Information, Volume 13, Issue 3 (March 2022) – 57 articles

With the prevalence of smartphones and the maturation of indoor positioning techniques, predicting the movement of a large number of customers in indoor environments has become a promising and challenging line of research in recent years. While most of the current predicting approaches that take advantage of mathematical methods perform well in outdoor settings, they exhibit poor performance in indoor environments. To solve this problem, in this study, a sequential similarity-based prediction approach which combines the spatial and semantic contexts into a unified framework is proposed. We first present a revised Longest Common Sub-Sequence (LCSS) algorithm to compute the spatial similarity of the indoor trajectories, and then a novel algorithm considering the indoor semantic R-tree is proposed to compute the semantic similarities; after this, a unified algorithm is considered to group the trajectories, and then the clustered trajectories are used to train the prediction models. Extensive performance evaluations were carried out on a real-world dataset collected from a large shopping mall to validate the performance of our proposed method. The results show that our approach markedly outperforms the baseline methods and can be used in real-world scenarios. Full article

A lack of knowledge exists about individuals’ vaccination decisions and their relation to their tourism behaviors. In this regard, this study examines the willingness of international travelers to take a COVID-19 vaccine prior to traveling. A quantitative research design with a survey method and the ordinary least square (OLS) multiple regression analysis was used to test the hypotheses. The media encouragement positively affected the travelers’ attitude toward the behavior and their injunctive social norm, whereas the travelers’ attitude toward the behavior and injunctive social norm positively affected their COVID-19 vaccination intention. The regression results also proved the mediating effect of both attitudes toward the behavior and injunctive social norm in the relationship between media encouragement and COVID-19 vaccination intention. This research successfully provided evidence regarding the role of media encouragement in travelers’ willingness to take the COVID-19 vaccination. Full article

In an IoT (Internet of Things) system, the autonomy of battery-operated edge devices is of paramount importance. When such devices operate intermittently, reducing power consumption during standby improves such a characteristic. The deep-sleep operation mode obtains such a result: it keeps on power only the hardware needed to wake up the unit after a timeout or an external trigger. For this reason, deep sleep exhibits the issue of losing the working memory, which prevents its use with applications depending on long-lasting or stateful computations. A way to circumvent such an issue consists of saving a snapshot of the working memory on a remote repository. However, such a solution is not always convenient since it exhibits an energy footprint due to checkpoint transmission. This article analyzes the applicability of such a solution. Firstly, by comparing its energy footprint against keeping the working memory on power. The analysis follows a formal, technology-agnostic methodology based on a mathematical model for energy consumption. It yields a discriminant inequality identifying the use cases where remote checkpointing is of interest. Once justified the approach, the article proceeds by defining an architecture and a secure protocol for data transport and storage. Finally, the description of a prototype implementation provides concrete insights. Full article

The advent of 5G has created an expectation for the provision of a much better user experience (UX) for utilising video streaming services. However, apart from quality, there are many other factors believed to influence users’ experiences of video streaming services in the era of 5G. The question then arises: what will determine the UX of streaming video services in the 5G era? This study aimed to discover what factors would influence 5G UX by using a conceptual framework involving measures of users’ predictive judgements of 5G combined with users’ measurements of current 4G UX, considering that there is no specific standard for measuring UX. Our case study is Indonesia, the world’s fourth-largest market, so the predictions may later become a reference for the priorities of implementing 5G technology. Our conceptual framework utilised the structural equation model (SEM) approach, based on the primary data of a 254-respondent sample of the Indonesian market. The questionnaire assessed 10 factors of user experience, ranging from service attractiveness to economic-related aspects. The results imply that the current user’s experience of 4G has an effect with a significance value of 0.42 to their later experience when accessing 5G video services. It means that many users will continue to use 4G for video streaming and still need tangible evidence of 5G. Among the 10 factors, a satisfaction value of 0.72 in 4G technology and perspicuity of 0.81 in 5G are the highest correlations. These factors are the most influential for video service experience. Full article

