Reviewerly/exHarmony huggingface dataset:
Sensor-Assisted Fault Mitigation in Quantum Computation

We propose a method to assist fault mitigation in quantum computation through the use of sensors co-located near physical qubits. Specifically, we consider using transition edge sensors co-located on silicon substrates hosting superconducting qubits to monitor for energy injection from ionizing radiation, which has been demonstrated to increase decoherence in transmon qubits. We generalize from these two physical device concepts and explore the potential advantages of co-located sensors to assist fault mitigation in quantum computation. In the simplest scheme, co-located sensors beneficially assist rejection of calculations potentially affected by environmental disturbances. Investigating the potential computational advantage further required development of an extension to the standard formulation of quantum error correction. In a specific case of the standard three-qubit, bit-flip quantum error-correction code, we show that given a 20% overall error probability per qubit, approximately 90% of repeated calculation attempts are correctable. However, when sensor-detectable errors account for 45% of overall error probability, the use of co-located sensors uniquely associated with independent qubits boosts the fraction of correct final-state calculations to 96%, at the cost of rejecting 7% of repeated calculation attempts.
Temporal Cues from Socially Unacceptable Trajectories for Anomaly Detection

State-of-the-Art (SoTA) deep learning-based approaches to detect anomalies in surveillance videos utilize limited temporal information, including basic information from motion, e.g., optical flow computed between consecutive frames. In this paper, we compliment the SoTA methods by including long-range dependencies from trajectories for anomaly detection. To achieve that, we first created trajectories by running a tracker on two SoTA datasets, namely Avenue and Shanghai-Tech. We propose a prediction-based anomaly detection method using trajectories based on Social GANs, also called in this paper as temporal-based anomaly detection. Then, we hypothesize that late fusion of the result of this temporal-based anomaly detection system with spatial-based anomaly detection systems produces SoTA results. We verify this hypothesis on two spatial-based anomaly detection systems. We show that both cases produce results better than baseline spatial-based systems, indicating the usefulness of the temporal information coming from the trajectories for anomaly detection. We observe that the proposed approach depicts the maximum improvement in micro-level Area-Under-the-Curve (AUC) by 4.1% on CUHK Avenue and 3.4% on Shanghai-Tech over one of the baseline method. We also show a high performance on cross-data evaluation, where we learn the weights to combine spatial and temporal information on Shanghai-Tech and perform evaluation on CUHK Avenue and vice-versa.
Designing Hyperchaos and Intermittency in Semiconductor Superlattices

Weakly coupled semiconductor superlattices under dc voltage bias are excitable systems with many degrees of freedom that may exhibit spontaneous chaos at room temperature and act as fast physical random number generator devices. Superlattices with identical periods exhibit current self-oscillations due to the dynamics of charge dipole waves but chaotic oscillations exist on narrow voltage intervals. They disappear easily due to variation in structural growth parameters. Based on numerical simulations, we predict that inserting two identical sufficiently separated wider wells increases superlattice excitability by allowing wave nucleation at the modified wells and more complex dynamics. This system exhibits hyperchaos and varieties of intermittent chaos in extended dc voltage ranges. Unlike in ideal superlattices, our chaotic attractors are robust and resilient against noises and against controlled random disorder due to growth fluctuations.
Assessment of Arsenic Levels in Water, Sediment, and Human Hair around Ie Seu’um Geothermal Manifestation Area, Aceh, Indonesia

The concentration of arsenic in water, sediment, and resident hair in the Ie Seu’um geothermal manifestation area has been analyzed using the Atomic Graphite Furnace Atomic Absorption Spectrophotometry (GF-AAS) method. Sediment and hair samples were prepared by wet digestion using an acid solution. The measurement results were validated by linearity, Limit of Detection (LoD), Limit of Quantification (LoQ), Relative Standard Deviation (%RSD), and %Recovery. The validation test showed that this method is very linear, sensitive, accurate, and precise with a correlation coefficient of 0.9999, LoD of 0.009 μg/L, LoQ of 0.027 μg/L, recovery values of 89.117–101.027% for hair samples and 88.337–105.175% for sediment samples, and RSD of 1.067%. The sample test results showed that the hot springs contained the highest arsenic with levels of 166.73 ± 0.0081 μg/L (IS1). In comparison, the water samples with the lowest arsenic levels were in the rivers that had not been in direct contact with the hot water (IS2) flow, which is 0.80 ± 0.0036 μg/L. All the river water samples that had been in contact with hot water were boreholes, and the resident boreholes located around hot water streams/rivers contaminated with arsenic were above the threshold. Only the river water that had not been in contact with hot water, refill water, and borehole waters outside the hot water flow were detected to be below the threshold level. All the sediment samples showed arsenic contamination, with levels ranging from 2.56–6.86 mg/kg, and it was still within the normal limits recommended by the National Oceanic and Atmospheric Administration (NOAA). Arsenic exposures in communities living around the Ie Seu’um geothermal area, Mesjid Raya Sub-District, Aceh Besar District, Aceh Province, were very high, where 9 out of 10 respondents were positive for arsenic with levels ranging from 54.59–164.57 mg/kg, which was already above the threshold set by the researchers.
Automatic 3-D Measurement Method for Nonuniform Moving Objects

