Reviewerly/exHarmony huggingface dataset:
Blockchain Technology Perception in Supporting the Digital Transformation of Supply Chain Management: A Preliminary Study

The globalization of the world economy increased the role of value chains, where disruptions in the realization of the logistics processes occur repeatedly. Pandemics, military conflicts, and climate change further increase the difficulties in matching demand and supply. Therefore, proper Supply Chain Management (SCM) is crucial and the digital transformation of SCM is considered an important solution for several SCM concerns. Extensive subject matter literature research highlighted that several technologies have been studied and implemented as SCM solutions contrary to the blockchain. As a result, the research aim of the article is to examine the current and future challenges of SCM with particular emphasis on blockchain technology perception in this context. Proper research has been conducted with the use of structured in-depth interviews with SCM experts. Pilot study results highlighted that blockchain is perceived as a technology with significant potential to support the creation of Digital Supply Chains. They also pointed out that blockchain is not the technology of the first choice when planning the digital transformation of SCM.
Development and Visualization of PS-Dict: a Programmer Skills Dictionary

Programmers need to have a wide range of skills to perform their tasks. Using programming tools is not enough for purveying full functionality and development and thus should be combined with essential abilities such as problem-solving, along with in-depth knowledge about operating systems, hardware, and software. Hence, knowledge concerning these various aspects is important. In this study, we propose an interactive programmer skills dictionary in both English and Thai called PS-Dict. Our system produces a Knowledge Graphs using with the first paragraph from a Wikipedia Infobox. The comparison results show that PS-Dict could provide useful information in terms of skill descriptions and a list of related knowledge.
The Design and Implementation of Testbed Middleware that Enables to Perform Wireless Emulation on Wired-based Network Testbed

Many technologies are implemented on the assumption that they will use a wireless environment. When evaluating these technologies, it is necessary to consider wireless links in various situations because the status of the wireless environment is affected by many physical phenomena and obstacles. Thus, it is not easy to evaluate these technologies. We have been studying and developing technology that enables us to evaluate software and hardware implementations for wireless technologies without transmitting radio waves by reproducing wireless sections on a wired network testbed. In this paper, we explain a detailed implementation of testbed middleware to easily introduce newly proposed technologies to the testbed. The architecture also enables us to introduce functions for wireless emulation to our testbed. Taking a small radio experiment as an example, we introduce a detailed implementation that enables wireless emulation in cooperation with the entire testbed.
Artificial intelligence technology in battlefield situation awareness

In order to make further significant researches on battlefield situation awareness, the paper summarized the challenges in the field at first, which can be listed as the following: the massive and large data makes situation understanding difficult; the rapid change of data makes situation prediction difficult; the complex and heterogeneous data makes situation display difficult. Aiming at the above problems, the paper then discussed the application of artificial intelligence technology, such as knowledge graph, natural language processing and expert system. The appropriate applyment of relevant artificial intelligence technology will greatly improve the capability of battlefield situation awareness.
EVALUATION OF INFERENCE OPTIMIZED IMAGE CLASSIFICATION MODELS ON IOS PLATFORM

In this paper, we evaluate MobileNetV2 and EfficientNet network architectures performance on the iOS platform. Even though both networks are well researched, there is a certain lack of empirical data when it comes to the iOS platform. In this paper, we provide experimental data for top-1 accuracy and inference time, as well as RAM footprint collected on iPhone X and iPhone 12 Pro. The evaluation was performed using CPU, GPU, and Neural Engine (iPhone 12 only). Also, a full int8 quantization evaluation was done for MobileNetV2. Challenges around EfficientNet quantization are also described. The scoring metrics and dataset used to train models were provided as part of Mobile AI 2021 Real-Time Camera Scene Detection Challenge: https://competitions.codalab.org/compet¬iti¬o¬n¬s¬/¬2¬8¬1¬13 .
Challenges of pricing data assets: a literature review

Data has tremendous value. However, too much of this value is lost because the data today is not reusable outside of its original context. A major key to taking full advantage of ever more and better data is opening up silos and sharing data. In the near future, data should become an asset that companies can sell and buy and that is accessible to anyone who has an idea on how to use it. To realize the vision of a thriving data market, one must first address the question of how to price data. In this paper, we have examined the existing body of research on this issue and highlighted the challenges of data pricing from a scientific perspective. In this way, we gain a comprehensive overview of the state of the art and prepare the ground for future research.
Predictive Analytics Comparison of Indonesia Government Budget With or Without Covid-19 Pandemic

