Reviewerly/exHarmony huggingface dataset:
Compression and Denoising of Medical Images Using Autoencoders

In medical image analysis Image compression and denoising is an important processing steps for remote analytics. A number of algorithms are proposed in the literature with varying degrees of denoising performances. In this paper, a 3-layer autoencoder model to compress and denoise grayscale medical images is proposed. Gaussian noise with a noise factor of 0.5 is added and passes through 3 convolution and 3 max pool layers. The reverse process is made for image denoising. The proposed model learns to denoise data with a high PSNR and a high compression ratio after training. Most of the denoised images show over 30 dB PSNR with a 4:1 compression ratio. Images can be combined to increase sample size and overall denoising performance.
Unveiling and Unraveling Aggregation and Dispersion Fallacies in Group MCDM

Abstract Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with solutions based on compositional data analysis to avoid misusing statistical operations. First, we use a compositional approach to aggregate the priorities of a group of DMs and show that the outcome of the compositional analysis is identical to the normalized geometric mean, meaning that the arithmetic mean should be avoided. Furthermore, a new aggregation method is developed, which is a robust surrogate for the geometric mean. We also discuss the errors in computing measures of dispersion, including standard deviation and distance functions. Discussing the fallacies in computing the standard deviation, we provide a probabilistic criteria ranking by developing proper Bayesian tests, where we calculate the extent to which a criterion is more important than another. Finally, we explain the errors in computing the distance between priorities, and a clustering algorithm is specially tailored based on proper distance metrics.
Thermal infrared spectroscopy studies on skarn minerals for exploration of the Jiama Cu-Mo deposit, Tibet, China

The zonation patterns of skarn minerals, including garnet, diopside, and wollastonite, are important for exploration of skarn deposits. Thermal infrared (TIR) spectroscopy, which covers the wavelength range of 6–14 μm, is a new eco-friendly technology for skarn mineral identification. In this study, the TIR spectra data were collected, using an Agilent 4300 Handheld FTIR Spectrometer from nine drill holes within Jiama Cu-Mo deposit in Tibet, China. This study focuses on the TIR characteristics of grandite-series garnets (i.e., grossular to andradite), which have twin peaks in the 10–13 μm range and a diagnostic absorption valley (T absorption valley) at around 11.5 μm shifting based on the contents of Al2O3 and Fe2O3. The T absorption valley in the zoned garnet sample showed an undulating pattern, shifting towards longer wavelengths from the core to the rim, indicating the increase in Fe2O3 content and a decrease in Al2O3, MnO, and TiO2 content. The mixed spectral features of three significant skarn minerals including garnet, diopside, and wollastonite were used to classify the skarn zonation into garnet skarns, diopside-garnet skarns, and wollastonite-garnet skarns. The results from nine drill holes of the No. 24 exploration line in the cross section showed that the garnet T absorption valley shifted towards shorter wavelengths from the proximal to the distal skarn, as well as from the lower to the upper skarn. Furthermore, intensive copper mineralization occurred in the skarn when the garnet T absorption valley exceeded 11.50 μm. In comparison, the garnet T absorption valley at 11.35–11.50 μm is related to more intense molybdenum mineralization. This study suggests that TIR spectroscopy has the potential to rapidly identify skarn zonation in the field, and providing a suitable vector tool for skarn ore exploration.
Randomization is all you need: A privacy-preserving federated learning framework for news recommendation

News recommendation systems represent a critical solution to the problem of information overload, as they can suggest news that may be of interest to a particular user. Traditional recommendation systems require the collection of private information, which can lead to serious privacy concerns. Federated learning is a privacy-preserving framework that allows multiple users to train a global model without sharing their private data. In federated learning, users keep their private data locally and calculate the local gradients. In recommendation systems, however, the situation is the opposite, as users need to share their preferences with the server. Notably, user preferences are highly relevant to user privacy. The difference between recommendation systems and federated learning may lead to user privacy leakage. Accordingly, in this paper, we propose RD-FedRec, which follows a paradigm commonly used in real-world recommendation systems. First, we propose a randomized decomposition method to protect the privacy of user preferences, which has good compatibility and can preserve the privacy of recommendation results. Second, to improve recommendation efficiency, we introduce a recall phase that roughly filters news, thereby reducing the time overhead of the ranking phase. We implement RD-FedRec and evaluate its performance on two real-world datasets. Experimental results show that the accuracy and efficiency of RD-FedRec are comparable to state-of-the-art recommendation systems that do not provide privacy guarantees, and moreover that our proposed randomized decomposition method is compatible with most recommendation systems.
Probably not a game: Playing with the AI in the ritual of taking pictures on the mobile phone