Phase-shifting profilometry (PSP) via sequential fringe projection is applicable for high-accuracy 3-D measurements of static objects. However, moving objects cannot be static or quasi-static during one projection cycle, thereby causing surface misalignment and phase errors in 3-D measurements. This paper proposes a motion-estimated PSP based on the fringeoriented synthetic phase correlation algorithm (FO-SPCA), which achieves automatic 3-D measurements of objects in nonuniform 2-D motion. To robustly estimate the motion of objects among fringe images, the proposed fringe-oriented feature extraction technique obtains the surface features by eliminating the modulation influence of sinusoidal illumination. Furthermore, the FO-SPCA is developed to accurately detect the subpixel displacements of moving objects according to object surface features. With the subpixel displacements, the alignment procedure is completed through a reverse 2-D transformation and then the phase errors are compensated accordingly. The simulation and experimental results verify the feasibility and robustness of the motion-estimated PSP for the in-situ 3-D measurements of nonuniform moving objects.
CARE: coherent actionable recourse based on sound counterfactual explanations

Abstract Counterfactual explanation (CE) is a popular post hoc interpretability approach that explains how to obtain an alternative outcome from a machine learning model by specifying minimum changes in the input. In line with this context, when the model’s inputs represent actual individuals, actionable recourse (AR) refers to a personalized CE that prescribes feasible changes according to an individual’s preferences. Hence, the quality of ARs highly depends on the soundness of underlying CEs and the proper incorporation of user preferences. To generate sound CEs, several data-level properties, such as proximity and connectedness, should be taken into account. Meanwhile, personalizing explanations demands fulfilling important user-level requirements, like coherency and actionability. The main obstacles to inclusive consideration of the stated properties are their associated modeling and computational complexity as well as the lack of a systematic approach for making a rigorous trade-off between them based on their importance. This paper introduces CARE, an explanation framework that addresses these challenges by formulating the properties as intuitive and computationally efficient objective functions, organized in a modular hierarchy and optimized using a multi-objective optimization algorithm. The devised modular hierarchy enables the arbitration and aggregation of various properties as well as the generation of CEs and AR by choice. CARE involves individuals through a flexible language for defining preferences, facilitates their choice by recommending multiple ARs, and guides their action steps toward their desired outcome. CARE is a model-agnostic approach for explaining any multi-class classification and regression model in mixed-feature tabular settings. We demonstrate the efficacy of our framework through several validation and benchmark experiments on standard data sets and black box models.
An optimal feature enriched region of interest (ROI) extraction for periocular biometric system

With the onset of COVID-19 pandemic, wearing of face mask became essential and the face occlusion created by the masks deteriorated the performance of the face biometric systems. In this situation, the use of periocular region (region around the eye) as a biometric trait for authentication is gaining attention since it is the most visible region when masks are used. One important issue in periocular biometrics is the identification of an optimal size periocular ROI which contains enough features for authentication. The state of the art ROI extraction algorithms use fixed size rectangular ROI calculated based on some reference points like center of the iris or centre of the eye without considering the shape of the periocular region of an individual. This paper proposes a novel approach to extract optimum size periocular ROIs of two different shapes (polygon and rectangular) by using five reference points (inner and outer canthus points, two end points and the midpoint of eyebrow) in order to accommodate the complete shape of the periocular region of an individual. The performance analysis on UBIPr database using CNN models validated the fact that both the proposed ROIs contain enough information to identify a person wearing face mask.
Self-Supervised Contrastive Learning for Volcanic Unrest Detection

