Reviewerly/exHarmony huggingface dataset:
A vector-agent approach to (spatiotemporal) movement modelling and reasoning

Modelling a complex system of autonomous individuals moving through space and time essentially entails understanding the (heterogeneous) spatiotemporal context, interactions with other individuals, their internal states and making any underlying causal interrelationships explicit, a task for which agents (including vector-agents) are specifically well-suited. Building on a conceptual model of agent space-time and reasoning behaviour, a design guideline for an implemented vector-agent model is presented. The movement of football players was chosen as it is appropriately constrained in space, time and individual actions. Sensitivity-variability analysis was applied to measure the performance of different configurations of system components on the emergent movement patterns. The model output varied more when the condition of the contextual actors (players' role-areas) was manipulated. The current study shows how agent-based modelling can contribute to our understanding of movement and how causally relevant evidence can be produced, illustrated through a spatiotemporally constrained football case-study.
BURST: Benchmarking uniform random sampling techniques

BURST is a benchmarking platform for uniform random sampling (URS) techniques. Given: i) the description of a sampling space provided as a Boolean formula (DIMACS), and ii) a sampling budget (time and strength of uniformity), BURST evaluates ten samplers for scalability and uniformity. BURST measures scalability based on the time required to produce a sample, and uniformity based on the state-of-the-art and proven statistical test Barbarik. BURST is easily extendable to new samplers and offers: i) 128 feature models (for highly-configurable systems), ii) many other models mined from the artificial intelligence/satisfiability solving benchmarks. BURST envisions supporting URS assessment and design across multiple research communities.
(Adversarial) Electromagnetic Disturbance in the Industry

Faults occur naturally and are responsible for reliability concerns. Faults are also an interesting tool for attackers to extract sensitive information from secure chips. In particular, non-invasive fault attacks have received a fair amount of attention. One easy way to perturb a chip without altering it is the so-called Electromagnetic Fault Injection (EMFI). Such attack has been studied in great depth, and nowadays, it is part and parcel of the state-of-the-art. Indeed, new capabilities have emerged where EM experimental benches are used to cryptanalyze chips. The progress of this “field” is fast, in terms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reproducibility</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accuracy</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">number of use-cases</i> . However, there is too little awareness about such advances. In this paper, we aim to expose the true harmfulness of EMFI (including reproducibility) to enable reasonable security quotations. We also analyze protections (at hardware/firmware/system levels) in light of their efficiency. We characterize the specificity of EM fault injection compared to other injection means (laser, glitch, probing).
How do task demands and aging affect lexical prediction during online reading of natural texts?

Facilitated identification of predictable words during online reading has been attributed to the generation of predictions about upcoming words. But highly predictable words are relatively infrequent in natural texts, raising questions about the utility and ubiquity of anticipatory prediction strategies. This study investigated the contribution of task demands and aging to predictability effects for short natural texts from the Provo corpus. The eye movements of 49 undergraduate students (mean age 21.2) and 46 healthy older adults (mean age 70.8) were recorded while they read these passages in two conditions: (a) reading for meaning to answer occasional comprehension questions; (b) proofreading to detect "transposed letter" lexical errors (e.g., clam instead of calm) in intermixed filler passages. The results suggested that the young adults, but not the older adults, engaged anticipatory prediction strategies to detect semantic errors in the proofreading condition, but neither age group showed any evidence of costs of prediction failures. Rather, both groups showed facilitated reading times for unexpected words that appeared in a high constraint within-sentence position. These findings suggest that predictability effects for natural texts reflect partial, probabilistic expectancies rather than anticipatory prediction of specific words. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Enhanced chimp optimization algorithm for high level synthesis of digital filters

The HLS of digital filters is a complex optimization task in electronic design automation that increases the level of abstraction for designing and scheming digital circuits. The complexity of this issue attracting the interest of the researcher and solution of this issue is a big challenge for the researcher. The scientists are trying to present the various most powerful methods for this issue, but keep in mind these methods could be trapped in the complex space of this problem due to own weaknesses. Due to shortcomings of these methods, we are trying to design a new framework with the mixture of the phases of the powerful approaches for high level synthesis of digital filters in this work. This modification has been done by merging the chimp optimizer with sine cosine functions. The sine cosine phases helped in enhancing the exploitation phase of the chimp optimizer and also ignored the local optima in the search area during the searching of new shortest paths. The algorithms have been applied on 23-standard test suites and 14-digital filters for verifying the performance of the algorithms. Experimental results of single and multi-objective functions have been compared in terms of best score, best maxima, average, standard deviation, execution time, occupied area and speed respectively. Furthermore, by analyzing the effectiveness of the proposed algorithm with the recent algorithms for the HLS digital filters design, this can be concluded that the proposed method dominates the other two methods in HLS digital filters design. Another prominent feature of the proposed system in addition to the stated enhancement, is its rapid runtime, lowest delay, occupied area and lowest power in achieving an appropriate response. This could greatly reduce the cost of systems with broad dimensions while increasing the design speed.
BPDST: Blockchain-Based Privacy-Preserving Data Sharing on Thin Client for Electronic Medical Records

