this study is to improve cognitive abilities through the use of digital learning media Baamboozle in group B at Kindergarten Aba Wasur II Merauke which will be held in the even semester of the 2021/2022 academic year. The subjects in this study were the children of Aba Wasur II Merauke Kindergarten, totaling 11 students consisting of 5 girls and 6 boys with an age range of 5-6 years. Based on the analysis of the teacher's teaching activity data in the first cycle, it reached 78.61%. Cycle one children's learning activities reached 71.47%. The learning outcomes of students in improving cognitive abilities through digital learning media Baamboozle in cycle one reached 54.58%. Cycle two shows that the analysis of teacher teaching activities has increased to 92.9%. The second cycle of children's learning activities experienced an increase of 92.9%. Based on the learning outcomes of students in improving cognitive abilities through digital learning media Baamboozle in cycle two it reached 90.9%. Thus it can be concluded that cognitive abilities can be improved through digital learning media Baamboozle in group B at TK Aba Wasur II Merauke.
Lattice-Based Group Signature with Message Recovery for Federal Learning

Federal learning and privacy protection are inseparable. The participants in federated learning need to be the targets of privacy protection. On the other hand, federated learning can also be used as a tool for privacy attacks. Group signature is regarded as an effective tool for preserving user privacy. Additionally, message recovery is a useful cryptographic primitive that ensures message recovery during the verification phase. In federated learning, message recovery can reduce the transmission of parameters and help protect parameter privacy. In this paper, we propose a lattice-based group signature with message recovery (GS-MR). We then prove that the GS-MR scheme has full anonymity and traceability under the random oracle model, and we reduce anonymity and traceability to the hardness assumptions of ring learning with errors (RLWE) and ring short integer solution (RSIS), respectively. Furthermore, we conduct some experiments to evaluate the sizes of key and signature, and make a performance comparison between three lattice-based group signature schemes and the GS-MR scheme. The results show that the message–signature size of GS-MR is reduced by an average of 39.17% for less than 2000 members.
Authentication Technique based on Image Recognition: Example of Quantitative Evaluation by Probabilistic Model Checker

A probabilistic model checker is a software tool for formal modeling and analysis of systems that exhibit random or probabilistic behaviour. Over probabilistic models, we analyze an innovative online authentication process based on image recognition. For true positive identification, the user needs to recognize the relationship between identified objects on distinct images which we call an outer relation, and the relation between objects in the same image which we call an inner relation. We use probabilistic computational tree logic formulas (PCTL) to quantify false-negative detection and analyze the proposed authentication process. That helps to tune up the process and make it more convenient for the user while maintaining the integrity of the authentication process.
Understanding Geolocation Data: Learning Scenarios for School Informatics

In this study, we present two learning scenarios for informatics education in a formal school setting, which aim to make pupils understand geolocation data through authentic outdoor experience with mobile technology. The aim of the study is to comprehensively evaluate the quality of proposed scenarios in terms of the quality of the educational resources, tools and learning activities used. The learning activities in the proposed learning scenarios are inquiry-based. They are mainly collaborative and focused on fostering higher-order thinking skills. The learning scenarios were successfully validated by many primary school teachers and were evaluated as useful, effective, and engaging. For misconceptions or failures of pupils (teachers) that were identified by the qualitative analysis of pupils’ products (teachers’ reports), the causes were described and the instructions it the learning scenarios were refined.
Adaptive Kevin: A Multipurpose AI Assistant for Higher Education

We present an innovative open domain question-answering (ODQA) intelligent workflow architecture that combines a fine-tuned retriever-reader model with a generative question-answering model with Google search functionality capable of answering questions both within and outside the scope of the domain represented by the documents indexed by the retriever. The retriever-reader architecture acts as the default responder, but if the question is outside of its scope or if its confidence score falls below a predefined threshold, the workflow logic triggers the execution of the generative model that reaches out to Google to formulate an answer. The paper describes the proposed workflow architecture, provides a quantitative evaluation of the retriever-reader performance and demonstrates the system’s flow of execution through several use cases. The paper will be of interest to both researchers and practitioners interested in deploying modern AI-driven ODQA systems.
Development and Evaluation of a Trusted Achievement Record of Accomplishments for Students in Higher Education Using Blockchain

