Reviewerly/exHarmony huggingface dataset:
Prototype of a wearable force-feedback mechanism for free-range immersive experience

The recent rise in popularity of head-mounted displays (HMDs) for immersion into virtual reality has resulted in demand for new ways to interact with virtual objects. Most solutions utilize generic controllers for interaction within virtual environments and provide limited haptic feedback. We describe a prototype of an ambulatory (allowing walking) haptic feedback stylus with primary use in computer-aided design. Our stylus is a modified 3D Systems Touch force-feedback arm mounted on a wearable platform carried in front of a user. The wearable harness also holds a full-sized laptop which drives a Meta Quest 2 HMD, also worn by the user. Such design provides six degrees-of-freedom without tethered limitations, while ensuring high precision of force-feedback from virtual interaction. Our solution also provides an experience wherein a mobile user can explore different haptic feedback simulations as well as create, arrange, and deform general shapes.
Technology in Practice

Abstract Ethnomethodology has made an important and highly influential contribution to our understanding of technology. It has provided the analytic and methodological resources with which to reconsider a number of key concepts and ideas within the social and cognitive sciences and has provided the foundation to a substantial corpus of studies of technology in action. To exemplify these analytic concerns, the authors consider a surveillance technology and the ways in which personnel use the system to detect and manage the problems and difficulties that arise in the operation of a major urban transport network. They consider how the use of the technology, to survey and manage incidents and events that might ordinarily pass unnoticed, is dependent upon the operators’ working knowledge of the environment, its characteristics and contingencies, and their familiarity with the routine patterns of conduct and interaction that arise within certain locales. In short, the system’s use is embedded within practice and practical action. By driving analytic attention toward the endogenous and situated character of technology in action, ethnomethodology has had a significant impact on contemporary research and practice throughout a range of disciplines in both the human and physical sciences.
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits

The ongoing 'digital transformation' fundamentally changes audit evidence's nature, recording, and volume. Nowadays, the International Standards on Auditing (ISA) requires auditors to examine vast volumes of a financial statement's underlying digital accounting records. As a result, audit firms also 'digitize' their analytical capabilities and invest in Deep Learning (DL), a successful sub-discipline of Machine Learning. The application of DL offers the ability to learn specialized audit models from data of multiple clients, e.g., organizations operating in the same industry or jurisdiction. In general, regulations require auditors to adhere to strict data confidentiality measures. At the same time, recent intriguing discoveries showed that large-scale DL models are vulnerable to leaking sensitive training data information. Today, it often remains unclear how audit firms can apply DL models while complying with data protection regulations. In this work, we propose a Federated Learning framework to train DL models on auditing relevant accounting data of multiple clients. The framework encompasses Differential Privacy and Split Learning capabilities to mitigate data confidentiality risks at model inference. Our results provide empirical evidence that auditors can benefit from DL models that accumulate knowledge from multiple sources of proprietary client data.
Homomorphic Asymmetric Encryption Applied to the Analysis of IoT Communications

In this paper, we describe the use of homomorphic encryption techniques in order to not only ensure the data are transmitted in a confidential way, but also to use the encrypted data to provide the manager with statistics that allow them to detect the incorrect functioning of a sensor node or a group of sensors due to either malicious data injection, data transmission, or simply sensor damage (miscalibration, faulty sensor functioning). Obtaining these statistical values does not need decryption, so the process is sped up and can be developed in real time. Operating the data in this way ensures privacy and removes the need to maintain a shared key infrastructure between the sensor nodes and the manager nodes that are part of the blockchain infrastructure. In this work, we focus on operations with the sensor nodes that provide data that will be, later, treated as part of the business logic in the agribusiness sector (for example), hence the importance of having fast checking mechanisms in terms of data quality. The results obtained on conventional configurations of sensor nodes encourage the use of this technique in the aforementioned infrastructure.
Development of Augmented-Reality-Based Magnetic Field Visualization System as an Educational Tool

Electromagnetism is a difficult subject to learn because the phenomenon is not observed directly and does not have a perceivable, concrete image. The visualization of the phenomenon will greatly help beginners understand electromagnetics. This paper proposes an augmented-reality-based visualization system of the magnetic flux density around current-generating objects. The system can be realized with a portable magnetic field sensor and a familiar device-a smartphone. The effectiveness of the system is verified through experiments, and our findings suggest that the system can effectively help an operator gain an intuitive understanding of the magnetic flux density.
Novel optical-markers-assisted point clouds registration for panoramic 3D shape measurement