Information Communication Technology (ICT) and social networks have significant impact on everyday life. One the one hand, Internet users enjoy promoting themselves and feel free to disseminate information about themselves through websites and social networks, but on the other hand, people feel forced to reveal information about them on the Internet. Web technologies enable self-promotion for many reasons, i.e., social relations development, acquiring a new job, or research career support. This paper concerns autoethnography application for social science researcher profile cultivation. Autoethnography belongs to qualitative methods and focuses on deep analysis of experiences and competencies in a narrative way. In this study, autoethnography is self-reflection for personal development strategy. This study methodology includes the literature survey and case study. The Literature Survey (LS) on autoethnographic research is included to answer the question for what purposes autoethnography is applied. In the case study, the author proposes to expand autoethnography and presents that beyond stories, statistical data can be used to reveal researcher’s experiences and personality, and data anonymization is a solution for privacy protection in autoethnographic research. The results indicate that perception of individual profile is significantly influenced by ICT, Internet services, and social networks platforms and portals. Contemporary researchers are evaluated by Web statistical measures. The researcher’s profiling is much more complex and statistical measures and metrics provide a general view of the researcher. Application of statistical measures leads to concluding on general competencies of the researcher and precludes a deep focus on local scientific specificity of the researcher. This paper has added value because of presenting the academic community integration with the Internet social networks, e.g., Facebook, LinkedIn, or SciVal. The paper emphasizes transparency and visibility of researchers’ profiles, as well as the necessity to analyze their activities and publications in academic community context and in comparisons with others. Full article

The counting of wheat heads is labor-intensive work in agricultural production. At present, it is mainly done by humans. Manual identification and statistics are time-consuming and error-prone. With the development of machine vision-related technologies, it has become possible to complete wheat head identification and counting with the help of computer vision detection algorithms. Based on the one-stage network framework, the Wheat Detection Net (WDN) model was proposed for wheat head detection and counting. Due to the characteristics of wheat head recognition, an attention module and feature fusion module were added to the one-stage backbone network, and the formula for the loss function was optimized as well. The model was tested on a test set and compared with mainstream object detection network algorithms. The results indicate that the mAP and FPS indicators of the WDN model are better than those of other models. The mAP of WDN reached 0.903. Furthermore, an intelligent wheat head counting system was developed for iOS, which can present the number of wheat heads within a photo of a crop within 1 s. Full article

Mining opinion on social media microblogs presents opportunities to extract meaningful insight from the public from trending issues like the “yahoo-yahoo” which in Nigeria, is synonymous to cybercrime. In this study, content analysis of selected historical tweets from “yahoo-yahoo” hash-tag was conducted for sentiment and topic modelling. A corpus of 5500 tweets was obtained and pre-processed using a pre-trained tweet tokenizer while Valence Aware Dictionary for Sentiment Reasoning (VADER), Liu Hu method, Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and Multidimensional Scaling (MDS) graphs were used for sentiment analysis, topic modelling and topic visualization. Results showed the corpus had 173 unique tweet clusters, 5327 duplicates tweets and a frequency of 9555 for “yahoo”. Further validation using the mean sentiment scores of ten volunteers returned R and R2 of 0.8038 and 0.6402; 0.5994 and 0.3463; 0.5999 and 0.3586 for Human and VADER; Human and Liu Hu; Liu Hu and VADER sentiment scores, respectively. While VADER outperforms Liu Hu in sentiment analysis, LDA and LSI returned similar results in the topic modelling. The study confirms VADER’s performance on unstructured social media data containing non-English slangs, conjunctions, emoticons, etc. and proved that emojis are more representative of sentiments in tweets than the texts. Full article

