Reviewerly/exHarmony huggingface dataset:
Blockchain urbanism: Evolving geographies of libertarian exit and technopolitical failure

Libertarian “exit” imaginaries project new social, political, and economic structures separate from existing institutions in which “sovereign individuals” can opt-in to the governing system that fits their ideals. This paper traces libertarian exit imaginaries through a variety of territorial and technological projects. Demonstrating how these imaginaries evolve, it describes a recent proposal to build a semi-autonomous, blockchain-based smart city in Nevada. Reflecting on these projects, the paper highlights (1) their inevitable failure as they confront reality, (2) their role as spectacle, spreading libertarian ideology, and (3) their real-life impacts on distinct places and communities even when they fail or never materialize.
Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis

Rapid and precise intraoperative diagnosing systems are required for improving surgical outcomes and patient prognosis. Because of the poor quality and time-intensive process of the prevalent frozen section procedure, various intraoperative diagnostic imaging systems have been explored. Microscopy with ultraviolet surface excitation (MUSE) is an inexpensive, maintenance-free, and rapid imaging technique that yields images like thin-sectioned samples without sectioning. However, pathologists find it nearly impossible to assign diagnostic labels to MUSE images of unfixed specimens; thus, AI for intraoperative diagnosis cannot be trained in a supervised learning manner. In this study, we propose a deep-learning pipeline model for lymph node metastasis detection, in which CycleGAN translate MUSE images of unfixed lymph nodes to formalin-fixed paraffin-embedded (FFPE) sample, and diagnostic prediction is performed using deep convolutional neural network trained on FFPE sample images. Our pipeline yielded an average accuracy of 84.6% when using each of the three deep convolutional neural networks, which is a 18.3% increase over the classification-only model without CycleGAN. The modality translation to FFPE sample images using CycleGAN can be applied to various intraoperative diagnostic imaging systems and eliminate the difficulty for pathologists in labeling new modality images in clinical sites. We anticipate our pipeline to be a starting point for accurate rapid intraoperative diagnostic systems for new imaging modalities, leading to healthcare quality improvement.
A survey on energy‐efficient workflow scheduling algorithms in cloud computing

Abstract The advancements in computing and storage capabilities of machines and their fusion with new technologies like the Internet of Thing (IoT), 5G networks, and artificial intelligence, to name a few, has resulted in a paradigm shift in the way computing is done in a cloud environment. In addition, the ever‐increasing user demand for cloud services and resources has resulted in cloud service providers (CSPs) expanding the scale of their data center facilities. This has increased energy consumption leading to more carbon dioxide emission levels. Hence, it becomes all the more important to design scheduling algorithms that optimize the use of cloud resources with minimum energy consumption. This paper surveys state‐of‐the‐art algorithms for scheduling workflow tasks to cloud resources with a focus on reducing energy consumption. For this, we categorize different workflow scheduling algorithms based on the scheduling approaches used and provide an analytical discussion of the algorithms covered in the paper. Further, we provide a detailed classification of different energy‐efficient strategies used by CSPs for energy saving in data centers. Finally, we describe some of the popular real‐world workflow applications as well as highlight important emerging trends and open issues in cloud computing for future research directions.
Human Keypoint Change Detection for Video Violence Detection Based on Cascade Transformer

Human action recognition remains a challenging task in computer vision. We focus on violence detection, a subset of action recognition. In this paper, we present a video violence detection model, which introduced cascaded Transformer for human pose estimation and 3D-CNN for capturing motion present in spatial-temporal dimension. The cascaded Transformer consist of person detecting Transformer and human keypoints detection Transformer. We trained 3D-CNN network to learn spatialtemporal features from human keypoints sequence, and output whether violence is present in video. We analyzed the influence of frame amount and human keypoints connection way on motion feature extraction. The proposed model achieves an accuracy rate of 83.2% on the RWF-2000 validation set and 90.9% on our own campus violent video dataset.
Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning

In games, as in many other domains, design validation and testing is a significant challenge as systems are growing in size and manual testing is becoming infeasible. In this position paper we outline an approach to automated game validation based on an imitation learning technique, and provide an analysis of the potential benefits to automated game testing. The method leverages a data-driven technique, which requires little effort and time and no knowledge of machine learning or programming, that designers can use to efficiently train game testing agents. We evaluate the validity of our claim by conducting a user study with industry experts. The survey results presented in this paper demonstrate the potential of a data-driven approach to reduce effort and enhance the quality of game testing. Moreover, the survey reveals several open challenges. To this end, we analyze the identified challenges and provide a basis for further research and discussion, as well as to help guide the development of imitation learning for game testing.
Summarizing Strategy Card Game AI Competition