Panoramic 3D shape measurement for larger-scale and complex objects inevitably requires multi-view point clouds registration. However, traditional registration methods often use expensive devices or inflexible physical markers. In this paper, a novel optical-markers-assisted registration (OMAR) method is proposed, which substitutes traditional physical markers with optical markers for the registration. Specifically, a markers design strategy is first proposed to generate the desired optical markers according to different requirements. The designed markers can be projected by using a low-cost projector or a laser with grating. The camera captures these projected markers from the object surface, and the 3D sensor collaboratively acquires the corresponding 3D point clouds. Finally, a markers-guided algorithm is specially designed to suit optical markers, which can make full use of the optical markers and recover the 360-degree 3D shape. The provided experimental results demonstrate both the accuracy and efficiency of the proposed OMAR in measuring large-scale, complex and texture-less objects. The proposed OMAR also enables flexible and non-destructive registration without losing accuracy compared with traditional registration methods.
Sensing What’s New: Considering Ethics When Using Sensor Data in Journalistic Practices

As data are becoming increasingly central to journalistic practice, a number of technology-driven approaches are emerging among data journalists. This article focuses on sensor journalism, which brings new practical and ethical concerns to journalism. By interviewing and working with data journalists and journalism scholars, we analyze the new technological and ethical challenges that sensors bring to journalism. The results contribute to the knowledge on how data journalists implicitly embed ethical values into their everyday work. Furthermore, they suggest that general ethical values are revisited and extended by the influence of sensors.
Validity and Reliability of a Depth Camera–Based Quantitative Measurement for Joint Motion of the Hand

Quantitative measurement of hand motion is essential in evaluating hand function. This study aimed to investigate the validity and reliability of a novel depth camera-based contactless automatic measurement system to assess hand range of motion and its potential benefits in clinical applications.Five hand gestures were designed to evaluate the hand range of motion using a depth camera-based measurement system. Seventy-one volunteers were enrolled in performing the designed hand gestures. Then, the hand range of motion was measured with the depth camera and manual procedures. System validity was evaluated based on 3 dimensions: repeatability, within-laboratory precision, and reproducibility. For system reliability, linear evaluation, the intraclass correlation coefficient, paired t-test and bias were employed to test the consistency and difference between the depth camera and manual procedures.When measuring phalangeal length, repeatability, within-laboratory precision, and reproducibility were 2.63%, 12.87%, and 27.15%, respectively. When measuring angles of hand motion, the mean repeatability and within-laboratory precision were 1.2° and 3.3° for extension of 5 digits, 2.7° and 10.2° for flexion of 4 fingers, and 3.1° and 5.3° for abduction of 4 metacarpophalangeal joints, respectively. For system reliability, the results showed excellent consistency (intraclass correlation coefficient = 0.823; P < .05) and good linearity with the manual procedures (r = 0.909-0.982, approximately; P < .001). Besides, 78.3% of the measurements were clinically acceptable.Our depth camera-based evaluation system provides acceptable validity and reliability in measuring hand range of motion and offers potential benefits for clinical care and research in hand surgery. However, further studies are required before clinical application.This study suggests a depth camera-based contactless automatic measurement system holds promise for assessing hand range of motion in hand function evaluation, diagnosis, and rehabilitation for medical staff. However, it is currently not adequate for all clinical applications.
Deep-learning density functionals for gradient descent optimization

Machine-learned regression models represent a promising tool to implement accurate and computationally affordable energy-density functionals to solve quantum many-body problems via density functional theory. However, while they can easily be trained to accurately map ground-state density profiles to the corresponding energies, their functional derivatives often turn out to be too noisy, leading to instabilities in self-consistent iterations and in gradient-based searches of the ground-state density profile. We investigate how these instabilities occur when standard deep neural networks are adopted as regression models, and we show how to avoid them by using an ad hoc convolutional architecture featuring an interchannel averaging layer. The main testbed we consider is a realistic model for noninteracting atoms in optical speckle disorder. With the interchannel average, accurate and systematically improvable ground-state energies and density profiles are obtained via gradient-descent optimization, without instabilities nor violations of the variational principle.
Autonomous DRL-based energy efficient VM consolidation for cloud data centers