An accurate budget forecast of Indonesia before starting a financial year is very important, the budget can be used to direct socio-economic development, ensure sustainability, and improve the quality of life of the people. Since the pandemic covid occurred in 2020 many countries carry out social restrictions to regional or national lockdown that impact the national budget. To estimate the Indonesian government's budget and accurately at the planning stage with the assumption the pandemic will continue or end. The solution can be figured out by using predictive analytics. Predictive analytics seeks to predict the future by examining historical data, detecting patterns or relationships in these data, and connecting all historical data patterns in the future. This solution can be used to predict how much the Indonesia Budget will be with the assumption previously mention.
Effect of Immersive VR on Student-Tutor Interaction in Design Crits

Immersive Virtual Reality (iVR) systems form a representational medium for student-tutor collaboration in studio crits through a shared presence in a life scale display of the design, making them relevant for design crits where students and tutors interact in developing a solution to a design problem.iVRs are known to support design activity.However, research focusing on the impact of iVRs on student-tutor interaction is scarce, creating a gap in integrating these systems as educational settings.In response, this research analyzes a natural case study of student-tutor interaction in two architecture studio crits that used the iVR and non-immersive media.We employed the Function-Behaviour-Structure ontology to track the design issues generated for each medium.Further analysis identified the distribution of cognitive effort measured by problemsolution indexes.Preliminary results show large differences when using the two media: The iVR was found to support a higher frequency of solution-focused issues generated by the student and to maintain a lower Problem-Solution Index for the student than for the tutor, serving as an empirical foundation for further research on the effects of deploying this technology in the design studio.
Investigating Classification Methods using Fixation Patterns to Predict Visual Tasks

Studies have shown the possibility to classify user tasks from eye-movement data. We present a new way to determine the optimal model for different visual cognitive tasks using data that includes two types of visual search tasks, a visual exploration task, a blank screen task, and a task where a user needs to fixate at the center of any scene. We used CNN and SVM models on RGB images generated from fixation scan paths from these tasks. We also used AdaBoost on filtered eye movement data as a baseline. Our study shows that deep learning gives the best accuracy for classifying between visual search tasks but misclassified between visual search and visual exploration tasks. Machine learning-based methods performed with high accuracy classifying tasks that involve minimal visual search. Our study gives insight on the best model to choose by type of visual task using eye movement data.
CellTypeGraph: A New Geometric Computer Vision Benchmark

Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial layout of the organ including symmetries. To allow the convenient testing of new geometrical learning methods, the benchmark of Arabidopsis thaliana ovules is made available as a PyTorch data loader, along with a large number of precomputed features. Finally, we benchmark eight recent graph neural network architectures, finding that DeeperGCN currently works best on this problem.
The use of information technologies for distance education in the study of a foreign language

Distance education is based on the use of computer information technologies. This allows you to study at a convenient time and place for the student. In distance education, a significant part of the educational material must be mastered by the student independently. The distance form of education cannot fully replace the full-time form of education, but it successfully complements it if the full-time form of education is difficult. Distance education creates an opportunity for the practical implementation of an individual approach to teaching each student based on his abilities and success. Effective distance learning is possible only if there is a high motivation of the trainees. Distance education can be considered an addition to full-time education, it allows you to make learning more productive, as well as extraterritorial. The use of Internet technologies in the educational process allows creating conditions for more rapid interaction of the student with the teacher, and also allows for faster access to educational information resources.
“I Am Bloody Amazing and So Are You!”: The (Im)politeness of Self-Praise in the Instagram Posts of Fashion and Lifestyle Influencers

Recent studies of self-praise in CMCComputer-mediated communication (CMC) (Dayter, 2016, 2018; Matley, 2018)Dayter, D. indicateMatley, D. that, in contrast to conversational normsConversational norm of face-to-face interaction, self-praise is a frequent and well-rooted practice online. The present study aims to contribute to this burgeoning field of research by examining the (im)politeness(im)politeness of self-praise in InstagramInstagram posts of fashion and lifestyle influencersInfluencers. Since their popularity is contingent on being liked by others, influencersInfluencers are highly cautious of their positive facePositive face (Brown & Levinson, 1987), andBrown, P. their self-presentationSelf-presentation thus represents a potentially rich source of data. 1,000 posts shared by five British influencersInfluencers were coded for the presence of self-praise, positive politeness, and mitigation strategiesMitigation strategies. The analysis showed that influencersInfluencers recognize the face-threateningFace-threatening nature of self-praise—as such behavior could drive less devoted followers away—and employ a variety of strategies oriented toward saving their own and their followers’ positive facePositive face. InfluencersInfluencers convey self-praise primarily indirectly or with modification, obstructing the image of a narcissist and ego-centric user which the accompanying photo might paint. The analysis also identified cases of self-praise bald on recordBald on record which, however, functions as an attention-getter transforming the illocutionary forceIllocutionary force of the utterance, e.g., “I’m bloody amazing and so are you” being used to raise the awareness of the lack of access to sanitary products for women around the world.
Privacy Preservation using Federated Learning and Homomorphic Encryption: A Study

