Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Industrial Machinery Using IoT Devices and Machine Learning: A Systematic Mapping

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety.Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection (AD).However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually.Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data.Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems.However, a large portion of the existing systematic mapping studies on AD primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector.Additionally, the related literature do not cover the challenges involved in using ML for AD in industrial machinery within the context of the IoT ecosystems.Therefore, this paper presents a systematic mapping study on AD for industrial machinery using IoT devices and ML algorithms to address this gap.Our primary objective is to investigate the use of ML models for anomaly detection within an industrial setting, particularly within IoT ecosystems.The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of AD research.Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types.Additionally, this review identifies application areas and points to future challenges and research opportunities.
Student of Games: A unified learning algorithm for both perfect and imperfect information games

Games have a long history as benchmarks for progress in artificial intelligence. Approaches using search and learning produced strong performance across many perfect information games, and approaches using game-theoretic reasoning and learning demonstrated strong performance for specific imperfect information poker variants. We introduce Student of Games, a general-purpose algorithm that unifies previous approaches, combining guided search, self-play learning, and game-theoretic reasoning. Student of Games achieves strong empirical performance in large perfect and imperfect information games-an important step toward truly general algorithms for arbitrary environments. We prove that Student of Games is sound, converging to perfect play as available computation and approximation capacity increases. Student of Games reaches strong performance in chess and Go, beats the strongest openly available agent in heads-up no-limit Texas hold'em poker, and defeats the state-of-the-art agent in Scotland Yard, an imperfect information game that illustrates the value of guided search, learning, and game-theoretic reasoning.
FAIR sharing of health data: a systematic review of applicable solutions

Abstract Purpose Data sharing is essential in health science research. This has also been acknowledged by governments and institutions who have set-up a number of regulations, laws, and initiatives to facilitate it. A large number of initiatives has been trying to address data sharing issues. With the development of the FAIR principles, a set of detailed criteria for evaluating the relevance of such solutions is now available. This article intends to help researchers to choose a suitable solution for sharing their health data in a FAIR way. Methods We conducted a systematic literature review of data sharing platforms adapted to health science research. We selected these platforms through a query on Scopus, PubMed, and Web of Science and filtered them based on specific exclusion criteria. We assessed their relevance by evaluating their: implementation of the FAIR principles, ease of use by researchers, ease of implementation by institutions, and suitability for handling Individual Participant Data (IPD). Results We categorized the 35 identified solutions as being either online or on-premises software platforms. Interoperability was the main obstacle for the solutions regarding the fulfilment of the FAIR principles. Additionally, we identified which solutions address sharing of IPD and anonymization issues. Vivli and Dataverse were identified as the two most all-round solutions for sharing health science data in a FAIR way. Conclusions Although no solution is perfectly adapted to share all type of health data, there are work-arounds and interesting solutions to make health research data FAIR.
PENGELOLAAN PERPUSTAKAAN YANG BAIK MENDUKUNG PROSES PEMBELAJARAN YANG EFEKTIF DAN EFISIEN

The library is the heart of a school which has a very vital function in supporting the learning process. Therefore, the school library with the ranks of librarians in it, has an obligation to "turn on" the library either through the services provided to visitors, or through other activities carried out inside or outside the library. Through the Institute of Research and Community Service of Nommensen HKBP University, several lecturers carried out community service activities to Senior High School Nasrani 1 Medan carrying out workshops in terms of fostering library management according to the demands and needs of the school . This activity is very important and meaningful, because it can help and better understand in managing in the field of collection procurement, collection inventory, collection classification, and main cataloging. Through this workshop, the librarian will further broaden his knowledge in carrying out his duties in serving students, teachers and other visitors who are registered and not in the library
Unmasking the digital deception - a comprehensive survey on image forgery and detection techniques

