Reviewerly/exHarmony huggingface dataset:
TEACHING TECHNOLOGY FORESIGHT TO BUSINESS AND MANAGEMENT STUDENTS

Predicting the future is an important, yet highly difficult management task. Disruptive technologies create new ways of working, shopping, and consuming, offering both risk and opportunity. Leading experts struggle to make accurate predictions about which technology will become ubiquitous, and which will face obscurity. However, 82% of jobs require digital skills, and while the current skillset is known, future requirements are unknown. Therefore, developing student’s ability to understand and capitalise on technology change is a problematic, but worthwhile endeavour. Technology foresight, sometimes called road-mapping is the process of looking at current trends and identifying how a new technology might impact a country, industry, market, or firm, should the future application and posited benefits be realised. This is problematic because – contrary to other forms of forecasting in which ex post forecasts can be compared to events – the dynamic nature of technology means that comparison is extremely difficult. This paper reports on the design and delivery of a module called Trends in Technology, to third year undergraduate Marketing students at a UK University. Hitherto, there would appear not to be an academic model to support teaching technology foresight. Therefore, through synthesis of literature of technology foresight and pedagogy with regard to design thinking, a process was developed that could be taught. Design thinking was selected because it is particularly useful when a solution cannot be judged in absolute terms and where user or customer perceptions of the same solution may differ based on a wide range of heuristics, which may be unknown to the designer. Students reported that a design thinking approach to technology application enabled them to relate to it and how it may impact their everyday lives. This fostered engagement. Furthermore, the potential career impact was recognised, with, for example, assessment artefacts being used as evidence to support job applications and interviews. Contributions include presentation of a process for teaching technology foresight and, based on qualitative findings, a theoretical model that explains how technology foresight can be applied to marketing theory to create both firm and consumer value.
A Study on Kitchen Equipment Adoption Using Technology Acceptance Model (TAM) in School Canteen: A Conceptual Framework

Literatures on food safety have widely accepted that one of the reasons leading to food poisoning cases is the mishandling of foods to proper storage and temperature. The said problems require appropriate kitchen equipment to ensure the abuse is kept at bay. In order to probe into the food handlers’ intention to use the equipment, this paper proposes an adapted Technology Acceptance Model (TAM) to answer this query. The related constructs for independent variables include perceived usefulness, perceived ease of use, and willingness to invest. Questionnaires will be distributed among school canteen food handlers using established TAM items that will be modified to suit the setting of the study. The results of this study will indicate the intention to adopt the kitchen equipment among school canteen food handlers.
A Crisis of Rights and Democracy in India

In the recent global history of constitutional democracies, it is difficult to name a single crisis that has plagued them simultaneously, until the COVID-19 pandemic. It presented an opportunity for crisis management without compromising principles of democratic governance and human rights guarantees. Some countries marginally succeeded in this test while in others, concerns about democratic decline and rights erosion were amplified. India unfortunately belongs to the latter camp. This chapter argues that four features define the Indian response to COVID-19: lack of transparency, executive monopoly, suppression of civil liberties and reckless management. The executive perpetuated a rule of secrecy by announcing lockdowns belatedly, setting up an opaque relief fund and promoting misinformation on crucial aspects of the pandemic response. During the crisis period, India witnessed a concentration of powers at the centre, with very little role for the states and other players in decision making. Legislative and judicial accountability suffered patent setbacks. Further, the government succeeded in curtailing human rights, including the right to political mobilisation and speech, through arrests, vilification and propaganda. The country needs to revive the principles of transparency, accountability and protection of human rights, to resist the sliding away of democracy.
Super-resolution of infrared image

The super-resolution of infrared images was discussed. Three classical super-resolution algorithms as Bicubic Interpolation, Projection Onto Convex Sets(POCS) and convolutional neural network(SRCNN) were applied. The experimental results showed that the three algorithms on infrared images were not as good as on visible images due to the imaging differences between infrared image and visible image. According to the results of PSNR(peak signal to noise ratio) and SSIM(structure similarity image measure) index parameters, the reconstruction effects of SRCNN algorithm were better than the bicubic interpolation and the projection on to converge sets (POCS) algorithm. The super-resolution effect of model 2 using infrared image database as training sample was better than that of model 1 using visible image database as training sample. It can be deduced that the effect of super-resolution of infrared image based on convolutional neural network can be improved using infrared images as training sample database.
Accelerating Open and FAIR Data Practices Across the Earth, Space, and Environmental Sciences

