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Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy. Full article

Based on expectation disconfirmation theory, this study analyzes how attitudes (satisfaction and loyalty) influence interaction intention (sWOM) and, consequently, active and passive sWOM behavior. It does so by assessing the mediating role of social presence on sWOM intention and behavior. The empirical results provide several contributions. First, knowing how to increase active sWOM contributes to bridging the gap regarding how to enhance interactions between users. Second, fostering active sWOM on social commerce websites will provide companies with more positive user-generated content, since this active sWOM comes from satisfied and loyal users, and it is assumed that they will rate the product positively and report a good experience. Third, companies can benefit more from users if users interact with other users by sharing their experiences. This study sheds light on how social presence can mediate the relationship between intention and behavior, particularly when it comes to increasing active participation and brand promotion. Full article

The COVID-19 pandemic affected many nations around the globe, including Ghana, in the first quarter of 2020. To avoid the spread of the virus, the Ghanaian government ordered universities to close, although most of them had only just begun the academic year. The adoption of Emergency Remote Teaching (ERT) had adverse effects, such as technostress, notwithstanding its advantages for both students and academic faculty. This study examined two significant antecedents: digital literacy and technology dependence. In addition, the study scrutinized the effects of technostress on two relevant student qualities: academic achievement and academic productivity. A descriptive correlational study method was used to discern the prevalence of technology-induced stress among university students in Ghana. The technostress scale was used with a sample of 525 students selected based on defined eligibility criteria. A confirmatory factor analysis (CFA) was employed to calculate the measurement models and structural models. The divergent validity and convergent validity were estimated with the average variance extracted (AVE) and coefficients of correlation between the constructs. The online survey of 525 university students inferred that technology dependence and digital literacy contributes significantly to technostress. Additionally, technostress has adverse effects on academic achievement and academic productivity. Practical implications, limitations, and future directions for the study were also discussed. Full article

Hajj, the fifth pillar of Islam, is held annually in the month of Dhul Al-Hijjah, the twelfth month, in the Islamic calendar. Pilgrims travel to Makkah and its neighbouring areas—Mina, Muzdalifah, and Arafat. Annually, about 2.5 million pilgrims perform spatiotemporally restricted rituals in these holy places that they must execute to fulfil the pilgrimage. These restrictions make the task of transportation in Hajj a big challenge. The shuttle bus service is an essential form of transport during Hajj due to its easy availability at all stages and ability to transport large numbers. The current shuttle service suffers from operational problems; this can be deduced from the service delays and customer dissatisfaction with the service. This study provides a system to help in planning the operation of the service for one of the Hajj Establishments to improve performance by determining the optimal number of buses and cycles required for each office in the Establishment. We will also present a case study in which the proposed model was applied to the non-Arab Africa Establishment shuttle service. At the same time, we will include the mechanism for extracting the information required in the tested model from the considerably large GPS data of 20,000+ buses in Hajj 2018. Full article

Apple flower detection is an important project in the apple planting stage. This paper proposes an optimized detection network model based on a generative module and pruning inference. Due to the problems of instability, non-convergence, and overfitting of convolutional neural networks in the case of insufficient samples, this paper uses a generative module and various image pre-processing methods including Cutout, CutMix, Mixup, SnapMix, and Mosaic algorithms for data augmentation. In order to solve the problem of slowing down the training and inference due to the increasing complexity of detection networks, the pruning inference proposed in this paper can automatically deactivate part of the network structure according to the different conditions, reduce the network parameters and operations, and significantly improve the network speed. The proposed model can achieve 90.01%, 98.79%, and 97.43% in precision, recall, and mAP, respectively, in detecting the apple flowers, and the inference speed can reach 29 FPS. On the YOLO-v5 model with slightly lower performance, the inference speed can reach 71 FPS by the pruning inference. These experimental results demonstrate that the model proposed in this paper can meet the needs of agricultural production. Full article

Even a cursory glance at scholarly literature from over a decade ago related to games can show authors variously prefacing their contributions with explanations of the newness of games, the impressive growth of the digital games industry, and the interdisciplinary nature of games [...] Full article

Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were used. Telematics applied to vehicles and to a broad range of economic activities has opened up new opportunities for transportation engineers, allowing a significant increase in the volume and detail level of data collected. One of the options for obtaining information on the daily activity of private cars now consists of processing data from automated vehicle monitoring (AVM). Therefore, in this context, and in order to explore the opportunity offered by telematics, this paper presents a methodology for obtaining origin–destination flows through basic info extracted from AVM/floating car data (FCD). Then, the benefits of such a procedure are evaluated through its implementation in a real test case, i.e., the Veneto region in northern Italy where full-day AVM/FCD data were available with about 30,000 vehicles surveyed and more than 388,000 trips identified. Then, the goodness of the proposed methodology for O-D flow estimation is validated through assignment to the road network and comparison with traffic count data. Taking into account aspects of vehicle-sampling observations, this paper also points out issues related to sample representativeness, both in terms of daily activities and spatial coverage. A preliminary descriptive analysis of the O-D flows was carried out, and the analysis of the revealed trip patterns is presented. Full article

