Reviewerly/exHarmony huggingface dataset:
Enhancing Supply Chain through Implementation of Key IIoT Technologies

The purpose of this study is to empirically assess the combined impact that Blockchain technology (BC), the Industrial Internet of Things (IIoT) technologies, and agile production have on supply chain performance. This study is narrowly based on the analysis of data from 303 U.S. manufacturing managers. The proposed theoretical model is analyzed following a covariance-based structural equation modeling methodology (CB/SEM). This is the first study to empirically assess the combined impact of Blockchain technology, IIoT technologies, and agile production on supply chain performance. The results support the conclusion that Blockchain and IIoT technologies are complementary and yield greater agility. Managers are provided with evidence that combining Blockchain, IIoT technologies, and agile production will lead to greater improvements in supply chain performance than using these technologies stand-alone.
Leveraging a cloud-native architecture to enable semantic interconnectedness of data for cyber threat intelligence

Cloud technologies have several merits including the elimination of cost incurred when traditional technologies are adopted. Despite the benefits, the cloud is still facing security challenges thereby calling for cyber threat intelligence capable of identifying threats and providing possible solutions. However, dependence on traditional security mechanisms and approaches for security solutions within cloud environments presents challenges. This calls for cloud-native solutions which leverages cloud features for design and development of solutions for data and applications hosted and running within the cloud. Past studies have suggested the adoption of semantic technologies for cloud-based security mechanisms. However, the semantic processing of data faces challenges of data interconnectedness due to aggregation of data from diverse heterogenous sources. Hence, this study proposes a cloud-native architecture capable of connecting security-related data from different sources in the cloud to enhance cyber threat intelligence. It presents a proof-of-concept implementation of the proposed solution on Amazon AWS cloud, within an auto-scaling group for scalability and across multiple availability zones for high availability.
EfficientMask-Net for face authentication in the era of COVID-19 pandemic

Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This research develops an Efficient Mask-Net method for low-power devices, such as mobile and embedding models with low-memory requirements. The method identifies face mask-wearing conditions in two different schemes: I. Correctly Face Mask (CFM), Incorrectly Face Mask (IFM), and Not Face Mask (NFM) wearing; II. Uncovered Chin IFM, Uncovered Nose IFM, and Uncovered Nose and Mouth IFM. The proposed method can also be helpful to unmask the face for face authentication based on unconstrained 2D facial images in the wild. In this study, deep convolutional neural networks (CNNs) were employed as feature extractors. Then, deep features were fed to a recently proposed large margin piecewise linear (LMPL) classifier. In the experimental study, lightweight and very powerful mobile implementation of CNN models were evaluated, where the novel "EffientNetb0" deep feature extractor with LMPL classifier outperformed well-known end-to-end CNN models, as well as conventional image classification methods. It achieved high accuracies of 99.53 and 99.64% in fulfilling the two mentioned tasks, respectively.
Effects of Security Knowledge, Self-Control, and Countermeasures on Cybersecurity Behaviors

Organizations whose employees participate in risky cybersecurity behaviors (RCySecB) could suffer disastrous consequences either directly or indirectly from actions linked to such behaviors. This study used conceptualizations facilitated by Attribution Theory to examine the effects of dispositional factors (self-control and knowledge of IS security threats/risks and their potential consequences) and situational factors (security, education, training, and awareness (SETA) programs and computer monitoring) in reducing employee participation in RCySecB. Data was collected from a survey of working professionals in India, and relevant hypotheses were formulated. The PLS technique was used for data analysis. This study investigated the direct effects of the factors on the dependent construct and analyzed the interacting effects of the constructs as well. The results indicate that all factors reduce employee participation in RCySecB. However, the joint effects of the situational factor elements and the dispositional factor of personal knowledge security threats/risks and their potential consequences were insignificant in the model.
A Personalized Recommendation Method for Short Drama Videos Based on External Index Features

