Reviewerly/exHarmony huggingface dataset:
Image Semantic Segmentation Fusion of Edge Detection and AFF Attention Mechanism

Deep learning has been widely used in various fields because of its accuracy and efficiency. At present, the improvement of image semantic segmentation accuracy has become the area of most concern. In terms of increasing accuracy, improved semantic segmentation models have attracted more attention. In this paper, a hybrid model is proposed to solve the problems of edge splitting and small objects disappearing from complex scene images. The hybrid model consists of three parts: (1) an improved HED network, (2) an improved PSP-Net, (3) an AFF attention mechanism. Continuous edges can be obtained by combining the improved HED network with an improved PSP-Net. The AFF attention mechanism can improve the segmentation effect of small target objects by enhancing its response recognition ability for specific semantic scenes. The experiments were carried out on Cityspaces, SIFT Flow, NYU-V2 and CamVid datasets, and the experimental results show that the segmentation accuracy of our method is improved by 2% for small target objects, and by 3% for scenes with complex object edges.
Village fund cash credit direct assistance recipient decision support system using the Simple Multi Attribute Rating Technique (SMART) method

Cash Direct Assistance is a program from the government due to the covid-19 pandemic conditions where with this Cash Direct Assistance is expected to be able to help restore the economy of people affected by government policies to stay at home and maintain their respective health. In determining the recipients of this assistance, the smallest government, namely the village government, still uses manual selection according to the criteria so that it requires a lot of time and high accuracy and a sophisticated application program is needed to overcome it. So in the design of the BLT Dana Desa application, the author designed using the PHP programming language with a decision-making system based on the SMART method where this application system was designed based on changing the processing system in the manual assessment of the recipient's family who was directly selected, now the author changes the assessment system for prospective recipients of cash direct assistance based on an application where this application will facilitate the determination of eligible families get assistance through a government program, namely direct cash assistance for village funds.
Cost-Effective and Energy-Aware Resource Allocation in Cloud Data Centers

Cloud computing supports the fast expansion of data and computer centers; therefore, energy and load balancing are vital concerns. The growing popularity of cloud computing has raised power usage and network costs. Frequent calls for computational resources may cause system instability; further, load balancing in the host requires migrating virtual machines (VM) from overloaded to underloaded hosts, which affects energy usage. The proposed cost-efficient whale optimization algorithm for virtual machine (CEWOAVM) technique helps to more effectively place migrating virtual machines. CEWOAVM optimizes system resources such as CPU, storage, and memory. This study proposes energy-aware virtual machine migration with the use of the WOA algorithm for dynamic, cost-effective cloud data centers in order to solve this problem. The experimental results showed that the proposed algorithm saved 18.6%, 27.08%, and 36.3% energy when compared with the PSOCM, RAPSO-VMP, and DTH-MF algorithms, respectively. It also showed 12.68%, 18.7%, and 27.9% improvements for the number of virtual machine migrations and 14.4%, 17.8%, and 23.8% reduction in SLA violation, respectively.
A Weak SNR Signal Extraction Method for Near-Bit Attitude Parameters Based on DWT

In practice, the near-bit signals are usually accompanied by noises generated by drilling tool vibration and high-speed rotary. This study introduces discrete wavelet transform to a well drilling area and presents a new weal signal extraction algorithm, which can eliminate the vibration and rotary noises effectively and obtain the useful gravity attitude signals based on the discrete wavelet transform through a hard threshold method. The effectiveness and usefulness of our method are verified via Matlab simulation and actual drilling data.
An online intelligent electronic medical record system via speech recognition

Traditional electronic medical record systems in hospitals rely on healthcare workers to manually enter patient information, resulting in healthcare workers having to spend a significant amount of time each day filling out electronic medical records. This inefficient interaction seriously affects the communication between doctors and patients and reduces the speed at which doctors can diagnose patients’ conditions. The rapid development of deep learning–based speech recognition technology promises to improve this situation. In this work, we build an online electronic medical record system based on speech interaction. The system integrates a medical linguistic knowledge base, a specialized language model, a personalized acoustic model, and a fault-tolerance mechanism. Hence, we propose and develop an advanced electronic medical record system approach with multi-accent adaptive technology for avoiding the mistakes caused by accents, and it improves the accuracy of speech recognition obviously. For testing the proposed speech recognition electronic medical record system, we construct medical speech recognition data sets using audio and electronic medical records from real medical environments. On the data sets from real clinical scenarios, our proposed algorithm significantly outperforms other machine learning algorithms. Furthermore, compared to traditional electronic medical record systems that rely on keyboard inputs, our system is much more efficient, and its accuracy rate increases with the increasing online time of the proposed system. Our results show that the proposed electronic medical record system is expected to revolutionize the traditional working approach of clinical departments, and it serves more efficient in clinics with low time consumption compared with traditional electronic medical record systems depending on keyboard inputs, which has less recording mistakes and lows down the time consumption in modification of medical recordings; due to the proposed speech recognition electronic medical record system is built on knowledge database of medical terms, so it has a good generalized application and adaption in the clinical scenarios for hospitals.
Classifying changes to models via changeset metrics