Verifying the legitimacy of academic accomplishments like CVs and diplomas has become increasingly difficult as the number of institutions participating in the global education market continues to expand. This has resulted in a heightened risk of academic fraud. The distributed ledger technology known as blockchain has the potential to be an instrumental factor in the resolution of this issue. The purpose of this study is to present a blockchain-based achievement record system that generates a record of accomplishments that can be verified. The purpose of the proposed system is to make the process of authenticating and validating certificates more trustworthy, straightforward, and expedient by capitalising on the one-of-a-kind capabilities provided by Blockchain technology (the public Ethereum Blockchain), as well as smart contracts. Here, we will discuss the planning and execution of the system, as well as its components and tools. After that, we conduct a number of studies to evaluate the system in order to measure its usability, effectiveness, performance, and cost. An evaluation using the System Usability Scale (SUS) produced a score of 77.1. We demonstrate through a review of the relevant literature that this system is a significant improvement over legacy systems, both in terms of its friendliness to users and its level of efficiency. In addition to this, we carry out an in-depth cost analysis and have a conversation about the benefits and drawbacks of various alternative blockchain solutions.
SIGIR 2023 Workshop on Retrieval Enhanced Machine Learning (REML @ SIGIR 2023)

Most machine learning models are designed to be self-contained and encode both "knowledge" and "reasoning" in their parameters. However, such models cannot perform effectively for tasks that require knowledge grounding and tasks that deal with non-stationary data, such as news and social media. Besides, these models often require huge number of parameters to encode all the required knowledge. These issues can be addressed via augmentation with a retrieval model. This category of machine learning models, which is called Retrieval-enhanced machine learning (REML), has recently attracted considerable attention in multiple research communities. For instance, REML models have been studied in the context of open-domain question answering, fact verification, and dialogue systems and also in the context of generalization through memorization in language models and memory networks. We believe that the information retrieval community can significantly contribute to this growing research area by designing, implementing, analyzing, and evaluating various aspects of retrieval models with applications to REML tasks. The goal of this full-day hybrid workshop is to bring together researchers from industry and academia to discuss various aspects of retrieval-enhanced machine learning, including effectiveness, efficiency, and robustness of these models in addition to their impact on real-world applications.
Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning

Federated Learning (FL) is a machine learning (ML) strategy that is performed in a decentralized environment. The training is performed locally by the client on the global model shared by the server. Federated learning has recently been used as a service (FLaaS) to provide a collaborative training environment to independent third-party applications. However, the widespread adoption in distributed settings of FL has opened venues for a number of security attacks. A number of studies have been performed to prevent multiple FL attacks. However, sophisticated attacks, such as label-flipping attacks, have received little or no attention. From the said perspective, this research is focused on providing a defense mechanism for the aforesaid attack. The proposed approach is based on Type-based Cohorts (TC) with Kernel Principal Component Analysis (KPCA) to detect and defend against label-flipping attacks. Moreover, to improve the performance of the network, we will deploy Multi-path Service Routing (MSR) for edge nodes to work effectively. The KPCA will be used to secure the network from attacks. The proposed mechanism will provide an effective and secure FL system. The proposed approach is evaluated with respect to the following measures: execution time, memory consumption, information loss, accuracy, service request violations, and the request’s waiting time.
Quantum machine learning for natural language processing application

Quantum computing is a speedily emerging area that applies quantum mechanics properties to solve complex problems that are difficult for classical computing. Machine learning is a sub-field of artificial intelligence which makes computers learn patterns from experiences. Due to the exponential growth of data, machine learning algorithms may be insufficient for big data, whereas on other side quantum computing can do fast computing. A combination of quantum computing and machine learning gave rise to a new field known as quantum machine learning. Quantum machine learning algorithms take advantage of the fast processing of quantum computing and show speedup compared to their classical counterpart. Natural language processing is another area of artificial intelligence that enables the computer to understand human languages. Now, researchers are trying to take advantage of quantum machine learning speedup in natural language processing applications. In this paper, first, we discuss the path from quantum computing to quantum machine learning. Then we review the state of the art of quantum machine learning for natural language processing applications. We also provide classical and quantum-based long short-term memory for parts of speech tagging on social media code mixed language. Our experiment shows that quantum-based long short-term memory performance is better than classical long short-term memory for parts of speech tagging of code-mixed datasets.
Elementary School Teachers’ Perception Survey on Smart Device based Teaching &amp; Learning and AI Education

