Reviewerly/exHarmony huggingface dataset:
Guaranteed-accuracy quantum annealing

Quantum chemistry calculations constitute an important application of quantum annealing (QA). For practical applications in quantum chemistry, it is essential to estimate the ground-state energy of the Hamiltonian with chemical accuracy. However, there is no known method for guaranteeing the accuracy of the energy estimated via QA. Here, we propose a method for guaranteeing the accuracy of the energy estimated via QA using the Weinstein and Temple bounds. In our scheme, before QA is performed, the energies of the ground state and first excited state must be preestimated with some error bars (corresponding to the possible estimation error) via classical computation with some approximations. We show that, if the expectation value and standard deviation of the energy of the state after QA are lower than certain threshold values (that we can calculate from the preestimation), the ground-state energy estimated by QA is closer to the ground-state energy than the preestimation. As the expectation value and standard deviation of the energy can be experimentally measured via QA, our results pave the way for accurate estimation of the ground-state energy using QA.
Blockchain and the Disruption of Corporate Organizations

This chapter explores how businesses can use new digital technologies—specifically distributed ledger (blockchain) technologies—to build more decentralized organizations for all stakeholders. Such unmediated, tech-driven organizations are increasingly attractive and can offer a competitive advantage in attracting talent, raising capital, finding suitable partners, and, perhaps most importantly, remaining relevant in today’s hypercompetitive global markets.
Global Attention Super-Resolution Algorithm for Nature Image Edge Enhancement

Single-image super-resolution (SR) has long been a research hotspot in computer vision, playing a crucial role in practical applications such as medical imaging, public security and remote sensing imagery. However, all currently available methods focus on reconstructing texture details, resulting in blurred edges and incomplete structures in the reconstructed images. To address this problem, an edge-enhancement-based global attention image super-resolution network (EGAN) combining channel- and self-attention mechanisms is proposed for modeling the hierarchical features and intra-layer features in multiple dimensions. Specifically, the channel contrast-aware attention (CCA) module learns the correlations between the intra-layer feature channels and enhances the contrast in the feature maps for richer features in the edge structures. The cyclic shift window multi-head self-attention (CS-MSA) module captures the long-range dependencies between layered features and captures more valuable features in the global information network. Experiments are conducted on five benchmark datasets for × 2, × 3 and × 4 SR. The experimental results show that for × 4 SR, our network improves the average PSNR by 0.12 dB, 0.19 dB and 0.12 dB over RCAN, HAN and NLSN, respectively, and can reconstruct a clear and complete edge structure.
How Real is Augmented Reality in Preschool? Examination of Young Children's AR Experiences

The research aims to examine augmented reality (AR) technology during the preschool years. For this purpose, we created activities with AR cards and compared them to activities with other materials. We designed the study as a case study, a qualitative research model. We used the convenience sampling method to select the study group, and we used the criterion sampling method from the selection methods. The study group consists of 18 children, nine boys and nine girls attending kindergarten. Children are between 54-and 77 months. We gathered data through semi-structured interviews, observation, and audio and video recordings. We used the content analysis method to analyze the data, creating codes and themes. Findings show that using AR technology in the preschool period attracts children's attention, gives them a sense of reality, embodies the content, supports peer relationships positively, and allows children to participate in activities willingly and focus on the activity. Furthermore, we have seen that AR technology can be a distraction. When comparing AR technology to other materials, we discovered that children prefer materials that directly connect to real life. They prefer materials that contain AR to materials that do not. We presented recommendations for the use of AR in the preschool period and for future research.
Research on MapReduce Heuristic Multi Table Join Algorithm Based on Binary Optimization and Pancake Parallel Strategy

Background: With the development of technology, the data amount has increased significantly. In data processing, the multi table query is the most frequent operation. Because the join keys cannot correspond one by one, there will be much redundant data transmission, resulting in a waste of network bandwidth. Objective: In order to solve the problems of network overhead and low efficiency, this paper proposes a heuristic multi table join optimization method. By sharing information, the unconnected tuples are eliminated so as to reduce the amount of data transmitting. This shortens response time and improves execution performance. Method: Firstly, the join key information of one table is compressed by the algorithm to make the filtered information for sharing. Then, the concurrent execution is controlled according to the pancake parallel strategy. Finally, the selection strategy of multi table join order is proposed. Results/Discussion: The experiments show that the proposed algorithm can filter a large amount of useless data and improve query efficiency. At the same time, the proposed algorithm reduces a lot of network overhead, improves the algorithm performance, and better solves the problem of low efficiency of multi table join. Conclusion: This paper introduces the heuristic strategy to optimize the algorithm, so that it can perform the join tasks in parallel, which further improves the performance of multi table join. The algorithm creatively combines heuristic data filtering, which greatly improves the quality of data processing. The algorithm is worth popularizing and applying.
Research on DUAL-ADGAN Model for Anomaly Detection Method in Time-Series Data