&amp;lt;p&amp;gt;Data underlying published studies is difficult to find or access, which can hinder new scientific research. Currently, only about 20% of published papers have their supporting data in discoverable and accessible repositories. The AGU, working with our partners (Dryad, CHORUS, ESIP, Wiley), and supported by the National Science Foundation (NSF), will focus on improving guidance and workflows to properly manage, link, and track data and software references throughout the publication pipeline. The resulting best practices will serve as a resource for AGU editors, reviewers, and authors and help advance data and software publication policies. Beyond AGU, this work will serve as a model for linking information across funders, data repositories, and publishers, and improving public access to research outputs. In this talk, current publication practices as they relate to the FAIR principles will be described, together with lessons learned, and how workflows and guidance are being improved.&amp;lt;/p&amp;gt;
Empirical Study on the Smart Collaborative Guiding Mechanism of Full-time Staff and Counselors Considering Information Flow Data Mining

This article is based on the goal of “cultivating high-quality talents” based on the research of information flow data mining, and exerts common advantages; builds a communication platform of “three main bodies” for graduate students, tutors, and counselors to promote effective communication; The respective work characteristics of the instructors can complement the advantages of the two; and through the establishment of a collaborative education model, the relevant mechanism should be improved to strengthen the connection between the postgraduate instructors and the instructors, so that the postgraduate instructors and instructors can coordinate and cooperate in the postgraduate training process, Walk in the same direction, focus on students, pay attention to students, and serve students to improve postgraduate training in colleges and universities.
A Meta-Probabilistic-Programming Language for Bisimulation of Probabilistic and Non-Well-Founded Type Systems

We introduce a formal meta-language for probabilistic programming, capable of expressing both programs and the type systems in which they are embedded. We are motivated here by the desire to allow an AGI to learn not only relevant knowledge (programs/proofs), but also appropriate ways of reasoning (logics/type systems). We draw on the frameworks of cubical type theory and dependent typed metagraphs to formalize our approach. In doing so, we show that specific constructions within the meta-language can be related via bisimulation (implying path equivalence) to the type systems they correspond. This allows our approach to provide a convenient means of deriving synthetic denotational semantics for various type systems. Particularly, we derive bisimulations for pure type systems (PTS), and probabilistic dependent type systems (PDTS). We discuss further the relationship of PTS to non-well-founded set theory, and demonstrate the feasibility of our approach with an implementation of a bisimulation proof in a Guarded Cubical Type Theory type checker.
Automatic Detection of Speech Sound Disorder in Child Speech Using Posterior-based Speaker Representations

This paper presents a macroscopic approach to automatic detection of speech sound disorder (SSD) in child speech. Typically, SSD is manifested by persistent articulation and phonological errors on specific phonemes in the language. The disorder can be detected by focally analyzing the phonemes or the words elicited by the child subject. In the present study, instead of attempting to detect individual phone- and word-level errors, we propose to extract a subject-level representation from a long utterance that is constructed by concatenating multiple test words. The speaker verification approach, and posterior features generated by deep neural network models, are applied to derive various types of holistic representations. A linear classifier is trained to differentiate disordered speech in normal one. On the task of detecting SSD in Cantonese-speaking children, experimental results show that the proposed approach achieves improved detection performance over previous method that requires fusing phone-level detection results. Using articulatory posterior features to derive i-vectors from multiple-word utterances achieves an unweighted average recall of 78.2% and a macro F1 score of 78.0%.
In-N-Out Generative Learning for Dense Unsupervised Video Segmentation

In this paper, we focus on unsupervised learning for Video Object Segmentation (VOS) which learns visual correspondence (i.e., the similarity between pixel-level features) from unlabeled videos. Previous methods are mainly based on the contrastive learning paradigm, which optimize either in image level or pixel level. Image-level optimization (e.g., the spatially pooled feature of ResNet) learns robust high-level semantics but is sub-optimal since the pixel-level features are optimized implicitly. By contrast, pixel-level optimization is more explicit, however, it is sensitive to the visual quality of training data and is not robust to object deformation. To complementarily perform these two levels of optimization in a unified framework, we propose the In-aNd-Out (INO) generative learning from a purely generative perspective with the help of naturally designed class tokens and patch tokens in Vision Transformer (ViT). Specifically, for image-level optimization, we force the out-view imagination from local to global views on class tokens, which helps capture high-level semantics, and we name it as out-generative learning. As to pixel-level optimization, we perform in-view masked image modeling on patch tokens, which recovers the corrupted parts of an image via inferring its fine-grained structure, and we term it as in-generative learning. To discover the temporal information better, we additionally force the inter-frame consistency from both feature and affinity matrix levels. Extensive experiments on DAVIS-2017 val and YouTube-VOS 2018 val show that our INO outperforms previous state-of-the-art methods by significant margins.
A lightweight convolutional network for infrared object detection and tracking

