Reviewerly/exHarmony huggingface dataset:
Overcoming Cloud Concerns with Trusted Execution Environments? Exploring the Organizational Perception of a Novel Security Technology in Regulated Swiss Companies

Trusted execution environments are a new approach for isolating data, specific parts of code, or an entire application within untrusted cloud environments.This emerging security technology could also enable the migration to cloud infrastructures for organizations working with highly sensitive data.As current research does not address the organizational perception of trusted execution environments (TEEs), we conducted an explorative study to clarify the technological, environmental, and organizational views on this technology by health care, life sciences, and banking companies in Switzerland.The interview findings show that in these industries, missing technological knowledge as well as privacy and process regulation are perceived to be the most critical driver for organizational adoption of TEEs.The identified low intrinsic motivation to adopt novel technologies permits us to conclude that clarifying the regulatory impact of TEEs could drive future adoption by organizations.
Uncertainty-Aware Forward Correction for Weakly Supervised Solar Panel Mapping From High-Resolution Aerial Images

Solar panel mapping from high-resolution aerial images is becoming increasingly crucial to grid planning and operation, where weakly supervised approach has been explored. To cope with the noisy nature of pseudo-labels (PLs) generated by weakly supervised object localization, we propose an effective uncertainty-aware forward correction (UA-FC) method to learn clean predictions from the noisy PLs. The proposed method consists of two steps: heteroscedastic uncertainty estimation and forward correction procedure. The purpose of the first step is to produce uncertainty as an indicator for the instance-dependent noise. The second step includes a target mapping network to produce clean predictions and a transition function to model the relationship between clean predictions and noisy PLs. As estimating every probability of one class flipped into another is difficult and time-consuming, we introduce heteroscedastic uncertainty as a measurement and propose an uncertainty-based transformation function to map clean predictions into noisy ones. By minimizing the errors between the noisy predictions and noisy labels, the target mapping network is able to offer clean predictions close to the actual objects. Extensive experiments on an aerial dataset reveal that the proposed method outperforms other state-of-the-art methods by a large margin, especially in recovering the boundary of the objects.
Aspect-Based Sentiment Analysis Using Mini-Window Locating Attention for Vietnamese E-commerce Reviews

This article illustrates a system developed to tackle Aspect-based sentiment classification for Vietnamese E-commerce reviews. We employ supervised learning models based on Deep Learning application and multiple classic classifiers such as Random Forest, Decision Tree, Support Vector Machine, etc. to sort out the model performs best with our dataset. Our method obtained the maximum Micro-Average and Macro-Average Performance of 95%. Furthermore, we present how our Vietnamese manually-annotated multi-aspect dataset in two domains: Technology and Mother & Baby was prepared.
Clustering of the Blendshape Facial Model

Digital human animation relies on high-quality 3D models of the human face-rigs. A face rig must be accurate and, at the same time, fast to compute. One of the most common rigging models is the blendshape model. We present a novel approach for learning the inverse rig parameters at increased accuracy and decreased computational cost at the same time. It is based on a two fold clustering of the blendshape face model. Our method focuses exclusively on the underlying space of deformation and produces clusters in both the mesh space and the controller space-something that was not investigated in previous literature. This segmentation finds intuitive and meaningful connections between groups of vertices on the face and deformation controls, and further these segments can be observed independently. A separate model for solving the inverse rig problem is then learnt for each segment. Our method is completely unsupervised and highly parallelizable.
Water Loss Meter Tracking GIS Mobile Application

One of the most challenging tasks in providing water services is proportion of water loss or known as non-revenue water (NRW). NRW is water supplied to the consumer, but it is lost before it reaches the consumer. The losses of NRW is related with water billing and water produced by local agencies such as Air Selangor. One of the ways to overcome NRW is by changing inaccurate or malfunctioned water meter, but the local department’s water meter still uses the manual method. An innovative geospatial mobile application for water loss meter tracking system was developed at AIR Selangor s, Shah Alam. The main stages used in this study were identifying the problem, designing the app, developing the app and lastly evaluating the app’s performance. Selected officers and contractors of the department agreed with the recommendation made by the authors to develop a digitized system based on the current system. The app has technical capabilities to improve the current system which uses pen and paper to manage water loss via meter system at the site. This geographical information system (GIS)-based application could also update and locate all the coordinates of water meters efficiently so that the contractor can easily find the malfunctioned water meters. The respondents agreed that the design and practical functions developed in the app are relevant in dealing with the water problem.
A Literature Review of Data Governance and Its Applicability to Smart Cities

