Reviewerly/exHarmony huggingface dataset:
A Spontaneous Adoption of AR Technology a manufacturing industrial context

Digital transformation is a process encompassing all organizations, requiring a proactive attitude and willingness to change. The Covid-19 pandemic highlighted the relevance of digitization through an increased awareness and implementation of digital tools for working life. The next wave of successful innovation in industry demands high-pitched adoption of technologies for production and workplace learning systems. Organizations are trying to understand which technologies to invest in, based on usability measures, cost effectiveness, and sustainability. It can be hard to predict which technology is best suited for specific tasks. This implies a growing risk regarding investments in technology. This paper describes the spontaneous use of technology for augmented reality (AR, Microsoft HoloLens 2) in a Norwegian manufacturing company during Covid-19. The case illustrates how AR technology can be used in assembling, installation and acceptance testing of machinery for selective soldering in the production of circuit boards. Data were collected through case study research and a qualitative research design, through observation and interviews with the participants. The results show that Microsoft HoloLens 2 is easy to adopt and could contribute to immediate and real value creation in industrial production companies. We believe that the spontaneous usage of AR technology in such extraordinary circumstances as a pandemic could motivate and guide other businesses facing important decisions related to technology implementation. The original value of this article is a contribution to the discussion on the Technology Acceptance Model, which is chosen as a theoretical framework for the paper.
An Efficient Parallel Hybrid Feature Selection Approach for Big Data Analysis

Classification algorithms face runtime complexity due to high data dimension, especially in the context of big data. Feature selection (FS) is a technique for reducing dimensions and improving learning performance. In this paper, the authors proposed a hybrid FS algorithm for classification in the context of big data. Firstly, only the most relevant features are selected using symmetric uncertainty (SU) as a measure of correlation. The features are distributed into subsets using Apache Spark to calculate SU between each feature and target class in parallel. Then a Binary PSO (BPSO) algorithm is used to find the optimal FS. The BPSO has limited convergence and restricted inertial weight adjustment, so the authors suggested using a multiple inertia weight strategy to influence the changes in particle motions so that the search process is more varied. Also, the authors proposed a parallel fitness evaluation for particles under Spark to accelerate the algorithm. The results showed that the proposed FS achieved higher classification performance with a smaller size in reasonable time.
In Memorial and Serenity

Macao inhabit a population of 683,100. The birth rate has been dropping while the death rate has risen compared to two years ago. Cemeteries are becoming crowded, and burial spots are demanding. In this case, video calls and social media can be the solution. How about our beloved ancestors? Can we video call them on their memorial days? This paper presents a VR experience of immersing oneself in the 3D VR of the Chapel of St. Michael of Macao to create a peaceful atmosphere for grave mourning. The chapel is also a personal space where we can be truly isolated in serenity. It is a retreat to pray, disconnect, and reconnect to the beloved deaths that may not be buried in an easily accessible location. The authors propose a possible future of mourning our loved ones through virtual reality and telepresence: an immersive experience connected with Macao's extraordinary and cultural unicity.
AUTOMATED SYSTEM OF LUNG DISEASE DIAGNOSIS ON X-RAY IMAGES

Currently, automated detection of pneumonia in images is a priority in information technology. A promising option for solving this problem is the use of convolutional neural networks. The purpose of the study is to develop an automated system for diagnosing pneumonia from X-rays. This paper presents research and development of software for the automated system for analysis and classification of X-ray images of the lungs in order to automatically determine the signs of the disease, in particular pneumonia, which is most relevant due to the COVID-19 pandemic. This work is an example of the creation of a decision support system that is designed to assist a doctor to make decisions, analyze X-ray images of the lungs, classify them, and also allows to store all the necessary information about patients in one repository. For the automated system software was developed using C# and the user interface development environment - WPF. During the implementation process there were used the MVVM architecture and ML.NET as a tool for the neural network setup. Software was created based on the developed mathematical model through the integrated training of neural networks. To fulfill the main goal of software development, convolutional neural networks were used. As a result of the experiments, the coefficient of correctly recognized images was obtained – 97 %, which is close to the coefficient of recognition by a doctor.
Exceptional crooked functions

Crooked functions are combinatorial objects of great interest. It is already known that the only monomial and binomial crooked functions are quadratic. In this paper, we investigate conditions on the shape of a polynomial to be crooked. Furthermore, the notion of exceptional crooked is introduced, similarly to those of APN or PN exceptional functions. Via a connection with algebraic varieties over finite fields, we provide non-existence results of exceptional crooked functions.
Fashion Recommendation with a real Recommender System Flow