The exponential data growth and demands for high computing resources lead to excessive resource use in cloud data centers, which cause an increase in energy consumption and high carbon emissions in the environment. So, the high energy consumption, inefficient resource usage, and quality of service assurance (QoS) are major challenges for cloud data centers. The dynamic consolidation of Virtual Machines (VMs) is proven to be an efficient way to tackle these issues while reducing energy consumption and improving resource utilization in data centers. It reduces the number of active hosts for energy efficiency by switching under-utilized and idle hosts into lower power mode. So, several heuristics and Artificial Intelligence (AI) based VM consolidation approaches have been published in papers. Most existing approaches rely on the aggressive consolidation of VMs for energy efficiency, thus causing performance degradation and high SLA violation. However, an automated solution is needed to reduce energy consumption and SLA violation by ensuring efficient resource usage in the cloud data center environment. Therefore, this article proposes an energy-efficient autonomous VM consolidation (AVMC) mechanism that has Deep Reinforcement Learning (DRL) based agent for performing VM consolidation decisions. The DRL agent learns the optimal distribution of VMs in the data center, considering energy efficiency and QoS assurance. The real-time workload traces from PlanetLab have been used to validate the proposed mechanism. Experimental results reveal the superiority of the proposed AVMC system over the existing models. AVMC reduced the energy consumption and SLA violation rate significantly.
HTML violations and where to find them

With the increased interest in the web in the 90s, everyone wanted to have their own website. However, given the lack of knowledge, such pages contained numerous HTML specification violations. This was when browser vendors came up with a new feature - error tolerance. This feature, part of browsers ever since, makes the HTML parsers tolerate and instead fix violations temporarily. On the downside, it risks security issues like Mutation XSS and Dangling Markup. In this paper, we asked, do we still need to rely on error tolerance, or can we abandon this security issue?
PhishInPatterns

Despite phishing attacks and detection systems being extensively studied, phishing is still on the rise and has recently reached an all-time high. Attacks are becoming increasingly sophisticated, leveraging new web design patterns to add perceived legitimacy and, at the same time, evade state-of-the-art detectors and web security crawlers.
Stop, DROP, and ROA

We analyze the properties of 712 prefixes that appeared in Spamhaus' Don't Route Or Peer (DROP) list over a nearly three-year period from June 2019 to March 2022. We show that attackers are subverting multiple defenses against malicious use of address space, including creating fraudulent Internet Routing Registry records for prefixes shortly before using them. Other attackers disguised their activities by announcing routes with spoofed origin ASes consistent with historic route announcements, and in one case, with the ASN in a Route Origin Authorization. We quantify the substantial and actively-exploited attack surface in unrouted address space, which warrants reconsideration of RPKI eligibility restrictions by RIRs, and reconsideration of AS0 policies by both operators and RIRs.
Agriculture Supply Chain Management Based on Blockchain Architecture and Smart Contracts

Since the commercialization of agriculture technology, there has been a surge in interest in agricultural data. However, these data are notoriously chaotic, and analysts are concerned about their authenticity because there is a big possibility that others may have influenced data quality at various points along the data stream. This article suggests a new blockchain architecture to protect the integrity of agricultural data. The goal of this architecture is to provide farmers with safe storage. The agriculture data inserted cannot be modified without some rules. Many procedures are completed automatically using smart contracts to limit the danger of manipulation. One of the suggested architectures is the proof of concept. It connects a traditional farm system with the blockchain accompanied by smart contracts to facilitate the entire agri-supply chain. The conceptual architecture will eliminate the flaws discovered in prior studies. Sensors are used in this approach to provide us with environmental data. As a result, we store our data in blocks using the blockchain system. Then, we built some unique agricultural smart contracts to handle all transactions and automatize decisions based on the source code of these automated contracts. This strategy would be more efficient and secure.
An Efficient Algorithm of Star Subgraph Queries on Urban Traffic Knowledge Graph

Abstract Knowledge graph has wide applications in the field of computer science. In the knowledge service environment, the information is large and explosive, and it is difficult to find knowledge of common phenomena. The urban traffic knowledge graph is a knowledge system that formally describes urban traffic concepts, entities and their interrelationships. It has great application potential in application scenarios such as user travel, route planning, and urban planning. This paper first defines the urban traffic knowledge graph and the star subgraph query of the urban traffic knowledge graph. Then, the road network data and trajectory data are collected to extract the urban traffic knowledge, and the urban traffic knowledge graph is constructed with this knowledge. Finally, a star subgraph query algorithm on the urban traffic knowledge graph is proposed. The discussion of the star subgraph query mode gives the corresponding application scenarios of our method in the urban traffic knowledge graph. Experimental results verify the performance advantages of this method.
An adaptive biogeography-based optimization with cumulative covariance matrix for rule-based network intrusion detection