Data governance have been relevant for companies for a long time.Yet, in the broad discussion on smart cities, research on data governance in particular is scant, even though data governance plays an essential role in an environment with multiple stakeholders, complex IT structures and heterogeneous processes.Indeed, not only can a city benefit from the existing body of knowledge on data governance, but it can also make the appropriate adjustments for its digital transformation.Therefore, this literature review aims to spark research on urban data governance by providing an initial perspective for future studies.It provides a comprehensive overview of data governance and the relevant facets embedded in this strand of research.Furthermore, it provides a fundamental basis for future research on the development of an urban data governance framework.
Social Relations and Methods in Recommender Systems: A Systematic Review

With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations.Currently, authors consider the user's historical behavior and find contextual information about the user, such as social relationships, time information, and location.In this work, a systematic review of the literature on recommender systems that use the information on social relationships between users was carried out.As the main findings, social relations were classified into three groups: trust, friend activities, and user interactions.Likewise, the collaborative filtering approach was the most used, and with the best results, considering the methods based on memory and model.The most used metrics that we found, and the recommendation methods studied in mobile applications are presented.The information provided by this study can be valuable to increase the precision of the recommendations.
Optimal Model for Direct Power Purchase by Large Consumers Based on Blockchain

The direct power purchase by large consumers (DPLC) is an important part of the reform of the electricity market, and the development of renewable energy has led to a trend of decentralization on the supply side. Blockchain, as an emerging distributed database technology, has a good application prospect in the context of the current energy Internet construction. The article first introduces the principle of blockchain technology in detail and analyzes its application value in electricity trading. Starting from the traditional direct purchase transaction model, a framework for direct purchase of electricity for large consumers is proposed. Combining the characteristics of direct power purchase transactions, the distributed consensus mechanism is researched and improved, the smart contract is designed in combination with the transaction process, and the communication protocol and interaction relationship at each level are analyzed from the overall system architecture. Finally, the challenges faced by the system in practical application are analyzed, which provides ideas for follow-up research.
Context-Fused Guidance for Image Captioning Using Sequence-Level Training

Recent image captioning models based on the encoder-decoder framework have achieved remarkable success in humanlike sentence generation. However, an explicit separation between encoder and decoder brings out a disconnection between the image and sentence. It usually leads to a rough image description: the generated caption only contains main instances but neglects additional objects and scenes unexpectedly, which reduces the caption consistency of the image. To address this issue, we proposed an image captioning system within context-fused guidance in this paper. It incorporates regional and global image representation as the compositional visual features to learn the objects and attributes in images. To integrate image-level semantic information, the visual concept is employed. To avoid misleading decoding, a context fusion gate is introduced to calculate the textual context by selectively aggregating the information of visual concept and word embedding. Subsequently, the context-fused image guidance is formulated based on the compositional visual features and textual context. It provides the decoder with informative semantic knowledge. Finally, a captioner with a two-layer LSTM architecture is constructed to generate captions. Moreover, to overcome the exposure bias, we train the proposed model through sequence decision-making. The experiments conducted on the MS COCO dataset show the outstanding performance of our work. The linguistic analysis demonstrates that our model improves the caption consistency of the image.
Collision Avoidance of Unmanned Aerial Vehicles Using Fuzzy Inference System-Aided Enhanced Potential Field

In recent, unmanned aerial vehicles (UAVs) have been utilized in various areas. This may lead to increasing the number of UAVs and eventually the risk of collisions. To deal with such potential risks, this work proposes a collision avoidance (CA) approach for UAVs using a combination of fuzzy inference systems (FISs) and the enhanced potential field (EPF). The FISs and EPF play unique roles in determining the magnitude and direction of the potential field, respectively. To find a near-optimal path in terms of the path length, the parameters that define the FISs are optimized by a genetic algorithm. For training, this work considers a single scenario that represents the well-known issues in the field of CA. For testing, two scenarios containing various static and dynamic obstacles are simulated for validating the performance of the proposed CA technique. The numerical simulation results show that the proposed method generates feasible trajectories that allow the UAVs to reach the target position without any collisions and a minimized path length.
Formation of university graduates employment culture