In this technical report, we present our solution of RecSys Challenge 2022 focusing on the fashion recommendation. We produce recommendations in two steps: (i) the retrieval step, which generates a candidate item set based on multiple cheap-to-compute strategies; (ii) the ranking step: which rearranges the candidate items with a richer set of features. Specifically, we conduct various strategies to retrieve as many positive samples as possible in retrieval step and obtain the retrieval scores from these retrieval channels meanwhile. Then these scores along with some extracted features are involved in the ranking stage for modeling to generate the purchase prediction. In the final submission, we use six effective retrieval strategies in retrieval step and ensemble five ranking models by taking average of their outputs. Using our method, our team doubleQ achieved MRR 0.2013 on final test set which wins the 10 place, and the solution codes are available via https://github.com/doubleQ2018/recsys-challenge-2022.
Supporting open science

The ability to share information is crucial to scientific progress, yet it is not easy to manage and share the vast amounts of data generated at Photon and Neutron Research Infrastructures (PaN RIs). We spoke to Professor Dr Patrick Fuhrmann and Dr Sophie Servan about the work of the ExPaNDS project in creating a framework for effective data management and supporting open science
Semantic Foundations for Cost Analysis of Pipeline-Optimized Programs

In this paper, we develop semantic foundations for precise cost analyses of programs running on architectures with multi-scalar pipelines and in-order execution with branch prediction. This model is then used to prove the correction of an automatic cost analysis we designed. The analysis is implemented and evaluated in an extant framework for high-assurance cryptography. In this field, developers aggressively hand-optimize their code to take maximal advantage of micro-architectural features while looking for provable semantic guarantees.
Multi-attention guided and feature enhancement network for vehicle re-identification

Vehicle Re-Identification (Re-ID) aims to discover and match target vehicles in different cameras of road surveillance. The high similarity between vehicle appearances and the dramatic variations in viewpoints and illumination cause great challenges for vehicle Re-ID. Meanwhile, in safety supervision and intelligent traffic systems, one needs a quick efficient method of identifying target vehicles. In this paper, we propose a Multi-Attention Guided Feature Enhancement Network (MAFEN) to extract robust vehicle appearance features. Specifically, the Fusing Spatial-Channel information multi-receptive fields Feature Enhancement module (FSCFE) is first proposed to aggregate richer and more representative multi-receptive fields features at different receptive fields sizes. It also learned the spatial structure information and channel dependencies of the multi-receptive fields features and embedded them to enhance the feature. Then, we construct the Spatial Attention-Guided Adaptive Feature Erasure (SAAFE) module, which uses spatial attention to erase the most distinguishing features. The network’s attention is shifted to potentially salient features to strengthen the ability of the network to extract salient features. In addition, a multi-loss knowledge distillation (MLKD) method using MAFEN as a teacher network is designed to improve computational efficiency. It uses multiple loss functions to jointly supervise the student network. Experimental results on three public datasets demonstrate the merits of the proposed method over the state-of-the-art methods.
Augmented Dimension Portal Types

This article classifies one kind of portal that can be used in the Augmented Dimension. Firstly, it defines the origins of Augmented Dimension, which comes from the Augmented Reality. Then define what the Augmented Dimension is. Afterwards, defining what a portal is and how it can be classified. The classification is based on a previous study about categorization scheme for Augmented Reality. Lastly, discussing the classification of the Augmented Dimension portals and their possible future work in the educational field.
Multi-Objective Cluster Ensemble Based on Filter Refinement Scheme

Cluster ensemble improves the robustness and stability of clustering performances by utilizing multiple solutions. Although traditional cluster ensemble methods have achieved promising performances, they are not adaptive enough to cope with data sets that have multiple levels of complexities. Besides, these methods may contain noisy and redundancy members which have negative effects. To mitigate the above issues, in this paper, we propose a multi-objective filter refinement scheme (MOFRS). First, we perform various clustering methods on different representations of data to generate diverse solutions. Second, we propose a solution filter to select a proper method and reduce the number of initial partitions for a given data set. Third, four stability indices are designed to split instances into stable and unstable groups. Fourth, objective functions based on diversity and quality are utilized to quantify the goodness of base clustering solutions. Finally, we design an improvement oriented multi-objective evolutionary algorithm to optimize these objective functions. Extensive experimental results conducted on 27 real-world data sets show that MOFRS outperforms most cluster ensemble selection methods, and achieves statistically significant improvements, compared with full ensemble methods.
Object Detection and Recognition based on Improved Faster R-CNN

