Reviewerly/exHarmony huggingface dataset:
Empowerment or Disempowerment: The (Dis)empowering Processes and Outcomes of Co-Designing with Rural Craftspeople

Despite the significance of human development for sustainability, the actual effects of craft-design collaboration on craftspeople have scarcely been discussed. Moreover, despite its wide usage, the meaning of empowerment in design discourse is unclear and its discussions are barely linked to mature ones in other disciplines. In addition, disempowerment has been mentioned far less often than empowerment. From the perspective of empowerment, this article examines the elements in the processes of craft-design collaborations that can exert positive or negative influence on craftspeople, and analyzes both the empowering and disempowering effects on them as co-design outcomes. This research builds on two empirical cases based on Tiao Hua, a cross-stitching handwork. We identify five stages of co-design processes for empowerment analysis. The data was mainly collected from the designers (D1–D16) and craftswomen (C1–C8) through observations, interviews, and reflective sessions. We sorted and codified the qualitative data as sticky note statements. Higher-level themes and sub-themes emerged from the codified statements through affinity analysis. Finally, we presented an analytical framework of craftspeople (dis)empowerment process and outcome. The (dis)empowering process covers four dimensions: organization, instrumentation, participation, and interaction, while the outcome involves emotional, cognitive, behavioral, and relational components. We contend craftspeople (dis)empowerment should be discussed dialectically in four aspects.
An in-situ authentication with privacy preservation scheme for accident response in Internet of Vehicles

Real-time monitoring and handling of traffic in large urban cities, remains challenging due to a broad range of technical and non-technical factors. For example, while real-time incident reporting with supporting data (e.g., images or video feeds) can inform decision-making and facilitate traffic control, there are operational challenges (e.g., bandwidth and security). Hence, we propose an identity authentication and privacy preservation scheme based on both blockchain and ring signature. Specifically, in our approach, blockchain is leveraged to maintain anonymity yet guaranteeing traceability. We also design a new ring signature scheme based on the SM2 algorithm and analyze its security. To demonstrate utility, we evaluate our proposed approach using Ethereum smart contracts, and the findings demonstrate that our approach is efficient, supports privacy preservation, and is resilient to a range of common attacks.
A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing

The maturity of 5G technology enabled crowd-sensing services to collect multimedia data via wireless network, so it promoted the applications of crowd-sensing services in different fields, but also brings more privacy security challenges, most general of which is privacy leakage. As a privacy protection technology combining data integrity check and identity anonymity, ring signature is widely used in the field of privacy protection. However, introducing signature technology leads to additional signature verification overhead. In the scenario of crowd-sensing, the existing signature schemes have low efficiency in multi-signature verification. Therefore, it is necessary to design an efficient multi-signature verification scheme while ensuring security. In this paper, a batch-verifiable signature scheme is proposed based on the crowd-sensing background, which supports the sensing platform to verify the uploaded multiple signature data efficiently, so as to overcoming the defects of the traditional signature scheme in verifying multiple signatures. In our proposal, a method for linking homologous data was presented, which was valuable for incentive mechanism and data analysis. Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.
Integrated Quantitative Evaluation Method of SAR Filters

An excellent quantitative evaluation method of SAR de-speckling filters needs to contain a comprehensive evaluation of both noise smoothing and edge preservation. However, most existing evaluation models only evaluate a single aspect, while a few comprehensive indicators lack robustness. For this reason, a novel integrated quantitative evaluation method of de-speckling filters is proposed. The proposed evaluation method is weighted by two sub-indicators: the coherent equivalent number of looks and edge preservation evaluation. The evaluation indicator of the coherent equivalent number of looks is built to evaluate the noise-smoothing ability of de-speckling filters, whereas the indicator of edge preservation evaluation is built to evaluate the edge-preserving performance of filtered image. Six filters with an excellent performance, five real synthetic aperture radar images with three bands, four polarization modes, four resolutions, and five common evaluation indexes were used in the experiment. The experimental results show that the evaluation results of the proposed evaluation method were consistent with the visual effect and other indicators, and its feasibility was verified.
The Link between the English Language and Digital Competences

In this digital era, students are required to understand and apply the basic knowledge of digital media because of the transformation in the learning environment. This paper investigates the impact of the English language on the students&rsquo; achievements in regard to their digital competencies, that is, to what extent university students whose mother tongue is not English feel confident in their ability to learn and practice new digital skills. The findings reveal that most students have positive attitudes towards technologies, however they find English language as a barrier in developing their digital competencies.
Intelligent hybrid hand gesture recognition system using deep recurrent neural network with chaos game optimisation

