Reviewerly/exHarmony huggingface dataset:
Leader-member exchange theory and its relationship to teachers' intrapreneurial behaviour, teachers' work engagement and entrepreneurial orientation

The current study aimed to explore how the relationship between teachers and their principals (leader-member exchange – LMX) promotes intrapreneurial behaviours (i.e., entrepreneurial and innovative activities within existing organisations). The hypotheses were tested using paper-based surveys and scales distributed to 105 teachers. Findings indicated that the relationship between teachers and school principals (LMX) was significantly positively associated with intrapreneurial activities. A high quality relationship was also connected to entrepreneurial orientation (EO) and mediated by the teachers' work engagement. In addition, EO mediated between LMX, work engagement and intrapreneurial activities. In addition, four different machine learning algorithms were used to evaluate the hypotheses by classifying the study data. Naïve Bayes was found to outperform logistic regression and the other classification methods. This study is one of the few to explore intrapreneurial behaviour in educational institutions and thus contributes to theory and practice in understanding how innovation behaviours take place in schools, and its implications for the education arena.
Exploring the Repetition Problem in Empathetic Dialogue Generative Model

We explore the task of generating an empathetic response of dialogue agents. Recent empathetic dialogue agents have shown their abilities to generate empathetic responses equipped with additional modules, such as mixture-of-experts, emotion mimicry, or external knowledge. However, we find even these agents prefer to generate generic and repetitive responses like &#x201C;I am sorry to hear that &#x2026;&#x201D;, which is the empathy statement in the task of empathetic dialogue generation. From the analysis, we reveal that this tendency is attributed to the nature of Empatheticdialogues dataset. Through the experiment, we confirm that only reducing repetition problem in the empathetic dialogue generation is not optimized solution.
Enhancing Semantics-Driven Recommender Systems with Visual Features

Content-based semantics-driven recommender systems are often used in the small-scale news recommendation domain, founded on the TF-IDF measure but also taking into account domain semantics through semantic lexicons or ontologies. This work explores the application of content-based semantics-driven recommender systems to large-scale recommendations on the example of movie domain. We propose methods to extract semantic features from various item descriptions, including images. In particular, we use computer vision to extract semantic features from images and use these for recommendation together with various features extracted from textual information. The semantics-driven approach is scaled up with pre-computation of the cosine similarities and gradient learning of the model. The results of the study on a large-scale MovieLens dataset of user ratings demonstrate that semantics-driven recommenders can be extended to more complex domains and outperform TF-IDF on ROC, PR, $$F_1$$ , and Kappa metrics.
AI Companionship or Loneliness: How AI-Based Chatbots Impact Consumer’s (Digital) Well-Being: An Abstract

With the recent proliferation of “smart” technologies, consumers increasingly interact with these technologies the same way they would interact with their fellow humans (e.g., having a conversation with Siri or Alexa) (Novak and Hoffman 2019). Indeed, some consumers are turning to technology to fulfill desires for friendship and/or romance (Olson 2020). While technology can be successfully designed to positively influence consumer well-being (Dekker et al. 2020; Peters et al. 2018), scant research exists examining this phenomenon beyond a conceptual level. Recently, however, scholars show that the detrimental impact of technology on consumer well-being (i.e., consisting of overall life satisfaction and positive and negative affective states; Burroughs and Rindfleisch 2002) may be less harmful than previously thought (Orben and Pryzbylski 2018; Pryzbylski and Weinstein 2016). In contrast, other research shows that the detrimental effect of technology may exist under certain conditions, such as long-term isolation (e.g., Lastovicka and Sirianni 2011). Therefore, technology can have beneficial and/or detrimental impacts on consumer well-being (Hefner and Vorderer 2017; Mick and Fournier 1998; Pryzbylski et al. 2012). Understanding the relationship between technology and consumer well-being is further marred by data collection issues such as research participants being unable to accurately estimate their own technology usage (Scharkow 2016). As such, the purpose of this research was to develop a deeper understanding of how technology and the interaction with technology can impact consumer well-being. Specifically, we develop theory regarding how consumer’s interact with technology via two qualitative studies following a grounded theory approach (Corbin and Strauss 1990). Because our data are cross-sectional, future research would benefit from a longitudinal perspective to assess when technology might improve (digital) well-being (e.g., potentially initially) and where technology might worsen (digital) well-being overtime due to increased isolation (e.g., if used for a long period) (Lastovicka and Sirianni 2011).
Deceptive security to protect your business