Automated classification of software changes can help to understand the reason why a change was made, guide the adoption of quality control practices as bugfix trends are observed, and cluster related sets of changes for similar management of the changed artifacts, thereby reducing maintenance efforts. A number of change classification techniques have been developed based on information extracted from the change author, change message, change size, or changed file. However, most of these approaches have targeted textual general-purpose programming languages. Furthermore, some of these approaches are computationally expensive because they often require the analysis of the whole source code, while others rely on the developers' ability to describe a commit via a well-written message. In this paper, we present an approach to classify changes to models into the appropriate maintenance type via a set of metrics that are extracted from the version history of models. We developed seven metrics related to changes applied to models and model elements. We then conducted an empirical study involving 10 classifiers to determine the classifier that offers the best performance for automating the change classification process. These classifiers were trained on over 300 changesets extracted from the version history of 28 Simulink repositories. The results of the study show that the Random Forest classifier offers the best performance. The Random Forest classifier has also been evaluated by comparing its results with labels extracted from the discussions within the issues reported in a similar time frame. The evaluation results show that the Random Forest classifier is able to achieve an F-1 score of 0.83, thereby showing its ability to classify changes into the appropriate categories intended by the original developers.
Telepathology: A Transforming Practice for the Efficient, Safe, and Best Patient Care at the Regional Veteran Affairs Medical Center

Abstract Introduction/Objective Telepathology is the practice of remote pathology using telecommunication links to enable transmission of digital pathology images for primary diagnosis, quality assurance, education, research, or second opinion diagnoses. Once properly validated and implemented, telepathology has great potential to transform the practice of pathology by increasing productivity, savings in costs, improving the quality of care, and participating in innovations to improve future care. These improvements would help ensure equity throughout all remote sites that may be served by the central regional Veteran Affairs Medical Center (VAMC), even with the long-term declining number of pathologists in the country. The validation and implementation of telepathology has been further refined by recent College of American Pathologist (CAP) and American Telemedicine Association (ATA) clinical guidelines and recommendations for the validation and implementation of telepathology, but otherwise the literature is sparse for recent telepathology validation and implementation after the promulgation of these guidelines and recommendations in the Veteran Affairs setting. Methods/Case Report An initial narrative literature review was conducted to ascertain the current clinical guidelines and practices of telepathology to ensure that practices during validation and implementation meet and exceed the standard of care. Furthermore, meetings including the appropriate stakeholders in information technology, privacy, facilities, and pathology were held. The telepathology platform from Leica Biosystems Aperio (Deer Park IL) was brought in house at the regional VAMC’s pathology department for validation and future implementation. Results (if a Case Study enter NA) Planning included following all appropriate guidelines from ATA and CAP regarding telepathology to ensure that the program met the standard of care. The arrival of the instrument was installed successfully within the pathology. Conclusion The installation of telepathology for use at a regional VAMC represents an important step to transform pathology to increase access, including even remote access, to pathology resources and subspecialty expertise throughout the Veteran Affairs Medical System.
Investigating the understandability of XAI methods for enhanced user experience: When Bayesian network users became detectives

In the medical domain, the uptake of an AI tool crucially depends on whether clinicians are confident that they understand the tool. Bayesian networks are popular AI models in the medical domain, yet, explaining predictions from Bayesian networks to physicians and patients is non-trivial. Various explanation methods for Bayesian network inference have appeared in literature, focusing on different aspects of the underlying reasoning. While there has been a lot of technical research, there is little known about the actual user experience of such methods. In this paper, we present results of a study in which four different explanation approaches were evaluated through a survey by questioning a group of human participants on their perceived understanding in order to gain insights about their user experience.
Constraints on parameter choices for successful time-series prediction with echo-state networks