In today's digital landscape, digital images are widely used for communication across various platforms, but they are also vulnerable to tampering and deception. The proliferation of image editing tools and software has made it easier for individuals to manipulate images, but extensive tampering can compromise the accuracy of conveyed information. The human eye is difficult to distinguish between original and tampered images, leading to potential misinformation. Therefore, robust mechanisms using powerful algorithms are crucial to examining image authenticity and detecting forgery. This is especially important in fields like forensics, where evidence manipulation can have significant implications. This work conducts a survey providing a structured overview of research on image forgery techniques and detection approaches. The survey discusses various approaches, including source camera identification, type-dependent and type-independent methods, and recent deep-learning approaches. The survey also presents benchmark datasets for training and validation. The survey aims to improve understanding of research paths in this field and demonstrate how techniques initially developed for a domain can be applied to other areas. It aims to assist other researchers in enhancing their expertise in digital forensics and forgery detection techniques.
Towards enhancing security of IoT-Enabled healthcare system

The Internet-of-Things (IoT)-based healthcare systems are comprised of a large number of networked medical devices, wearables, and sensors that collect and transmit data to improve patient care. However, the enormous number of networked devices renders these systems vulnerable to assaults. To address these challenges, researchers advocated reducing execution time, leveraging cryptographic protocols to improve security and avoid assaults, and utilizing energy-efficient algorithms to minimize energy consumption during computation. Nonetheless, these systems still struggle with long execution times, assaults, excessive energy usage, and inadequate security. We present a novel whale-based attribute encryption scheme (WbAES) that empowers the transmitter and receiver to encrypt and decrypt data using asymmetric master key encryption. The proposed WbAES employs attribute-based encryption (ABE) using whale optimization algorithm behaviour, which transforms plain data to ciphertexts and adjusts the whale fitness to generate a suitable master public and secret key, ensuring security against unauthorized access and manipulation. The proposed WbAES is evaluated using patient health record (PHR) datasets collected by IoT-based sensors, and various attack scenarios are established using Python libraries to validate the suggested framework. The simulation outcomes of the proposed system are compared to cutting-edge security algorithms and achieved finest performance in terms of reduced 11 s of execution time for 20 sensors, 0.121 mJ of energy consumption, 850 Kbps of throughput, 99.85 % of accuracy, and 0.19 ms of computational cost.
Typical Situational-version Features of the Initial Stage of Investigating Unlawful Impact on Critical Information Infrastructure: Criminal Law and Criminalistic Aspects

The authors examine the questions of ensuring national security on the basis of incorporating modern trends of the digitization of society into the classical approach when investigating criminal cases connected with unlawful impact on critical information infrastructure. Using the situational approach, clauses of criminal legislation and the theory of crime qualification, the authors determine the circumstances included in the subject of proof for the criminal cases of unlawful influence on critical information infrastructure, which serves as a foundation for building general and specific criminalistic versions, and is also the basic scheme for making plans during the preliminary investigation. The authors conclude that, in order to obtain a realistic picture, it is necessary and unavoidable that an aggregate of official criminalistic techniques and scientific approaches should be used to study criminal activities in the sphere of information systems. They stress the necessity of studying and incorporating various data of criminal law research into modern investigative practice. The dominant features of the initial stage are identified, which make it possible to formulate its key tasks. The description of the tasks at this stage predetermines its key functions — the search and cognitive work of investigators and the subjects of investigation they interact with, the use of expert knowledge, the leading role of investigative, operative search versions and expert versions. The specific character of information and search for it predetermine the dominance of search, communicative and experimental investigative actions in the structure of the investigation. The analysis of the specific features of carrying out procedural and other actions at the initial stage of investigation allowed the authors to make conclusions about the importance of using tactical complexes in the investigation. They specify a complex of criminalistic recommendations of technical, tactical and methodological character to be implemented in the practice of crime investigation with the purpose of optimizing the counteraction to unlawful impact on critical information infrastructure.
‘Hacking the hackers’: reflections on state-implemented disruption as a ‘new model’ for cyber policing

Following the most significant data breaches in Australia's history, targeting Optus and Medibank, in November 2022 Australia's Minster for Home Affairs and Cybersecurity Clare O'Neil announced 'a new model of policing' involving a collaboration between the Australian Federal Police and Australian Signals Directorate. This collaboration would result in a new taskforce focused on 'hacking the hackers' or disrupting criminal groups responsible for high-profile cyber-attacks against Australian entities. Since this announcement, significant discussion has ensued regarding the meaning of state-implemented 'disruption' and questions have been raised about the potential mandate and operations of the newly announced taskforce. This paper explores the concept of disruption as it applies to cyber-criminal groups. It then turns to considerations for policy and practice regarding the pursuit of disruption. The paper aims to advance existing understanding of disruption and encourage further research and policy debate into the policing of cybercrime as the Australian government commences work on the 2023–2030 Australian Cyber Security Strategy.
Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic system

