Reviewerly/exHarmony huggingface dataset:
ECiS: Encryption prior to compression for digital image security with reduced memory

Digital images are a popular source of information and play an important role in several applications. However, the large amount of available data in the form of images causes serious security concerns. Although digital images are widely available, the storage of these images requires a large amount of data. Compression is an efficient way to reduce image size and efficiently utilize network resources. In this study, a joint encryption and compression technique , namely, ECiS, is developed to solve the two major issues with digital images outlined above. To achieve high security, we encrypt an image by using DNA, SHA-256, and a chaotic-based encryption technique . To reduce the bandwidth or storage space demand, we compress the encrypted image by using a compression technique based on zero memory set partitioned embedded block (ZM-SPECK). The strength of our ECiS technique is performing the analysis by applying several standard tests, including key analysis, statistical analysis, differential analysis, and time cost evaluation on a USC-SIPI dataset. Furthermore, the extensive evaluations on the dataset demonstrate that the proposed technique is secure and has a lower memory and encryption overhead than similar techniques.
Automatic Performance Tuning for Distributed Data Stream Processing Systems

Distributed data stream processing systems (DSPSs) such as Storm, Flink, and Spark Streaming are now routinely used to process continuous data streams in (near) real-time. However, achieving the low latency and high throughput demanded by today's streaming applications can be a daunting task, especially since the performance of DSPSs highly depends on a large number of system parameters that control load balancing, degree of parallelism, buffer sizes, and various other aspects of system execution. This tutorial offers a comprehensive review of the state-of-the-art automatic performance tuning approaches that have been proposed in recent years. The approaches are organized into five main categories based on their methodologies and features: cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. The categories of approaches will be analyzed in depth and compared to each other, exposing their various strengths and weaknesses. Finally, we will identify several open research problems and challenges related to automatic performance tuning for DSPSs.
Fast Adaptive Similarity Search through Variance-Aware Quantization

With the explosive growth of high-dimensional data, approximate methods emerge as promising solutions for nearest neighbor search. Among alternatives, quantization methods have gained attention due to the fast query responses and the low encoding and storage costs. Quantization methods decompose data dimensions into non-overlapping subspaces and encode data using a different dictionary per subspace. The state-of-the-art approach assigns dictionary sizes uniformly across subspaces while attempting to balance the relative importance of subspaces. Unfortunately, a uniform balance is not always achievable and may lead to unsatisfactory performance. Similarly, hardware-accelerated quantization methods may sacrifice accuracy to speed up the query execution. We propose a Variance-Aware Quantization (VAQ) method to encode data by intelligently adapting dictionary sizes to subspaces to alleviate these significant drawbacks. VAQ exploits intrinsic dimensionality reduction properties to derive the subspaces and only partially balances the importance of subspaces. Then, VAQ solves a constrained optimization problem to assign dictionary sizes proportionally to the importance of each subspace. In addition, VAQ accelerates the query execution by skipping data and subspaces through a hardware-oblivious algorithmic solution. To demonstrate the robustness of VAQ, we perform an extensive evaluation against quantization, hashing, and indexing methods using five large-scale benchmarking datasets. VAQ significantly outperforms the strongest hashing and quantization methods in accuracy while achieving up to 5× speedup. Compared to the fastest but less accurate hardware-accelerated method, VAQ achieves a speedup@recall performance up to 14×. Importantly, a rigorous statistical comparison using over one hundred datasets reveals that VAQ significantly outperforms rival methods even with a half budget. Notably, VAQ's simple data skipping solution achieves competitive or better performance against index-based methods, highlighting the need for new indices for quantization methods.
Capture, Analyze, Diagnose: Realizability Checking Of Requirements in FRET

Abstract Requirements formalization has become increasingly popular in industrial settings as an effort to disambiguate designs and optimize development time and costs for critical system components. Formal requirements elicitation also enables the employment of analysis tools to prove important properties, such as consistency and realizability. In this paper, we present the realizability analysis framework that we developed as part of the Formal Requirements Elicitation Tool ( FRET ). Our framework prioritizes usability, and employs state-of-the-art analysis algorithms that support infinite theories. We demonstrate the workflow for realizability checking, showcase the diagnosis process that supports visualization of conflicts between requirements and simulation of counterexamples, and discuss results from industrial-level case studies.
Keeping synthetic patients on track: feedback mechanisms to mitigate performance drift in longitudinal health data simulation