Hand gesture recognition is considered an essential task in various human-computer interaction (HCI) applications. Therefore, developing a robust system for hand gesture recognition is a challenge. This work proposed a new hybrid approach named hybrid deep recurrent neural network (RNN) incorporated with a chaos game optimisation (CGO) algorithm for efficiently recognising hand gestures. The main objective of a hybrid recurrent neural network with chaos game optimisation (RNN-CGO) is to achieve the recognition of alphabet signs from 2D gesture images. The different stages included in this technique are pre-processing, feature extraction, feature selection, and classification. The proposed approach is executed using American Sign Language (ASL) dataset with the help of the PYTHON platform. The precision, accuracy, f1-score, and recall obtained for the proposed approach are 99.28%, 99.96%, 99.25%, and 99.28%, respectively. The inference time required for the proposed approach is 0.121 s. The proposed technique has less computational complexity. The results showed that the proposed approach is better than the existing hand gesture recognition model approaches.
Multiband longwave infrared reflectance removal using blackbody channel prior

Drawing from techniques used to dehaze visible, three-channel RGB images, we propose an approach to separate emissive and reflected radiance in images at short distances with a microbolometer-based longwave infrared (LWIR) camera system. The best case for optimal contrast and with the most descriptive information about the scene in an LWIR image would be where no external sources are radiating toward the scene. We introduce the concept of a blackbody channel prior (BCP) to multiband LWIR imaging to describe pixels that represent objects that behave most similarly to perfect blackbodies with an emissivity near unity. Most LWIR images of outdoor scenes are degraded largely in part by reflected sky path radiance. We can estimate scene radiance with a minimized reflective component producing images with enhanced contrast. Experiments on a number of multiband images are present to demonstrate this spectral-based BCP technique and show its potential for preserving scene information while achieving contrast enhancement.
Earthquake Early Warning System (EEWS) empowered by Time‐Dependent Neo‐Deterministic Seismic Hazard Assessment (TD‐NDSHA)

Abstract In the network‐based on‐site earthquake early warning system (EEWS), the ‘blind zone’, namely the zone where the issued warning arrives later than the destructive S and surface waves, is one of the challenges affecting its effectiveness. The ‘blind zone’ is determined by the interstation distance, or equivalently the density of seismic stations, of the network. In this paper, we suggest a practical approach according to which, when in a region a temporary increase of seismic hazard is declared, additional stations are deployed in such a way that the blind zone is temporarily reduced. In the procedure, the time‐dependent neo‐deterministic seismic hazard assessment (TD‐NDSHA) plays a vital role in the identification of the regions potentially exposed to high macroseismic intensities. As a showcase example, we consider the scenario of year 2014 at the Sichuan‐Yunnan border of southwest China. The TD‐NDSHA is based on the standard NDSHA procedure at regional scale (bedrock conditions), with the ‘controlling earthquakes’ defined on the basis of the Annual Consultation. We show that the ‘blind zone’ can be reduced in the identified areas of interest (e.g., MMI ≥ VI), by deploying a limited number of additional seismic stations. In the case where false alarms can be tolerated, significant reduction of the ‘blind zone’ can be implemented by moving from a network‐based EEWS to a single‐sensor‐based EEWS and skipping the process of location and magnitude‐determination/prediction procedures.
Super-Resolution of Compressed Images Using Residual Information Distillation Network

Super-Resolution (SR) is a fundamental computer vision task, which reconstructs high-resolution images from low-resolution ones. Existing SR methods mainly recover images from clear low-resolution images, leading to unsatisfactory results when processing compressed low-resolution images. In the paper, we propose a two-stage SR method for compressed images, which consists of the Compression Artifact Removal Module (CARM) and Super-Resolution Module (SRM). The compressed low-resolution image is used to reconstruct the clear low-resolution image by CARM, and the high-resolution image is obtained by SRM. In addition, we propose a residual information distillation block to learn the texture details which are lost during the compression process. The proposed method has been validated and compared with the state of the art, and experimental results show that the proposed method outperforms other super-resolution methods in terms of visual effects and objective evaluation metrics.
Relational Action Bank with Semantic–Visual Attention for Few-Shot Action Recognition