The resurgence of Artificial Intelligence (AI) and its broader adoption into major fields has revolutionized the operations of organizations and applications. AI depends on Machine Learning (ML) models that are suitably trained using data generated by global users. Preserving the privacy and security of global user’s data in ML and Deep Learning (DL) has emerged as a major concern. The advent of Federated Learning (FL) - an ML paradigm, has emerged as a solution for the privacy concerns related to traditional training models. FL is a decentralized training method that enables computation of well-trained models over remote data sources located at different geographical locations without sharing the training data to a centralized location. The aim of this study is to provide a detailed understanding of FL. This work focuses on the significant challenges and attacks associated with data privacy in FL and also the solutions that are available in addressing these attacks. This paper highlights the features and importance of Homomorphic Encryption (HE) towards privacy preservation in FL. This work also discusses open challenges and potential future scope and applications of FL.
Cyberpunk and Cypherpunk: A Philosophical Analysis Comparing Two Views of the Metaverse

As ancestors of the idea of the metaverse - Cyberpunks and Cypherpunks - propose two distinct attitudes towards Cyberspace, and this difference also infuses their attitudes towards the metaverse. These two attitudes will powerfully influence the developmental direction of the metaverse and how it will be recognized and accepted by society. Given that the visual space is an artificial reality created by technology, they not only illustrate two different ideas regarding “realities other than the physical one of human society”, but also illustrate two different ideas regarding technology itself. The Cyberpunk view of the metaverse could be seen as techno-pessimistic or Luddiism. Cypherpunk’s metaverse view, on the other hand, is “technological transformationism”, which carries multi-possibilities of living styles of human being, and it almost incompatible with Luddism.
An Analysis of Sentiment Using Aspect-Based Perspective

Opinions play a major role in almost every human practice. Finding product and service reviews is made easy online. Product reviews are readily available in huge quantities. Considering each review and making a concise decision about a product is not feasible or even possible. Aspect-based sentiment analysis (ABSA) is one of the best solutions to this problem. Summary and online review’s analysis is delivered in this paper. ABSA has made extensive use of machine learning techniques. Recent years have seen deep learning take off due to the growth of computer processing power and digitalization. When applied to various deep learning techniques, numerous NLP tasks produced futuristic results. An overview of various deep learning models used in the field of ABSA is presented in this chapter after an introduction to ABSA.
Emotion Recognition from Facial Expression using Explainable Deep Learning

Facial expressions give important information about people emotions. Understanding facial expressions accurately is one of the challenging tasks for interpersonal relationships. Automatic emotion recognition using facial expressions is an area of interest within various fields as, for instance, computer science, healthcare, bio-engineering and psychology. In this paper we propose a method for automatic emotional recognition from facial expressions by exploiting deep learning techniques. The proposed method is able to classify a facial image under analysis in one of three different emotions: happiness, neutral or sadness. Moreover the proposed method is able to show on the facial images the areas symptomatic of a certain prediction, thus highlighting the characteristics of the face that are related to a particular emotion: in this way the model is able to explain the reason why it predicts a certain emotion. A preliminary experimental analysis performed on a 1,500 different facial expression images shows that the proposed method is promising in emotion recognition.
Worst Case Matters for Few-Shot Recognition

Few-shot recognition learns a recognition model with very few (e.g., 1 or 5) images per category, and current few-shot learning methods focus on improving the average accuracy over many episodes. We argue that in real-world applications we may often only try one episode instead of many, and hence maximizing the worst-case accuracy is more important than maximizing the average accuracy. We empirically show that a high average accuracy not necessarily means a high worst-case accuracy. Since this objective is not accessible, we propose to reduce the standard deviation and increase the average accuracy simultaneously. In turn, we devise two strategies from the bias-variance tradeoff perspective to implicitly reach this goal: a simple yet effective stability regularization (SR) loss together with model ensemble to reduce variance during fine-tuning, and an adaptability calibration mechanism to reduce the bias. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed strategies, which outperforms current state-of-the-art methods with a significant margin in terms of not only average, but also worst-case accuracy.
Correct-by-Construction Runtime Enforcement in AI – A Survey