Dramatic short videos have quickly gained a huge number of user views in the current short video boom. The information presentation dimension of short videos is higher, and it is easier to be accepted and spread by people. At present, there are a large number of drama short video messages on the Internet. These short video messages have brought serious information overload to users and also brought great challenges to short video operators and video editors. Therefore, how to process short videos quickly has become a research hotspot. The traditional episode recommendation process often adopts collaborative filtering recommendation or content-based recommendation to users, but these methods have certain limitations. Short videos have fast dissemination speed, strong timeliness, and fast hot search speed. These have become the characteristics of short video dissemination. Traditional recommendation methods cannot recommend short videos with high attention and high popularity. To this end, this paper adds external index features to extract short video features and proposes a short video recommendation method based on index features. Using external features to classify and recommend TV series videos, this method can quickly and accurately make recommendations to target customers. Through the experimental analysis, it can be seen that the method in this paper has a good effect.
Performance Analysis of FFBP-LM-ANN Based Hourly GHI Prediction Using Environmental Variables: A Case Study in Chennai

Sun is the sustainable and abundantly available alternative resource on the planet Earth. The uncertain nature of the source caused due to various environmental factors increases the need to quantify the irradiation potential at the targeted location, especially for power smoothing processes, solar-fed electrical applications, and the utility grid. The irradiation measuring devices lead to appropriate maintenance and more expenses and are ineffective under dynamically varying irradiation conditions. Hence, a robust ML-based forecasting technique is sufficient to predict GHI under dynamically varying irradiation profiles caused by various environmental factors. Therefore, this study focuses on short-term (hourly) GHI forecasting using the FFBP-LM-ANN approach. In most of the related articles, the selection of independent variables and designing the network is being a challenge and obstacle in attaining fast and efficient prediction. Such a scenario increases the overall computation complexity and understanding. Hence, this study was intended to use a simple process to crucially select 5 environmental factors and to optimally choose the number of neurons for designing the network for better irradiation prediction. Thus, the network is designed with an input layer of 5 selected environmental variables and one hidden layer with 20 neurons, and hourly GHI in the output layer is performed. The approach is trained using the Levenberg-Marquardt BP algorithm in MATLAB toolbox, with the help of a 4-year dataset received for the rooftop panels of VIT University, Chennai, from NREL. The performance of the model during training and testing was validated and analyzed using 9 performance matrices. As a result, FFBP-LM-ANN satisfactorily predicts hourly GHI for the targeted location based on rRMSE of 7.21%, MAE of 0.042, MBE of 0.000492, R of 0.96, MAPE of 44.4%, MRE of 9.5%, and NSE of 94% obtained under testing process. Moreover, the model has performed much better when compared with 9 related models that exist in literature based on the input variables used, network design, epoch, and RMSE. Subsequently, such a predictor will be adaptable and more suitable for the explicit prediction of hourly GHI for different regions across the world having varying climatic conditions, since the study model is designed for locations facing robust climatic nature. More importantly, the designed model is superior with only environmental variables, which are rarely found in the article, rather than geographical variables, which are predominantly used in most of the related literature.
An integrated approach of designing functionality with security for distributed cyber-physical systems

In this work, we propose a multi-tier architectural model to separate functionality and security concerns for distributed cyber-physical systems. On the line of distributed computing, such systems require the identification of leaders for distribution of work, aggregation of results, etc. Further, we propose a fault-tolerant leader election algorithm that can independently elect the functionality and security leaders. The proposed election algorithm identifies a list of potential leader capable nodes to reduce the leader election overhead. It keeps identifying the highest potential node as the leader, whenever needed, including the situation when one has failed. We also explain the proposed architecture and its management method through a case study. Further, we perform several experiments to evaluate the system performance. The experimental results show that the proposed architectural model improves the system performance in terms of latency, average response time, and the number of real-time tasks completed within the deadline.
Modality-Balanced Embedding for Video Retrieval

Video search has become the main routine for users to discover videos relevant to a text query on large short-video sharing platforms. During training a query-video bi-encoder model using online search logs,\textit we identify a modality bias phenomenon that the video encoder almost entirely relies on text matching, neglecting other modalities of the videos such as vision, audio, \etc This modality imbalance results from a) modality gap: the relevance between a query and a video text is much easier to learn as the query is also a piece of text, with the same modality as the video text; b) data bias: most training samples can be solved solely by text matching. Here we share our practices to improve the first retrieval stage including our solution for the modality imbalance issue. We propose \modelname (short for Modality Balanced Video Retrieval) with two key components: manually generated modality-shuffled (MS) samples and a dynamic margin (DM) based on visual relevance. They can encourage the video encoder to pay balanced attentions to each modality. Through extensive experiments on a real world dataset, we show empirically that our method is both effective and efficient in solving modality bias problem. We have also deployed our ~\modelname~ in a large video platform and observed statistically significant boost over a highly optimized baseline in an A/B test and manual GSB evaluations.
Designing Tangible as an Orchestration Tool for Collaborative Activities