This paper proposes a supervised machine learning system to detect fake news in online sources published in Romanian. Additionally, this work presents a comparison of the obtained results by using recurrent neural networks based on long short-term memory and gated recurrent unit cells, a convolutional neural network, and a Bidirectional Encoder Representations from Transformers (BERT) model, namely RoBERT, a pre-trained Romanian BERT model. The deep learning architectures are compared with the results achieved by two classical classification algorithms: Naïve Bayes and Support Vector Machine. The proposed approach is based on a Romanian news corpus containing 25,841 true news items and 13,064 fake news items. The best result is over 98.20%, achieved by the convolutional neural network, which outperforms the standard classification methods and the BERT models. Moreover, based on irony detection and sentiment analysis systems, additional details are revealed about the irony phenomenon and sentiment analysis field which are used to tackle fake news challenges. Full article

The main purpose of this study is to explore the insights of customers’ reviews from the upscale hotel Atlantis, The Palm in the Dubai area. The data was collected from the SCTM 3.0 (smart crawling and text mining) platform developed by the Wellness & Tourism Big Data Institute at Kyungsung University. A total of 2051 online reviews were collected from the period from 29 October 2018 to 29 October 2021. The following steps were conducted by RStudio and UCINET 6.0 to analyze the collected data and to visualize the results. The results showed the top 50 keywords customers used in the reviews, such as ‘great’, ‘amazing’, or ‘service’. Exploratory factor analysis (EFA) and linear regression analysis were applied for an in-depth understanding of customer satisfaction. The analysis results demonstrated that the ‘value’ and ‘dining’ factors had a negative influence on overall customer satisfaction. These findings could provide managerial and marketing insights for upscale hotel managers when formulating and implementing strategies and tactics to improve customer satisfaction. Full article

Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis. Full article

A potential myocardial injury can be induced by intensive sporting activities, which may be due to ventricular tachycardia or fibrillation when individuals continue to exercise during the maximum physical loading period (the aerobic capability plateau, ACP). Herein, we conducted an incremental exercise test with the RR-interval and SV-series measurements as the input and output of the circulatory system. Through time and frequency analyses, we aimed to identify the indicators for distinguishing the normal stage (S1), last stage before ACP (S2), and ACP stage (S3) during different incremental physical loads. The cross-correlation results of the RR interval and SV series showed that the maximum coefficient of S2 was significantly greater (p < 0.05) than that of S1 (median 0.91 to 0.87), and also significantly lower (p < 0.05) than that of S3 (median 0.87 to 0.60). The corresponding spectrum shows that the decreasing correlation coefficient of SVV and Heart rate variability can be used to assess whether the body has reached the ACP. These findings can be used as a guide for exercise healthcare. Pausing or reducing the exercise load before entering the ACP could effectively reduce the risk of myocardial injury. Full article

Recently, in the field of visual object tracking, visual object tracking algorithms combined with visual object segmentation have achieved impressive results while using mask to label targets in the VOT2020 dataset. Most of the trackers get the object mask by increasing the resolution through multiple upsampling modules and gradually get the mask by summing with the features in the backbone network. However, this refinement pathway does not fully consider the spatial information of the backbone features, and therefore, the segmentation results are not perfect. In this paper, the cross-stage and cross-resolution (CSCR) module is proposed for optimizing the segmentation effect. This module makes full use of the semantic information of high-level features and the spatial information of low-level features, and fuses them by skip connections to achieve a very accurate segmentation effect. Experiments were conducted on the VOT dataset, and the experimental results outperformed other excellent trackers and verified the effectiveness of the algorithm in this paper. Full article

The integration of Information and Communication Technology (ICT) tools into mechanical devices in routine use within the aviation industry has heightened cyber-security concerns. The extent of the inherent vulnerabilities in the software tools that drive these systems escalates as the level of integration increases. Moreover, these concerns are becoming even more acute as the migration within the industry in the deployment of electronic-enabled aircraft and smart airports gathers pace. A review of cyber-security attacks and attack surfaces within the aviation sector over the last 20 years provides a mapping of the trends and insights that are of value in informing on future frameworks to protect the evolution of a key industry. The goal is to identify common threat actors, their motivations, attacks types and map the vulnerabilities within aviation infrastructures most commonly subject to persistent attack campaigns. The analyses will enable an improved understanding of both the current and potential future cyber-security protection provisions for the sector. Evidence is provided that the main threats to the industry arise from Advance Persistent Threat (APT) groups that operate, in collaboration with a particular state actor, to steal intellectual property and intelligence in order to advance their domestic aerospace capabilities as well as monitor, infiltrate and subvert other sovereign nations’ capabilities. A segment of the aviation industry commonly attacked is the Information Technology (IT) infrastructure, the most prominent type of attack being malicious hacking with intent to gain unauthorised access. The analysis of the range of attack surfaces and the existing threat dynamics has been used as a foundation to predict future cyber-attack trends. The insights arising from the review will support the future definition and implementation of proactive measures that protect critical infrastructures against cyber-incidents that damage the confidence of customers in a key service-oriented industry. Full article