Information systems are being increasingly exposed to cyber attacks, intrusion attempts and sophisticated malware. For organisations, one of the main challenges associated with these risks is gaining a better understanding of the actors behind the threats, as well as the methods that they employ. So how can organisations gather information about the specific cyberthreats that they face and improve their threat intelligence strategy? One solution is a honeypot.
A Novel Distributed Database Architectural Model for Mobile Cloud Computing

Cloud computing is the way by which we connect to servers, large systems into a distributed secure manner without worrying about local memory limits. Here, in this paper, we proposed a Novel distributed database architectural model for mobile cloud computing (NDDAMMCC). Accelerating the exponential growth of wireless technologies and Internet which are following Nielsen’s Law of Internet Bandwidth, we are in the new era of cloud computing. In the recent technological era, smart mobile devices play a big role in all sort of day-by-day human needs. The applicability is so huge that the number of apps install on a mobile system becomes a hazard due to local memory limitations for mobile phone users and demands an alternative approach to solve this local memory problems. Mobile cloud computing (MCC) is the ultimate mechanism to this issue, and our model presents a promising path in this new kind of cloud computing technology.
Information Extraction of the Vehicle from High-Resolution Remote Sensing Image Based on Convolution Neural Network

Aims: To effectively detect vehicle targets in remote sensing images, it can be widely used in traffic management, route planning, and vehicle flow detection. YOLOv3 deep learning neural network, which mainly studies the vehicle target detection in remote sensing images and carries out the target detection suitable for the characteristics of remote sensing images. Objective: This paper studies the information extraction of vehicle high-resolution remote sensing images based on a convolution neural network. Method: The YOLOv3 network model of vehicle target detection in satellite remote sensing images is optimized. The iterations are set to 50002000045000, and the learning rate is 0.001. At the same time, the comparative experiments of RCNN, Fast RCNN, fast RCNN, and yolov3 network models are carried out. Result: The ca-yolov3 network model can be applied to target detection in satellite images. After 40500 times of learning, the loss function value of the model is reduced to about 0.011. Conclusion: The IOU value of the model also has a good performance in the training process, which makes the yolov3 neural network model more accurate in the image small target detection.
Methodological Grounds for Personification of School Educational Environment

The aim of the study is to substantiate the methodological foundations of the personification of the school educational environment in the context of system-synergetic, environmental, personal-activity and cultural approaches, the provisions of existential pedagogy, the concept of education individualization, a pupil’s self-determination and a subjective position development in education. Based on the analysis of theoretical and methodological provisions, the article substantiates the strategies of personification of the school educational environment: a subjective position development and a pupil’s personality self-realization in the conditions of the non-linear organization of education; saturation of the school educational environment based on the principle of events; organization of psychological and pedagogical support and implementation of tutor technologies. The scientific novelty of the study lies in the fact that the methodological foundations for the personification of the school educational environment are determined, which consist in a combination of theoretical and methodological approaches and methods that allow it to be studied and designed. As a result, methodological foundations for the personification of the school educational environment have been substantiated, a generalization of the pedagogical concepts of differentiation, individualization, personalization, education personification has been carried out, strategies for the personification of the school educational environment have been determined.
Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm

Customer opinion is an important aspect in determining the success of a company or service provider. By determining the sentiment of the existing opinion, the company can use it as an evaluation material to improve the quality of the service or product provided. Sentiment analysis can be used as a measure of opinion sentiment with input data in the form of a corpus which will be classified into positive or negative classes to obtain the level of customer satisfaction with a product or service. Aspect-based sentiment analysis can be used by companies to analyze more specifically and find out what aspects need to be improved. In this research, an aspect-based sentiment analysis was conducted on Telkomsel users on Twitter. The data used is 16,992 tweets from users who discuss several aspects such as Telkomsel's services and signals in Twitter. In this research Word2Vec was used for feature expansion to minimize vocabulary mismatch caused by limited words in tweets. The results showed that Word2Vec, Synthetic Minority Oversampling Technique (SMOTE), and Boosting algorithm combination with Logistic Regression classifier achieve highest accuracy of 95.10% for signal aspect and using hyperparameters makes the service aspect get the highest accuracy of 93.34%.&#x0D;
GPK-YOLOv5s: Content-Aware Reassembly of Features and Self Attention for High Altitude Parabolic Detection and Tracking