Synthetic data are increasingly relied upon to share electronic health record (EHR) data while maintaining patient privacy. Current simulation methods can generate longitudinal data, but the results are unreliable for several reasons. First, the synthetic data drifts from the real data distribution over time. Second, the typical approach to quality assessment, which is based on the extent to which real records can be distinguished from synthetic records using a critic model, often fails to recognize poor simulation results. In this article, we introduce a longitudinal simulation framework, called LS-EHR, which addresses these issues.LS-EHR enhances simulation through conditional fuzzing and regularization, rejection sampling, and prior knowledge embedding. We compare LS-EHR to the state-of-the-art using data from 60 000 EHRs from Vanderbilt University Medical Center (VUMC) and the All of Us Research Program. We assess discrimination between real and synthetic data over time. We evaluate the generation process and critic model using the area under the receiver operating characteristic curve (AUROC). For the critic, a higher value indicates a more robust model for quality assessment. For the generation process, a lower value indicates better synthetic data quality.The LS-EHR critic improves discrimination AUROC from 0.655 to 0.909 and 0.692 to 0.918 for VUMC and All of Us data, respectively. By using the new critic, the LS-EHR generation model reduces the AUROC from 0.909 to 0.758 and 0.918 to 0.806.LS-EHR can substantially improve the usability of simulated longitudinal EHR data.
Analyzing diversification benefits of cryptocurrencies through backfill simulation

• Perform long-term analysis on diversification effects of cryptocurrencies • Simulation is performed with backfilled returns of cryptocurrencies • Risk factors of cryptocurrencies are identified for modeling cryptocurrency returns • Maximum likelihood estimation is used for simulating historical returns • Including cryptocurrencies appear to improve portfolio return but also increase risk The cryptocurrency market provides an interesting diversification opportunity for asset allocation due to its fundamental differences compared to traditional asset classes. Even though the cryptocurrency market experienced a surge until 2017, the market has relatively stabilized since the bubble along with increased participation from institutional investors. Instead of only performing analysis for the recent years, which is short for testing asset allocation benefits, we focus on the post-bubble market condition but backfill cryptocurrency returns into the past in order to analyze a longer investment horizon. In our backfill simulation, investment in cryptocurrencies show higher return but mixed results in terms of portfolio efficiency for risk-based optimized portfolios. Even though risk-based models allocate a small portion in cryptocurrencies, its high volatility limits diversification benefits since even small allocations often lead to higher portfolio risk.
An empirical study of question discussions on Stack Overflow

Stack Overflow provides a means for developers to exchange knowledge. While much previous research on Stack Overflow has focused on questions and answers (Q&A), recent work has shown that discussions in comments also contain rich information. On Stack Overflow, discussions through comments and chat rooms can be tied to questions or answers. In this paper, we conduct an empirical study that focuses on the nature of question discussions. We observe that: (1) Question discussions occur at all phases of the Q&A process, with most beginning before the first answer is received. (2) Both askers and answerers actively participate in question discussions; the likelihood of their participation increases as the number of comments increases. (3) There is a strong correlation between the number of question comments and the question answering time (i.e., more discussed questions receive answers more slowly). Our findings suggest that question discussions contain a rich trove of data that is integral to the Q&A processes on Stack Overflow. We further suggest how future research can leverage the information in question discussions, along with the commonly studied Q&A information.
Hybrid Path Planning Model for Multiple Robots Considering Obstacle Avoidance