Sharing medical data can improve the quality of medical services and reduce costs. However, the current Electronic Medical Records (EMRs) are scattered and easily tampered with, which is not conducive to the sharing of EMRs and is not compatible with thin clients. Fortunately, blockchain technology is tamper-proof, decentralized, auditable, and meets the above requirements. To solve these problems, we first propose Blockchain-Based Privacy-Preserving Data Sharing on Thin-Client for Electronic Medical Records (BPDST) approach that combines the k-anonymity and cloud storage, which thin clients can run like a full-node user and safeguard user's EMRs privacy concurrently. Using this approach, patients can control their own EMRs, while the consortium blockchain is responsible for the transaction process and sending the correct results to the patients. BPDST can also share information without leaking or tampering with EMRs' privacy, achieving the purpose of sharing medical data and privacy protection. In the medical field, this study can effectively protect users' privacy when sharing medical data to provide convenience for users and break the "island" phenomenon among various medical institutions. Security analysis and extensive experiments show that BPDST is secure and practical for sharing EMRs.
Coherently coupled quantum oscillators for quantum reservoir computing

We analyze the properties of a quantum system composed of two coherently coupled quantum oscillators and show through simulations that it fulfills the two properties required for reservoir computing: non-linearity and fading memory. We first show that the basis states of this system apply a set of nonlinear transformations on the input signals and thus can implement neurons. We then show that the system exhibits a fading memory that can be controlled by its dissipation rates. Finally we show that a strong coupling between the oscillators is important in order to ensure complex dynamics and to populate a number of basis state neurons that is exponential in the number of physical devices.
An Earthquake Early Warning Method Based on Bayesian Inference

Earthquake early warning (EEW) systems can give people warnings before damaging ground motions reach their site and can reduce earthquake-related losses. It has been shown that Bayesian reference can be used to estimate earthquake magnitude, location, and the distribution of peak ground motion using observed ground-motion amplitudes, predefined prior information, and appropriate attenuation relationships. In this article, we describe a Bayesian approach to earthquake early warning (EEW) systems for the estimation of magnitude (M) and peak ground-motion velocity (PGV) using the Gutenberg–Richter relation as a priori probability derived from statistical historical earthquake information from the Japanese KiK-net. Moreover, using the magnitude Mj = 4.5 and PGV = 0.4 cm/s as the warning threshold, an earthquake hazard discrimination model was established. Following this, the proposed approach was compared with the traditional fitting method to analyze the earthquake hazard discrimination using an Mj = 4.3 earthquake in the Mt. Fuji area and an Mj = 5.5 earthquake in the Ibaraki area of Japan as examples. The results are as follows: (1) The probabilistic algorithm founded on the predictive model of the magnitude from the average period (τc) and PGV from the displacement amplitude (Pd) from the initial 3 s of the P wave is able to provide a fast and accurate estimation of the final magnitude and location. (2) The Bayesian inference approach for the earthquake hazard discrimination model has a 2.94% miss rate and a 5.88% false alarm rate, which is lower than that of the traditional fitting method, thus increasing the accuracy and reliability of earthquake hazard estimation.
Judging Distances to Virtual Objects Generated by Optical and Video See-Through Augmented Reality

Prior work on depth perception in commercial augmented reality (AR) has indicated that egocentric distances are underestimated. One hypothesis as to why distances may be misjudged is an inability of the viewer to correctly perceive that a virtual object has surface contact with the ground rather than is floating above it. There are several factors in AR that can contribute to this effect, and they differ across types of AR technologies, making the question of how to improve depth perception in AR difficult. We carried out an experiment to examine egocentric depth judgments to virtual objects generated in state-of-the-art commercial optical and video see-through AR head-mounted displays (HMDs). In addition, we examined the influence of a rendered cast shadow on depth judgments for objects placed both above and on the ground. Although factors such as field of view, inertial characteristics of the HMDs, and rendering quality of the virtual objects differ, it is useful to make direct comparisons of technologies to understand any tradeoffs in spatial perception between them. Thus, we evaluated egocentric distance judgments in the Microsoft HoloLens 2, an optical see-through AR display, and the Varjo XR-3, a video see-through AR display. For each display, we asked participants (N = 24) to verbally report distance judgments to both grounded and floating targets that were rendered either with or without a cast shadow along the ground. We found significant underestimation of distance in both HMDs. We also found a significant difference between the two HMDs with the video see-through device (Varjo XR-3) showing more distance underestimation than the optical see-through device (HoloLens 2). Finally, we found that the vertical height of a virtual object and the presence of a cast shadow influence distance judgments
Sprite-from-Sprite