Smart manufacturing using the Internet of Things (IoT) ensures uninterrupted and human intervention-less automation in industries for precision outcomes. As the smart manufacturing encloses chaotic systems the point of security is always demandable due to external threats. For mitigating the authorization issues in chaotic systems, a Smart Reviving Authorization Model using Advanced Encryption Standard (SRAM-AES) is designed in this article. This model is selective for chaotic systems for reviving their conventional operation cycles and preventing failures. A machine/controller's performance is monitored for its point of instability through differential access. The malicious access and its cause for controller unstableness are verified using IoT elements (remotely) and deep recurrent learning algorithms. Such identified instances are recovered by providing alternate controller recommendations from the IoT platform. In the recurrent learning process, the unstable to stable point possibilities are verified; the passing controllers are equipped with AES mitigating the previous authorizations. For a stable-functioning controller, the AES deficiency in authorization is verified in its completion cycles for consecutive production instances. Thus this model stands reliable for preventing unauthorized access, controller downtime reduction, and production failures.
An eco-friendly remote sensing assisted development procedure to install renewable energy infrastructure for highest techno-economic gain

By taking socio-economic, terrain, and meteorological impacts into account, an eco-friendly methodology to sort the potential locations for installation of renewable energy infrastructure has been proposed, which is also able to address the most important challenge in the field, i.e., determination of the best contribution of the renewable energy sources. For this purpose, three advanced approaches, namely, remote sensing by geographic information system (GIS), optimization, and weighted decision-making are employed. Moreover, a sensitivity analysis is conducted to evaluate the influence of land prices, nominal capacity, and relative importance of technical and economic indicators in decision-making. Iran is chosen as the case study because it has significant solar and wind energy potential and also has serious energy, economic, and environmental issues. The findings indicate an optimal land price of $200 per m2, a hub nominal capacity of 50 MW, and an equitable weighting of objective functions for the case study. The average energy generation, levelized cost of energy (LCOE), PV ratio, and land utilization are projected to reach 288.5 GWh.year-1, 0.32 $.(kWh)-1, 4.15%, and 2362334 m2, respectively when the optimal procedure is applied. The sensitivity study also shows that relative to PV, the contribution of wind turbines increases with plant size.
Unsupervised industrial anomaly detection with diffusion models

Due to the limitations of autoencoders and generative adversarial networks, the performance of reconstruction-based unsupervised image anomaly detection methods are not satisfactory. In this paper, we aim to explore the potential of a more powerful generative model, the diffusion model, in the anomaly detection problem. Specifically, we design a Reconstructed Diffusion Models (RecDMs) based on conditional denoising diffusion implicit models for image reconstruction. To eliminate the stochastic nature of the generation process, our key idea is to use a learnable encoder to extract meaningful semantic representations, which are then used as signal conditions in an iterative denoising process to guide the model in recovering the image, while avoiding falling into an ”identical shortcut” to meaningless image reconstruction. To accurately locate anomaly regions, we introduce a discriminative network to obtain the pixel-level anomaly segmentation map based on the reconstructed image. Our experiments demonstrate the effectiveness of the proposed method, achieving a new state-of-the-art image-level AUC score of 98.1% and a pixel-level AUC score of 94.6% on the MVTec AD dataset, among all reconstruction-based methods. We also show the significant potential and promising future of our method on the challenging real-world dataset, the CHL AD dataset.
Less payment and higher efficiency: A verifiable, fair and forward-secure range query scheme using blockchain

