Reviewerly/exHarmony huggingface dataset:
A method for evaluating the learning concentration in head-mounted virtual reality interaction

Abstract In education, learning concentration is closely related to the quality of learning, and teachers can adjust their teaching methods accordingly to improve the learning outcomes of students. Particularly in head-mounted virtual reality interactions, current methods for assessing learning concentration cannot be fully applied to new interactive environments because immersion shaping and cognitive formation differ from the conventional education. Therefore, in this study, a learning concentration assessment method is proposed to measure the learning concentration of students in head-mounted virtual interaction, using the expression score, visual focus rate, and task mastery as evaluation indicators. In addition, the weights of the evaluation indicators can be configured to be included in the calculation of learning concentration depending on the characteristics of different types of courses. The results of a usability evaluation indicate that the learning concentration of students can be effectively evaluated using the proposed method. By developing and implementing strategies for optimizing learning effects, the learning concentration and assessment scores of students increased by 18% and 15.39%, respectively.
Exploring Interactions with Printed Data Visualizations in Augmented Reality

This paper presents a design space of interaction techniques to engage with visualizations that are printed on paper and augmented through Augmented Reality. Paper sheets are widely used to deploy visualizations and provide a rich set of tangible affordances for interactions, such as touch, folding, tilting, or stacking. At the same time, augmented reality can dynamically update visualization content to provide commands such as pan, zoom, filter, or detail on demand. This paper is the first to provide a structured approach to mapping possible actions with the paper to interaction commands. This design space and the findings of a controlled user study have implications for future designs of augmented reality systems involving paper sheets and visualizations. Through workshops ( N=20) and ideation, we identified 81 interactions that we classify in three dimensions: 1) commands that can be supported by an interaction, 2) the specific parameters provided by an (inter)action with paper, and 3) the number of paper sheets involved in an interaction. We tested user preference and viability of 11 of these interactions with a prototype implementation in a controlled study ( N=12, HoloLens 2) and found that most of the interactions are intuitive and engaging to use. We summarized interactions (e.g., tilt to pan) that have strong affordance to complement "point" for data exploration, physical limitations and properties of paper as a medium, cases requiring redundancy and shortcuts, and other implications for design.
Towards achieving a high degree of situational awareness and multimodal interaction with AR and semantic AI in industrial applications

Abstract With its various available frameworks and possible devices, augmented reality is a proven useful tool in various industrial processes such as maintenance, repairing, training, reconfiguration, and even monitoring tasks of production lines in large factories. Despite its advantages, augmented reality still does not usually give meaning to the elements it complements, staying in a physical or geometric layer of its environment and without providing information that may be of great interest to industrial operators in carrying out their work. An expert’s remote human assistance is becoming an exciting complement in these environments, but this is expensive or even impossible in many cases. This paper shows how a machine learning semantic layer can complement augmented reality solutions in the industry by providing an intelligent layer, sometimes even beyond some expert’s skills. This layer, using state-of-the-art models, can provide visual validation and new inputs, natural language interaction, and automatic anomaly detection. All this new level of semantic context can be integrated into almost any current augmented reality system, improving the operator’s job with additional contextual information, new multimodal interaction and validation, increasing their work comfort, operational times, and security.
Bibliometric Survey on Particle Swarm Optimization Algorithms (2001–2021)

Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. “Conference papers” is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that “particle swarm optimization (PSO)” occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature.
Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

As data grows in size, search engines face new challenges in extracting more relevant content for users' searches.As a result, a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user's requirements.Unfortunately, most existing indexes and ranking algorithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations, making it impossible to deliver exceptionally accurate results.As a result, this study investigates and analyses how search engines work, as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank (PR) algorithm, which is one of the most widely used page ranking algorithms We propose weighted PageRank (WPR) algorithms to test the relationship between these various measures.The Weighted Page Rank (WPR) model was used in three distinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.The findings of utilizing the Weighted Page Rank model showed that using multiple criteria to rank final pages is better than using only one, and that some criteria had a greater impact on ranking results than others.
Differential privacy protection scheme based on community density aggregation and matrix perturbation