We present an approach to decompose cartoon animation videos into a set of "sprites" --- the basic units of digital cartoons that depict the contents and transforms of each animated object. The sprites in real-world cartoons are unique: artists may draw arbitrary sprite animations for expressiveness, where the animated content is often complicated, irregular, and challenging; alternatively, artists may also reduce their workload by tweening and adjusting sprites, or even reuse static sprites, in which case the transformations are relatively regular and simple. Based on these observations, we propose a sprite decomposition framework using Pixel Multilayer Perceptrons (Pixel MLPs) where the estimation of each sprite is conditioned on and guided by all other sprites. In this way, once those relatively regular and simple sprites are resolved, the decomposition of the remaining "challenging" sprites can simplified and eased with the guidance of other sprites. We call this method "sprite-from-sprite" cartoon decomposition. We study ablative architectures of our framework, and the user study demonstrates that our results are the most preferred ones in 19/20 cases.
Typification of gold deposits for evaluation and exploration purposes

The issues of choosing the parameters of the exploration network based on quantitative criteria of exploration: the accuracy of the assessment of average grades and the error of geometrization are considered. Acceptable values of criteria for C1 category reserves are established based on the results of the analysis of actual data. The typification of gold deposits is based on an assessment of the scale of deposits, their morphology and fractal dimension; an assessment of the variability of gold grade in samples or composites is also used. To assess geometrization errors at the early stages of geological exploration, a method for determining them based on the positions of fractal geometry and on the results of geostatistical studies is proposed.
Implementation of Extreme Learning Machine to Predict Distribution Power Transformer Lifetime

The estimated remaining life of transformers is the most critical factor in maintaining service continuity and quality assurance in distributing electrical energy to customers. Therefore, an accurate approach is needed to predict the remaining life of a distribution power transformer. In this paper, an approach to predict transformer lifetime is carried out by utilizing transformer current, voltage, and temperature. The resulting model estimates the transformer age through Extreme Learning Machine in order to extract the maximum information from the transformer. Tests were performed using the proposed and other classification methods on a 20 kV/380-220V transformer whose remaining life spans between 14 and 26 years. Results show that the proposed method yields the lowest minimum error outperforming the other methods with the selection feature MAE at 0.1076 and MSE at 0.0179. The results further validate that the proposed method can achieve accuracy better than the ones obtained from other transformer remaining service life prediction methods.
Synthetic Minority Oversampling Technique Based on Adaptive Noise Optimization and Fast Search for Local Sets for Random Forest

The classification is usually degraded due to the imbalanced class distribution. Synthetic minority oversampling technique (SMOTE) has been successful in improving imbalanced classification and has received great praise. Overgeneralization is one of the most challenges in SMOTE. Although multiple SMOTE-based variations are proposed against overgeneralization, they still have the following shortcomings: (a) creating too many synthetic samples in high-density regions; (b) removing suspicious noise directly instead of modifying them; (c) relying on many parameters. This paper proposes a new SMOTE based on adaptive noise optimization and fast search for local sets (SMOTE-ANO-FLS) to overcome the overgeneralization and the shortcomings of existing works. First, SMOTE-ANO-FLS uses the [Formula: see text]-D tree to fast search the local sets for each sample. Second, a new noise detection method based on local sets and the imbalanced ratio is proposed to detect suspicious noise. Third, a new adaptive noise optimization method is proposed to modify detected suspicious noise instead of removing them. Finally, a new probability weight based on local sets is proposed to help create more synthetic minority class samples in borderline and sparse regions. The effectiveness of SMOTE-ANO-FLS is proven by employing 7 oversampling methods and random forest on the extensive synthetic and real data sets.
Correlation between Margin Width and Time to Local and Systemic Recurrence of Ductal Carcinoma in Situ