Abstract As an important application of computer vision, visual tracking has being a fundamental topic. Compared with visible image, infrared image has the characteristic of low resolution, blurred contour and single color feature. Thus, it is still a challenge for infrared object tracking. Further, it is difficult to balance the real-time performance and accuracy. This paper proposed a method for target detection and tracking, with a deeper and lightweight MobileNet V2 structure as the backbone network. In the end, the tracker is tested on various datasets. Result shows that the tracker can get a balance between tracking accuracy and inference speed, which is crucial for deployment on mobile devices.
Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection

This article provides an optimisation method using a Genetic Algorithm approach to apply feature selection techniques for large data sets to improve accuracy. This is achieved through improved classification, a reduced number of features, and furthermore it aids in interpreting the model. A clinical dataset, based on heart failure, is used to illustrate the nature of the problem and to show the effectiveness of the techniques developed. Clinical datasets are sometimes characterised as having many variables. For instance, blood biochemistry data has more than 60 variables that have led to complexities in developing predictions of outcomes using machine-learning and other algorithms. Hence, techniques to make them more tractable are required. Genetic Algorithms can provide an efficient and low numerically complex method for effectively selecting features. In this paper, a way to estimate the number of required variables is presented, and a genetic algorithm is used in a “wrapper” form to select features for a case study of heart failure data. Additionally, different initial populations and termination conditions are used to arrive at a set of optimal features, and these are then compared with the features obtained using traditional methodologies. The paper provides a framework for estimating the number of variables and generations required for a suitable solution.
An Intelligent AI for Transportation System

Public transportation is used by many people all over the world to visit new places. Individuals are in general make a plan to trip new places and for special occasions they might feel totally lost in new place. At this time our Chatbot acts the helper for travellers. Our Chatbot helps the people to find their desire source and destination. It is a application that is work on web, desktop and also on telegram application. This Chatbot uses the Natural Language Processing(NLP) and Deep Learning methods. It acts as the live human being as a Graphical User Interface(GUI). This AI Chatbot ask user for current location and destination location by asking some questions. It analyze the user’s queries and associate with data set of the Chatbot. The deep learning techniques are used in this Chatbot are responsible for understanding the user’s questions perfectly to avoid any misunderstanding. Once the users question has been understand, then the Chatbot provides the most appropriate reply for the users query request. Then the users gets all the information related to the bus names with their numbers so the person can travel to the desired location. Our Chatbot implemented by using Pythons Keras library and Tkinter is used for GUI purpose.
Analysis of the Effectiveness of Online Education of Liberal Arts Coding Classes in the AI Era: The Mediating Effect of Learning Flow

Purpose: This study aims to analyze the effectiveness of online education in liberal arts coding classes that have different characteristics from general online theory classes. Through this, it is a study conducted to find out what factors to pay attention to in order to increase the educational effect of online liberal arts coding classes and to derive various implications for improving learners learning outcomes in online liberal arts coding classes. Method: To this end, a survey was conducted on 125 college students who took coding classes. SPSS/WIN 23.0 was used for data analysis, and multiple regression analysis was performed by using learning motivation, class content, and learning flow as independent variables, and inputting learning satisfaction and academic achievement as dependent variables. In order to find out the mediating effect of learning flow, a hierarchical regression analysis proposed by Baron and Kenny was conducted, and the significance of the mediating effect was additionally verified through the Sobel test. Results: In online liberal arts coding classes, learning motivation and class content were found to have a direct effect on academic achievement. There was no mediating effect of learning flow in the relationship between learn-ing motivation, class content, and academic achievement. In online liberal arts coding classes, learning flow was found to have a complete mediating effect in the relationship between learning motivation and learning satisfaction. In addition, learning flow was found to partially mediate between class content and learning satisfaction. Conclusion: Through this study, it can be confirmed that learning motivation and class content factors directly affect academic achievement and significantly affect learning satisfaction of online liberal arts coding classes through learning flow.
ENGLISH LANGUAGE ERROR ANALYSIS OF DESCRIPTIVE TEXT PRODUCED BY SEVENTH GRADER