This paper introduces a new method for determining the shape similarity of complex three-dimensional (3D) mesh structures based on extracting a vector of important vertices, ordered according to a matrix of their most important geometrical and topological features. The correlation of ordered matrix vectors is combined with perceptual definition of salient regions in order to aid detection, distinguishing, measurement and restoration of real degradation and digitization errors. The case study is the digital 3D structure of the Camino Degli Angeli, in the Urbino’s Ducal Palace, acquired by the structure from motion (SfM) technique. In order to obtain an accurate, featured representation of the matching shape, the strong mesh processing computations are performed over the mesh surface while preserving real shape and geometric structure. In addition to perceptually based feature ranking, the new theoretical approach for ranking the evaluation criteria by employing neural networks (NNs) has been proposed to reduce the probability of deleting shape points, subject to optimization. Numerical analysis and simulations in combination with the developed virtual reality (VR) application serve as an assurance to restoration specialists providing visual and feature-based comparison of damaged parts with correct similar examples. The procedure also distinguishes mesh irregularities resulting from the photogrammetry process. Full article

Changes in the information age have induced the necessity for a more efficient and effective self-decision-making requirement. A method of extracting and constructing naval operations decision-making rules based on scenario analysis is proposed. The template specifications of Event Condition Action (ECA) rules are defined, and a consistency detection method of ECA rules based on SWRL is proposed. The logical relationships and state transitions of the naval operational process is analyzed in detail, and the association of objects, events, and behaviors is realized. Finally, the operation of the proposed methods is illustrated through an example process, showing the method can effectively solve the problems of self-decision-making rule extraction and construction among naval battlefield decision environment, and avoid relying on artificial intelligence, which may have brought some uncertain factors. Full article

Industrial engineering design is a crucial issue in manufacturing. To meet the competitive global market, manufacturers are continuously seeking solutions to design industrial products and systems inventively. Su-Field analysis, which is one of the TRIZ analysis tools for inventive design problems, has been used to effectively improve the performance of industrial systems. However, the inventive standards used for engineering design are summarized and classified according to a large number of patents in different fields. They are built on a highly abstract basis and are independent of specific application fields, making their use require much more technical knowledge than other TRIZ tools. To facilitate the use of invention standards, in particular to capture the uncertainty or imprecision described in the standards, this paper proposes a rule-based heuristic approach. First, Su-Field analysis ontology and fuzzy analysis ontology are constructed to represent precise and fuzzy knowledge in the process of solving inventive problems respectively. Then, SWRL (Semantic Web Rule Language) reasoning and fuzzy reasoning are executed to generate heuristic conceptual solutions. Finally, we develop a software prototype and elaborate the resolution of “Auguste Piccard’s Stratostat ” in the prototype. Full article

Over the years, there has been a significant increase in the adoption of game-based interventions for behaviour change associated with many fields such as health, education, and psychology. This is due to the significance of the players’ intrinsic motivation that is naturally generated to play games and the substantial impact they can have on players. Many review papers measure the effectiveness of the use of gaming on changing behaviours; however, these studies neglect the game features involved in the game design process, which have an impact of stimulating behaviour change. Therefore, this paper aimed to identify game design mechanics and features that are reported to commonly influence behaviour change during and/or after the interventions. This paper identified key theories of behaviour change that inform the game design process, providing insights that can be adopted by game designers for informing considerations on the use of game features for moderating behaviour in their own games. Full article