In recent years, anomaly detection techniques in time-series data have been widely used in manufacturing, cybersecurity, and other fields. Meanwhile, various anomaly detection models based on generative adversarial networks (GAN) are gradually used in time-series anomaly detection tasks. However, there are problems of unstable generator training, missed detection of anomalous data, and inconsistency between the discriminator's discriminant and the anomaly detection target in GAN networks. Aiming at the above problems, the paper proposes a DUAL-ADGAN (Dual Anomaly Detection Generative Adversarial Networks) model for the detection of anomalous data in time series. First, the Wasserstein distance satisfying the Lipschitz constraint is used as the loss function of the data reconstruction module, which improves the stability of the traditional GAN network training. Second, by adding a data prediction module to the DUAL-ADGAN model, the distinction between abnormal and normal samples is increased, and the rate of missing abnormal data in the model is reduced. Third, by introducing the Fence-GAN loss function, the discriminator is aligned with the anomaly detection target, which effectively reduces the anomaly data false detection rate of the DUAL-ADGAN model. Finally, anomaly scores derived from the DUAL-ADGAN model are compared with dynamic thresholds to detect anomalies. The experimental results show that the average F1 of the DUAL-ADGAN model is 0.881, which is better than the other nine baseline models. The conclusions demonstrate that the DUAL-ADGAN model proposed in the paper is more stable in training while effectively solving the problems of anomaly miss detection and discriminator inconsistency with the anomaly detection target in the anomaly detection task.
Disturbance rejection of T–S fuzzy systems: a membership function-dependent EID method

The disturbance rejection problem of T–S fuzzy systems is concerned. Since the T–S fuzzy system is characterised by its membership function, less conservative stabilisation conditions can be derived from membership function-dependent Lyapunov function which contributes to the improvement of disturbance rejection performance. Specifically, we utilise a configuration composed of a membership function-dependent state observer for the state estimation, a membership function-dependent equivalent-input-disturbance estimator for the estimation and compensation of disturbance and an internal model for the reference tracking. It is revealed that this membership function-dependent Lyapunov function naturally leads to control gains switching in accordance with the derivative signs of the normalised premise variables. The switching rules and the design conditions for all control gains are obtained explicitly. In particular, the free-weighting-matrix approach is used to lessen the conservatism in the stability condition. Moreover, a concrete procedure for the controller design including the switching rule is given. Finally, the developed method is tested via simulations. The advantage of the membership function-dependent equivalent-input-disturbance method is validated by comparing with conventional methods.
How does quality of institutions affect the impact of technology adoption on export performance in Africa?

This paper investigates the role of technology adoption and quality of institutions on the export performance of African countries. Data on 49 African countries for the period 1995–2019 is used while the two-step system generalized method of moment estimation technique is employed for analyses. The results reveal that, in both the short run and long run, technology adoption and quality of institutions are crucial in driving export performance in Africa. More importantly, our results clearly show that, in both periods, quality of institutions serve as important channel through which technology adoption has its largest positive impact on export performance in Africa. Based on these outcomes, policies designed to improve the technology adoption and quality of institutions would be beneficial to the performance of exports in African countries.
Strong coupling in the entanglement dynamics of two qubits interacting with a graphene nanodisk

We investigate the entanglement dynamics of two qubits interacting with a graphene nanodisk using the macroscopic quantum electrodynamics method. By modifying the free-space decay rate of each qubit, we study the coupling strength between the nanoparticle and the qubits. We find that as the free-space decay rate increases, the decaying Rabi oscillations featured in the qubit population dynamics change to complex non-Markovian dynamical population evolution. This is also reflected on the concurrence, which at weak or moderate light–matter coupling conditions, attains values up to 0.5, while as the coupling conditions become stronger, larger values are also transiently observed. Our findings indicate that graphene nanostructures can provide a platform for the realization of high degree of entanglement in the strong coupling regime at the nanoscale, essential for quantum technology applications.
Metaheuristic–Boltzmannian optimization model: A new methodology for convergence using the Jensen–Shannon metric in continuous optimization problems

