Reviewerly/exHarmony huggingface dataset:
Digital skills recruitment challenges in small enterprises: the role of blockchain

Industry reports estimate blockchain can potentially generate around $3.1 trillion in new businesses by 2030. These latest digital technologies present more opportunities for all sizes of enterprises to grow. However, they also create new challenges for the small enterprises in adopting such technologies for their individual business needs. While the growth of digital technologies and their impact is explicit, it is also evidenced that small enterprises struggle to recruit and train the digital skills required to effectively use these innovations. This study conducted a literature survey to analyse how blockchain can contribute to small enterprises benefits. Based on the blockchain social ecosystems’ analysis, this study proposes a National Digital Skills Recruitment Chains (NDSRC) model that explicates how blockchain characteristics (digital trust, data immutability, privacy, and security) can play a role in addressing the digital skills recruitment challenges in small enterprises.
Sound-based Two-Factor Authentication: Vulnerabilities and Redesign

Reducing the level of user effort involved in traditional two-factor authentication (TFA) constitutes an important research topic. An interesting representative approach, Sound-Proof , leverages ambient sounds to detect the proximity between the second-factor device (phone) and the login terminal (browser), and eliminates the need for the user to transfer PIN codes. In this paper, we identify a weakness of the Sound-Proof system that makes it completely vulnerable to passive “environment guessing” and active “environment manipulating” remote attackers and proximity attackers. Addressing these security issues, we propose Listening-Watch , a new TFA mechanism based on a wearable device (watch/bracelet) and active browser-generated random speech sounds. As the user attempts to log in, the browser populates a short random code encoded into speech, and the login succeeds if the watch’s audio recording contains this code (decoded using speech recognition ), and is similar enough to the browser’s audio recording. The remote attacker, who has guessed/manipulated the user’s environment, will be defeated since authentication success relies upon the presence of the random code in watch’s recordings. The proximity attacker will also be defeated unless it is extremely close (&lt; 50 cm) to the watch since the wearable microphones are usually designed to capture only nearby sounds (e.g., voice commands).
A pseudo-sluicing analysis of reduced embedded questions in Chakhar Mongolian

Abstract This paper provides a detailed description of reduced embedded questions in Chakhar Mongolian and proposes to analyze them in terms of so-called pseudo-sluicing. It has been noted in the literature that the comparable construction in Khalkha Mongolian does not exhibit the so-called case-matching effect, a phenomenon in which the case of a remnant interrogative phrase matches that of its correlate in the preceding context. We show that it also holds in Chakhar Mongolian. We argue that reduced embedded questions in the language have a pseudo-sluicing structure, which straightforwardly accounts for the absence of the relevant effect. Our proposal is supported by the appearance of a copula and pronominal subjects in those reduced questions and by the fact that reduced questions can be pragmatically controlled.
Attribute network joint embedding based on global attention

The attribute network not only has a complex topology, but its nodes also contain rich attribute information. Attribute network embedding methods extract both network topology and node attribute information to learn low-dimensional embedding of large attribute networks, which are of great importance for network analysis. In this paper, we propose attribute network joint embedding based on global attention (GAJE). First, GAJE uses the structure information of the attribute network to obtain the structure embedding vectors of nodes. Second, we propose a global attention method to obtain the attribute embedding vectors of nodes. This method captures the relationships of different attributes within and between nodes through convolutional neural networks and attention mechanisms, respectively. Finally, GAJE concatenates the structure embedding vectors and the attribute embedding vectors to obtain the final joint embedding vectors which simultaneously reflects the network structure and attributes information. For link prediction and node classification, we compared GAJE with nine well-known network embedding methods in four real datasets. The experimental results show that the algorithm has good attribute network embedding effects. Our tensorflow implementation of the GAJE is available at https://github.com/Andrewsama/GAJE-master.
Relevant edge probability‐based adaptively weighted active contour for medical image segmentation

Abstract The level‐set based active contours have been found popular for medical image segmentation tasks, because of their inherent support for the topological changes—splitting and merging. Meanwhile, contour's leakage through the weak edges and premature convergence due to intensity inhomogeneity diminish its accuracy. Adjusting energy weights according to image features, local to the contour can be helpful. However, weight adjusted as deterministic function of the features is not adequate, limiting the segmentation accuracy. To address the problem, a new relevant edge probability based adaptively weighted level‐set evolution (REP‐WLSE) method for medical image segmentation is investigated. The weights used in this proposal are adaptive to an image relative value, obtained statistically from the feature‐explorations during the contour's evolution. The value is basically an estimate of contour's probability of finding relevant boundary edges on the image plane. Spatial intensity‐range filtering provides the feature space. An adaptive time‐step management scheme is also implemented, which controls the speed variation of the contour evolution. Time‐step is adjusted as a function of contour's alignment with the edges. The merits of the suggested methodology are—(i) reduced leakage through the weak edges, (ii) ability to handle the inhomogeneity, and (iii) increased chances of convergence around the foreground/region of interest (ROI). Experimental results using brain MR, abdominal ultrasound, and breast ultrasound images are presented. State‐of‐the‐art methods are compared using different metrics. The suggested methodology achieved better results.
Promoting Renewable Energy Consumption in Sub-Saharan Africa: How Capital Flight Crowds Out the Favorable Effect of Foreign Aid