With the development and popularity of social networks, the security issues of personal data await effective solution. We propose a differential privacy protection scheme for social network data based on community density aggregation and matrix perturbation (MPCD) and introduce the correlation coefficient k to preserve differential privacy and availability of edge-related data. We adopt a community structure rapid identification algorithm to identify social network structure labels, to realize the label aggregation of community nodes. Combined with the structure of a binary tree, we use the adaptive method to identify the dense regions for the generated upper triangular adjacency matrix. We perturb the edge connection probability of the identified dense regions and reconstruct a disturbed adjacency matrix by edge optimal allocation to publish the final network graph, which can improve the efficiency of the algorithm and data availability. In addition, we experimentally evaluate our approach on four social network datasets and show that the published network graph has better data availability and privacy protection, with higher execution efficiency than the four classical schemes.
Anchor link prediction across social networks based on multiple consistency

With the development of social applications, users began to participate in multiple online social networks. If the same user in different social networks can be identified, it can provide powerful help for cross-platform recommendation, information diffusion, etc. Traditional methods use interlayer or intralayer structures alone for anchor link prediction and lack the comprehensive utilization of interlayer and intralayer structures. Therefore, this paper proposes an anchor link prediction method based on multiple consistency (MC). It uses interlayer structure information in an iterative manner and intralayer structure information through network representation learning. When using the intralayer structural information, a matrix factorization-based network representation learning method is used to learn embedding vectors that contain global structural features of nodes. A radial basis neural network is then trained as a mapping function to map embedding vectors in different spaces to the same space. Finally, the anchor links between node pairs are predicted by considering the interlayer and intralayer structures together. Experiments in several real networks show that our method generally outperforms the current approach.
Design a new image encryption algorithm based on a 2D-ASCC map

Abstract In this paper, a new model of 2D absolute sine-cosine coupling (2D-ASCC) is proposed. In comparison with other 2D chaos map, the proposed system has higher complexity and better property of pseudo-random. It can effectively improve the security performance of encryption algorithm, if the proposed chaos map is applied in the design of image encryption algorithm based on chaos. Then, a new diffusion algorithm is designed based on the jumping diffusion. The plaintext is divided into two groups, and each group of plaintext is encrypted with a different formula. The pixel value of each ciphertext is composed of several non-adjacent pixels and the pseudo-random values generated by the proposed chaos map. In comparison with some existing encryption scheme, by using the design encryption scheme, the plaintext image can be completely encrypted in one iteration, it can significantly increase the security of algorithm and reduces the correlation between adjacent pixels. Finally, by using the numerical simulation and the security analysis, the effectiveness of the encryption algorithm is verified, and the comparison results show the higher security of the design encryption algorithm.
Towards Sentiment Analysis for Romanian Twitter Content

With the increased popularity of social media platforms such as Twitter or Facebook, sentiment analysis (SA) over the microblogging content becomes of crucial importance. The literature reports good results for well-resourced languages such as English, Spanish or German, but open research space still exists for underrepresented languages such as Romanian, where there is a lack of public training datasets or pretrained word embeddings. The majority of research on Romanian SA tackles the issue in a binary classification manner (positive vs. negative), using a single public dataset which consists of product reviews. In this paper, we respond to the need for a media surveillance project to possess a custom multinomial SA classifier for usage in a restrictive and specific production setup. We describe in detail how such a classifier was built, with the help of an English dataset (containing around 15,000 tweets) translated to Romanian with a public translation service. We test the most popular classification methods that could be applied to SA, including standard machine learning, deep learning and BERT. As we could not find any results for multinomial sentiment classification (positive, negative and neutral) in Romanian, we set two benchmark accuracies of ≈78% using standard machine learning and ≈81% using BERT. Furthermore, we demonstrate that the automatic translation service does not downgrade the learning performance by comparing the accuracies achieved by the models trained on the original dataset with the models trained on the translated data.
Automated deep learning-based wide-band receiver