Introduction. Successful employment of university graduates is necessary for the productive solution of socio-economic problems of modern society. It is a particularly urgent task in the context of the global economic crisis and the significant changes of labor and employment area caused by it. Many students are not able to get a job, which means a significant lack of qualified specialists with higher education, who are strategic resources for the progressive development of society. This article aims to develop and give scientific justification for the extracurricular program of the Formation of University Graduates Employment Culture based on a modular approach. Materials and methods. The study involved 564 senior students of Russian universities (bachelor’s degree, master’s degree). Online survey was conducted to identify problem areas and prospects for employment. The parameters and characteristics of the formation of student’s employment culture are diagnosed using a set of valid diagnostic techniques. The reliability of the data obtained was checked using the Pearson’s chi-squared (χ2) test. Results. The author’s program for the formation of university graduates' employment culture was developed in the course of the study. The essence of the program is the development of students’ skills of job search, self-presentation, self-promotion, self-image, CV writing, effective communication, teamwork, creative problem solving, forecasting the trajectory of employment after graduation. A significant increase in the formation of student’s employment culture level was recorded as a result of the program implementation in the experimental group (χ2emp = 26.554) compared with the students of the control group who did not show positive dynamics in the level of the measured personal characteristics (χ2emp = 0.334) during the experimental work. Discussion and conclusion. The study has resulted that the effectiveness of the formation of university graduates' employment culture increases with the use of modular structuring for extracurricular programs (communicative, facilitation, socio-legal, administrative, psychological and pedagogical, project, information and interactive modules).
Research on Resource Carbon Emission Allocation Efficiency Based on Blockchain Consensus Algorithm

This paper takes the secondary low-carbon supply chain composed of a manufacturer and a retailer as the research object and takes the government subsidy strategy of low-carbon supply chain as the research theme, the government invests in the construction of blockchain technology application platform, and supply chain enterprises use blockchain technology for compensation. Under this background, considering consumers’ low-carbon preference and green trust, a three-stage game model dominated by the government and followed by manufacturers and retailers under the four scenarios of emission reduction technology input subsidy and low-carbon product output subsidy before and after the application of blockchain technology is established. By comparing the optimal social welfare, carbon emission reduction rate, and low-carbon product output under the four scenarios, the optimal problem of government low-carbon subsidy strategy is discussed. It is found that when the product of consumers’ low-carbon preference coefficient and green trust coefficient is greater than a certain fixed value, the government can obtain higher social welfare and promote enterprises’ emission reduction through output subsidies. The government can always promote the demand for low-carbon products through output subsidies. When the emission reduction cost coefficient approaches infinity, the demand for low-carbon products when the government implements output subsidies are four times that when the government implements technology subsidies. When the single cycle shared cost of the blockchain platform and the unit cost of enterprise application of blockchain technology are less than a certain threshold, the government’s construction of the blockchain platform can obtain higher social welfare, promote enterprise emission reduction, and improve consumers’ demand for low-carbon products.
“It’s our fault!”: Insights Into Users’ Understanding and Interaction With an Explanatory Collaborative Dialog System

Human-AI collaboration, a long standing goal in AI, refers to a partnership where a human and artificial intelligence work together towards a shared goal.Collaborative dialog allows human-AI teams to communicate and leverage strengths from both partners.To design collaborative dialog systems, it is important to understand what mental models users form about their AI-dialog partners, however, how users perceive these systems is not fully understood.In this study, we designed a novel, collaborative, communication-based puzzle game and explanatory dialog system.We created a public corpus from 117 conversations and post-surveys and used this to analyze what mental models users formed.Key takeaways include: Even when users were not engaged in the game, they perceived the AIdialog partner as intelligent and likeable, implying they saw it as a partner separate from the game.This was further supported by users often overestimating the system's abilities and projecting human-like attributes which led to miscommunications.We conclude that creating shared mental models between users and AI systems is important to achieving successful dialogs.We propose that our insights on mental models and miscommunication, the game, and our corpus provide useful tools for designing collaborative dialog systems.
Deep Learning for Human Activity Recognition using on-Node Sensors