Biogeography-based optimization (BBO) is a well-known population-based metaheuristic optimization algorithm. The historical population distribution information is so underutilized that the global optimal value may not be searched efficiently in existing variants of BBO. To address the limitation, we propose in this paper a BBO framework based on cumulative covariance matrix (CCM) and dub it as CCM-BBO. The CCM operator is used in CCM-BBO to establish Eigen coordinate system and enables the migration operator independent on fixed coordinate system. By embedding CCM operator into BBO, the migration operator is implemented in the Eigen coordinate system to enhance the rotation invariance of BBO. To verify the performance of CCM operator, CCM-BBO has been applied to three state-of-the-art variants of BBO: SBBO, DE/BBO and EMBBO. The experimental results on 25 benchmark functions of CEC2005, 10 benchmark functions of CEC2020, and a real-world intrusion detection optimization problem have demonstrated that CCM-BBO is an effective framework to enhance the performance of BBO.
Non-Pauli errors in the three-dimensional surface code

A powerful feature of stabiliser error correcting codes is the fact that stabiliser measurement projects arbitrary errors to Pauli errors, greatly simplifying the physical error correction process as well as classical simulations of code performance. However, logical non-Clifford operations can map Pauli errors to non-Pauli (Clifford) errors, and while subsequent stabiliser measurements will project the Clifford errors back to Pauli errors the resulting distributions will possess additional correlations that depend on both the nature of the logical operation and the structure of the code. Previous work has studied these effects when applying a transversal $T$ gate to the three-dimensional colour code and shown the existence of a non-local "linking charge" phenomenon between membranes of intersecting errors. In this work we generalise these results to the case of a $CCZ$ gate in the three-dimensional surface code and find that many aspects of the problem are much more easily understood in this setting. In particular, the emergence of linking charge is a local effect rather than a non-local one. We use the relative simplicity of Clifford errors in this setting to simulate their effect on the performance of a single-shot magic state preparation process (the first such simulation to account for the full effect of these errors) and find that their effect on the threshold is largely determined by probability of $X$ errors occurring immediately prior to the application of the gate, after the most recent stabiliser measurement.
Assessing forensic ballistics three-dimensionally through graphical reconstruction and immersive VR observation

Abstract A crime scene can provide valuable evidence critical to explain reason and modality of the occurred crime, and it can also lead to the arrest of criminals. The type of evidence collected by crime scene investigators or by law enforcement may accordingly effective involved cases. Bullets and cartridge cases examination is of paramount importance in forensic science because they may contain traces of microscopic striations, impressions and markings, which are unique and reproducible as “ballistic fingerprints”. The analysis of bullets and cartridge cases is a complicated and challenging process, typically based on optical comparison, leading to the identification of the employed firearm. New methods have recently been proposed for more accurate comparisons, which rely on three-dimensionally reconstructed data. This paper aims at further advancing recent methods by introducing a novel immersive technique for ballistics comparison by means of Virtual Reality. Users can three-dimensionally examine the cartridge cases shapes through intuitive natural gestures, from any vantage viewpoint (including internal iper-magnified views), while having at their disposal sets of visual aids which could not be easily implemented in desktop-based applications. A user study was conducted to assess viability and performance of our solution, which involved fourteen individuals acquainted with the standard procedures used by law enforcement agencies. Results clearly indicated that our approach lead to faster adaptation of users to the UI/UX and more accurate and explainable ballistics examination results.
Principal component analysis on face recognition using artificial firefirefly swarm optimization algorithm

In the modern environment, the innovations emerging in information technology has driven us to focus on strengthening the security process. This led to the recent advancement in face recognition technology and special attention is given to the recognition process by applying a biometric system for personal identification. Face recognition is renowned as one of the efficacious applications of picture study, popularly applied for reliable biometric where security is the important quality attribute to be achieved. In this paper, a highly effective face recognition system has been proposed by incorporating genetic algorithms for better search strategy. The proposed model works in two-step processes: face feature extraction and face pattern matching. The Haralick features and features extracted from face databases using PCA are used for face recognition. The most eminent artificial firefirefly swarm optimization algorithm is employed for better searching and matching of facial features. From the simulation experiments performed on the faces warehoused in the OUR database, the result has shown that the model is highly efficient, the PCA method has achieved 80.6% of recognition rate and the AFSA has acquired 88.9% accuracy in correct recognition rate.