This research explores mediated ritual interactions in the form of pictures taken using a mobile device in tandem with two critical designs: Probably Not and World to GIF. We take our point of departure in Rich Ling’s understanding of mobile interaction as a ritual of social cohesion and social bonding to explore sociotechnical interactions with mobile technology. By means of playful critical interaction design methods, we explore how deviance from the ritual of mediated interaction through taking pictures on the mobile phone may enhance our understanding of artificial agents. Building upon Ling’s work, we mobilize the ritual aspect of mobile communication to open possibilities for imagining alternatives of seeing the world through artificial intelligence. We conclude by arguing that playful designs in mobile communication research may allow us to disassemble and reassemble mobile apps which enables critical reflection on the role of artificial agents in mobile rituals.
The application of knowledge graphs in the Chinese cultural field: the ancient capital culture of Beijing

Abstract A methodology is proposed to introduce knowledge graphs into the study of the Chinese cultural field for use in a newly designed, complete application. At present, the combination of culture and information technology has become a trend. Among various technologies, knowledge graphs are a very promising option. The contributions of this paper are as follows: it supplies for the first time a knowledge graph in the cultural field of the ancient capital of Beijing, establishes a domain knowledge base, and develops a platform for visual analysis and interactive question and answer. In this process, a framework for applying knowledge graphs to research in the cultural field is summarized, providing ideas for research in the cultural field.
Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization

A metaheuristic approach based on the nature-inspired and well-known Grey Wolf Optimization algorithm (GWO) was employed in this study to design an approach for retrieving strong designs of 8×8 substitution boxes (S-boxes). The GWO was developed as a novel metaheuristic based on inspiration from grey wolves and how they hunt. The ability of the GWO to quickly explore the search space for the near/optimal feature subsets that maximize any given fitness function (in consideration of its distinctive hierarchical structure) aids in the construction of strong S-boxes that can satisfy the required criteria. However, when tackling optimization problems, GWO may experience the problem of premature convergence. Therefore, a variant of GWO called Crossover Grey Wolf Optimizer (XGWO) has been proposed in this study. The performance of the proposed novel approach was evaluated using numerous cryptographic performance metrics, including bijective property, bit independence, strict avalanche, linear probability, and I/O XOR distribution and the result was contrasted with a couple of existing S-box creation techniques. Overall, the results of the experiment showed that the suggested S-box design had adequate cryptographic features.
Construction of smart older adults care service model driven by primary health care

In recent years, with the arrival of an overall aging population, how to provide for the elderly has become the focus of government departments and society. The traditional elderly care service model has problems such as backward construction of information platforms, low quality of elderly care services, and "digital divide". For this reason, based on grassroots medical and health care, this paper improves the quality of elderly care services by establishing a smart elderly care service model. Through experiments, it can be found that compared with the traditional elderly care service model, the intelligent elderly care service model has an absolute advantage in the identification of nursing data. The recognition accuracy rate of the smart elderly care service model for all kinds of daily care data is above 94%, while the recognition accuracy rate of the traditional elderly care service model is below 90%. Therefore, it is of great significance to study the smart elderly care service model driven by primary medical care and health.
Adaptive fusion affinity graph with noise-free online low-rank representation for natural image segmentation

Affinity graph-based segmentation methods have become a major trend in computer vision. The performance of these methods rely on the constructed affinity graph, with particular emphasis on the neighborhood topology and pairwise affinities among superpixels. However, these graph-based methods ignore the noisy data from images, that influence the accuracy of pairwise similarities. Multiscale combinatorial grouping and graph fusion also generate a higher computational complexity. In this paper, we propose an adaptive fusion affinity graph with noise-free low-rank representation in an online manner for natural image segmentation. An input image is first over-segmented into superpixels at different scales and then filtered by an improved kernel density estimation method. Moreover, we select global nodes of these superpixels on the basis of their subspace-preserving presentation, which reveals the feature distribution of superpixels exactly. To reduce time complexity while improving performance, a sparse representation of global nodes based on noise-free online low-rank representation is used to obtain a global graph at each scale. Experimental results on BSD300, BSD500, MSRC, SBD, and PASCAL VOC show the effectiveness of our method in comparison with the state-of-the-art approaches. The code is available at https://github.com/Yangzhangcst/AFA-graph.
Factors affecting team social networking and performance: The moderation effect of team size and tenure