Runtime enforcement refers to the theories, techniques, and tools for enforcing correct behavior with respect to a formal specification of systems at runtime. In this paper, we are interested in techniques for constructing runtime enforcers for the concrete application domain of enforcing safety in AI. We discuss how safety is traditionally handled in the field of AI and how more formal guarantees on the safety of a self-learning agent can be given by integrating a runtime enforcer. We survey a selection of work on such enforcers, where we distinguish between approaches for discrete and continuous action spaces. The purpose of this paper is to foster a better understanding of advantages and limitations of different enforcement techniques, focusing on the specific challenges that arise due to their application in AI. Finally, we present some open challenges and avenues for future work.
Conclusions, Pedagogical Implications, and New Research Directions

In this final chapter, I draw conclusions with regard to the role of affective factors in second language learning tasks based on the cumulative findings of the three empirical studies reported in the book. I aim to demonstrate the benefits of investigating a phenomenon, namely the role of affective factors, at micro, meso, and macro levels. By reflecting on a number of themes (the learning experience, the school climate, the flow state, and emotions in particular) as they appeared in the different empirical studies, I offer a synthesis of the findings deriving from the different studies conducted at the micro, meso, and macro levels. Besides presenting this synthesis and pointing out limitations of my investigations, I also draw attention to the most important pedagogical implications of the findings and outline future research directions.
Spatiotemporal Neural Network for Video-Based Pose Estimation

The application of 2D pose estimation methods often suffers from performance degeneration because of the severe video quality degradation. To mitigate the problem, a novel model is proposed, namely spatiotemporal net (STNet). STNet utilizes convolution modules to extract the 2D joint heatmaps of each frame and exploits recurrent convolution modules to encode the time information between the adjacent frames. This decoupling learning of spatiotemporal information improves the temporal coherence and spatial accuracy of the estimated poses and reduces the difficulty of extracting spatiotemporal features. The application of ConvGRU effectively reduces the computational cost while ensuring recognition accuracy. Proposed model is compared with other existing methods on two benchmarks: Penn Action and Sub-JHMDB. The results show that STNet can better trade-off prediction performance and computational complexity and have more practical value.
Ecological Cognition for Measuring Psi-Hit and Psi-Miss Online: Using K-Scores to Understand Anomalistic Psychology in Project Management Teams

The human-side of project management is a topic that has been explored in project management, but not to the extent that it should be. Health and safety is of paramount importance in project management, particularly construction project management. What is often overlooked is the mental health side of health and safety and how anomalistic psychological experiences can have an adverse impact on project success. This paper proposes using ‘k-scores’ to measure psi-hit and psi-miss, or in other words the extent to which a person thinks they have had an anomalistic experience or that they have not. The measure of k-scores are derived from combining the ecological cognition framework with a parametric user model. The benefit of this is that it allows the collection of data online, such as through Microsoft Teams, Skype or Zoom.
Highlighting the Main Poles of Political Performance and Governance Capacity at EU Level. Implications in the Context of COVID-19 Crisis

The paper aims to examine the political performance and governance capacity at EU level. The study is conducted using data from Sustainable Governance Indicators Database (SGI). Statistical methods based on composing aggregate indices are proposed using 21 indicators grouped in 11 dimensions, and countries are classified according to the performance obtained by the cluster method. The principal component analysis revealed six main factors: “PCT Patent Applications,” “Life expectancy,” “Old age dependency ratio,” “Tax system complexity,” “Inflation,” and “Fertility rate.” After calculating the final value of the index of political performance and governance capacity, we have identified three main groups of countries, as follows: Finland, Sweden, Denmark, Greece, Austria, Germany, Spain, Belgium, France, Italy, and the Netherlands with high political and governance performance, countries with an increasing trend (Estonia, Croatia, Ireland, Lithuania, Luxembourg, the United Kingdom), and countries with poor performance (Bulgaria, Cyprus, Czech Republic, Latvia, Malta, Poland, Romania, Slovakia, Hungary). Romania ranks last in terms of political performance and governance capacity.