High altitude parabolic is difficult to identify because of its small size, fast speed and changeable state, which makes it difficult for subsequent forensics and accountability. This paper proposes a high-altitude parabolic detection and tracking method GPK-YOLOv5s, which integrates Content-Aware Reassembly of Features (CARAFE) and self-attention to realize parabolic detection and tracking. For the detection network, the backbone integrates C3Ghost module to extract effective features and simplify the network. C3Transformer module is embedded in the feature extraction and fusion layer to pay attention to the global context information. The feature fusion layer uses CARAFE module for up sampling to perceive effective features, and integrates shallow features and deep features to form a new large-scale detection layer (Output4) to further obtain smaller receptive fields. Improved multi-scale detection heads are embedded with CBAM to enhance the expression ability of targets. To overcome the frame loss of real-time detection, improved multiscale detection heads are externally connected with Kalman filter to track targets. This experiment verifies that the detection Precision, Recall and F1 value of GPK-YOLOv5s reached 99.0%, 98.6% and 98.8% respectively, which are 2.8%, 4.1% and 3.5% higher than YOLOv5s respectively. And GPK-YOLOv5s is lighter, and the calculation consumption is reduced by 0.4 GFLOPs.
IoT in Healthcare in Times of Pandemic (COVID-19)

The novel corona virus (COVID-19) has created a challenge for health experts and doctors to take control of the current situation. There has been continuous research made worldwide to combat this global public health crisis. The infectious cases have already crossed over 223 million cases as per WHO, so far. Internet of things (IoT) is one of the main tools that have to break the ground with its numerous benefits. The IoT has changed the outlook of the modern healthcare system. Various devices embedded with the internet are used to minimize the spread of COVID-19. These devices help in early diagnosing, performing necessary procedures, and monitoring patients’ health. This paper outlines the role of IoT based technologies as to how it is used for combating the novel severe respiratory syndrome corona virus-2 and also proposes a better solution for the patients to identify the efficient healthcare unit when suffering from a health issue. This helps the common man to identify which healthcare unit is specialized in treating them.
Readiness of young university teachers for professional activities in the digital educational space

The dynamic development of digital technologies leads to a continuously increasing use of information products and services in all areas of modern society, incl. and in education. Technologies, platforms and services appear here that provide unlimited opportunities for obtaining information, mastering various training courses and programmes in a distance format, online and offline learning. Young teachers are at the centre of these changes. They are required not only to be ready to accept the ongoing transformations, quickly master new opportunities, adapt them to teaching of disciplines, but also continuously improve the educational process, taking into account the experience gained and its problematic aspects that are identified. The article presents the results of a study of the readiness of young university teachers to work in a dynamically developing information educational environment, to develop and create electronic educational courses and enhance the use of ICT in the educational process. The paper provides a comparative analysis of the results of surveys conducted in 2020 and 2021 аt Tsiolkovsky Kaluga State University. The authors conclude the necessity of creating a system of work at the university to support young teachers, in terms of both professional and personal development.
Physics-Informed Feature Space Evaluation for Diagnostic Power Monitoring

Sensing solutions provide a rich feature set for electromechanical load monitoring and diagnostics. For example, qualities that describe the operation of an electromechanical load can include measurements of power, torque, vibration, electrical current demand, and electrical harmonic content. If properly interpreted, these measurements can be utilized for energy management, condition-based maintenance, and fault detection and diagnostics. When monitoring several loads from an aggregate data stream, a well-posed feature space will permit not only load identification, but also the characterization of faults and gradual changes in the health of an individual machine. Many feature selection methods assume static and generalizable data, without consideration of concept drift and evolving behavior over time. This article presents a method for evaluating load separability in a feature space prior to the application of a pattern classifier while accounting for changing operating conditions and load variability. A four-year load dataset is used to validate the method.
A multi-intent based multi-policy relay contrastive learning for sequential recommendation

Sequential recommendations have become a trending study for their ability to capture dynamic user preference. However, when dealing with sparse data, they still fall short of expectations. The recent contrastive learning (CL) has shown potential in mitigating the issue of data sparsity. Many item representations are destined to be poorly learned due to data sparsity. It is better to pay more attention to learn a set of influential latent intents that have greater impacts on the sequence evolution. In this article, we devise a novel multi-intent self-attention module, which modifies the self-attention mechanism to break down the user behavior sequences to multiple latent intents that identify the different tastes and inclinations of users. In addition to the above change in the model architecture, we also extend in dealing with multiple contrastive tasks. Specifically, some data augmentations in CL can be very different. Together they cannot cooperate well, and may stumbling over each other. To solve this problem, we propose a multi-policy relay training strategy, which divides the training into multiple stages based on the number of data augmentations. In each stage we optimize the relay to the best on the basis of the previous stage. This can combine the advantage of different schemes and make the best use of them. Experiments on four public recommendation datasets demonstrate the superiority of our model.
Keypoints class distribution based entropy for weighting scheme on image classification

