Reviewerly/exHarmony huggingface dataset:
A Systematic Review and Meta-analysis of the Effectiveness of Body Ownership Illusions in Virtual Reality

Body ownership illusions (BOIs) occur when participants experience that their actual body is replaced by a body shown in virtual reality (VR). Based on a systematic review of the cumulative evidence on BOIs from 111 research articles published in 2010 to 2021, this article summarizes the findings of empirical studies of BOIs. Following the PRISMA guidelines, the review points to diverse experimental practices for inducing and measuring body ownership. The two major components of embodiment measurement, body ownership and agency, are examined. The embodiment of virtual avatars generally leads to modest body ownership and slightly higher agency. We also find that BOI research lacks statistical power and standardization across tasks, measurement instruments, and analysis approaches. Furthermore, the reviewed studies showed a lack of clarity in fundamental terminology, constructs, and theoretical underpinnings. These issues restrict scientific advances on the major components of BOIs, and together impede scientific rigor and theory-building.
CyTran: A cycle-consistent transformer with multi-level consistency for non-contrast to contrast CT translation

We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans and the other way around. Solving this task has two important applications: (i) to automatically generate contrast CT scans for patients for whom injecting contrast substance is not an option, and (ii) to enhance the alignment between contrast and non-contrast CT by reducing the differences induced by the contrast substance before registration. Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran. Our neural model can be trained on unpaired images, due to the integration of a multi-level cycle-consistency loss. Aside from the standard cycle-consistency loss applied at the image level, we propose to apply additional cycle-consistency losses between intermediate feature representations, which enforces the model to be cycle-consistent at multiple representations levels, leading to superior results. To deal with high-resolution images, we design a hybrid architecture based on convolutional and multi-head attention layers. In addition, we introduce a novel data set, Coltea-Lung-CT-100W, containing 100 3D triphasic lung CT scans (with a total of 37,290 images) collected from 100 female patients (there is one examination per patient). Each scan contains three phases (non-contrast, early portal venous, and late arterial), allowing us to perform experiments to compare our novel approach with state-of-the-art methods for image style transfer. Our empirical results show that CyTran outperforms all competing methods. Moreover, we show that CyTran can be employed as a preliminary step to improve a state-of-the-art medical image alignment method. We release our novel model and data set as open source at: https://github.com/ristea/cycle-transformer. Our qualitative and subjective human evaluations reveal that CyTran is the only approach that does not introduce visual artifacts during the translation process. We believe this is a key advantage in our application domain, where medical images need to precisely represent the scanned body parts.
TRacer: Scalable Graph-Based Transaction Tracing for Account-Based Blockchain Trading Systems

Security incidents such as scams and hacks, have become a major threat to the health of the blockchain ecosystem, causing billions of dollars in losses each year for blockchain users. To reveal the real-world entities behind the pseudonymous blockchain account and recover the stolen funds from the massive transaction data, much effort has been devoted to tracing the flow of illicit funds in blockchains recently. However, most current tracing approaches based on heuristics and taint analysis have limitations in terms of universality, effectiveness, and efficiency. This paper models the blockchain transaction records as a blockchain transaction graph and tackles blockchain transaction tracing as a graph searching task. We propose TRacer, a scalable transaction tracing tool for account-based blockchains. To infer the relevance between accounts during graph searching, we develop a novel personalized PageRank method in TRacer based on the directed, weighted, temporal, and multi-relationship blockchain transaction graphs. To the best of our knowledge, TRacer is the first intelligent transaction tracing tool in account-based blockchains that can handle complex transaction actions in decentralized finance (DeFi). Experimental results and theoretical analysis prove that TRacer can complete the transaction tracing task effectively at a low cost. All codes of TRacer are available at GitHub.
Retinal motion statistics during natural locomotion

Walking through an environment generates retinal motion, which humans rely on to perform a variety of visual tasks. Retinal motion patterns are determined by an interconnected set of factors, including gaze location, gaze stabilization, the structure of the environment, and the walker's goals. The characteristics of these motion signals have important consequences for neural organization and behavior. However, to date, there are no empirical in situ measurements of how combined eye and body movements interact with real 3D environments to shape the statistics of retinal motion signals. Here, we collect measurements of the eyes, the body, and the 3D environment during locomotion. We describe properties of the resulting retinal motion patterns. We explain how these patterns are shaped by gaze location in the world, as well as by behavior, and how they may provide a template for the way motion sensitivity and receptive field properties vary across the visual field.
Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs' potential for enhanced T2D screening.
Toward Collaborative Occlusion-free Perception in Connected Autonomous Vehicles