Searchable symmetric encryption (SSE) has recently been under the spotlight in a cloud data storage system due to its high efficiency. SSE allows a client to outsource his private data while maintaining data searchability. To ensure the correctness of query results, verifiable SSE has attracted a lot of attention from both academia and industry to enable reliable encrypted search over the untrusted cloud server. However, most traditional verifiable SSE schemes focus on point queries instead of range queries. Moreover, no effective countermeasures are available to prevent clients from maliciously rejecting the correct result for denying the payment. In this paper, we take the first step to study verifiable range queries with fairness and forward security. First, to support efficient range query over numerical values, we propose Prefix Tree-like Keyword Set (PTKS), a novel data structure to largely reduce the number of search tokens. Specifically, the number of search tokens is up to a logarithmic value (e.g., 7) of the query range width (e.g., [0, 1000]) with PTKS. Then, we propose a double-layer verification mechanism including client-side verification and on-chain verification via the smart contract on the blockchain to achieve cost-effective fair verification. In addition, our range query scheme supports forward-secure update operations. Based on these, we propose a blockchain-based verifiable, fair and forward-secure range query scheme. Finally, extensive experiments demonstrate the efficiency of our scheme.
Supervised feature compression based on counterfactual analysis

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, an optimal Decision Tree can be trained that resembles the black-box model, but that is more interpretable and compact. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity.
A crowdsource based framework for Bengali scene text data collection and detection

Scene texts are the type of texts that we see in our surroundings when we are outside. They have custom fonts, sizes, shapes and colors that are often different from the fonts or styles typically found in documents. Scene text recognition has grown in popularity due to its significance in the development of smarter automated systems. Despite the fact that numerous types of research on scene text detection and recognition have been conducted in different languages, similar work on the Bengali language is limited by the lack of large-scale usable datasets. In this paper, we propose a multipurpose system for Bengali Scene text that allows for easy dataset collection via crowdsourcing as well as annotation, classification and detection tasks to be performed on the same platform. It comes with an Android-based application that can be used to capture or load images from the device, select Bengali texts over the images and annotate them. The images and labels are stored in the cloud. A Python-based script is used to perform real-time data processing, analysis, and detection tasks. The proposed system has also been tested on a crowdsourced Bengali scene text dataset collected by this system. The classification model achieved an accuracy score of 97%, and the detection model achieved a Mean Average Precision score of 92 on the on these crowd contributed dataset.
Applications of Remote Sensing and GIS in Mineral Exploration

Abstract: Mineral exploration is a task that we need to approach with maximum information. Missing out on rare metals and minerals can easily occur, and the process of searching for them is a costly risk. That is one of the reasons for remote sensing in mineral exploration being so important. Remote sensing can be used to measure, variations in acoustic wave distributions, force distributions and also electromagnetic energy distributions. The latest progress in the field of remote sensing and origin of new computer software such as Geographical Information System (GIS), ENVIS (Environmental Information Software) have transformed the world and made life much easier for mineral explorers.
Potential Risks of Cloud Computing in Financial Institutions in Tanzania: Perspectives from CRDB Bank Plc

The adoption of cloud computing introduces a range of potential risks that financial institutions must navigate with prudence. Cloud service providers are entrusted with valuable customer information, and any compromise could have severe consequences, including financial losses and reputational damage. The main objective of this research was to assess the potential risks of cloud computing in financial institutions in Tanzania. This is done in the context of CRDB bank. The research employed a mixed methods approach, incorporating both quantitative and qualitative data collection methods. The data was acquired through questionnaires, specifically targeting the employee population of CRB bank. The data underwent quantitative analysis. The research sampled population is 201 respondents from ICT, legal and procurement departments at the financial institution. Cloud computing poses hazards that financial organizations must carefully manage. Security of sensitive financial data comes first. Any compromise of cloud service providers' client data could result in financial losses and reputational damage. Data privacy risks occur as legislative contexts change. Cross-border cloud services can challenge data sovereignty and local legislation. Another crucial factor is operational continuity. Financial institutions depend on uninterrupted service, putting them exposed to cloud service provider outages and technical issues. Maintaining financial services and client satisfaction are crucial. The regulatory compliance challenge is unique. Cloud computing requires vigilance in local and international legal systems. To retain financial ecosystem confidence, financial institutions must ensure their cloud-based solutions meet industry standards and laws. The study stressed the importance of a holistic strategy to cloud computing in financial institutions like CRDB Bank PLC. Cloud technology has many benefits, but stakeholders must be cautious and implement risk management and mitigation strategies. The conclusions of this study can help CRDB Bank PLC and other Tanzanian financial institutions make educated cloud technology implementation decisions. These decisions must prioritize financial system security, privacy, and resilience. The results also highlight the need for financial industry-regulatory cooperation to keep the regulatory framework up to date with technology.