In bag-of-visual words (BoVW), a weighting scheme is applied to improve the discriminative power of visual words, which affect the performance of image classification. However, the weighting schemes in the BoVW model did not utilize the information class of keypoints. In this paper, we propose an algorithm to measure the certainty of visual words which employ the information class of keypoints, called keypoints class distribution based entropy (KCDE). The algorithm is then combined with the existing weighting scheme namely term frequency-term distribution (TF-TD). The results show that the proposed weighting schemes give better classification results than the baseline weighting schemes.
Speeding Up Action Recognition Using Dynamic Accumulation of Residuals in Compressed Domain

Temporal redundancy and the sheer size of raw videos are the two most common problematic issues related to video processing algorithms. Most of the existing methods mainly focused on increasing accuracy by exploring consecutive frames, which is laborious and cannot be considered for real-time applications. Since videos are mostly stored and transmitted in compressed format, these kinds of videos are available on many devices. Compressed videos contain valuable information, including Motion Vectors and quantized coefficients. Proper use of this available information can greatly improve the video understanding methods' performance. This paper presents an approach for using residuals, which can be obtained by a light partially decoding procedure. In addition, a method for accumulating similar residuals is proposed, dramatically reducing the number of processed frames. Feeding neural networks with accumulated residuals in the compressed domain accelerate performance, while the classification results are highly competitive with previous traditional approaches.
IoT efficient data exploitation process using Model Driven Engineering

Sensor networks are present in various fields such as medicine, agriculture, industry, or urban areas at different scales from building to city-scale. They are useful for numerous applications, often using different kinds of sensor technologies bringing in their own particularities, thus contributing to the heterogeneity of such networks. In this context, there is an in-creasing need for scalable and rapidly deployable infrastructures to allow interoperability for the management and processing of collected data. In this paper, we focus on an approach aiming to facilitate the automatic deployment of heterogeneous sensor networks. We propose to introduce a Model-Driven Engineering (MDE) approach providing a high-level model to describe such infrastructures along with automatic code generation to facilitate the exploitation of collected data through a graphical interface. Hence, we present a model allowing to describe heterogeneous sensor networks with a data exploitation point of view. We use a high-level intermediary generic code structure to allow automatic code generation towards different targets. Such automation is intended to speed-up establishing new services from existing infrastructures as well as upgrades that might follow. The evaluation of our model is done through a real example.
Foveated Stochastic Lightcuts

Foveated rendering provides an idea for accelerating rendering algorithms without sacrificing the perceived rendering quality in virtual reality applications. In this paper, we propose a foveated stochastic lightcuts method to render high-quality many-lights illumination effects in high perception-sensitive regions. First, we introduce a spatiotemporal-luminance based lightcuts generation method to generate lightcuts with different accuracy for different visual perception-sensitive regions. Then we propose a multi-resolution light samples selection method to select the light sample for each node in the lightcuts more efficiently. Our method supports full-dynamic scenes containing over 250k dynamic light sources and dynamic diffuse/specular/glossy objects. It provides frame rates up to 110fps for high-quality many-lights illumination effects in high perception-sensitive regions of the HVS in VR HMDs. Compared with the state-of-the-art stochastic lightcuts method using the same rendering time, our method achieves smaller mean squared errors in the fovea and periphery. We also conduct user studies to prove that the perceived quality of our method has a high visual similarity with the results of the ground truth rendered by using the stochastic lightcuts with 2048 light samples per pixel.
Oracle Machine Learning in Autonomous Database

Oracle Machine Learning (OML) harnesses the massive power of a database to process data for the machine learning process, without the hassle of moving the data outside the database, or risking the data security. OML supports three programming languages: SQL (OML4SQL), R (OML4R), and Python (OML4Py). SQL and Python are automatically available in an Oracle Autonomous Database via their APIs. Soon, also R will most likely be available. Currently R is only available on Oracle Database, not on Autonomous Database.