To solve the problem that the single robot task execution capability is not enough to meet the whole handling task demand under complex conditions, the hybrid path planning models such as multi-robot path planning and formation cooperative control considering obstacle avoidance are studied. Firstly, for the robot global path finding problem, on the basis of the construction for a robot working environment model based on the geometric map model building method, an improved particle swarm algorithm-based global path planning model is proposed to solve the problems of low robot path planning solution efficiency and easy to fall into local optimal solutions. Secondly, for the multi-robot cooperative formation control and obstacle avoidance and inter-robot collision avoidance problems, a multi-robot formation local path planning model based on the improved artificial potential field method is constructed, a simulated annealing algorithm is introduced to optimize the traditional artificial potential field method, and a multi-robot formation control strategy, robot obstacle avoidance, and inter-robot collision avoidance methods are designed in combination with the pilot-following method to improve the robot formation path exploration The proposed method can improve the path exploration capability and handling efficiency of robot formation. Finally, the global path planning model of the robot based on the improved particle swarm algorithm is simulated and analyzed using Matlab 7.0 to verify the outstanding performance of the model in pathfinding capability. Then the local path planning model of multi-robot formation based on the improved artificial potential field is simulated and analyzed to verify the improved algorithm has good path planning as well as obstacle avoidance performance. The hybrid path planning model is applied to a real case and simulated, and the results show that the improved algorithm improves the exploration capability of the robot formation, effectively avoids obstacles, and verifies its reliability and superiority in the hybrid path planning process.
An exploratory assessment of the effectiveness of geomasking methods on privacy protection and analytical accuracy for individual-level geospatial data

ABSTRACTABSTRACTThe widespread use of personal geospatial data raises serious geoprivacy concerns for sharing these data, which may limit the reproducibility of research findings. One widely used method for securely sharing confidential geospatial information is applying geomasking techniques before sharing. Geomasking may reduce the usability of the data. Thus, researchers need to strike a balance between privacy protection and analytical accuracy. Although many geomasking methods have been proposed, there is no systematic evaluation of these methods or guidance on which method to use and how to apply it properly. To address this gap, we evaluate eight geomasking methods with simulated geospatial data with various spatial patterns and investigate their performance on privacy protection and analytical accuracy. We propose not only a set of preliminary guidelines for applying the proper geomasking methods when using different spatial analysis methods but also an evaluation framework for assessing geomasking methods for other spatial analysis methods. The findings will help researchers to properly apply geomasking for sensitive geospatial data and thus promote data sharing and interdisciplinary collaboration while protecting personal geoprivacy.KEYWORDS: Geomaskinggeoprivacydata sharingdata confidentialityanalytical accuracy AcknowledgmentsThe authors would like to thank the editors and anonymous reviewers for their helpful comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available with the identifier at https://doi.org/10.17605/OSF.IO/HXD5W and subject to Creative Commons license (CC-By Attribution 4.0 International).Supplemental dataSupplemental data for this article can be accessed here.Additional informationFundingThis research was supported by the U.S. National Science Foundation (Grant No. BCS-2025783) and the University of Toronto (UTM Start-up Funding).
Trainify: A CEGAR-Driven Training and Verification Framework for Safe Deep Reinforcement Learning

Abstract Deep Reinforcement Learning (DRL) has demonstrated its strength in developing intelligent systems. These systems shall be formally guaranteed to be trustworthy when applied to safety-critical domains, which is typically achieved by formal verification performed after training. This train-then-verify process has two limits: (i) trained systems are difficult to formally verify due to their continuous and infinite state space and inexplicable AI components ( i.e. , deep neural networks), and (ii) the ex post facto detection of bugs increases both the time- and money-wise cost of training and deployment. In this paper, we propose a novel verification-in-the-loop training framework called Trainify for developing safe DRL systems driven by counterexample-guided abstraction and refinement. Specifically, Trainify trains a DRL system on a finite set of coarsely abstracted but efficiently verifiable state spaces. When verification fails, we refine the abstraction based on returned counterexamples and train again on the finer abstract states. The process is iterated until all predefined properties are verified against the trained system. We demonstrate the effectiveness of our framework on six classic control systems. The experimental results show that our framework yields more reliable DRL systems with provable guarantees without sacrificing system performance such as cumulative reward and robustness than conventional DRL approaches.
Comparison of Pretraining Models and Strategies for Health-Related Social Media Text Classification