This paper concludes five years of AI competitions based on Legends of Code and Magic (LOCM), a small Collectible Card Game (CCG), designed with the goal of supporting research and algorithm development. The game was used in a number of events, including Community Contests on the CodinGame platform, and Strategy Card Game AI Competition at the IEEE Congress on Evolutionary Computation and IEEE Conference on Games. LOCM has been used in a number of publications related to areas such as game tree search algorithms, neural networks, evaluation functions, and CCG deckbuilding. We present the rules of the game, the history of organized competitions, and a listing of the participant and their approaches, as well as some general advice on organizing AI competitions for the research community. Although the COG 2022 edition was announced to be the last one, the game remains available and can be played using an online leaderboard arena.
Regulatory capacity capture: the United Kingdom’s online safety regime

Drawing from 33 elite interviews, this study develops a capacity-based approach to analyse the emergent regulatory system for online safety in the UK. By examining the capacity constellations of government and platform actors, the research reveals critical deficiencies in the UK government's capacity to regulate platforms, presenting challenges to regulation and increasing the
Neural networks memorise personal information from one sample

Deep neural networks (DNNs) have achieved high accuracy in diagnosing multiple diseases/conditions at a large scale. However, a number of concerns have been raised about safeguarding data privacy and algorithmic bias of the neural network models. We demonstrate that unique features (UFs), such as names, IDs, or other patient information can be memorised (and eventually leaked) by neural networks even when it occurs on a single training data sample within the dataset. We explain this memorisation phenomenon by showing that it is more likely to occur when UFs are an instance of a rare concept. We propose methods to identify whether a given model does or does not memorise a given (known) feature. Importantly, our method does not require access to the training data and therefore can be deployed by an external entity. We conclude that memorisation does have implications on model robustness, but it can also pose a risk to the privacy of patients who consent to the use of their data for training models.
Consolidation of Ground Truth Sets for Weakness Detection in Smart Contracts

Smart contracts are small programs on the blockchain that often handle valuable assets. Vulnerabilities in smart contracts can be costly, as time has shown over and over again. Countermeasures are high in demand and include best practice recommendations as well as tools supporting development, program verification, and post-deployment analysis. Many tools focus on detecting the absence or presence of a subset of the known vulnerabilities, delivering results of varying quality. Most comparative tool evaluations resort to selecting a handful of tools and testing them against each other. In the best case, the evaluation is based on a smallish ground truth. For Ethereum, there are commendable efforts by several author groups to manually classify contracts. However, a comprehensive ground truth is still lacking. In this work, we construct a ground truth based on publicly available benchmark sets for Ethereum smart contracts with manually checked ground truth data. We develop a method to unify these sets. Additionally, we devise strategies for matching entries that pertain to the same contract, such that we can determine overlaps and disagreements between the sets and consolidate the disagreements. Finally, we assess the quality of the included ground truth sets. Our work reduces inconsistencies, redundancies, and incompleteness while increasing the number of data points and their heterogeneity.
Is artificial intelligence and machine learning changing the ways of banking: a systematic literature review and meta analysis

Abstract This study aims to analyze the research work and present an SLR (Systematic Literature Review) on the initiatives taken by players in the banking sector, especially toward the technological innovations done in the field of AI (Artificial Intelligence) and ML (Machine Learning) that have contributed to the growth of the sector as well towards the ease of doing and building trust amongst consumers in the digitalized era of Banking. Growing security risks in cyberspace accompany the enormous and complex digital versions of virtual services. In total, 734 articles were reviewed to explore how Artificial Intelligence and Machine Learning have been used in various banking facets and how they have evolved to cater to different banking sector requirements. The findings reveal the areas where these modern-age technologies have been adopted and where the same can be used efficiently. Lastly, this study elaborates and evaluates the applications of these technologies on the parameters of scalability going further, keeping in mind the unprecedented dynamic times, and serving the very purpose of banks and how the banks are dealing with all the opportunities and challenges these new technologies bring in. This SLR also provides an overview of the research that can be taken up in future by the researchers in this domain.
Is ChatGPT a Good English to Arabic Machine Translation Tool?