With the rapid development of deep learning methods, object detection, as one of the most basic and challenging tasks in the field of computer vision, has made remarkable progress. Graph convolutional network (GCN) [1] extracts image spatial features, which can effectively integrate semantic information and geometric information in a graph structure for learning. In this paper, the feature extraction network of the Faster R-CNN [2] object detection algorithm is changed from the original Vgg16 network to GCN, which improves the detection effect of the algorithm on multiple targets and improves the detection speed. In the fully connected layer of the network, GCN is used to process the target frame area extracted by the Faster-RCNN model to obtain a target frame that is more in line with the semantic information of the image, and then the final task classification and target recognition are performed to effectively utilize the essential features of the object. and intrinsic connections improve the generalization ability and robustness of the model. In the experimental comparison between PASCAL VOC 2007 [3] and Microsoft COCO [4] datasets, the detection speed of the method in this paper is increased by 12% and 13.5%, and the average detection accuracy is increased by 3.8% and 5%, which reduces the amount of calculation and improves the recognition accuracy.
Fault Tolerant Scheme in Steganographic Video Streaming Using n - Repetition Code

The rapid exchange of information increases the need for information security, particularly for confidential data information. Confidential data can be secured using a steganography technique by inserting the data into a cover media, in this case, the cover media is in the form of video. This video becomes a medium for sending a message in real time, which is known as video streaming. However, video streaming has the potential for packet loss. This paper proposes a fault tolerant scheme in steganographic video streaming by using repetition code for ensuring the reception of hidden information in a noisy channel such as packet drop in video streaming. This idea comes from the simplest error correction that can minimize errors in the transmission process of data information with the aim of finding the best fault-tolerant value for video steganography. The method used in this study during video streaming is repetition code with n = odd and multiples of 3. This study describes the embedding and extraction process using the Discrete Wavelet Transform (DWT) method on the YUV color space - Luminance(Y) Chrominance (”U” and ”V”), especially Luminance (Y) channel. The measurement of packet loss effect is done by using Peak Signal to Noise Ratio (PSNR) calculation, in which the higher the PSNR value, the higher the quality of the reconstruction. The use of the DWT method which offers high resolution at low frequencies provides a PSNR value of 131.49 dB with the use of the H.265 codec when the packet drop is at a percentage of 15%, as well as message insertion and repetition in every odd frame (1, 3, 5, 7, …853).
Rainfall Prediction Using Gated Recurrent Unit Based on DMI and Nino3.4 Index

Rainfall variability has a severe impact in Sidoarjo, Indonesia. The significant increase in extreme rainfall caused a hydrometeorological disaster and expanded the Sidoarjo mudflow. Rainfall prediction can reduce risks and anticipate hydrometeorological disasters. This study predicts rainfall based on the Dipole Mode Index (DMI) and Niño3.4 Index and several other parameters such as temperature, humidity, duration of sunshine, and wind speed. This study uses monthly time-series data to predict rainfall and compare the results of the 1D-CNN, RNN, LSTM, and GRU methods. The best prediction was made by GRU with a Mean Arctangent Absolute Percentage Error (MAAPE) value of 0.42 and R-square value of 0.79 with 32 hidden neurons, 32 batch sizes, and 0.001 learning rate. Predictions indicate that the rainfall intensity will increase from 50 mm to 200 mm per month from September 2021 to January 2022, or the rainfall intensity will increase by 30 mm per month.
Infrared Target Classification Based on Single Shot MultiBox Detector

The task of infrared target recognition is challenging for three reasons. First, there are few features that can be extracted from infrared images. Second is that the infrared feature similarity between the target and the background is high. Lastly the image is blurry. To solve the problem, it is necessary to propose a method that can capture these changes and is more robust for classification and recognition. Compared with the method of manually extracting features, the deep learning methods automatically learns and extracts features based on the details of the learning environment, which is more stable and adjustable. This paper proposes to use the Single Shot MultiBox (VGG) backbone network to automatically classify and recognize (VGG) backbone network to automatically classify and recognize images in the “KAIST Pedestrian Dataset”. SSD adopts pyramid structure which performs softmax classification and position regression on multiple feature maps. Changing the size of the prior box is beneficial to small IR target detection. The classification task includes four categories: person (clear pedestrians), people (fuzzy pedestrians), person? (unknown), cyclist (bicycle riders). The accuracy of the algorithm for the four target categories is 67%, 63%, 82%, 62%, the testing mAP is 67.83%, and the training mAP is 69.08%, the predicting mAP is 68.51%. The network recognition speed is 57.29 FPS. Faster RCNN is used as a comparison experiment. From the experimental results, SSD is more suitable for infrared target classification and recognition tasks in real-time monitoring environment with small samples than Faster RCNN.
On Convergence Probability of Direct Acyclic Graph-Based Ledgers in Forking Blockchain Systems