Pretrained contextual language models proposed in the recent past have been reported to achieve state-of-the-art performances in many natural language processing (NLP) tasks, including those involving health-related social media data. We sought to evaluate the effectiveness of different pretrained transformer-based models for social media-based health-related text classification tasks. An additional objective was to explore and propose effective pretraining strategies to improve machine learning performance on such datasets and tasks. We benchmarked six transformer-based models that were pretrained with texts from different domains and sources-BERT, RoBERTa, BERTweet, TwitterBERT, BioClinical_BERT, and BioBERT-on 22 social media-based health-related text classification tasks. For the top-performing models, we explored the possibility of further boosting performance by comparing several pretraining strategies: domain-adaptive pretraining (DAPT), source-adaptive pretraining (SAPT), and a novel approach called topic specific pretraining (TSPT). We also attempted to interpret the impacts of distinct pretraining strategies by visualizing document-level embeddings at different stages of the training process. RoBERTa outperformed BERTweet on most tasks, and better than others. BERT, TwitterBERT, BioClinical_BERT and BioBERT consistently underperformed. For pretraining strategies, SAPT performed better or comparable to the off-the-shelf models, and significantly outperformed DAPT. SAPT + TSPT showed consistently high performance, with statistically significant improvement in three tasks. Our findings demonstrate that RoBERTa and BERTweet are excellent off-the-shelf models for health-related social media text classification, and extended pretraining using SAPT and TSPT can further improve performance.
Decoding China’s COVID-19 Health Code Apps: The Legal Challenges

Heath code apps, along with robust testing, isolation, and the care of cases, are a vital strategy for containing the spread of the COVID-19 outbreak in China. They have remained stable and consistent, allowing China to extensively restore its social and economic development. However, the ethical and legal boundaries of deploying health code apps for disease surveillance and control purposes are unclear, and a rapidly evolving debate has emerged around the promises and risks of their fast promotion. The article outlines the legal challenges by applying the core values of the Personal Information Protection Law (PIPL), the fundamental law for personal information protection in China, into the context of the nationwide use of health code apps. It elaborates on the balance between the demands for upholding individuals' rights to the security of their personal information and those for public access to such information to prevent the spread of infectious diseases. It identifies the current gaps in addressing personal information harms during the use of the apps, particularly with regard to user consent, transparency, necessity, storage duration, and security safeguards.
Classification of breast cancer using a manta-ray foraging optimized transfer learning framework

Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework's adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.
Hardware Accelerators for Real-Time Face Recognition: A Survey

Real-time face recognition has been of great interest in the last decade due to its wide and variant critical applications which include biometrics, security in public places, and identification in login systems. This has encouraged researchers to design fast and accurate embedded and portable systems that are capable to detect and recognize a large number of faces at almost video frame rate. Due to the increasing volume of reference faces, traditional general purpose computing engines such as the ones based on Intel&#x2019;s Pentium processors have shown not to be adequate and various dedicated hardware accelerators based on either Graphical Processing Unit (GPU), Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuit (ASIC), or even multi-core Central Processing Units (CPU) have emerged. Earlier published review papers on face detection/recognition have discussed face detection and face recognition algorithms enhancement that improve the detection accuracy. Nevertheless, none of them has reviewed the hardware accelerators used for this application. Accordingly, this paper aims to provide a comprehensive review of the most recent face recognition algorithms and associated embedded hardware systems targeting real-time performance. A detailed comparison between neural network and non-neural network-based algorithms in terms of accuracy and processing time is provided. Discussions on their suitability to be implemented into parallel hardware architectures such as Single Instruction Multiple Thread (SIMT) or Single Instruction Multiple Data (SIMD) is also discussed.
Federated Learning Approach Decouples Clients From Training a Local Model and With the Communication With the Server

Traffic sign recognition and autonomous vehicles computing are a few of the innovative applications which are emerging in the domain of mobile edge computing. Distributed machine learning in the form of Federated Learning (FL) has been applied to mobile edge computing through a range of methodologies and techniques for intelligent feature classification approaches. The challenges that research on such FL methods is facing is twofold: identify an optimal distributed architecture and algorithm components to each side to meet the demand of heavy data processing, and enhance the algorithm components with heuristics that fit to the problem domain and optimize the key parameters of the algorithms. In this prospect, we present a Federated Learning implementation based on a neural network architecture with emphasis to traffic sign image recognition. Our benchmark was tested with two FL strategies seeking an optimal performance model and in reference to a corresponding data set. We present the results of this work while we define the scope of future improvements to our model.