We propose a modular and full-fledged physical layer receiver design for Orthogonal Frequency Division Multiplexing (OFDM) wireless systems leveraging the advances of deep neural networks (DNN). We adopt a detailed modular design that includes proper and utmost domain knowledge in each element and train it using data collected both via simulations as well as over-the-air and emulated wireless transmissions. We then unify all the modules into an end-to-end automated deep learning-based wide-band receiver and fine-tune it to further improve its accuracy. Our combined pipeline analysis exhibits superior performance by showing bit error rate values up to 8 times lower if compared to the traditional approaches for wireless communications.
Geostatistical modeling of heterogeneous geo-clusters in a copper deposit integrated with multinomial logistic regression: An exercise on resource estimation

Resource estimation is the main and primary step in the development of a mining project. Principally, it is necessary to first identify the geological domains through boreholes, model them at unsampled locations, and then evaluate the grade(s) of interest inside each built domain. The traditional determination of these categorical domains over the sampling points is suboptimal as it considers mostly-one or two variables from core logging. This leads to the neglect of the influence of other significant variables. To circumvent the problem of estimation domain identification, spatially dependent clustering machine learning algorithms can be of great help in detecting such domains. However, one problem that may appear when using these techniques is that the resulting geo-domains (geo-clusters) obtained by the clustering technique might be heterogeneous and show a non-stationary property. The reason is that the aim of these spatially dependent techniques is to produce compact and spatially contiguous clusters, which are well suited to establishing non-stationary geo-domains. This makes the procedure of modelling challenging as it necessitates the use of advanced geostatistical techniques to propagate the heterogeneous geo-clusters at unsampled locations. An algorithm is presented in this study that employs a non-stationary sequential indicator simulation paradigm to model such complex variability of heterogeneous geo-clusters. Since the spatial trends of underlying geo-clusters are required in this simulation method, in this study, we propose the use of multinomial logistic regression to infer these trends. The algorithm was tested using an actual case study from a porphyry copper deposit in Iran, where Cu, Mo, Au, Rock Quality Designation (RQD), mineralization zones, alteration types, and rock types were employed to identify and spatially model the heterogeneous geo-domains in the entire deposit. The results were compared with a conventional sequential indicator simulation where no trend was used. An examination of the resulting maps using several evaluation criteria including visual inspection of the realizations, probability maps, reproduction of proportion of each geo-cluster, connectivity measures, and trend analysis, showed that the findings of the proposed algorithm were superior in modelling heterogeneous geo-domains.
A dream job? Skill demand and skill mismatch in ICT

We analyse labour demand and mismatch for educational traits in information and communication technology (ICT) occupations, taking into account the areas of education, occupations and skills in demand, and the supply of labour. We based our analysis on almost 40 million online job offers and a CAWI survey of people aged 18–65 in Poland. The analysis uses official classifications, and considers both job-related and transversal skills. To analyse an objective measure of mismatch, we compare job requirements stated in job offers to the declared characteristics of potential labour supply, and present demand and mismatch for skills across occupations. We also model a declared educational mismatch and its determinants, including educational profiles of surveyed participants. In order to further investigate the mismatches in ICT fields of education, occupations and skills, we compare them with the mismatches in other occupational groups. The analysis concludes with direct educational policy recommendations, including the Sectoral Qualifications Framework for ICT occupations in Poland. Our approach shows the complete educational characteristics of skill demands and skill mismatches.
Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions

Color is an important feature of vehicles, and it plays a key role in intelligent traffic management and criminal investigation. Existing algorithms for vehicle color recognition are typically trained on data under good weather conditions and have poor robustness for outdoor visual tasks. Fine vehicle color recognition under rainy conditions is still a challenging problem. In this paper, an algorithm for jointly deraining and recognizing vehicle color, (JADAR), is proposed, where three layers of UNet are embedded into RetinaNet-50 to obtain joint semantic fusion information. More precisely, the UNet subnet is used for deraining, and the feature maps of the recovered clean image and the extracted feature maps of the input image are cascaded into the Feature Pyramid Net (FPN) module to achieve joint semantic learning. The joint feature maps are then fed into the class and box subnets to classify and locate objects. The RainVehicleColor-24 dataset is used to train the JADAR for vehicle color recognition under rainy conditions, and extensive experiments are conducted. Since the deraining and detecting modules share the feature extraction layers, our algorithm maintains the test time of RetinaNet-50 while improving its robustness. Testing on self-built and public real datasets, the mean average precision (mAP) of vehicle color recognition reaches 72.07%, which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms.
Robust adaptive cubature Kalman filter for tracking manoeuvring target by wireless sensor network under noisy environment