As it derives from information technology, the internet revolution, and their consequences for business operations, social networking has received wide research interest. Therefore, this study aims to analyze the factors influencing team social networking and performance with the moderating effect of team size and tenure in SMEs in Jordan. Using a survey questionnaire, the study collected the data from this sector and analyzed it with SamartPLS to test the proposed research model. The results showed a mostly significant positive effect from seven factors (team communication, team conflict, team power, team regulation, team collaboration, team engagement, and team motivation). The study also revealed a moderating effect of both team size and tenure in the relationship between team social networking and SME performance in Jordan. The contributions provided through this work expand understanding of the factors’ importance while addressing the issue of team social networking and performance. In practice, this enables team leaders to focus on team size and tenure to develop their business performance.
Dual-decoder transformer network for answer grounding in visual question answering

Visual Question Answering (VQA) have made stunning advances by exploiting Transformer architecture and large-scale visual-linguistic pretraining. State-of-the-art methods generally require large amounts of data and devices to predict textualized answers and fail to provide visualized evidence of the answers. To mitigate these limitations, we propose a novel dual-decoder Transformer network (DDTN) for predicting the language answer and corresponding vision instance. Specifically, the linguistic features are first embedded by Long Short-Term Memory (LSTM) block and Transformer encoder, which are shared between the Transformer dual-decoder. Then, we introduce object detector to obtain vision region features and grid features for reducing the size and cost of DDTN. These visual features are combined with the linguistic features and are respectively fed into two decoders. Moreover, we design an instance query to guide the fused visual-linguistic features for outputting the instance mask or bounding box. The classification layers aggregate results from decoders and predict answer as well as corresponding instance coordinates at last. Without bells and whistles, DDTN achieves state-of-the-art performance and even competitive to pretraining models on VizWizGround and GQA dataset. The code is available at https://github.com/zlj63501/DDTN.
A Real-Time Incremental Video Mosaic Framework for UAV Remote Sensing

Unmanned aerial vehicles (UAVs) are becoming increasingly popular in various fields such as agriculture, forest protection, resource exploration, and so on, due to their ability to capture high-resolution images quickly and efficiently at low altitudes. However, real-time image mosaicking of UAV image sequences, especially during long multi-strip flights, remains challenging. In this paper, a real-time incremental UAV image mosaicking framework is proposed, which only uses the UAV image sequence, and does not rely on global positioning system (GPS), ground control points (CGPs), or other auxiliary information. Our framework aims to reduce spatial distortion, increase the speed of the operation in the mosaicking process, and output high-quality panorama. To achieve this goal, we employ several strategies. First, the framework estimates the approximate position of each newly added frame and selects keyframes to improve efficiency. Then, the matching relationship between keyframes and other frames is obtained by using the estimated position. After that, a new optimization method based on minimizing weighted reprojection errors is adopted to carry out precise position calculation of the current frame, so as to reduce the deformation caused by cumulative errors. Finally, the weighted partition fusion method based on the Laplacian pyramid is used to fuse and update the local image in real time to achieve the best mosaic result. We have carried out a series of experiments which show that our system can output high-quality panorama in real time. The proposed keyframe selection strategy and local optimization strategy can minimize cumulative errors, the image fusion strategy is highly robust, and it can effectively improve the panorama quality.
Immersive Virtual-Reality System for Aircraft Maintenance Education: A Case Study

Aircraft maintenance is a highly relevant procedure in many industries, yet obtaining qualified personnel to carry it out is a difficult task. Training in such techniques is complex and requires access to facilities and materials that are not readily available. Virtual reality can be a tool to improve this situation. This paper presents the whole process of design, development, and evaluation of a virtual environment that allows users to perform some of the main tasks required in aircraft maintenance after landing or for take-off. By following a user-centered design methodology and the Octalysis framework to apply motivation and engagement techniques, a gamified virtual environment was developed that allows the user to practice specific aircraft maintenance techniques. The environment was tested by users of different profiles who answered questionnaires to evaluate the perceived gamification, usability, and the feeling of sickness from the experience. The analysis of the data corroborates the good performance of the VR environment in these fields.
Leveraging Ethereum Platform for Development of Efficient Tractability System in Pharmaceutical Supply Chain