Abstract Echo-state networks are simple models of discrete dynamical systems driven by a time series. By selecting network parameters such that the dynamics of the network is contractive, characterized by a negative maximal Lyapunov exponent, the network may synchronize with the driving signal. Exploiting this synchronization, the echo-state network may be trained to autonomously reproduce the input dynamics, enabling time-series prediction. However, while synchronization is a necessary condition for prediction, it is not sufficient. Here, we study what other conditions are necessary for successful time-series prediction. We identify two key parameters for prediction performance, and conduct a parameter sweep to find regions where prediction is successful. These regions differ significantly depending on whether full or partial phase space information about the input is provided to the network during training. We explain how these regions emerge.
Three-dimensional shape measurement method based on composite cyclic phase coding

Phase coding is widely used in 3D measurement due to its good anti-interference and robustness. However, the measurement accuracy is affected by the limitation of the number of codewords. To solve this problem, we propose a 3D shape measurement method based on composite cyclic phase coding. The traditional phase coding is quantized cyclic without adding extra patterns, further adopting composite coding, using the composite cyclic phase coding grayscale values to distinguish the same cyclic codewords, and finally integrating them into a new fringe order sequentially for phase unwrapping to achieve effective expansion of codewords. The related experimental results show that the proposed method stably achieves high accuracy 3D reconstruction, which overcomes the misjudgment of codewords caused by traditional phase coding under high-frequency fringes due to system nonlinearity and noise. Meanwhile, compared with the improved phase coding method of temporal domain combined with spatial domain information, such as the method of quantized phase coding and connected region labeling, it can effectively avoid the phenomenon of error propagation, with high robustness and low algorithm complexity.
Usability Testing on the Simponik Website using the System Usability Scale (SUS)

SIMPONIK is one of the websites developed by the Furniture and Wood Processing Industry Polytechnic to answer the challenges during online learning due to covid 19 and the large number of guests visiting because it is located in an Industrial Estate. To improve the website's service and user experience, there must be testing of the website's usability. The purpose of this study is to test the usability of the SIMPONIK website as a basis for improving the appearance and services available on the website. This study uses the System Usability Scale (SUS) method by distributing questionnaires to 100 respondents. From the results of this study, the SUS score was 76,025, which means the SUS category is acceptable (71-100). Although the results are categorized as acceptable, there must be improvements must be made, especially in terms of practicality and convenience for users.
Aqua: Leveraging Citizen Science to Enhance Whale-Watching Activities and Promote Marine-Biodiversity Awareness

Life-supporting ecosystems are facing impending destruction. The human–computer interaction (HCI) community must rethink how to design technological interventions that reconcile concepts and theories for ecological computing. Proponents of sustainable HCI have pushed for tools and systems that aim to decenter the human in a shift toward posthuman design—a theoretical approach that challenges the assumption that only humans are stakeholders of technology as it increasingly shapes the future. Building on the iconic value of whales and the economic impact of whale watching as a form of ecotourism, we developed Aqua, a digital tool that leverages the potential of citizen science to engage tourists in marine-biodiversity awareness and conservation. This manuscript is advancing the field of sustainable HCI and tourism applications in two ways: first, we deliver an artifact contribution by designing and implementing a digital tool to enhance whale-watching activities. Second, we offer an empirical research contribution through observation and data gathering while comparing participants’ experiences of a whale-watching trip with and without the digital tool. Finally, preliminary insights are provided to inform the design of future digital tools aimed at promoting environmental conservation and citizen-science approaches among tourists. This work presents progression in understanding and informs the design of digital tools to engage tourists in novel and sustainable experiences.
Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems

Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based iterated local search (EA-ILS) hyper-heuristic that applies a novel mutation operator to control the selection of perturbative low-level heuristics in searching for optimal sequences for performance improvement. The algorithm was compared to existing ones in the hyper-heuristics flexible (HyFlex) framework to demonstrate its performance across the problem domains of knapsack, quadratic assignment, and maximum cut. The comparative results have shown that the EA-ILS hyper-heuristic can obtain the best median objective function values on 22 out of 30 instances in the HyFlex framework. Moreover, it has achieved superiority in its generalization capability when compared to the reported top-performing hyper-heuristic algorithms.
A two-segment LSTM based data center temperature prediction model