This study aimed to investigate and analyze the perceptions of elementary school teachers regarding the use of smart devices in classrooms and AI education, in order to identify stable strategies for the successful implementation of AI education. The study surveyed and analyzed the satisfaction levels of smart device-based classroom activities, as well as the perceived usefulness, acceptance, and interest in AI education. Additionally, the study aimed to examine the influence of satisfaction levels with smart device-based classroom activities on the perceived usefulness, acceptance, and interest in AI education. A sample of 107 elementary school teachers affiliated with the K Education Office was selected, and an online survey was developed and utilized as the research tool. The collected data were analyzed using SPSS 25.0 software.&#x0D; The research findings are as follows: Firstly, the teachers expressed high satisfaction with smart device-based classroom activities. Secondly, there was generally a high level of perceived usefulness, acceptance, and interest in AI education. Thirdly, it was observed that higher satisfaction with smart device-based classroom activities was associated with higher levels of perceived usefulness, acceptance, and interest in AI education. Therefore, it can be inferred that positive perceptions of smart device-based classroom activities are linked to positive perceptions of AI education.&#x0D; The implications of the study suggest that in order to foster positive perceptions of AI education among teachers, efforts should be made to enhance satisfaction levels with smart device-based classroom activities. Furthermore, it is recommended to provide training programs and teaching materials that help teachers recognize the usefulness of AI education and encourage them to actively embrace and increase their interest in it.&#x0D; However, it is important to note that this study was limited to elementary school teachers affiliated with the K Education Office. Therefore, future research should expand its scope to include not only teachers but also students and parents in order to obtain a broader understanding of their perceptions. Additionally, conducting longitudinal studies to investigate whether positive perceptions of AI education actually translate into practical acceptance is also suggested for future research.
Lateral variations in the Hosen 8‐2 vein in the Hishikari deposit, Japan: Implications for high Au‐grade zone

Abstract Lateral sampling of each blasting interval (~ 2.6 m) along a 79 m strike length was conducted for the Hosen 8‐2 vein on the −5 ML (mining level, meters relative to sea level) of the Main ore zone in the Hishikari epithermal Au deposit, Kagoshima, Japan. The horizontal variation of mineral textures, bulk chemical compositions, and fluid inclusion temperatures and apparent salinity of ore fluids were determined. The major gangue minerals of the quartz veins studied include quartz and adularia with minor amounts of calcite and smectite. The major ore minerals include electrum, galena, and sphalerite with minor pyrite, petzite, and hessite. Electrum commonly occurs in a discrete band with microcrystalline quartz, granular adularia, and smectite. The electrum‐bearing band is followed by tabular adularia, and finally by granular or comb quartz. Mineral textures that indicate high degrees of supersaturation with respect to amorphous silica (presently quartz with microcrystalline/mosaic and feathery textures) and adularia (with tabular, rhombic, and granular textures) were common throughout the vein strike. Bulk chemical analyses indicate that Au grade is positively correlated with Ag, Bi, Pb, and Te contents. A bonanza zone with Au grades up to 10,800 ppm occurs in the central part of the Hosen 8‐2 vein on the −5 ML, and is associated with high adularia content, calculated as adularia / (quartz + adularia). The wide lateral variation in the geochemical composition contrasts with the consistent association of electrum with microcrystalline quartz, granular adularia, and smectite throughout the Hosen 8‐2 vein on the −5 ML. Fluid inclusion microthermometry of primary and pseudosecondary inclusions in quartz and adularia yielded histogram modes of homogenization temperature between 160 and 240°C; the most frequent mode is 200–210°C, with most data within ±10°C from this value. The maximum ice‐melting temperature of most samples excluding late comb quartz is −1.5°C, which is equivalent to an apparent salinity of 2.6 wt% NaCl eq. The wide variation in ice‐melting temperatures and apparent salinities (up to 5.2 wt% NaCl eq.) may be due to dissolved CO 2 in the fluids, while the sharp decrease in apparent salinity with temperature decrease indicates CO 2 loss due to fluid boiling during vein formation. The intimate association of electrum with microcrystalline quartz that recrystallized from amorphous silica indicate sharp boiling and vapor loss as the primary mechanism of Au deposition in the Hosen 8‐2 vein.
Applying Transfer Learning ResNet-50 for Tracking and Classification of A Coral Reef in Development The Mobile Application with Scrum Framework

A Coral Reef has an important role in keeping a balanced ecosystem in the ocean. This research aims to develop a mobile application to track and classify to identify Coral Reefs with a presentation of information names of species, the type of classification of coral (hard or soft coral), accuracy, and area distribution that has been recorded before in certain areas. This research, utilized Flutter for deploying a mobile application, Deep Learning of Transfer Learning (TL) ResNet-50 for the classification of Coral Reefs, and the Google Cloud Platform used to deploy Rest API and deploy model TL ResNet-50 has been created, then the MongoDB database for storing data of the Coral Reef. A species of Coral Reef that could be classified to identify i.e. Acropora Clathrata, Acropora Florida, Cyphastrea Microphthalma, Diploastrea Heliopora, and Pachyseris Speciosa. A total dataset of a Coral Reef has 2197 images and is trained with TL ResNet-50 and it has resulted in an accuracy of 0.9594 or 96% for the classification of Corel Reef. We used the Scrum framework to produce a product in a short time and assist to manage process application development.