Due to advancement in technology, availability of resources and by increased utilization of on node sensors enormous amount of data is obtained. There is a necessity of analyzing and classifying this physiological information by efficient and effective approaches such as deep learning and artificial intelligence. Human Activity Recognition (HAR) is assuming a dominant role in sports, security, anti-crime, healthcare and also in environmental applications like wildlife observation etc. Most techniques work well for processing offline instead of real- time processing. There are few approaches which provide maximum accuracy for real time processing of large-scale data, one of the compromising approaches is deep learning. Limitation of resources is one of the causes to restrict the usage of deep learning for low power devices which can be worn on our body. Deep learning implementations are known to produce precise results for different computing systems.We suggest a deep learning approach in this paper which integrates features and data learned from inertial sensors with complementary knowledge obtained from a collection of shallow features which generates the possibility of performing real time activity classification accurately. Eliminating the obstructions caused by using deep learning methods for real-time analysis is the aim of this integrated design. Before passing the data into the deep learning framework, we perform spectral analysis to optimize the planned methodology for on-node computation. The accuracy obtained by combined approach is tested by utilizing datasets obtained from laboratory and real world controlled and uncontrolled environment. Our outcomes demonstrate the legitimacy of the methodology on various human action datasets, beating different techniques, including the two strategies utilized inside our consolidated pipeline. We additionally exhibit that our integrated design's classification times are reliable with on node real-time analysis criteria on smart phones and wearable technology.
Colour Image Encryption Algorithm With Quantum Coding

In image processing, quantum mechanics has wide applications. This paper proposes a lower-complexity encryption scheme based on Novel Enhanced Quantum Representation (NEQR) and bit-level adjacent exchange operations. The chaotic henon map and quantum coding were used to create this work. The image is further diffused by XORing key streams from the henon map. This scheme further undergoes a scrambling procedure using the key generated from SHA-256. The encryption procedure done step by step is applied separately on three channels R, G, and B, of a color image. Finally, performance analysis and simulation results confirm that the encryption scheme is robust and secure.
Controlling camera movement in VR colonography

Immersive Colonography allows medical professionals to navigate inside the intricate tubular geometries of subject-specific 3D colon images using Virtual Reality displays. Typically, camera travel is performed via Fly-Through or Fly-Over techniques that enable semi-automatic traveling through a constrained, well-defined path at user-controlled speeds. However, Fly-Through is known to limit the visibility of lesions located behind or inside haustral folds. At the same time, Fly-Over requires splitting the entire colon visualization into two specific halves. In this paper, we study the effect of immersive Fly-Through and Fly-Over techniques on lesion detection and introduce a camera travel technique that maintains a fixed camera orientation throughout the entire medial axis path. While these techniques have been studied in non-VR desktop environments, their performance is not well understood in VR setups. We performed a comparative study to ascertain which camera travel technique is more appropriate for constrained path navigation in Immersive Colonography and validated our conclusions with two radiologists. To this end, we asked 18 participants to navigate inside a 3D colon to find specific marks. Our results suggest that the Fly-Over technique may lead to enhanced lesion detection at the cost of higher task completion times. Nevertheless, the Fly-Through method may offer a more balanced trade-off between speed and effectiveness, whereas the fixed camera orientation technique provided seemingly inferior performance results. Our study further provides design guidelines and informs future work.
A Complex-Valued Encoding Multichain Seeker Optimization Algorithm for Engineering Problems

This article comes up with a complex-valued encoding multichain seeker optimization algorithm (CMSOA) for the engineering optimization problems. The complex-valued encoding strategy and the multichain strategy are leaded in the seeker optimization algorithm (SOA). These strategies enhance the individuals’ diversity, enhance the local search, avert falling into the local optimum, and are the influential global optimization strategies. This article chooses fifteen benchmark functions, four proportional integral derivative (PID) control parameter models, and six constrained engineering problems to test. According to the experimental results, the CMSOA can be used in the benchmark functions, in the PID control parameter optimization, and in the optimization of constrained engineering problems. Compared to the particle swarm optimization (PSO), simulated annealing based on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multiverse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of the CMSOA are better than those of others algorithms.
Professional development of teachers in the system of formal and non-formal postgraduate education during quarantine restrictions period

The article is devoted to the problem of professional development of teachers.The issue is regarded as a component of transformational