The study assesses the effect of capital flight in the nexus between foreign aid and renewable energy consumption in 20 countries in sub-Saharan Africa using data for the period 1996 to 2018. The empirical technique employed is interactive quantile regressions, and the following findings are established. Foreign aid increases renewable energy consumption, while capital flight dampens the favorable effect of foreign aid on renewable energy consumption. The underlying significance and corresponding mitigating effect are exclusively relevant to the bottom (i.e., 10th) quantile of the conditional distribution of renewable energy consumption. The findings are robust to simultaneity and unobserved heterogeneity. Policy implications are discussed. JEL Classification: H10; Q20; Q30; O11; O55
Collaborative diagnosis in mixed-reality using deep-learning networks and RE-WAPICP algorithm

This investigation explores the use of mixed-reality in collaborative diagnosis by sharing medical data in real-time between multiple physicians using Head-Mounted Display (HMD) devices. Object detection and alignment of the digitized data with the object are the backbone in any mixed-reality application. In this paper, deep-learning networks are used in detecting the patient’s face in the physical world and the medical data is aligned to the patient via the Region-Enhanced-Weight-and-Perturb Iterative-Closest-Point (RE-WAPICP) algorithm. Experiments were performed by sharing a 3D digital model of intracerebral vascular with multi-viewers in a mix-reality environment and the results show that this approach is feasible.
Foggy image restoration using deep sub‐pixel reconstruction network

Abstract Light undergoes attenuation due to scattering and refraction when propagating through aerosols. In foggy conditions, Aerosol particles in the troposphere exhibit high mobility introducing intricate non‐linear noise into images. Foggy image restoration represents an ill‐posed problem, where traditional physical models and image enhancement techniques often prove inadequate in delivering effective solutions. This paper introduces a novel deep sub‐pixel reconstruction algorithm for foggy image restoration, pioneering the application of sub‐pixel reconstruction modules to this domain. This model employs convolutional layers to extract low‐level features and dense‐connected layers for high‐level feature extraction. Furthermore, a specialized sub‐pixel reconstruction module tailored for the task of foggy image restoration is designed, with the purpose of reconstructing dehazed images from latent vectors. During training, a generative adversarial training framework is adopted, incorporating a purpose‐designed discriminator. Additionally, a fusion loss is implemented to facilitate model refinement. Quantitative and qualitative evaluation experiments conducted on synthetic and real‐world image datasets demonstrate the effectiveness of the proposed method in preserving finer details. The Structural Similarity Index (SSIM) is observed to improve by 2.5%, attesting to enhanced perceptual quality for grayscale foggy images.
Multicriteria decision‐making–based framework for implementing DevOps practices: A fuzzy best–worst approach

Abstract Increasingly, software organizations are implementing DevOps culture to benefit from it in terms of continuous testing, delivery, improvement, and so forth. Implementing DevOps is difficult due to a lack of understanding about the practices and their effective application for its effective implementation. This paper aims to explore different DevOps practices that can be implemented in software organizations. The study involves conducting a systematic literature review (SLR) to identify DevOps implementation practices, followed by the utilization of the fuzzy best–worst method (FBWM) to establish a taxonomy or classification of software practices. We have used an SLR to investigate the practices, and subsequently, the survey method was followed to validate the identified practices. Moreover, the best–worst method (BWM) was considered to evaluate the significance and develop the taxonomy of the practices. The results of this study extracted 19 practices that have been identified in the SLR process. The identified factors are further classified into six core DevOps lifecycle phases. The results of the BWM approach are shown. The outcomes of the study conclude that the proposed taxonomy of the practices could help DevOps practitioners and researchers effectively implement them in software development organizations.
Data-driven satellite monitoring method applicable to various telemetry

It is difficult to detect signs of faults for the rule-based health monitoring systems currently installed on artificial satellites in principle, and manual monitoring of satellite telemetry is conducted. However, due to lack of human resources, much of the data is left not yet well reviewed. In this study, a systematic telemetry monitoring method that screens anomalous ones applicable to various time-series telemetry is proposed. The proposed method estimates the normal range of future telemetry values by focusing on quantile statistics of each telemetry. The demonstrative application results to the real telemetry data are also reported.
Natureness‐preserving tone mapping operator based on improved guided filter and adaptive Gamma curve