Ground deformation measured from Interferometric Synthetic Aperture Radar (InSAR) data is considered a sign of volcanic unrest, statistically linked to a volcanic eruption. Recent studies have shown the potential of using Sentinel-1 InSAR data and supervised deep learning (DL) methods for the detection of volcanic deformation signals, towards global volcanic hazard mitigation. However, detection accuracy is compromised from the lack of labelled data and class imbalance. To overcome this, synthetic data are typically used for finetuning DL models pre-trained on the ImageNet dataset. This approach suffers from poor generalisation on real InSAR data. This letter proposes the use of self-supervised contrastive learning to learn quality visual representations hidden in unlabeled InSAR data. Our approach, based on the SimCLR framework, provides a solution that does not require a specialized architecture nor a large labelled or synthetic dataset. We show that our self-supervised pipeline achieves higher accuracy with respect to the state-of-the-art methods, and shows excellent generalisation even for out-of-distribution test data. Finally, we showcase the effectiveness of our approach for detecting the unrest episodes preceding the recent Icelandic Fagradalsfjall volcanic eruption.
Students’ Perceptions Concerning Emergency Remote Teaching During COVID-19

Abstract The sudden shift from physical classroom education towards emergency remote teaching ( ERT ) in higher education during the unprecedented global pandemic SARS -CoV-2, or more commonly known as COVID -19, caused an abrupt change in the learning environment for students and educators alike. The disruptive overnight change to convert entire courses to emergency remote teaching caused distress for not only educators, but also students as they had little time to adapt to the new circumstances. While the embedment of technologies in the classroom is not a new concept, this quantitative research examined the impact on perceived student engagement as a result of the sudden change, and how students in higher education in Finland (n = 121) and Thailand (n = 137) responded to this change. One of the primary findings of the research was that students in Thailand indicated difficulties completing group assignments digitally, while, contrary to that finding, students in Finland consented to ease in digital collaboration concerning their group works. As a logical next step, it is recommended to conduct qualitative research to gain a more comprehensive insight into how the views on this particular statement differed significantly between both focus groups of students.
A Threat-Based Cybersecurity Risk Assessment Approach Addressing SME Needs

Cybersecurity incidents are commonplace nowadays, and Small- and Medium-Sized Enterprises (SMEs) are exceptionally vulnerable targets. The lack of cybersecurity resources available to SMEs implies that they are less capable of dealing with cyber-attacks. Motivation to improve cybersecurity is often low, as the prerequisite knowledge and awareness to drive motivation is generally absent at SMEs. A solution that aims to help SMEs manage their cybersecurity risks should therefore not only offer a correct assessment but should also motivate SME users. From Self-Determination Theory (SDT), we know that by promoting perceived autonomy, competence, and relatedness, people can be motivated to take action. In this paper, we explain how a threat-based cybersecurity risk assessment approach can help to address the needs outlined in SDT. We propose such an approach for SMEs and outline the data requirements that facilitate automation. We present a practical application covering various user interfaces, showing how our threat-based cybersecurity risk assessment approach turns SME data into prioritised, actionable recommendations.
Rational verification: game-theoretic verification of multi-agent systems

Abstract We provide a survey of the state of the art of rational verification : the problem of checking whether a given temporal logic formula ϕ is satisfied in some or all game-theoretic equilibria of a multi-agent system – that is, whether the system will exhibit the behavior ϕ represents under the assumption that agents within the system act rationally in pursuit of their preferences. After motivating and introducing the overall framework of rational verification, we discuss key results obtained in the past few years as well as relevant related work in logic, AI, and computer science.
A study on generic object detection with emphasis on future research directions

Object detection is an essential task in computer vision and image processing. It has many applications in various domains like medical diagnosis, civil military, video surveillance, security, etc. In some vision-related approaches, object detection used as an integral part, such as semantic segmentation, instance segmentation, pose estimation, suspicious activity detection, etc. The first stage in the pipeline is to detect an object. The survey begins with significant highlights of deep learning for object detection. It provides a comprehensive study on object representation; Convolution Neural Network (CNN) and different Deep Convolution Neural Network architecture. It presents a concise review of renowned datasets and definitive measurement metrics, forming the primitive baseline to evaluate the detection framework. Then studies in detail on detection framework one-stage and two-stage detectors and evaluates each framework with standard datasets listing its vital significance. The study also explores different issues of object detection like multi-scale, intra-class variations, generalization & security. Moreover, lists the primitive steps for creating object detector for different conditions from the reviewed survey. Finally, it proposes promising research directions.
Real-time VR Simulation of Laparoscopic Cholecystectomy based on Parallel Position-based Dynamics in GPU