Atmospheric impairment-induced attenuation is the prominent source of signal degradation in radio wave communication channels. The computation-based modeling of radio wave attenuation over the atmosphere is the stepwise application of relevant radio propagation models, data, and procedures to effectively and prognostically estimate the losses of the propagated radio signals that have been induced by atmospheric constituents. This contribution aims to perform a detailed prognostic evaluation of radio wave propagation attenuation due to rain, free space, gases, and cloud over the atmosphere at the ultra-high frequency band. This aim has been achieved by employing relevant empirical atmospheric data and suitable propagation models for robust prognostic modeling using experimental measurements. Additionally, the extrapolative attenuation estimation results and the performance analysis were accomplished by engaging different stepwise propagation models and computation parameters often utilized in Earth–satellite and terrestrial communications. Results indicate that steady attenuation loss levels rise with increasing signal carrier frequency where free space is more dominant. The attenuation levels attained due to rain, cloud, atmospheric gases, and free space are also dependent on droplet depths, sizes, composition, and statistical distribution. While moderate and heavy rain depths achieved 3 dB and 4 dB attenuations, the attenuation due to light rainfall attained a 2.5 dB level. The results also revealed that attenuation intensity levels induced by atmospheric gases and cloud effects are less than that of rain. The prognostic-based empirical attenuation modeling results can provide first-hand information to radio transmission engineers on link budgets concerning various atmospheric impairment effects during radio frequency network design, deployment, and management, essentially at the ultra-high frequency band. Full article

Recognition of biological tissue denaturation is a vital work in high-intensity focused ultrasound (HIFU) therapy. Multiscale permutation entropy (MPE) is a nonlinear signal processing method for feature extraction, widely applied to the recognition of biological tissue denaturation. However, the typical MPE cannot derive a stable entropy due to intensity information loss during the coarse-graining process. For this problem, an improved multiscale permutation entropy (IMPE) is proposed in this work. IMPE is obtained through refining and reconstructing MPE. Compared with MPE, the IMPE overcomes the deficiency of amplitude information loss due to the coarse-graining process when computing signal complexity. Through the simulation of calculating MPE and IMPE from white Gaussian noise, it is found that the entropy derived by IMPE is more stable than that derived by MPE. The processing method based on IMPE feature extraction is applied to the experimental ultrasonic scattered echo signals in HIFU treatment. Support vector machine and Gustafson–Kessel fuzzy clustering based on MPE and IMPE feature extraction are also used for biological tissue denaturation classification and recognition. The results calculated from the different combination algorithms show that the recognition of biological tissue denaturation based on IMPE-GK clustering is more reliable with the accuracy of 95.5%. Full article

Ontologies are logical theories that are used in computer science for describing different items such as web services, agents in multi-agent systems, or domain knowledge. Many ontologies exist, expressing various domains of knowledge with different abstraction levels (domain ontologies, top-level ontologies, and task ontologies are the usual categories). The conceptualization of the knowledge contained in an ontology is subject to change, whether because the context of its use changes, because the domain evolves, or because an ontology needs to interoperate with other elements using other ontologies. Change in logical theories is a form of defeasible reasoning, in which some formulas need to be added or removed from a knowledge base. Adaptive Logics (AL) is a logic managing defeasible reasoning that we investigate in this paper for managing change in ontologies expressed with Description Logics (DL). The adaptation of AL for DL will help express the context in which formulas remain valid or can be added to a DL knowledge base, and ease the interoperability between ontologies. Full article