We present a new methodology called J-SIGMA to infer state change information using the minimum Jensen–Shannon distance to describe a continuous optimization problem as a Boltzmannian system, as well as its applications to population-based metaheuristics. In general, a Boltzmannian system describes a macrostate from the statistics of the constituent microstate where the minimum energy is the most probable. If we model an optimization problem as a Boltzmann process, the global optimum would be the most probable state. To achieve this, we propose an analytical derivation of the minimum distance to the Boltzmann distribution using the parameters μ and σ2 to fit this model. As a case study, we implemented the J-SIGMA methodology on three families of population-based metaheuristics: Swarm, Evolutionary, and Estimation of Distribution, and used a set of continuous optimization functions from CEC’17 to evaluate their performance against other metaheuristics of each family. Finally, from a statistical analysis of the convergence performance, the evidence is shown to affirm that the J-SIGMA methodology can significantly improve the convergence performance of the algorithms, regardless of the metaheuristic family to which it belongs.
It is about time–Do exFAT implementations handle timestamps correctly?

Digital forensic investigations require that file metadata are interpreted correctly. In this paper we focus on the timestamps of the exFAT file system. How these timestamps are written may depend on the implementation of the file system. We have performed experiments using Windows, MacOS and Linux to examine whether the respective file system drivers for exFAT use timestamps in the same manner, and whether they take the directory entry UTCOffset fields into account. We have also studied whether the forensic tools: Autopsy, X-Ways Forensics, EnCase Examiner, and FTK Imager interpret the timestamps consistently. The results show that there are substantial inconsistencies both in the file system implementations and in how forensic tools handle these inconsistencies. For the unwary forensic examiner, there is a clear risk of interpreting timestamps incorrectly by a substantial margin. We conclude that timestamp interpretation during criminal investigations should not be based on the assumption that the file system specifications are followed flawlessly by the file system driver developers or necessarily interpreted and displayed correctly by the digital forensic tools.
Strategies of Intentional Friction in the User Interface of Digital Games

While deliberately designing friction in game user interfaces may be a fitting approach for challenging players’ assumptions and encouraging reflection, mainstream practices are often dominated by an overarching paradigm of user-friendliness and enjoyment. This study explores how designers can use intentional friction in the user interfaces of digital games as a strategy to create meaningful experiences and encourage reflection in their players. First, we review literature to frame what constitutes interface elements in game design, the user-friendly and enjoyment paradigm, and other perspectives that offer context to the employment of friction as strategy. Afterwards, we explore game instances that use user interface friction when appropriate as a strategy to express a point of view, challenge current systems, or foment critical reflection. The starting point for our observations is Donald Norman’s seven design principles and Jakob Nielsen’s usability heuristics. As a result, we identify six strategies: 1) explore memory shortcomings; 2) faulty feedback; 3) mismatched mental model; 4) impairment of ability; 5) deliberate inefficiency, and 6) oppressive constraints. Although this work is not focused on collecting all friction design approaches indiscriminately, the identified strategies suggest more nuanced techniques than just framing the principles to create a friendly design in reverse.
Mobile Social Recommendation Model Integrating Users’ Personality Traits and Relationship Strength under Privacy Concerns

Aiming at the problem of data sparsity, cold start, and privacy concerns in complex information recommendation systems, such as personalized marketing on Alibaba or TikTok, this paper proposes a mobile social recommendation model integrating users’ personality traits and social relationship strength under privacy concerns (PC-MSPR). Firstly, PC-MSPR focuses on specific personality traits, including openness, extraversion, and agreeableness, and their impacts on mobile users’ online behaviors. A personality traits calculation method that incorporates privacy preferences (PP-PTM) is then introduced. Secondly, a novel method for calculating the users’ relationship strength, based on their social network interactive activities and domain ontologies (AI-URS) is proposed. AI-URS divides the interactive activities into activity domains and calculates the strength of relationships between users belonging to the same activity domain; at the same time, the comprehensive relationship strength of users in the same domain, including direct relationships and indirect relationships, is calculated based on interactive activity documents. Finally, social recommendations are derived by integrating personality traits and social relationships to calculate user similarity. The proposed model is validated using empirical data. The results show the model’s superiority in alleviating data sparsity and cold-start problems, obtaining higher recommendation precision, and reducing the impact of privacy concerns regarding the users’ adoption of personalized recommendation services.
Test review: French examination of the College Scholastic Ability Test in Korea