Nowadays, data centers are critical infrastructure for the information industry. Thermal security is one of the most concerning problems of the data center efficiently providing service. The temperature prediction method is an effective way, which overcomes the lagging of the feedback control and rewards a high prediction accuracy. While the current LSTM based prediction methods are limited in accuracy and restricted in scalability due to the lack of knowledge of physical properties and consideration of time constant differences of features. To address this, we propose a data center temperature prediction model with two-segment LSTM for prediction separately for IT equipment load and other heat-related variables with different time constants. The model takes into account the physical properties of the equipment and achieves higher prediction accuracy. The experimental results show that the prediction accuracy of our method is 27.27% higher than the state-of-art single segment LSTM method.
Fusion steady‐state robust filtering for uncertain multisensor networked systems with application to autoregressive moving average signal estimates

Abstract This article is concerned with the centralized fusion (CF) robust steady‐state Kalman filtering problem for a class of multisensor networked systems with mixed uncertainties including state‐dependent and noise‐dependent multiplicative noises, missing measurements, packet dropouts, and uncertain noise variances. By using a model transformation approach, the initial multisensor system under study is transformed into a multi‐model multisensor system only with uncertain noise variances. By introducing an augmented state vector, the CF system is obtained. In the light of the minimax robust estimation method, and based on the worst‐case fusion system with conservative upper bounds of uncertain noise variances, the CF robust steady‐state Kalman estimators (predictor, filter, and smoother) are proposed in a unified form. By means of the permutation matrices and Lyapunov equation method, the robustness of CF estimators is proved. The accuracy relations among the robust local and fusion steady‐state estimators are proved. An example applied to autoregressive moving average signal estimation is put forward, and the robust CF steady‐state signal estimators are proposed. Simulation experiment shows the correctness of the proposed approach.
Modular Encryption Standard to Improve Security of Health Information in Mobile Cloud Computing

Despite the numerous and visible benefits of mobile cloud computing (MCC) in medical care, its advancement is hampered by protection and security concerns. Such difficulties necessitate the strongest consideration in order to comprehend their entire scope and efficient application. There is a need for health information to be distributed globally, regionally, and locally. To completely profit of the wellbeing administrations, it is significant to set up the requested security rehearses for the counteraction of safety breaks and weaknesses. As a result, this investigation is being considered to provide requirement-based health data security using the modular encryption standard (MES) in light of the tiered displaying of security efforts. In terms of better execution and helpful subjective security assuring methods, the offered work outperforms other commonly used calculations against the security of health data in the MCC atmosphere, according to the presentation assessment.
A large‐scale empirical study of low‐level function use in Ethereum smart contracts and automated replacement

Abstract The Ethereum blockchain stores and executes complex logic via smart contracts written in Solidity, a high‐level programming language. The Solidity language (in its early versions) provides features to exercise fine‐grained control over smart contracts, whose usage is discouraged by later‐released Solidity documentation, but nonetheless supported in later versions for backward compatibility. We define these features as low‐level functions. However, the high‐volume of transactions and the improper use of low‐level functions lead to security exploits with heavy financial loss. Consequently, the documentation suggests secure alternatives to the use of low‐level functions. In this article, we first perform an empirical study on the use of low‐level functions in Ethereum smart contracts. We study a smart contract dataset consisting of over 2,100,000 real‐world smart contracts. We find that low‐level functions are widely used and that the majority of these uses are gratuitous. We then propose GoHigh, a source‐to‐source transformation tool to eliminate low‐level function‐related vulnerabilities, by replacing low‐level functions with secure alternatives. Our experimental evaluation on the dataset shows that GoHigh successfully replaces all low‐level functions with 4.9% fewer compiler warnings. Further, no unintended side‐effects are introduced in 80% of the contracts, and the remaining 20% are not verifiable due to their external dependency. GoHigh saves more than 5% of the gas cost of the contract. Finally, GoHigh takes 7 s on average per contract.
Cybercrime Through Love Scams: What Women Should Know?

Due to various technological advancements in the modern era, cybercrime has become a global issue. Since the outbreak of the COVID-19 pandemic, most jobs and transactions have been done virtually, compromising our global cyber security. Many people are unaware of this alarming situation leading them to become victims of cybercrime, especially the vulnerable groups of the elderly, children, and women. This conceptual study discusses the scammers' modus operandi for monetary deception or militants’ recruitment through love scams. The discussion then continues with the factors that lead women to become potential victims, from the personality aspects, characteristics, and impacts after being victimised. This study concludes that precautious steps are necessary to avoid becoming a potential victim of cybercrime, and coping with the aftermath is equally important.