In recent years, virtual reality (VR) based training has greatly changed surgeons learning mode. It can simulate the surgery from the visual, auditory, and tactile aspects. VR medical simulator can greatly reduce the risk of the real patient and the cost of hospitals. Laparoscopic cholecystectomy is one of the typical representatives in minimal invasive surgery (MIS). Due to the large incidence of cholecystectomy, the application of its VR-based simulation is vital and necessary for the residents' surgical training. In this paper, we present a VR simulation framework based on position-based dynamics (PBD) for cholecystectomy. To further accelerate the deformation of organs, PBD constraints are solved in parallel by a graph coloring algorithm. We introduce a bio-thermal conduction model to improve the realism of the fat tissue electrocautery. Finally, we design a hybrid multi-model connection method to handle the interaction and simulation of the liver-gallbladder separation. This simulation system has been applied to laparoscopic cholecystectomy training in several hospitals. From the experimental results, users can operate in real-time with high stability and fidelity. The simulator is also evaluated by a number of digestive surgeons through preliminary studies. They believed that the system can offer great help to the improvement of surgical skills.
Neural Network Approach in the Task of Multi-label Classification of User Posts in Online Social Networks

This paper focuses on the issue of automating the multi-label classification of users' text posts in online social networks. This classification is based on previously developed criteria to correlate post types with the evaluation of the severity of psychological features of the user. The presented model for classification is based on a neural network with long short-term memory architecture. The results will help to partially automate the process of making recommendations to improve the protection of users from social engineering attacks.
BART Based Semantic Correction for Mandarin Automatic Speech Recognition System

Although automatic speech recognition (ASR) systems achieved significantly improvements in recent years, spoken language recognition error occurs which can be easily spotted by human beings. Various language modeling techniques have been developed on post recognition tasks like semantic correction. In this paper, we propose a Transformer based semantic correction method with pretrained BART initialization, Experiments on 10000 hours Mandarin speech dataset show that character error rate (CER) can be effectively reduced by 21.7% relatively compared to our baseline ASR system. Expert evaluation demonstrates that actual improvement of our model surpasses what CER indicates.
High-fidelity magic-state preparation with a biased-noise architecture

Magic state distillation is a resource intensive subroutine that consumes noisy input states to produce high-fidelity resource states that are used to perform logical operations in practical quantum-computing architectures. The resource cost of magic state distillation can be reduced by improving the fidelity of the raw input states. To this end, we propose an initialization protocol that offers a quadratic improvement in the error rate of the input magic states in architectures with biased noise. This is achieved by preparing an error-detecting code which detects the dominant errors that occur during state preparation. We obtain this advantage by exploiting the native gate operations of an underlying qubit architecture that experiences biases in its noise profile. We perform simulations to analyze the performance of our protocol with the XZZX surface code. Even at modest physical parameters with a two-qubit gate error rate of $0.7\%$ and total probability of dominant errors in the gate $O(10^3)$ larger compared to that of non-dominant errors, we find that our preparation scheme delivers magic states with logical error rate $O(10^{-8})$ after a single round of the standard 15-to-1 distillation protocol; two orders of magnitude lower than using conventional state preparation. Our approach therefore promises considerable savings in overheads with near-term technology.
A Shape Expression approach for assessing the quality of Linked Open Data in libraries

Cultural heritage institutions are exploring Semantic Web technologies to publish and enrich their catalogues. Several initiatives, such as Labs, are based on the creative and innovative reuse of the materials published by cultural heritage institutions. In this way, quality has become a crucial aspect to identify and reuse a dataset for research. In this article, we propose a methodology to create Shape Expressions definitions in order to validate LOD datasets published by libraries. The methodology was then applied to four use cases based on datasets published by relevant institutions. It intends to encourage institutions to use ShEx to validate LOD datasets as well as to promote the reuse of LOD, made openly available by libraries.
Identification of token contracts on Ethereum: standard compliance and beyond

Abstract Next to cryptocurrencies, tokens are a widespread application area of blockchains. Tokens are digital assets implemented as small programs on a blockchain. Being programmable makes them versatile and an innovative means for various purposes. Tokens can be used as investment, as a local currency in a decentralized application, or as a tool for building an ecosystem or a community. A high-level categorization of tokens differentiates between payment, security, and utility tokens. In most jurisdictions, security tokens are regulated, and hence, the distinction is of relevance. In this work, we discuss the identification of tokens on Ethereum, the most widely used token platform. The programs on Ethereum are called smart contracts, which—for the sake of interoperability—may provide standardized interfaces. In our approach, we evaluate the publicly available transaction data by first reconstructing interfaces in the low-level code of the smart contracts. Then, we not only check the compliance of a smart contract with an established interface standard for tokens, but also aim at identifying tokens that are not fully compliant. Thus, we discuss various heuristics for token identification in combination with possible definitions of a token. More specifically, we propose indicators for tokens and evaluate them on a large set of token and non-token contracts. Finally, we present first steps toward an automated classification of tokens regarding their purpose.