Abstract Mapping high dynamic range (HDR) images to low dynamic range (LDR) display devices while performing enhancement processing is a challenging task. To address the issues of chromatic drift, the imbalance between preserving naturalness and enhancing details during the mapping process, this paper proposed a tone mapping operator (TMO) based on an improved guided filter with multi‐layered processing. Firstly, the operator tackled the problem of insufficient extraction of detail layer information in complex scene images by redefining the formula of the guided filter and introducing adaptive regularization parameters. This enhancement enabled more effective extraction of details compared to traditional fixed‐parameter guided filters. Secondly, the algorithm employed a centre‐surround approach to subdivide and processed the detail layers individually, effectively suppressing halos and artefacts. Thirdly, image entropy was utilized to guide the fusion and enhancement processing of the detail layers, preventing excessive or insufficient enhancement. Finally, this paper proposed an adaptive gamma correction algorithm and a chromatic consistency algorithm to improve overall contrast and hue saturation. The results demonstrate convincing performance in terms of subjective visual evaluation and image quality assessment.
Bayesian Stackelberg game approach for cyber mission impact assessment

Cyber threats increasingly target mission-critical systems in the military and private sectors. CMIA has emerged to mitigate such cyber threats and assure the resilience of critical missions. Existing approaches merely provide partial, manual analysis methods to support CMIA and lack the ability to provide optimal defense measures. In this paper, we propose a novel CMIA framework enabling a streamlined, automated CMIA procedure, based on the Bayesian Stackelburg game. Unlike conventional approaches, our framework provides the most probable cyber threat and the optimal defense, i.e., countermeasures against the cyber threat in a given mission environment, securing the resilience of mission-critical systems.
DLSDHMS: Design of a deep learning-based analysis model for secure and distributed hospital management using context-aware sidechains

Designing an efficient hospital management solution requires the integration of multidomain operations that include secure storage, alert system modelling, infrastructure management, staff management, report analysis, and feedback-based learning tasks. Existing hospital management models are either highly complex or do not incorporate comprehensive deep learning analysis, which limits their deployment capabilities. Moreover, most of these models use mutable storage solutions, which restricts their trust levels under multi-patient to multi-doctor mapping scenarios. To overcome these issues, this article proposes the design of a novel deep Learning-based analysis model for secure and distributed hospital management via context-aware sidechains. The model initially collects large-scale information sets from different hospital entities via an IoT-based network and stores the information on context-sensitive sidechains. These context-sensitive sidechains store information sets related to Medicine Management, Doctor Management, Insurance and Billing Management, and Appointment Management operations. These chains are optimized via an Iterative Genetic Algorithm (IGA) that assists in improving storage and retrieval performance via intelligent merging and splitting operations. Information stored on these chains is processed via a combination of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), that assist in identifying patient-level diseases and issues. The information obtained from these classifiers is updated on the central repository and assists in the pre-emption of diseases for other patients. Due to these integrations, the proposed model is capable of reducing computational delay by 3.5 % and reducing storage cost by 8.3 % when compared to other blockchain-based deployments. The model is also able to pre-empt patient issues with 9.3 % higher accuracy and 4.8 % higher precision, which makes it useful for real-time clinical deployments.
<scp>GLRP</scp>: Global and local contrastive learning based on relative position for medical image segmentation on cardiac <scp>MRI</scp>

Abstract Contrastive learning, as an unsupervised technique, is widely employed in image segmentation to enhance segmentation performance even when working with small labeled datasets. However, generating positive and negative data pairs for medical image segmentation poses a challenge due to the presence of similar tissues and organs across different slices in datasets. To tackle this issue, we propose a novel contrastive learning strategy that leverages the relative position differences between image slices. Additionally, we combine global and local features to address this problem effectively. In order to enhance segmentation accuracy and reduce isolated mis‐segmented regions, we employ a two‐dimensional fully connected conditional random field for iterative optimization of the segmentation results. With only 10 labeled samples, our proposed method is able to achieve average dice scores of 0.876 and 0.899 on the public and private dataset heart segmentation tasks, surpassing the PCL method's 0.801 and 0.852. Experimental results on both public and private MRI datasets demonstrate that our proposed method yields significant improvements in medical segmentation tasks with limited annotated samples, outperforming existing semi‐supervised and self‐supervised techniques.
Deploying artificial intelligence in services to AID vulnerable consumers