Background: Recurrence after DCIS treatment is common and half are invasive cancers.Positive excision margin is significantly associated with recurrence, however; debate exists about close margin.We examine the pattern of treatment and recurrences at a single institute.Objective: To investigate, in women with DCIS treated at KFMC and long follow-up, the relationship between margin width and time to local or systemic recurrence of disease, controlling the other characteristics. Method:We retrospectively reviewed a prospectively maintained database of DCIS patients treated at single institution from 2014-2018.A multivariable Cox model was used to evaluate the association of margin width with recurrence while controlling for other variables. Results:We identified 56 cases with a median follow-up of 51 months (range 3 -120), of which four (7%) had a recurrence within 2 years of initial treatment.Three were invasive ductal cancer while one was high grade DCIS.After controlling the other variables, no association of margin width with recurrence was identified (p-value 0.81) in the current study, however; all recurrences were on the left side (p-value <0.001). Conclusion:The current study did not identify any correlation between margin width and LR, however; relation between site and LR was statistically significant as more LR was identified in left breast.Low rate of LR in current study could be due to more aged population of patients in present study.
Analysis of factors influencing regional economic expansion based on OOB coefficients under RF algorithm

In the context of China's economic growth, the economic situation of national new areas is particularly important, and it is of great significance to study its economic expansion factors. In this paper, the regression random forest algorithm (RF) is used and the economic panel data of Shanghai, China from 2011-2022 is selected for algorithm learning, which obtained algorithm with low error and high accuracy. After the algorithm was learned, the OOB coefficients of the influencing factors were obtained and analyzed to study their impact on regional economic inflation. The results show that the per capita disposable income and the ratio of the added value of the tertiary industry to the GDP play an important role in economic expansion. Finally, policy recommendations for regional economic development are offered, which can help address regional economic risks and contribute to regional economic improvement and growth. The research in this paper analyses the importance of factors influencing regional economic growth by using machine learning methods and quarterly and annual provincial panel data, making the conclusions drawn more innovative and robust.
Trends in publishing blockchain surveys: a bibliometric perspective

A large number of survey papers are being published in blockchain since the first survey appeared in 2016. A person entering into the field of blockchain is faced with the issue of which blockchain surveys to read and why?, Who is publishing these surveys and what is the nature of these surveys?, Which of the publishers are publishing more such surveys and what are the lengths of the published surveys?, Which kind of survey is getting more citations?, Which of the authors is collaborating on such surveys?, etc. All these questions motivated us to analyze the trends in publishing blockchain surveys. In this paper, we have performed a bibliometric analysis on 1088 surveys and review papers published in the field of blockchain in the last six years. We have analyzed the papers with respect to the publication type, publishers and venue, references, citations, paper length, different categories, year, countries, authors, and their collaborations and found interesting insights. To the best of our knowledge, this study is the first of its kind and hope to provide better understanding of the field.
AI proctoring for offline examinations with 2-Longitudinal-Stream Convolutional Neural Networks

In recent years, Artificial Intelligence (AI)-assisted proctoring techniques have developed rapidly, but most are for online exams. On the contrary, this study aims to explore the AI-based visual proctoring approach through surveillance cameras in real-world offline examination rooms (usually classrooms) to assist proctoring, with contributions from three perspectives: First, to proctor in the offline examination rooms, a visual assessment system is introduced that can be applied in the majority of various classrooms for proctoring after corresponding model training. Second, the new proposed 2-Longitudinal-Stream Convolutional Neural Networks model can adequately adapt to the large-scale examination room environment (i.e., the surveillance cameras are too far away from some examinees) with different lighting conditions. In the third aspect, this study proposes a dimensionality reduction technique called Mini-Batch Cutting for video data capable of converting video information into almost lossless image information with a corresponding data preprocessing method. Compared to existing studies, the proposed method has lower computational requirements and is easier to use in all exam rooms simultaneously. Finally, the study findings demonstrate that the proposed proctoring system has a better detection effect than humans in real-world large-scale offline examination environment tests.
Simplifying a classical-quantum algorithm interpolation with quantum singular value transformations

The problem of phase estimation (or amplitude estimation) admits a quadratic quantum speedup. Wang, Higgott, and Brierley [Wang, Higgott, and Brierley, Phys. Rev. Lett. 122, 140504 (2019)] have shown that there is a continuous tradeoff between quantum speedup and circuit depth [by defining a family of algorithms known as $\ensuremath{\alpha}$-quantum phase estimation $(\ensuremath{\alpha}\text{\ensuremath{-}}\mathrm{QPE})$]. In this paper, we show that the scaling of $\ensuremath{\alpha}\text{\ensuremath{-}}\mathrm{QPE}$ can be naturally and succinctly derived within the framework of quantum singular value transformation (QSVT). From the QSVT perspective, a greater number of coherent oracle calls translates into a better polynomial approximation to the sign function, which is the key routine for solving phase estimation. The better the approximation to the sign function, the fewer samples one needs to determine the sign accurately. With this idea, we simplify the proof of $\ensuremath{\alpha}\text{\ensuremath{-}}\mathrm{QPE}$, while providing an interpretation of the interpolation parameters, and show that QSVT is a promising framework for reasoning about classical-quantum interpolations.