In this paper, we study the performance of using ChatGPT as a machine translation tool for translating English text to Arabic. For evaluating ChatGPT as a machine translator, it was compared with fifteen well-known commercial tools that support English to Arabic Translation. First, fourteenth prompts were suggested and evaluated in order to select the best prompt that should be used with ChatGPT. Then, 100 samples of English sentences from a well-known dataset were used in the evaluation process, in which three metrics including BLEU, chrF, and TER are used. In terms of the BLEU metric, the highest value among the tested translators was 34.71 and the lowest value was 12.06 while the ChatGPT value was 18.63, which indicates that ChatGPT according to the chosen samples used from the dataset has not a good performance as English to Arabic MT tool.
AR-supported Human-Robot Collaboration: Facilitating Workspace Awareness and Parallelized Assembly Tasks

While technologies for human-robot collaboration are rapidly advancing, plenty of aspects still need further investigation, such as ensuring workspace awareness, enabling the operator to reschedule tasks on the fly, and how users prefer to coordinate and collaborate with robots. To address these, we propose an Augmented Reality interface that supports human-robot collaboration in an assembly task by (1) enabling the inspection of planned and ongoing robot processes through dynamic task lists and a path visualization, (2) allowing the operator to also delegate tasks to the robot, and (3) presenting step-by-step assembly instructions. We evaluate our AR interface in comparison to a state-of-the-art tablet interface in a user study, where participants collaborated with a robot arm in a shared workspace to complete an assembly task. Our findings confirm the feasibility and potential of AR-assisted human-robot collaboration, while pointing to some central challenges that require further work.
Who’s Watching Me?: Exploring the Impact of Audience Familiarity on Player Performance, Experience, and Exertion in Virtual Reality Exergames

Familiarity with audiences plays a significant role in shaping individual performance and experience across various activities in everyday life.This study delves into the impact of familiarity with non-playable character (NPC) audiences on player performance and experience in virtual reality (VR) exergames.By manipulating of NPC appearance (face and body shape) and voice familiarity, we explored their effect on game performance, experience, and exertion.The findings reveal that familiar NPC audiences have a positive impact on performance, creating a more enjoyable gaming experience, and leading players to perceive less exertion.Moreover, individuals with higher levels of self-consciousness exhibit heightened sensitivity to the familiarity with NPC audiences.Our results shed light on the role of familiar NPC audiences in enhancing player experiences and provide insights for designing more engaging and personalized VR exergame environments.
A Novel Approach to Modeling and Visualisation of Epidemic Outbreaks: Combining Manual and Automatic Calibration

In this paper, we present an approach which couples a calibration algorithm for a compartmental model of infection dynamics with a graphical user interface which allows to visualise the modeling results and to simulate control measures aimed at curtailing the spread of infection. Unlike the most popular approaches related to visualisation of disease dynamics, our method simultaneously allows to obtain the parameter values based on epidemic data and change these values in interactive mode. The simulation tool built according to this principle is intended to help the interdisciplinary teams to obtain parameter values based on objective and subjective (domain-specific) criteria. We demonstrate the usage of the resulting modeling and visualisation framework using influenza outbreak data in Saint Petersburg for two epidemic seasons.
Visualization and Bibliometric Analysis of Research Evolution on Digital Human

Digital humans play a central role in the Metaverse world. To gain a comprehensive understanding of the current status, hot topics, and trends in digital human research, we employed authoritative databases such as Web of Science as data sources, key literatures in the field of digital human research before June 2023 was retrieved through a combined search using multiple keywords. Visualization tools such as VOSviewer and CiteSpace were then employed to analyze the retrieved literatures. The findings indicate a significant increase in the number of publications related to digital humans over the past 27 years. The fields of biomedicine and entertainment media have played crucial roles as catalysts for advancements in digital human research. The current widespread popularity of digital humans is not coincidental, but rather the result of extensive multidisciplinary research, continual advancements in digital productivity, and collaborative efforts spanning various industries over an extended period of time. Throughout this historical journey, terms such as virtual human, digital human model, virtual character, and virtual reality have emerged prominently and gained considerable frequency. Furthermore, research on artificial intelligence and related fields has recently become a prominent focal point. These analysis results provide a scientific theoretical foundation and valuable references from diverse perspectives for researchers in this field.
DP-PQD: Privately Detecting Per-Query Gaps in Synthetic Data Generated by Black-Box Mechanisms