Orchestrating collaborative learning activities is a challenge, even with the support of technology. Tangibles as orchestration tools represent an ambient and embodied approach to sharing information about the learning content and flow of the activity, thus facilitating both collaboration and its orchestration. Therefore, we propose tangibles as a solution to orchestrate productive collaborative learning. Concretely, this paper makes three contributions toward this end: First, we analyze the design space for tangibles as an orchestration tool to support collaborative learning and identify twelve essential dimensions. Second, we present five tangible tools for collaborative learning activities in face-to-face and online classrooms. Third, we present principles and challenges to designing tangibles for orchestrating collaborative learning based on our findings from the evaluation of ten educational experts who evaluated these tools using a usability scale and open questions. The key findings were: (1) they had good usability; (2) their main advantages are ease of use and support for collaborative learning; (3) their main disadvantages are limited functions and the difficulty to scale them to more users. We conclude by providing reflections and recommendations for the future design of tangibles for orchestration.
Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction

Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity in sentences, some important contextual information can guide the semantic representation of entity mentions to improve the accuracy of relation extraction. However, most existing relation extraction models ignore the semantic guidance of contextual information to entity mentions and treat entity mentions in and the textual context of a sentence equally. This results in low-accuracy relation extractions. To address this problem, we propose a contextual semantic-guided entity-centric graph convolutional network (CEGCN) model that enables entity mentions to obtain semantic-guided contextual information for more accurate relational representations. This model develops a self-attention enhanced neural network to concentrate on the importance and relevance of different words to obtain semantic-guided contextual information. Then, we employ a dependency tree with entities as global nodes and add virtual edges to construct an entity-centric logical adjacency matrix (ELAM). This matrix can enable entities to aggregate the semantic-guided contextual information with a one-layer GCN calculation. The experimental results on the TACRED and SemEval-2010 Task 8 datasets show that our model can efficiently use semantic-guided contextual information to enrich semantic entity representations and outperform previous models.
Graph construction by incorporating local and global affinity graphs for saliency detection

The diffusion-based graph is one type of extensively studied method in saliency detection due to its simplicity and effectiveness. The key to success is a superior affinity graph and suitable initial seeds. In this paper, considering the local structure and global confidence of the affinity graph, we generate a joint global affinity graph from the deep-level and low-level image feature matrix based on the self-representation model, as well as a deep-level local affinity graph and a low-level local affinity graph using these two types of features, respectively. Based on the local and global affinity graph, we design three superior affinity graphs to benefit the corresponding three stages consisting of saliency estimation, saliency refinement, and saliency optimization. In particular, in the second and third stages, we embed the initial saliency values to robust the affinity graphs. Extensive experiments demonstrate that the proposed method over performing the state-of-the-art approaches on four benchmark datasets.
An efficient outlier detection method based on distance ratio of k-nearest neighbors

Outlier detection is an important topic, which has been investigated in different theoretical and applied sciences. So far, numerous methods and algorithms have been proposed for outlier detection; each of these methods has found a use in some fields and has advantages and disadvantages. The present study presents an efficient method for detecting outliers based on the distance ratio of the k-nearest neighbors. In this method, both criteria of density and distance between objects have been included. Contrary to a large number of the previous methods, the density of each object is measured in relation to each neighbor independently, and after considering the distance criterion, the outlierness score of each object is measured collectively. Another feature of this method is its resistance to the k parameter. The proposed method has been evaluated in two-dimensional synthetic and multi-dimensional real datasets and compared with other significant algorithms in this field. The results of the experiments proved the efficiency of the proposed method.
Research on Modeling of Underground Path Loss Prediction Based on SA-SVM

Abstract In view of the serious refraction and reflection of radio wave transmission in the complex environment of coal mines, serious multipath fading occurs, which affects the performance of the communication system. Therefore, we presents a simulated annealing algorithm to optimize the kernel function and regularization function parameters in LS-SVM, and establishes a wireless channel path loss model based on LS-SVM. Comparing the optimized and improved model based on the SA algorithm with the traditional model, the MATLAB simulation experiment results show that the SA+LS-SVM model is significantly superior to the pre-optimized model and the traditional model in prediction accuracy, and can more effectively predict the fading change to the channel Condition.
Security versus Compliance: An Empirical Study of the Impact of Industry Standards Compliance on Application Security