Abstract This study reviews the French examination of the College Scholastic Ability Test (CSAT), the most influential and highest-stakes test in Korea. The CSAT score has considerable influence on university admissions, and admission to a highly ranked university is generally a prerequisite for socioeconomic success. From this perspective, the significance of the CSAT is enormous. In general, the assessment is influenced by the goals and content of the curriculum, and it also plays an important role in curriculum implementation. In Korea, the national curriculum has been recently reformed. The main goal of the reformed French curriculum is to promote communicative skills and cultural literacy. However, the examination has been criticized for not uniformly or adequately evaluating overall communicative skills. Considering the importance of the CSAT and the interdependence of assessment and curriculum, the CSAT French examination has to be reviewed from the perspective of test quality. Due to its limited assessment of communicative skills, the validity of the French examination is debatable. The problem of low reliability also arises due to the uncertainty of assessment criteria and guidelines. Authenticity and interactiveness could be improved by avoiding fragmentary knowledge and memorization-oriented assessment. The negative washback effect needs to be addressed to resolve the low relevance of the curriculum to the test and the relatively low applicability of the CSAT French score in university admission policy.
Can information sharing predict fresh produce supply chain performance amid the COVID-19 pandemic? A social learning perspective

Purpose This research aims to provide a predictive model assessment on the effect of information sharing on fresh produce supply chain (FPSC) performance during the COVID-19 pandemic by incorporating information quality as an important part of information sharing, as well as cognitive and affective appraisals as part of a social learning process (mediators) into the model. Design/methodology/approach An online survey was conducted on 197 small fresh produce (fruits and vegetables) retailers in Indonesia during the COVID-19 pandemic. The data were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM) particularly PLSpredict supported by SmartPLS 4 software. Findings This research reveals that information sharing is positively and significantly associated with information quality and that the two constructs are not directly associated with FPSC performance. The path analysis suggests that the effect of information sharing on FPSC performance is fully mediated by cognitive and affective appraisals to the information-sharing activity. It also suggests that the effect of information quality on FPSC performance is fully mediated by affective rather than cognitive appraisal. This model shows a high predictive power and highlights the pivotal role of the learning process during the COVID-19 pandemic. Originality/value This research is the first to employ a predictive model assessment in PLS-SEM to empirically predict the effect of information sharing on FPSC performance using a social learning perspective, particularly in the context of the COVID-19 pandemic.
A text typology of social media

Abstract This paper introduces an initial text typology of social media posts from a multi-dimensional (MD) perspective. Text types are “[g]roupings of text that are similar in their linguistic form” ( Biber 1989 : 13). This text typology is based on a new MD analysis of social media messages presented in the paper. The corpus consists of 60,000 social media messages in English compiled from Facebook, Twitter, Instagram, Reddit, Telegram, and YouTube. After the texts were cleaned up, the corpus was tagged with the Biber Tagger and post-processed with the Biber Tag Count. Three dimensions of variation were determined, each representing an underlying parameter of variation. Once the texts were scored on each of the dimensions, a k-means cluster analysis was carried out, and the optimal number of clusters was determined using the Cubic Clustering Criterion statistic. A two-way typology was developed based on the dimensional characteristics of each cluster and on careful qualitative analysis of text samples.
A portable device for single point strain analysis in sheet metal forming processes

Strain measurement is one of the basic techniques in sheet metal forming for quantifying the deformation of a material under the effects of various loading conditions. The main components in the automatic strain measurement system are the image acquisition hardware and image processing software. This paper presents an automatic single point strain analysis system for sheet metal forming applications. The proposed system is very portable and consists of a USB microscope with different end caps for image acquisition. The software system was developed using Python - OpenCV library and provided with a graphical user interface (GUI).
Open-set learning under covariate shift

Open-set learning deals with the testing distribution where there exist samples from the classes that are unseen during training. They aim to classify the seen classes and recognize the unseen classes. Previous studies typically assume that the marginal distribution of the seen classes is fixed across the training and testing distributions. In many real-world applications, however, there may exist covariate shift between them, i.e., the marginal distribution of seen classes may shift. We call this kind of problem as open-set learning under covariate shift, aim to robustly classify the seen classes under covariate shift and be aware of the unseen classes.We present a new open-set learning framework with covariate generalization based on supervised contrastive learning, called SC–OSG, inspired by the latent connection between contrastive learning and representation invariance. Specifically, we theoretically justify supervised contrastive learning that could promote the conditional invariance of representations, a critical condition for covariate generalization. SC–OSG generates multi-source samples to promote the representation invariance and improve the covariate generalization. Based on this, we propose a detection score that is specific to the proposed training scheme. We evaluate the effectiveness of our method on several real-world datasets, on all of which we achieve competitive results with state-of-the-art methods.