Due to the complexity and uncertainty of objective things, interval-valued intuitionistic fuzzy (I-VIF) numbers are often used to describe the attribute values in multiple-attribute decision making (MADM). Sometimes, there are correlations between the attributes. In order to make the decision-making result more objective and reasonable, it is often necessary to take the correlation factors into account. Therefore, the study of MADM based on the correlations between attributes in the I-VIF environment has important theoretical and practical significance. Thus, in this paper, we propose new operators (AOs) for I-VIF information that are able to reflect the completeness of the information, attribute relevance, and the risk preference of decision makers (DMs). Firstly, we propose some new AOs for I-VIF information, including I-VIF generalized Heronian mean (I-VIFGHM), I-VIF generalized weighted Heronian mean (I-VIFGWHM), and I-VIF three-parameter generalized weighted Heronian mean (I-VIFTPGWHM). The properties of the obtained operators, including their idempotency, monotonicity, and boundedness are studied. Furthermore, an MADM method based on the I-VIFGWHM operator is provided. Finally, an example is provided to explain the rationality and feasibility of the proposed method. Full article

Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure. Full article

Science, technology, engineering, and mathematics (STEM) is a meta-discipline employing active, problem-centric approaches such as game-based learning. STEM competencies are an essential part of the educational response to the transformations caused by the fourth industrial revolution, spearheaded by the convergence of multiple exponential technologies. Teachers’ attitude is a critical success factor for any technology-enhanced learning innovation. This study explored in-service teachers’ views on the use of a digital educational escape room in virtual reality. Forty-one (n = 41) K-12 educators participated in a mixed research study involving a validated survey questionnaire instrument and an online debriefing session in the context of a teacher training program. The key findings revealed that such alternative instructional solutions can potentially enhance the cognitive benefits and learning outcomes, but further highlighted the shortcomings that instructional designers should consider while integrating them in contexts different than the intended. In line with this effort, more systematic professional development actions are recommended to encourage the development of additional teacher-led interventions. Full article

In order to investigate the key attributes of casino hotel customer eWOM and their structural relationships, this study selects two casino hotels located in Las Vegas and Macao. Through big data analytics, online reviews of two casino hotels from Google Travel were utilized. The frequency and CONCOR analyses showed the top 50 high-frequency words for each hotel and divided them into groups. The results of the factor analysis and linear regression analysis show that four factors, namely “Physical Environment”, “Entertainment”, “Experience”, and “Amenity”, in Las Vegas have a significant impact on customer satisfaction, while two factors, namely “Value” and “Physical Environment”, do in Macao. Through the results, the study points out the general characteristics affecting customer satisfaction of casino hotels, as well as the distinctions in influencing factors of their customer satisfaction in different source markets. Full article

The game ‘Mafia’ is a logic puzzle that has been a top-rated party game played worldwide. Many studies have been dedicated to determining the best character combination to keep players engaged while analyzing the overall death toll. Although it has only two-sided plays, there are multiple combinations of characters in which each character’s rules are different. This paper explores the game’s sophistication using the game refinement theory and motion in mind model while measuring the entertainment of each character’s actions. It then focuses on the dynamics of minority versus majority behaviors during the game process. Computer simulations were conducted to collect the data of each character and assess the entertainment impacts. Moreover, the energy value of each character was computed based on the motion in mind model. The results show that when the number of ‘Mafia’ and the number of ‘Sheriffs’ are equal, the sophistication of each character is maximized. In addition, the data indicates the player engagement in the following order: $Mafia>Sheriff>Citizen$. Thus, it can be concluded that the actions of the Mafia character are the most complicated and significantly impact the game. It is expected that the results in this study enable game designers to improve each character’s perspective and examine possible enhancements from the viewpoint of entertainment. Full article

Knowledge graph embedding can learn low-rank vector representations for knowledge graph entities and relations, and has been a main research topic for knowledge graph completion. Several recent works suggest that convolutional neural network (CNN)-based models can capture interactions between head and relation embeddings, and hence perform well on knowledge graph completion. However, previous convolutional network models have ignored the different contributions of different interaction features to the experimental results. In this paper, we propose a novel embedding model named DyConvNE for knowledge base completion. Our model DyConvNE uses a dynamic convolution kernel because the dynamic convolutional kernel can assign weights of varying importance to interaction features. We also propose a new method of negative sampling, which mines hard negative samples as additional negative samples for training. We have performed experiments on the data sets WN18RR and FB15k-237, and the results show that our method is better than several other benchmark algorithms for knowledge graph completion. In addition, we used a new test method when predicting the Hits@1 values of WN18RR and FB15k-237, named specific-relationship testing. This method gives about a 2% relative improvement over models that do not use this method in terms of Hits@1. Full article