The existing adaptive Kalman filters for tracking manoeuvring targets by wireless sensor networks can easily lose robustness when both the measurement and process noises are unknown and time-varying, resulting in large positioning errors. To solve this problem, a wireless sensor network manoeuvring target tracking algorithm using a novel robust adaptive cubature Kalman filter is proposed. This innovative robust adaptive cubature Kalman filter consists of a derived third-order biased noise statistic estimator and conventional cubature Kalman filter. This derived noise statistic estimator can simultaneously sense time-varying and unknown measurement and process noises and ensure that the adaptive cubature Kalman filter's robustness is not lost. The robustness of the novel robust adaptive cubature Kalman filter is strictly proven in this study. Extensive practical experiments and numerical simulations show that the proposed robust adaptive cubature Kalman filter always has higher target tracking accuracy than other existing adaptive Kalman filters, regardless of whether the mobile target is manoeuvring or not, the noise is unknown or time-varying, and the number of anchor nodes is few or many.
The implications of blockchain-coordinated information sharing within a supply chain: A simulation study

The profitability of a supply chain (SC) is proportional to the stability of all its stakeholders as well as their consistent information sharing with an effective and efficient communication mechanism. Various inefficiencies, such as the bullwhip effect (BWE) and product unavailability, may be caused by a lack of coordination in an SC. The importance of sharing consumer demand has been quantified by comprehensive studies under the assumption that all SC participants will access the same information. However, only a few studies have studied the effect of minimal coordination or limited visibility of information while considering their effect on the overall efficiency of an SC. This work primarily leverages blockchain technology (BCT) to create a simulation model. To do this, an SC BWE-based model is initially developed. Following that, a blockchain-based robust information sharing system is simulated. Furthermore, information sharing is challenging, and SC stakeholders may not really trust each other and hence be reluctant to share sensitive information. Considering that, this paper propose an improved proof-of-authority (PoA) consensus algorithm that will increase trust in a decentralized SC model. Multiple experiments are carried out to demonstrate the effectiveness of our approach, and the simulation results clearly demonstrate the effectiveness of information sharing in a supply chain via blockchain, as well as that trust between partners tends to increase overall SC efficiency and reduce BWE.
SCVS: On AI and Edge Clouds Enabled Privacy-preserved Smart-city Video Surveillance Services

Video surveillance systems are increasingly becoming common in many private and public campuses, city buildings, and facilities. They provide many useful smart campus/city monitoring and management services based on data captured from video sensors. However, the video surveillance services may also breach personally identifiable information, especially human face images being monitored; therefore, it may potentially violate the privacy of human subjects involved. To address this privacy issue, we introduced a large-scale distributed video surveillance service model, called Smart-city Video Surveillance (SCVS). SCVS is a video surveillance data collection and processing platform to identify important events, monitor, protect, and make decisions for smart campus/city applications. In this article, the specific research focus is on how to identify and anonymize human faces in a distributed edge cloud computing infrastructure. To preserve the privacy of data during video anonymization, SCVS utilizes a two-step approach: (i) parameter server-based distributed machine learning solution, which ensures that edge nodes can exchange parameters for machine learning-based training. Since the dataset is not located on a centralized location, the data privacy and ownership are protected and preserved. (ii) To improve the machine learning model’s accuracy, we presented an asynchronous training approach to protect data and model privacy for both data owners and data users, respectively. SCVS adopts an in-memory encryption approach, where edge computing nodes collect and process data in the memory of edge nodes in encrypted form. This approach can effectively prevent honest but curious attacks. The performance evaluation shows the presented privacy protection platform is efficient and effective compared to traditional centralized computing models as presented in Section 5 .