Consumer knowledge of the goods produced or processed by the numerous suppliers and processors is still relatively low due to the growing complexity of the structure of pharmaceutical supply chains. Information asymmetry in the pharmaceutical sector has an effect on welfare, sustainability, and health. (1) Background: In this respect, we wanted to develop a productive structure for a pharmaceutical supply chain that satisfies the consumer information needs and fosters consumer confidence in the pharmacy goods they buy. By using blockchain technology, the main goals were to develop and implement a pharmaceutical supply chain. (2) Objectives: The main objectives of this work were to leverage an Ethereum platform for the development of a tractability system in a pharmaceutical supply chain environment and to analyze the efficiency of MSMAChain with respect to the cost and execution of transactions based on our designed smart contracts. (3) Results: This research looked into a variety of issues related to the value, viability, and effects of blockchain technology for use in supply chain applications. The methods and creations in this environment were monitored and researched. It is vital to identify a number of crucial subjects including future research areas, in order to achieve the widespread acceptance of the supply chain traceability provided by blockchain technology. (4) Conclusions: MSMAChain, an Ethereum blockchain-based approach, leverages smart contracts and decentralized off-chain storage for efficient product traceability in terms of the cost and execution of transaction for a health care supply chain.
Grapharizer: A Graph-Based Technique for Extractive Multi-Document Summarization

In the age of big data, there is increasing growth of data on the Internet. It becomes frustrating for users to locate the desired data. Therefore, text summarization emerges as a solution to this problem. It summarizes and presents the users with the gist of the provided documents. However, summarizer systems face challenges, such as poor grammaticality, missing important information, and redundancy, particularly in multi-document summarization. This study involves the development of a graph-based extractive generic MDS technique, named Grapharizer (GRAPH-based summARIZER), focusing on resolving these challenges. Grapharizer addresses the grammaticality problems of the summary using lemmatization during pre-processing. Furthermore, synonym mapping, multi-word expression mapping, and anaphora and cataphora resolution, contribute positively to improving the grammaticality of the generated summary. Challenges, such as redundancy and proper coverage of all topics, are dealt with to achieve informativity and representativeness. Grapharizer is a novel approach which can also be used in combination with different machine learning models. The system was tested on DUC 2004 and Recent News Article datasets against various state-of-the-art techniques. Use of Grapharizer with machine learning increased accuracy by up to 23.05% compared with different baseline techniques on ROUGE scores. Expert evaluation of the proposed system indicated the accuracy to be more than 55%.
Comprehensive Analysis of Compressible Perceptual Encryption Methods—Compression and Encryption Perspectives

Perceptual encryption (PE) hides the identifiable information of an image in such a way that its intrinsic characteristics remain intact. This recognizable perceptual quality can be used to enable computation in the encryption domain. A class of PE algorithms based on block-level processing has recently gained popularity for their ability to generate JPEG-compressible cipher images. A tradeoff in these methods, however, is between the security efficiency and compression savings due to the chosen block size. Several methods (such as the processing of each color component independently, image representation, and sub-block-level processing) have been proposed to effectively manage this tradeoff. The current study adapts these assorted practices into a uniform framework to provide a fair comparison of their results. Specifically, their compression quality is investigated under various design parameters, such as the choice of colorspace, image representation, chroma subsampling, quantization tables, and block size. Our analyses have shown that at best the PE methods introduce a decrease of 6% and 3% in the JPEG compression performance with and without chroma subsampling, respectively. Additionally, their encryption quality is quantified in terms of several statistical analyses. The simulation results show that block-based PE methods exhibit several favorable properties for the encryption-then-compression schemes. Nonetheless, to avoid any pitfalls, their principal design should be carefully considered in the context of the applications for which we outlined possible future research directions.
Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model

Fight detection in videos is an emerging deep learning application with today's prevalence of surveillance systems and streaming media. Previous work has largely relied on action recognition techniques to tackle this problem. In this paper, we propose a simple but effective method that solves the task from a new perspective: we design the fight detection model as a composition of an action-aware feature extractor and an anomaly score generator. Also, considering that collecting frame-level labels for videos is too laborious, we design a weakly supervised two-stage training scheme, where we utilize multiple-instance-learning loss calculated on video-level labels to train the score generator, and adopt the self-training technique to further improve its performance. Extensive experiments on a publicly available large-scale dataset, UBI-Fights, demonstrate the effectiveness of our method, and the performance on the dataset exceeds several previous state-of-the-art approaches. Furthermore, we collect a new dataset, VFD-2000, that specializes in video fight detection, with a larger scale and more scenarios than existing datasets. The implementation of our method and the proposed dataset is available at https://github.com/Hepta-Col/VideoFightDetection.