Synthetic data generation methods, and in particular, private synthetic data generation methods, are gaining popularity as a means to make copies of sensitive databases that can be shared widely for research and data analysis. Some of the fundamental operations in data analysis include analyzing aggregated statistics, e.g., count, sum, or median, on a subset of data satisfying some conditions. When synthetic data is generated, users may be interested in knowing if their aggregated queries generating such statistics can be reliably answered on the synthetic data, for instance, to decide if the synthetic data is suitable for specific tasks. However, the standard data generation systems do not provide "per-query" quality guarantees on the synthetic data, and the users have no way of knowing how much the aggregated statistics on the synthetic data can be trusted. To address this problem, we present a novel framework named DP-PQD (differentially-private per-query decider) to detect if the query answers on the private and synthetic datasets are within a user-specified threshold of each other while guaranteeing differential privacy. We give a suite of private algorithms for per-query deciders for count, sum, and median queries, analyze their properties, and evaluate them experimentally.
Can Large Language Models Predict Data Correlations from Column Names?

Recent publications suggest using natural language analysis on database schema elements to guide tuning and profiling efforts. The underlying hypothesis is that state-of-the-art language processing methods, so-called language models, are able to extract information on data properties from schema text. This paper examines that hypothesis in the context of data correlation analysis: is it possible to find column pairs with correlated data by analyzing their names via language models? First, the paper introduces a novel benchmark for data correlation analysis, created by analyzing thousands of Kaggle data sets (and available for download). Second, it uses that data to study the ability of language models to predict correlation, based on column names. The analysis covers different language models, various correlation metrics, and a multitude of accuracy metrics. It pinpoints factors that contribute to successful predictions, such as the length of column names as well as the ratio of words. Finally, the study analyzes the impact of column types on prediction performance. The results show that schema text can be a useful source of information and inform future research efforts, targeted at NLP-enhanced database tuning and data profiling.
Efficient Dynamic Weighted Set Sampling and Its Extension

Given a weighted set S of n elements, weighted set sampling (WSS) samples an element in S so that each element a i ; is sampled with a probability proportional to its weight w ( a i ). The classic alias method pre-processes an index in O ( n ) time with O ( n ) space and handles WSS with O (1) time. Yet, the alias method does not support dynamic updates. By minor modifications of existing dynamic WSS schemes, it is possible to achieve an expected O (1) update time and draw t independent samples in expected O ( t ) time with linear space, which is theoretically optimal. But such a method is impractical and even slower than a binary search tree-based solution. How to support both efficient sampling and updates in practice is still challenging. Motivated by this, we design BUS , an efficient scheme that handles an update in O (1) amortized time and draws t independent samples in O (log n + t) time with linear space. A natural extension of WSS is the weighted independent range sampling (WIRS) , where each element in S is a data point from R. Given an arbitrary range Q = [ℓ, r ] at query time, WIRS aims to do weighted set sampling on the set S Q of data points falling into range Q. We show that by integrating the theoretically optimal dynamic WSS scheme mentioned above, it can handle an update in O (log n ) time and can draw t independent samples for WIRS in O (log n + t ) time, the same as the state-of-the-art static algorithm. Again, such a solution by integrating the optimal dynamic WSS scheme is still impractical to handle WIRS queries. We further propose WIRS-BUS to integrate BUS to handle WIRS queries, which handles each update in O (log n ) time and draws t independent samples in O (log 2 n + t ) time with linear space. Extensive experiments show that our BUS and WIRS-BUS are efficient for both sampling and updates.
ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems

The past decade has seen rapid growth of distributed stream data processing systems. Under these systems, a stream application is realized as a Directed Acyclic Graph (DAG) of operators, where the level of parallelism of each operator has a substantial impact on its overall performance. However, finding optimal levels of parallelism remains challenging. Most existing methods are heavily coupled with the topological graph of operators, unable to efficiently tune under-provisioned jobs. They either insufficiently use previous tuning experience by treating successively tuning independently, or explore the configuration space aggressively, violating the Service Level Agreements (SLA). To address the above problems, we propose ContTune, a continuous tuning system for stream applications. It is equipped with a novel Big-small algorithm, in which the Big phase decouples the tuning from the topological graph by decomposing the job tuning problem into sub-problems that can be solved concurrently. We propose a conservative Bayesian Optimization (CBO) technique in the Small phase to speed up the tuning process by utilizing the previous observations. It leverages the state-of-the-art (SOTA) tuning method as conservative exploration to avoid SLA violations. Experimental results show that ContTune reduces up to 60.75% number of reconfigurations under synthetic workloads and up to 57.5% number of reconfigurations under real workloads, compared to the SOTA method DS2.