&#x0D; This study aims to describe the error on descriptive text written by seventh C grader in SMPN 1 Porong and to identify the causes of error. The research method of this research is descriptive qualitative method. The researcher used interactive model by Miles and Huberman that consists of three steps, they are; data condensation, data display, and drawing verifying conclusions. The researcher used the document observation sheet and questionnaire form as the instrument of this research. The researcher only selected 20 descriptive texts written by the students. Based on the research finding, it can be concluded that the students produced error on their descriptive texts. The highest error is miss-formation error (48,87%) because the students lack on writing practice. The researcher found the causes of error from the questionnaire is communication strategy-based error as 50% because the students get misunderstanding in receiving descriptive text material from the teacher's explanation. The teacher should change the method in the teaching process to help the students understand descriptive text well. For the next researcher, this research can be a reference for error analysis on other English skills.
Full-separability and bi-separability of qubits using Bell operators, partial transpose, witnesses and explicit (full/bi) separability

Using the Hilbert–Schmidt (HS) decomposition we suggest new possible choices of Bell operators and entanglement witnesses (EWs) for [Formula: see text] ([Formula: see text]) qubits systems for (full/bi) separability. The latter give upper bounds for (full/bi) separability. Also using the HS decomposition, we find explicitly (full/bi) separable forms for some qubits states which give lower bounds for (full/bi) separability. When the lower bounds and upper bounds coincide it means that the EW is optimal. In the case of full separability, the positive transpose method can sometimes give optimal results. As concrete examples, we give results for the GHZ(3), [Formula: see text] and cluster [Formula: see text] states.
E-Book Production and Online Communication Education Practice for Public Value

Purpose: At a time when talents with convergence and consilience who can create creative ideas at the center of talents with various knowledge through infusion and memorization education with experts in a specific field become the object of envy, talents who fit the trend of the times in universities, which are higher education institu-tions The need for nurturing is increasing, and creative convergence talents including mutual collaboration ability and self-directed learning are in the spotlight rather than individuals. Two-way interactive communication education is absolutely necessary at the center of nurturing creative fusion talents, and this is to foster creative fusion talents through mutual communication education. Method: Creative convergence talent refers to talent with humanistic imagination and creativity in science and technology, and collaboration-oriented communication education is needed rather than individual-centered education. Tools are required In this study, through the production and use of e-books, convergence thinking skills are enhanced, teachers and learners can share with each other, and creative imagination can be enhanced through class-centered teaching methods. Results: In this study, questionnaire evaluation and analysis of learners were conducted, and the responses of learners were evaluated by evaluating the satisfaction of teaching methods using various educational models and flip learning. Through this, an important foundational material was prepared for designing the right direction for future education. Conclusion: This study presented the e-Book Production and Interactive Communication Education Practice, and the results of the class using a learning method based on various communication tools were very satisfactory and it was considered that a major change occurred in the class. In the future, follow-up research such as the de-velopment of new smart devices that can expand learner-centered capabilities and the development of creative content will continue.
Do Humanity Student New Needs Meet the State Decisions of Distance Learning during the COVID-19 Epidemic in Ukraine?

This study aims to verify do the humanity student new needs meet the State decisions of distance learning in distance learning during COVID-19 epidemic in Ukraine. It was used the research methods: general scientific research methods; study of official documents and scientific resources; statistical methods, including verification of statistical hypotheses. It was 77 students of humanity specialities. Verification of statistical hypotheses led to the Alternative hypothesis. The principal result is that the humanity student new needs in distance learning do not meet the state decisions of distance learning related to the COVID-19 epidemic in Ukraine. The result is highly statistically significant (99.0%). The major conclusions are: 1) the state decisions of student health are distance learning in the amount of 100%; 2) the humanity student new needs in distance learning are 25.00% - 46.25%; 3) this amount is less than it was just before the pandemic came (54.20% - 68.66%). The new scientific knowledge has important practical and theoretical significance. The authors prepared some multi-level recommendations about distance learning during the COVID-19 epidemic for Ukrainian authority and universities. This study is helpful for higher education institutions who use distance learning.
Podiy: A System for Design and Production of Pouches by Novices

We have developed a system called "Podiy" that supports handicraft production of pouch-style bags using three-dimensional (3-D) computer graphics. We divide the making of a pouch into four steps: pouch design, fabric design, pattern design, and production, each supported by different panels. In pouch design mode, the user can design both the size and pattern. In fabric design mode, the user can design two types of checkered patterns. In cloth cutout mode, the user can choose from three ways of printing the pattern onto the fabric. In production mode, the system shows the user a 3-D illustration view using the user-designed fabrics. Using Podiy, even a handicraft novice can design an original pouch and produce it successfully.