Recently, few-shot learning has attracted significant attention in the field of video action recognition, owing to its data-efficient learning paradigm. Despite the encouraging progress, identifying ways to further improve the few-shot learning performance by exploring additional or auxiliary information for video action recognition remains an ongoing challenge. To address this problem, in this paper we make the first attempt to propose a relational action bank with semantic–visual attention for few-shot action recognition. Specifically, we introduce a relational action bank as the auxiliary library to assist the network in understanding the actions in novel classes. Meanwhile, the semantic–visual attention is devised to adaptively capture the connections to the foregone actions via both semantic correlation and visual similarity. We extensively evaluate our approach via two backbone models (ResNet-50 and C3D) on HMDB and Kinetics datasets, and demonstrate that the proposed model can obtain significantly better performance compared against state-of-the-art methods. Notably, our results demonstrate an average improvement of about 6.2% when compared to the second-best method on the Kinetics dataset.
The phenomenon of decision oscillation: A new consequence of pathology in game trees

Abstract Random minimaxing studies the consequences of using a random number for scoring the leaf nodes of a full width game tree and then computing the best move using the standard minimax procedure. Experiments in Chess showed that the strength of play increases as the depth of the lookahead is increased. Previous research by the authors provided a partial explanation of why random minimaxing can strengthen play by showing that, when one move dominates another move, then the dominating move is more likely to be chosen by minimax. This paper examines a special case of determining the move probability when domination does not occur. Specifically, we show that, under a uniform branching game tree model, whether the probability that one move is chosen rather than another depends not only on the branching factors of the moves involved, but also on whether the number of ply searched is odd or even. This is a new type of game tree pathology, where the minimax procedure will change its mind as to which move is best, independently of the true value of the game, and oscillate between moves as the depth of lookahead alternates between odd and even.
Studying the Effect of Introducing Chaotic Search on Improving the Performance of the Sine Cosine Algorithm to Solve Optimization Problems and Nonlinear System of Equations

The development of many engineering and scientific models depends on the solution of nonlinear systems of equations (NSEs), and the progress of these fields depends on their efficient resolution. Due to the disadvantages in solving them with classical methods, NSEs are amenable to modeling as an optimization issue. The purpose of this work is to propose the chaotic search sine cosine algorithm (CSSCA), a new optimization approach for solving NSEs. CSSCA will be set up so that it employs a chaotic search to get over the limitations of optimization techniques like a lack of diversity in solutions, exploitation’s unfair advantage over exploration, and the gradual convergence of the optimal solution. A chaotic logistic map has been employed by many studies and has demonstrated its effectiveness in raising the quality of solutions and offering the greatest performance. So, it is used as a local search strategy. Three kinds of test functions—unimodal, multimodal, and composite test functions—as well as numerous NSEs—combustion problems, neurophysiology problems, arithmetic application, and nonlinear algebraic equations—were employed to assess CSSCA. To demonstrate the significance of the changes made in CSSCA, the results of the recommended algorithm are contrasted with those of the original SCA, where CSSCA’s average improvement rate was roughly 12.71, demonstrating that it is very successful at resolving NSEs. Finally, outcomes demonstrated that adding a chaotic search to the SCA improves results by modifying the chaotic search’s parameters, enabling better outcomes to be attained.
A Study of Weather-Image Classification Combining VIT and a Dual Enhanced-Attention Module

A weather-image-classification model combining a VIT (vision transformer) and dual augmented attention module is proposed to address the problems of the insufficient feature-extraction capability of traditional deep-learning methods with the recognition accuracy still to be improved and the limited types of weather phenomena existing in the dataset. A pre-trained model vision transformer is used to acquire the basic semantic feature representation of weather images. Dual augmented attention combined with convolutional self-attention and Atrous self-attention modules are used to acquire the low-level and high-level deep-image semantic representations, respectively, and the feature vectors are spliced and fed into the linear layer to obtain the weather types. Experimental validation is performed on the publicly available standard weather-image datasets MWD (Multi-class Weather Database) and WEAPD (Weather Phenomenon Database), and the two datasets are combined to enhance the comprehensiveness of the model for weather-phenomena recognition. The results show that the model achieves the highest F1 scores of 97.47%, 87.69% and 92.73% on the MWD, WEAPD and merged datasets, respectively. These scores are higher than the scores of recent deep-learning models with excellent performance in the experimental comparison, thereby, proving the effectiveness of the model.
Perceptions of Learning and Engagement with Mobile Technology in a University Setting

Mobile technology has changed the landscape of post-secondary education. This descriptive study examined student and instructor perceptions of student learning and engagement while using mobile technology. Overall, student perceptions of learning and engagement were neutral; however, instructors reported higher positive perceptions on both constructs. Positive aspects of mobile technology included themes of interactivity, convenience, note taking, and collaboration, whereas negative aspects included distraction, technology compatibility, and note taking. While there seems to be potential for the use of iPads in the classroom, caution should be taken when considering how to implement mobile technology into the curriculum.
Quantum finite difference solvers for physics simulation