The paper analyses the characteristics of Volunteer Geographic Information (VGI) and the need to assure and assess its quality for a possible use and re-use. Ontologies and soft ontologies are presented as means to support quality assurance and assessment of VGI by highlighting their limitations. A proposal of a possibilistic approach using fuzzy ontology is finally illustrated that allows to model both imprecision and vagueness of domain knowledge and epistemic uncertainty affecting observations. A case study example is illustrated. Full article

In this paper, we consider the task of assigning relevant labels to studies in the social science domain. Manual labelling is an expensive process and prone to human error. Various multi-label text classification machine learning approaches have been proposed to resolve this problem. We introduce a dataset obtained from the Finnish Social Science Archive and comprised of 2968 research studies’ metadata. The metadata of each study includes attributes, such as the “abstract” and the “set of labels”. We used the Bag of Words (BoW), TF-IDF term weighting and pretrained word embeddings obtained from FastText and BERT models to generate the text representations for each study’s abstract field. Our selection of multi-label classification methods includes a Naive approach, Multi-label k Nearest Neighbours (ML-kNN), Multi-Label Random Forest (ML-RF), X-BERT and Parabel. The methods were combined with the text representation techniques and their performance was evaluated on our dataset. We measured the classification accuracy of the combinations using Precision, Recall and F1 metrics. In addition, we used the Normalized Discounted Cumulative Gain to measure the label ranking performance of the selected methods combined with the text representation techniques. The results showed that the ML-RF model achieved a higher classification accuracy with the TF-IDF features and, based on the ranking score, the Parabel model outperformed the other methods. Full article

Background: The current pandemic caused by SARS-CoV-2 is an acute illness of global concern. SARS-CoV-2 is an infectious disease caused by a recently discovered coronavirus. Most people who get sick from COVID-19 experience either mild, moderate, or severe symptoms. In order to help make quick decisions regarding treatment and isolation needs, it is useful to determine which significant variables indicate infection cases in the population served by the Tijuana General Hospital (Hospital General de Tijuana). An Artificial Intelligence (Machine Learning) mathematical model was developed in order to identify early-stage significant variables in COVID-19 patients. Methods: The individual characteristics of the study subjects included age, gender, age group, symptoms, comorbidities, diagnosis, and outcomes. A mathematical model that uses supervised learning algorithms, allowing the identification of the significant variables that predict the diagnosis of COVID-19 with high precision, was developed. Results: Automatic algorithms were used to analyze the data: for Systolic Arterial Hypertension (SAH), the Logistic Regression algorithm showed results of 91.0% in area under ROC (AUC), 80% accuracy (CA), 80% F1 and 80% Recall, and 80.1% precision for the selected variables, while for Diabetes Mellitus (DM) with the Logistic Regression algorithm it obtained 91.2% AUC, 89.2% accuracy, 88.8% F1, 89.7% precision, and 89.2% recall for the selected variables. The neural network algorithm showed better results for patients with Obesity, obtaining 83.4% AUC, 91.4% accuracy, 89.9% F1, 90.6% precision, and 91.4% recall. Conclusions: Statistical analyses revealed that the significant predictive symptoms in patients with SAH, DM, and Obesity were more substantial in fatigue and myalgias/arthralgias. In contrast, the third dominant symptom in people with SAH and DM was odynophagia. Full article

This paper presents an automated inspection and classification system for automotive component remanufacturing industry, based on ensemble learning. The system is based on different stages allowing to classify the components as good, rectifiable or rejection according to the manufacturer criteria. A study of two deep learning-based models’ performance when used individually and when using an ensemble of them is carried out, obtaining an improvement of 7% in accuracy in the ensemble. The results of the test set demonstrate the successful performance of the system in terms of component classification. Full article

The growing convergence of various services characterizes wireless access networks. Therefore, there is a high demand for provisioning the spectrum to serve simultaneous users demanding high throughput rates. The load prediction at each access point is mandatory to allocate resources and to assist sophisticated network designs. However, the load at each access point varies according to the number of connected devices and traffic characteristics. In this paper, we propose a load estimation strategy based on a Markov’s Chain to predict the number of devices connected to each access point on the wireless network, and we apply an unsupervised machine learning model to identify traffic profiles. The main goals are to determine traffic patterns and overload projections in the wireless network, efficiently scale the network, and provide a knowledge base for security tools. We evaluate the proposal in a large-scale university network, with 670 access points spread over a wide area. The collected data is de-identified, and data processing occurs in the cloud. The evaluation results show that the proposal predicts the number of connected devices with 90% accuracy and discriminates five different user-traffic profiles on the load of the wireless network. Full article

Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers. Full article

The number of consumers playing virtual reality games is booming. To speed up product iteration, the user experience team needs to collect and analyze unsatisfying experiences in time. In this paper, we aim to detect the unsatisfying experiences hidden in online reviews of virtual reality exergames using a deep learning method and find out the unmet psychological needs of users based on self-determination theory. Convolutional neural networks for sentence classification (textCNN) are used in this study to classify online reviews with unsatisfying experiences. For comparison, we set eXtreme gradient boosting (XGBoost) with lexical features as the baseline of machine learning. Term frequency-inverse document frequency (TF-IDF) is used to extract keywords from every set of classified reviews. The micro-F1 score of textCNN classifier is 90.00, which is better than 82.69 of XGBoost. The top 10 keywords of every set of reviews reflect relevant topics of unmet psychological needs. This paper explores the potential problems causing unsatisfying experiences and unmet psychological needs in virtual reality exergames through text mining and makes a supplement for experimental studies about virtual reality exergames. Full article

As a unique device of railway networks, the normal operation of switch machines involves railway safe and efficient operation. Predictive maintenance becomes the focus of the switch machine. Aiming at the low accuracy of the prediction state and the difficulty in state visualization, the paper proposes a predictive maintenance model for switch machines based on Digital Twins (DT). It constructs a DT model for the switch machine, which contains a behavior model and a rule model. The behavior model is a high-fidelity visual model. The rule model is a high-precision prediction model, which is combined with long short-term memory (LSTM) and autoregressive Integrated Moving Average model (ARIMA). Experiment results show that the model can be more intuitive with higher prediction accuracy and better applicability. The proposed DT approach is potentially practical, providing a promising idea for switching machines in predictive maintenance. Full article

Recent years have seen a surge of interest in dialogue translation, which is a significant application task for machine translation (MT) technology. However, this has so far not been extensively explored due to its inherent characteristics including data limitation, discourse properties and personality traits. In this article, we give the first comprehensive review of dialogue MT, including well-defined problems (e.g., 4 perspectives), collected resources (e.g., 5 language pairs and 4 sub-domains), representative approaches (e.g., architecture, discourse phenomena and personality) and useful applications (e.g., hotel-booking chat system). After systematical investigation, we also build a state-of-the-art dialogue NMT system by leveraging a breadth of established approaches such as novel architectures, popular pre-training and advanced techniques. Encouragingly, we push the state-of-the-art performance up to 62.7 BLEU points on a commonly-used benchmark by using mBART pre-training. We hope that this survey paper could significantly promote the research in dialogue MT. Full article

From the functional equation $F\left(s\right)=F\left(1-s\right)$ of Riemann’s zeta function, this article gives new insight into Hadamard’s product formula. The function $S_1\left(s\right)=d\left(\ln F\left(s\right)\right)/ds$ and its family of associated $S_m$ functions, expressed as a sum of rational fractions, are interpreted as meromorphic functions whose poles are the poles and zeros of the $F$ function. This family is a mathematical and numerical tool which makes it possible to estimate the value $F\left(s\right)$ of the function at a point $s=x+iy=\dot{x}+½+iy$ in the critical strip $\mathcal{S}$ from a point $𝓈=½+iy$ on the critical line $ℒ$.Generating estimates $S_m^{\ast }\left(s\right)$ of $S_m\left(s\right)$ at a given point requires a large number of adjacent zeros, due to the slow convergence of the series. The process allows a numerical approach of the Riemann hypothesis (RH). The method can be extended to other meromorphic functions, in the neighborhood of isolated zeros, inspired by the Weierstraß canonical form. A final and brief comparison is made with the $\zeta$ and $F$ functions over finite fields. Full article

Today, many products and solutions are provided on the cloud; however, the amount and financial losses due to cloud security incidents illustrate the critical need to do more to protect cloud assets adequately. A gap lies in transferring what cloud and security standards recommend and require to industry practitioners working in the front line. It is of paramount importance to raise awareness about cloud security of these industrial practitioners. Under the guidance of design science paradigm, we introduce a serious game to help participants understand the inherent risks, understand the different roles, and encourage proactive defensive thinking in defending cloud assets. In our game, we designed and implemented an automated evaluator as a novel element. We invite the players to build defense plans and attack plans for which the evaluator calculates success likelihoods. The primary target group is industry practitioners, whereas people with limited background knowledge about cloud security can also participate in and benefit from the game. We design the game and organize several trial runs in an industrial setting. Observations of the trial runs and collected feedback indicate that the game ideas and logic are useful and provide help in raising awareness of cloud security in industry. Our preliminary results share insight into the design of the serious game and are discussed in this paper. Full article