In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 × 24 pixels IR array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end IR array sensors with even lower resolution (16 × 12 and 8 × 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 × 12. For frames of size 8 × 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection. Full article

Target tracking has always been an important research direction in the field of computer vision. The target tracking method based on correlation filtering has become a research hotspot in the field of target tracking due to its efficiency and robustness. In recent years, a series of new developments have been made in this research. However, traditional correlation filtering algorithms cannot achieve real-time tracking in complex scenes such as illumination changes, target occlusion, motion deformation, and motion blur due to their single characteristics and insufficient background information. Therefore, a scale-adaptive anti-occlusion correlation filtering tracking algorithm is proposed. First, solve the single feature problem of traditional correlation filters through feature fusion. Secondly, the scale pyramid is introduced to solve the problem of tracking failure caused by scale changes. In this paper, two independent filters are trained, namely the position filter and the scale filter, to locate and scale the target, respectively. Finally, an occlusion judgment strategy is proposed to improve the robustness of the algorithm in view of the tracking drift problem caused by the occlusion of the target. In addition, the problem of insufficient background information in traditional correlation filtering algorithms is improved by adding context-aware background information. The experimental results show that the improved algorithm has a significant improvement in success rate and accuracy compared when with the traditional kernel correlation filter tracking algorithm. When the target has large scale changes or there is occlusion, the improved algorithm can still keep stable tracking. Full article

The weigh-in-motion (WIM) system weighs the entire vehicle by identifying the dynamic forces of each axle of the vehicle on the road. The load of each axle is very important to detect the total weight of the vehicle. Different drivers have different driving behaviors, and when large trucks pass through the weighing detection area, the driving state of the trucks may affect the weighing accuracy of the system. This paper proposes YOLOv3 network model as the basis for this algorithm, which uses the feature pyramid network (FPN) idea to achieve multi-scale prediction and the deep residual network (ResNet) idea to extract image features, so as to achieve a balance between detection speed and detection accuracy. In the paper, spatial pyramid pooling (SPP) network and cross stage partial (CSP) network are added to the original network model to improve the learning ability of the convolutional neural network and make the original network more lightweight. Then the detection-based target tracking method with Kalman filtering + RTS (rauch–tung–striebel) smoothing is used to extract the truck driving status information (vehicle trajectory and speed). Finally, the effective size of the vehicle in different driving states on the weighing accuracy is statistically analyzed. The experimental results show that the method has high accuracy and real-time performance in truck driving state extraction, can be used to analyze the influence of weighing accuracy, and provides theoretical support for personalized accuracy correction of WIM system. At the same time, it is beneficial for WIM system to assist the existing traffic system more accurately and provide a highway health management and effective decision making by providing reliable monitoring data. Full article

Information and communication technologies (ICTs) are an integral part of our environment, and their uses vary across generations and among individuals. Today’s student population is made up of “digital natives” who have grown up under the ubiquitous influence of digital technologies, and for whom the use of ICT is common and whose daily activities are structured around media use. The aim of this study is to examine the impact of ICT use and digital skills on students’ academic performance and to explore the digital divide in France. Data were collected through face-to-face questionnaires administered to 1323 students enrolled in three French universities. Principal component analysis, a non-hierarchical k-means clustering approach and multilevel ordered logistic regression were used for data analysis and provide four main findings: first, poor investment in ICT affects students’ results; second, the ICT training offered by universities has little impact on students’ results; third, student performance improves with the innovative and collaborative use of ICTs; fourth, the acquisition of digital skills increases students’ academic performance. The results show that the digital divide still exists, and this raises questions about the effectiveness of education policies in France. They suggest also that organizational change in universities is essential to enable an exploitation of ICT. Full article