Abstract Physics systems are becoming increasingly complex and require more and more computing time. Quantum computing, which has shown its efficiency on some problems, such as the factorisation of a number with Shor's algorithm, may be the solution to reduce these computation times. Here, the authors propose two quantum numerical schemes for the simulation of physics phenomena, based on the finite difference method. The aim is to see if quantum versions of standard numerical schemes offer an advantage over their classical counterparts, either in accuracy, stability or computation time. First, the authors will present the different phenomena studied as well as the classical solution methods chosen. The authors will then describe the implementation of the quantum numerical schemes and present some results obtained on the different physics phenomena beforehand and then compare both approaches, classical and quantum.
A dual‐balanced network for long‐tail distribution object detection

Abstract Object detection on datasets with imbalanced distributions (i.e. long‐tail distributions) dataset is a significantly challenging task. Some re‐balancing solutions, such as re‐weighting and re‐sampling have two main disadvantages. First, re‐balancing strategies only utilise a coarse‐grained global threshold to suppress some of the most influential categories, while overlooking locally influential categories. Second, very few studies have specifically designed algorithms for object detection tasks under long‐tail distribution. To address these two issues, a dual‐balanced network for fine‐grained re‐balancing object detection is proposed. Our re‐balancing strategies are both in proposal and classification logic, corresponding to two sub‐networks, the Balance Region Proposal Network (BRPN) and the Balance Classification Network (BCN). The BRPN sub‐network equalises the number of proposals in the background and foreground by reducing the sampling probability of simple backgrounds, and the BCN sub‐network equalises the logic between head and tail categories by globally suppressing negative gradients and locally fixing the over‐suppressed negative gradients. In addition, the authors advise a balance binary cross entropy loss to jointly re‐balance the entire network. This design can be generalised to different two‐stage object detection frameworks. The experimental mAP result of 26.40% on this LVIS‐v0.5 dataset outperforms most SOTA methods.
Registered Keyword Searchable Encryption Based on SM9

The SM9 algorithm is an Identity-Based Encryption (IBE) algorithm independently made by China. The existing SM9 searchable encryption scheme cannot be effective against insider keyword guessing attacks and violates users’ data privacy. This article utilizes the SM9 encryption method to propose a Registered Public Keyword Searchable Encryption based on SM9 (RKSE-SM9), which uses the SM9 user keys in the registration keyword algorithm. For RKSE-SM9 to generate the keyword ciphertext or trapdoor, a secure server must first register the keyword, which effectively and reasonably protects users’ data and resists honest and curious cloud servers. From there, we also utilize Beaver’s triple to construct an improved registered keyword generation algorithm, defining and proving that the improved algorithm satisfies the concept of indistinguishability against registration keywords, achieving a higher level of privacy. In addition, compared with existing SM9 searchable encryption, our scheme proved to guarantee better security while reducing the computational efficiency by only 1%; compared with the existing registered keyword searchable encryption scheme, the overall operational efficiency increases by 63%.
Cross-Language Entity Alignment Based on Dual-Relation Graph and Neighbor Entity Screening

Graph convolutional network-based methods have become mainstream for cross-language entity alignment. The graph convolutional network has multi-order characteristics that not only process data more conveniently but also reduce the interference of noise effectively. Although the existing methods have achieved good results for the task of cross-language entity alignment, they have often overlooked the same entity names in the real corpus, resulting in an entity-matching result that was not ideal. Therefore, this study proposed a neighboring-entity-screening rule by combining the entity name and the attribute (NENA) to reduce the influence of these issues. We used the NENA-screening rule to filter and delete redundant equivalent entities and to construct a dual-relation graph as auxiliary evidence for scenarios when the attribute information may be insufficient.This study adopted a graph convolutional network in order to embed knowledge graphs and entity names into a unified vector space, and then a down-sampling method was used to extract the neighboring entities of each entity, thus forming sub-graphs of the two knowledge graphs. We embedded the sub-graphs into the GCN, as the new input, and then we used a cross-graph-matching module to finally achieve alignment. Our results on the DBP15K dataset showed that our approach significantly improved the overall entity alignment.On the sub-dataset ZH-EN of DBP15K, the value of Hits@1 improved by 1.38%, as compared to the best approach mentioned in this paper, and it was useful for the construction and completion of the open knowledge graph.