In connected autonomous vehicles (CAVs), the driving safety can be greatly deteriorated, in the presence of occlusions which are adverse to CAVs' perception of region-of-interest (RoI). Collaborative perception on the basis the information sharing of occlusions among CAVs, in a real-time and accurate manner, provides a means of the occlusion-free RoI perception for safe driving. In this paper, we propose a novel framework of <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ollaborative <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> cclusion- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</u> ree <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> erception ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COFP</b> ) in CAVs, to regain the real-time and accurate occlusion awareness. The innovative COFP targets two goals: well-balanced computation resource allocation, as well as fast and high-quality RoI information fusion. Specifically, the resource allocation problem, with the objective of minimizing CAVs' completion delay, is formulated as a multi-player continuous potential game and solved by a better response dynamics (BRD) algorithm. The RoI information fusion, with the objective of maximizing the overall object depiction quality, is formulated as a combinatorial optimization problem, and solved by a modified discrete salp swarm (MDSSA) algorithm. Experimental results show that the proposed COFP with 5GHz computing power can achieve full occlusion awareness for CAVs with 69.61% completion time reduction and 19.03% fusion quality improvement, compared to the existing methods.
Perspectives on Large Language Models for Relevance Judgment

When asked, large language models~(LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in evaluations of retrieval systems. In this perspectives paper, we discuss possible ways for~LLMs to support relevance judgments along with concerns and issues that arise. We devise a human--machine collaboration spectrum that allows to categorize different relevance judgment strategies, based on how much humans rely on machines. For the extreme point of 'fully automated judgments', we further include a pilot experiment on whether LLM-based relevance judgments correlate with judgments from trained human assessors. We conclude the paper by providing opposing perspectives for and against the use of~LLMs for automatic relevance judgments, and a compromise perspective, informed by our analyses of the literature, our preliminary experimental evidence, and our experience as IR~researchers.
Cybercrime Unmasked: Investigating Cases and Digital Evidence

The advent of rapid digital technology has opened doors to a new domain for criminal activities, commonly termed as computer crimes. Stringent penalties have been instituted by various countries and institutions to combat these offenses executed through computers or networks. Central to investigating these crimes is digital evidence, pivotal in the realm of digital forensics. The digital forensics process comprises five critical phases: acquisition, preservation, analysis, reconstruction, and presentation. Its core aim is to locate and present digital evidence in court, aiding in determining the culpability of individuals involved in computer crimes. This discipline encompasses specialized fields such as data recovery, conversion, erasure, file identification, encryption, decryption, and IP address tracing to apprehend culprits. This paper conducts a thorough examination of the digital forensics stages using sample cases that elucidate five distinct computer crimes. It delves into evidence origins, collection methods, and preservation techniques utilized in the investigation. Once this meticulous process is completed, the digital forensics team compiles documented findings for presentation in a court of law.
Introduction to Mineralogy and Petrology

Introduction to Mineralogy and Petrology presents the essentials of both disciplines through an approach accessible to industry professionals, academic researchers, and students. Mineralogy and petrology stand as the backbone of the geosciences. Detailed knowledge of minerals and rocks and the process of formation and association are essential for practicing professionals and advanced students. This book is designed as an accessible, step-by-step guide to exploring, retaining, and implementing the core concepts of mineral and hydrocarbon exploration, mining, and extraction. Each topic is fully supported by working examples, diagrams and full-color images. The inclusion of petroleum, gas, metallic deposits and economic aspects enhance the book's value as a practical reference for mineralogy and petrology. Authored by two of the world's premier experts, this book is a must for any young professional, researcher, or student looking for a thorough and inclusive guide to mineralogy and petrology in a single source. It is authored by two of the world's experts in mineralogy and petrology, who have more than 70 years of experience in research and instruction combined. It addresses the full scope of the core concepts of mineralogy and petrology, including crystal structure, formation and grouping of minerals and soils, definition, origin, structure and classification of igneous, sedimentary and metamorphic rocks. It features more than 150 figures, illustrations, and color photographs to vividly explore the fundamental principles of mineralogy and petrology. It offers a holistic approach to both subjects, beginning with the formation of geologic structures followed by the hosting of mineral deposits and concluding with the exploration and extraction of lucrative, usable products to improve the health of global economies.
Combining Model-Based Meta-Reasoning and Reinforcement Learning For Adapting Game-Playing Agents