Direct acyclic graph (DAG)-based ledger is a promising technology for the Internet of things (IoT). Compared with a single-chain topology, DAG and forking blockchain topology can solve some problems in IoT, such as high resource consumption, high transaction fee, low transaction throughput, and long confirmation delay. We propose the convergence probability to aid further analysis of the performance and security of DAG-based ledgers. Under unsteady load regimes, the convergence probability is the probability of each possible cumulative weight of the observed transaction when it is approved by all new arrival transactions. In this article, we derive a closed-form expression and an approximate expression of the convergence probability under the high-to-low regime (H2LR). Also, we verify the accuracy of the derived expressions through Markov chain Monte Carlo (MCMC) simulations. Numerical results shows that the simulation results match well with its analytical results, which indicates the accuracy of the exact expression and the approximate expression of the convergence probability.
A Double-stream Exchange Transformer Network for Intrinsic Image Decomposition

Intrinsic image decomposition separates the input image into several layers which reflect the attributes of the scene. In this paper, we present a double-stream exchange transformer network for intrinsic image decomposition, in which an independent and interrelated relationship is built between reflectance and shading. There are two core designs in the double-stream exchange transformer network (DSETNet). First, we propose a novel exchange transformer block, which performs the information exchange and reconstruction between reflectance and shading components in a window-based self-attention. Second, a residual structure is added into the exchange transformer block to form a residual exchange transformer block to eliminate artifacts caused by local window areas. We predict reflectance and shading constraint relationship between reflectance and shading is established through residual exchange transformer block. The evaluation results on two real and synthetic public datasets BOLD and ShapeNet show that the DSETNet achieves competitive results with other advanced algorithms.
Mutual Information-Based Modeling for Services Dependency

Web services composition has drawn a great attention in computing industries to build complex and large systems. However, web services composition modeling has major challenges, including dependency determination, complexity, user requests dependency, handling cycles within a composition, service redundancy and scalability concerns. The service dependency graph (SDG) between services in a repository should be accurate to ensure the quality of composition and the associated user requests’ responses. Despite of the crucial importance of accurate dependencies for adequate web services compositions, current modeling approaches do not provide any metric to evaluate the dependency between services for quality estimation. In this paper, the Mutual Information-based Services Dependency (MISD) model is proposed as a graph-based modeling approach, independent to any given user request. It constructs services dependency graphs based on Web Services Mutual Information (WSMI), a proposed modified version of mutual information as the dependency metric, along with other criteria for an accurate, efficient dependency evaluation. It finds the optimum composition representing the structure of the web services composition in a repository rather than the path of given user requests. The experimental dependency analysis emphasizes the efficiency of the generated OC and accuracy of the constructed SDG to be 76% and 86% higher than the state-of-the-art models respectively. The time cost to build the SDG and to find the OC is reduced dramatically up to 99% for different public repositories compared to the state-of-the-art studies as the number of user requests increases.
Modulation Classification of QAM Signals with Different Phase Noise Levels Using Deep Learning

Spectrum awareness gained attention to solve the emerging communication problems, e.g., overcoming spectrum shortage and performing precise interference management. Phase noise and fading are well-known concerns in wireless communication, which could result from the channel or thermal noise that worsens the communication systems. Deep learning showed outstanding results in solving communication system problems compared to traditional methods. Our work focuses on automatic modulation classification with different phase noise levels in the fading channel using constellation diagrams as input. Our neural network demonstrates its capability to capture a different modulation format under different phase noise levels with excellent classification accuracy.
COVID-19 Vaccine Ranking Using ANP Method

Wuhan Province in China reported the first case of novel corona virus as pneumonia outbreak during December 2019. The novel coronavirus was soon declared a pandemic by the World Health Organization. On 16th of July 2021, the number of COVID-19 confirmed cases was 188,128,952 globally, out of which 4,059,339 individuals succumbed to this deadly virus. In a short span of time, eight vaccines were approval for emergency use in different nations. The selection of vaccine depends upon many criteria. Concepts from multi-criteria decision making (MCDM) are appropriate to compare and rank them. The paper proposes analytical network processing (ANP) method to rank the eight vaccines according to seven criteria. The study proposes a decision tool to select the best vaccine among the candidate vaccines. A mathematical model based on ANP approach with three clusters having interrelationships within and among the clusters is proposed.