Abstract Despite offering substantial opportunities to tailor services to consumers’ wants and needs, artificial intelligence (AI) technologies often come with ethical and operational challenges. One salient instance of such challenges emerges when vulnerable consumers, consumers who temporarily or permanently lack resource access or control, are unknowingly discriminated against, or excluded from the marketplace. By integrating the literature on consumer vulnerability, AI for social good, and the calls for rethinking marketing for a better world, the current work builds a framework on how to leverage AI technologies to detect, better serve, and empower vulnerable consumers. Specifically, our AID framework advocates for designing AI technologies that make services more accessible , optimize customer experiences and journeys interactively , and to dynamically improve consumer decision-making. Adopting a multi-stakeholder perspective, we also discuss the respective implications for researchers, managers, consumers, and public policy makers.
Cross-domain Recommendation via Dual Adversarial Adaptation

Data scarcity is a perpetual challenge of recommendation systems, and researchers have proposed a variety of cross-domain recommendation methods to alleviate the problem of data scarcity in target domains. However, in many real-world cross-domain recommendation systems, the source domain and the target domain are sampled from different data distributions, which obstructs the cross-domain knowledge transfer. In this paper, we propose to specifically align the data distributions between the source domain and the target domain to alleviate imbalanced sample distribution and thus challenge the data scarcity issue in the target domain. Technically, our proposed approach builds a dual adversarial adaptation (DAA) framework to adversarially train the target model together with a pre-trained source model. Two domain discriminators play the two-player minmax game with the target model and guide the target model to learn reliable domain-invariant features that can be transferred across domains. At the same time, the target model is calibrated to learn domain-specific information of the target domain. In addition, we formulate our approach as a plug-and-play module to boost existing recommendation systems. We apply the proposed method to address the issues of insufficient data and imbalanced sample distribution in real-world Click-Through Rate (CTR)/Conversion Rate (CVR) predictions on two large-scale industrial datasets. We evaluate the proposed method in scenarios with and without overlapping users/items, and extensive experiments verify that the proposed method is able to significantly improve the prediction performance on the target domain. For instance, our method can boost PLE with a performance improvement of 15.4% in terms of Area Under Curve (AUC) compared with single-domain PLE on our private game dataset. In addition, our method is able to surpass single-domain MMoE by 6.85% on the public datasets. Code: https://github.com/TL-UESTC/DAA.
ANDIN-MU: Development of Android-Based Descriptive Text Interactive Multimedia Materials in High School English Subjects

In learning there is a process of changing the behavior of a person who is initially unaware of something then becomes aware. In other words, there is a process of conveying knowledge between teachers and students or using learning materials. The purpose of this study was to develop interactive multimedia based on Android descriptive text material for Class X SMA Pasuruan English. This study uses the ADDIE model. In this development model there are five steps in the process, but in this study the researchers modified some of the steps used and adapted them to the environmental conditions affected by the Covid-19 pandemic climate. The steps taken are 4 out of 5 steps, namely (1) analysis (2) design (3) development and (4) evaluation. Collecting data in this study through interviews, documentation and questionnaires. The results showed that the media validation results scored 80%, while the content validation results obtained a score of 78%. The Likert scale used in the data calculation process shows that the developed interactive multimedia is very feasible to use in Pasuruan High School
Multi-fuzzy clustering validity index ensemble: A Dempster-Shafer theory-based parallel and series fusion

Clustering validity evaluation is a key part in clustering process. To adapt the complex data structure, the traditional fuzzy clustering validity index (FCVI) is designed more complex. The weighted combined validity evaluation method (WCVEM) is simple in structure but difficult in weight selection. Therefore, this paper proposed an ensemble method based on multi-fuzzy clustering algorithms and multi-FCVI. Firstly, multi-FCVI are calculated by using the multiple sets of cluster centers and membership degrees that obtained by multi-fuzzy clustering algorithms. This can improve the robustness of the multi-FCVI. Secondly, multi-FCVI are ensembled by Dempster-Shafer (DS) theory. The validity index basic probability assignment function can be obtained by calculating the credibility of each validity index with different clusters number. Finally, the decision module is used to output the optimal clusters number. This paper ensembles multi-fuzzy clustering algorithms, multi-FCVI, and the DS theory by using series and parallel structure to verify performance of the proposed model and the degree of information retention of the FCVI. The proposed method is simple in structure and does not need to be select weighted. 6 artificial datasets and 12 UCI datasets were selected to simulate and verify the method. When facing different data, the simulation results show that the parallel structure has the highest accuracy, and the series structure is even worse than the weighted method in some datasets. In addition, the paper changes the value of fuzzy weighted, and experimental results show that the ensemble method has better stability than other methods in the face of different fuzzy weighted strategy.