The integration of security aspects into software development is an open topic, especially in highly regulated industries where standards are accompanied by a high degree of complexity. The research question of this paper relates to the misconception of industry standards compliance and security in the field of software development. Cyber attackers are constantly inventing new tools to penetrate systems and exploit even the most minor flaws, and adherence to an industry standard is not a solution. In this study, an empirical investigation is conducted over a six-month period to observe various customer relationship management (CRM) systems. To analyze and anticipate the vulnerabilities of various CRMs, penetration testing methodologies and cross-project prediction approaches are employed. Classification using multiple machine learning approaches is utilized in the study to increase the discovery of vulnerable components in each CRM. The Student [Formula: see text]-test is also used to assess if the mean values of the two CRM datasets are substantially different from each other in order to evaluate the efficacy of overall security and its features. The results show that security best practices during application development have a significant influence on applications created in regulated environments. The action research approach used to validate this study provided positive results and its feasibility in practice to optimize security throughout the application development. This study adds to the literature on information security management systems (ISMS) and best practices in application development in terms of creating and implementing opportunities based on broader information security management measures.
Evaluation of an interactive visualization tool to increase energy literacy in the building sector

The building sector, and especially residential households and office buildings, account for a large share of global emissions. Meanwhile, energy literacy is extremely low amongst residents and citizens in general, leading to insufficient evaluations of energy efficiency measures and technology equipment for buildings. To address this issue, we develop a research model and design an experiment to evaluate the ability of a website with interactive and vivid features to convey information in an engaging way, thus increasing the users' enjoyment and their intention to (re)-use and recommend the website as well as the usefulness for information retrieval and technology evaluation. We conduct an experiment with two treatments in which the participants interact with an animated and a static website, respectively. While participants' self-assessed knowledge improvement is statistically higher in the animated treatment, no difference was found in tested knowledge assessment or technology-specific knowledge. We find that the vividness of the website plays an important role for both the utilitarian and hedonic purpose of the website. However, somewhat contrasting to existing theories, interactivity did not increase enjoyment or diagnosticity.
Determining the Paleostress Regime during the Mineralization Period in the Dayingezhuang Orogenic Gold Deposit, Jiaodong Peninsula, Eastern China: Insights from 3D Numerical Modeling

The Dayingezhuang orogenic gold deposit, located in the northwestern Jiaodong Peninsula, is hosted by the Zhaoping detachment fault, but the paleostress regime during the mineralization period remains poorly understood. In this study, a series of numerical modeling experiments with variable stress conditions were carried out using FLAC3D software to determine the orientation of paleostress and the fluid migration processes during the ore-forming period. The results show that the simple compression or tension stress model led to fluid downward or upward flow along the fault, respectively, accompanying the expansion deformation near the hanging wall or footwall of the Zhaoping fault, which is inconsistent with the known NE oblique mineralization distribution at Dayingezhuang. The reverse and strike-slip model shows that the shear stress was distributed in the gentle dip sites of the fault, and the expansion space occurred in the geometric depression sites of the fault, which is also inconsistent with the known mineralization distribution. The normal and strike-slip model shows that shear stress was distributed in the sites where the fault geometry transforms from steep to gentle. In addition, the expansion deformation zones appeared at sites with dip angles of 35~60° in the footwall and extended along with the NE-trending distribution from shallow to deep levels. The numerical results are quite consistent with the known mineralization, suggesting that the fault movement during the mineralization stage is a combination of the local strike-slip and the NW–SE extension in the Dayingezhuang deposit. Under this stress regime (σ1 NE–SW, σ2 vertical, σ3 NW–SE), the NE dilation zones associated with fault deformation served as channels for the ore-forming fluid migration. Based on the numerical modeling results, the deeper NE levels of the No. 2 orebody in the Dayingezhuang deposit have good prospecting potential. Thus, our study not only highlights that gold mineralization at Dayingezhuang is essentially controlled by the detachment fault geometry associated with certain stress directions but also demonstrates that numerical modeling is a robust tool for identifying potential mineralization.