Human experience with interactive games will be enhanced if the software agents that play the game learn from their failures. Techniques such as reinforcement learning provide one way in which these agents may learn from their failures. Model-based meta-reasoning, a technique in which an agent uses a self-model for blame assignment, provides another. This paper evaluates a framework in which both these approaches are combined. We describe an experimental investigation of a specific task (defending a city) in a computer war strategy game called FreeCiv. Our results indicate that in the task examined, model-based meta-reasoning coupled with reinforcement learning enables the agent to learn the task with performance matching that of an expert designed agent and with speed exceeding that of a pure reinforcement learning agent.
Eye-Centric ICT Control

There are many ways of interfacing with ICT devices but where the end user is mobility restricted then the interface designer has to become much more innovative. One approach is to employ the user’s eye movements to initiate control operations but this has well known problems of the measured eye gaze location not always corresponding to the user’s actual visual attention. We have developed a methodology which overcomes these problems. The user’s environment is imaged continuously and interrogated, using SIFT image analysis algorithms, for the presence of known ICT devices. The locations of these ICT devices are then related mathematically to the measured eye gaze location of a user. The technical development of the approach and its current status are described.
Information system conflicts: causes and types

Conflicts are an inherent part of organizational life and managers deal with confrontations and conflicts on an almost daily basis. Information Systems (IS) implementations are a type of change that often leads to open or hidden conflicts. Managers and others involved can only deal with such conflicts effectively if they understand the nature and causes of information system conflicts (IS conflicts). To contribute to such an understanding, this study focuses on the analysis of IS conflicts. In so doing, it aims to identify various types of IS conflicts and to develop a framework that can be helpful in assessing these conflicts. To this end, we have conducted a meta-ethnographic study – that is, we synthesized earlier case studies in which IS conflicts are described. We purposefully selected 11 descriptions of IS conflicts and we analyzed the topics, contexts, and processes of these conflicts. Based on this analysis, we propose a two-dimensional framework of IS conflicts that leads to a categorization involving four IS conflict types: task; implementation process; structure; and value conflicts. Based on the conflicts that were studied, this paper also reveals that, in reality, many IS conflicts have a hybrid form and develop from one type to another over time.
Online Data Thinning via Multi-Subspace Tracking

In an era of ubiquitous large-scale streaming data, the availability of data far exceeds the capacity of expert human analysts. In many settings, such data is either discarded or stored unprocessed in data centers. This paper proposes a method of online data thinning, in which large-scale streaming datasets are winnowed to preserve unique, anomalous, or salient elements for timely expert analysis. At the heart of this proposed approach is an online anomaly detection method based on dynamic, low-rank Gaussian mixture models. Specifically, the high-dimensional covariance matrices associated with the Gaussian components are associated with low-rank models. According to this model, most observations lie near a union of subspaces. The low-rank modeling mitigates the curse of dimensionality associated with anomaly detection for high-dimensional data, and recent advances in subspace clustering and subspace tracking allow the proposed method to adapt to dynamic environments. Furthermore, the proposed method allows subsampling, is robust to missing data, and uses a mini-batch online optimization approach. The resulting algorithms are scalable, efficient, and are capable of operating in real time. Experiments on wide-area motion imagery and e-mail databases illustrate the efficacy of the proposed approach.
Good Initializations of Variational Bayes for Deep Models

Stochastic variational inference is an established way to carry out approximate Bayesian inference for deep models. While there have been effective proposals for good initializations for loss minimization in deep learning, far less attention has been devoted to the issue of initialization of stochastic variational inference. We address this by proposing a novel layer-wise initialization strategy based on Bayesian linear models. The proposed method is extensively validated on regression and classification tasks, including Bayesian DeepNets and ConvNets, showing faster and better convergence compared to alternatives inspired by the literature on initializations for loss minimization.
Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

The balance of neighborhood space around a central point is an important concept in cluster analysis. It can be used to effectively detect cluster boundary objects. The existing neighborhood analysis methods focus on the distribution of data, i.e., analyzing the characteristic of the neighborhood space from a single perspective, and could not obtain rich data characteristics. In this paper, we analyze the high-dimensional neighborhood space from multiple perspectives. By simulating each dimension of a data point's k nearest neighbors space (kNNs) as a lever, we apply the lever principle to compute the balance fulcrum of each dimension after proving its inevitability and uniqueness. Then, we model the distance between the projected coordinate of the data point and the balance fulcrum on each dimension and construct the DHBlan coefficient to measure the balance of the neighborhood space. Based on this theoretical model, we propose a simple yet effective cluster boundary detection algorithm called Lever. Experiments on both low- and high-dimensional data sets validate the effectiveness and efficiency of our proposed algorithm.
Evaluation of Selected Universities Library Websites Listed by National Institutional Ranking Framework (NIRF) during the Year 2017: A Webometric Analysis

The present study is carried out for analyzing the selected universities library websites ranked by National Institutional Ranking Framework (NIRF) for the year 2017. The paper investigates domain authority, a number of webpages, links and calculates the Web Impact Factor of 23 universities library websites. Though top 25 universities were selected for the study out of which 1 university library website found an error while fetching data and 1 more university was not having library website. The study revealed that the Internal Web Impact Factor and Simple Web Impact Factor of Birla Institute of Technology and Science, Rajasthan occupies at the top with 3309.52. Whereas, Jamia Millia Islamia occupies second place with 1250 IWIF and 1250.05 SWIF and the third place was occupied by Jawaharlal Nehru Centre for Advanced Scientific Research with 230.26 IWIF and 230.31 SWIF. Indian Institute of Science, Bangalore recorded highest EWIF with 11.67.
Analysis and Triage of Advanced Hacking Groups Targeting Western Countries Critical National Infrastructure: APT28, RED October, and Regin

Many organizations still rely on traditional methods to protect themselves against various cyber threats. This is effective when they deal with traditional threats, but it is less effective when it comes to Advanced Persistent Threat (APT) actors. APT attacks are carried by highly skilled (possibly state-sponsored) cyber criminal groups who have potentially unlimited time and resources. This paper analyzes three specific APT groups targeting critical national infrastructure of western countries, namely: APT28, Red October, and Regin. Cyber Kill Chain (CKC) was used as the reference model to analyze these APT groups activities. We create a Defense Triage Process (DTP) as a novel combination of the Diamond Model of Intrusion Analysis, CKC, and 7D Model, to triage the attack vectors and potential targets for these three APT groups. A comparative summary of these APT groups is presented, based on their attack impact and deployed technical mechanism. This paper also highlights the type of organization and vulnerabilities that are attractive to these APT groups and proposes mitigation actions.
The Coin Passcode: A Shoulder-Surfing Proof Graphical Password Authentication Model for Mobile Devices

Swiftness, simplicity, and security is crucial for mobile device authentication.Currently, most mobile devices are protected by a six pin numerical passcode authentication layer which is extremely vulnerable to Shoulder-Surfing attacks and Spyware attacks.This paper proposes a multi-elemental graphical password authentication model for mobile devices that are resistant to shoulder surfing attacks and spyware attacks.The proposed Coin Passcode model simplifies the complex user interface issues that previous graphical password models have, which work as a swift passcode security mechanism for mobile devices.The Coin Passcode model also has a high memorability rate compared to the existing numerical and alphanumerical passwords, as psychology studies suggest that human are better at remembering graphics than words.The results shows that the Coin Passcode is able to overcome the current shoulder-surfing and spyware attack vulnerability that existing mobile application numerical passcode authentication layers suffer from.
Learning Sublinear-Time Indexing for Nearest Neighbor Search.

Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) are based on space partitions of the ambient space $\mathbb{R}^d$. Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification. We instantiate this general approach with the KaHIP graph partitioner [Sanders, Schulz SEA 2013] and neural networks, respectively, to obtain a new partitioning procedure called Neural Locality-Sensitive Hashing (Neural LSH). On several standard benchmarks for NNS, our experiments show that the partitions found by Neural LSH consistently outperform partitions found by quantization- and tree-based methods.