Reviewerly/exHarmony huggingface dataset:
Improving Few-Shot Visual Classification with Unlabelled Examples

We propose a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized Mahalanobis-distance-based soft k-means clustering procedure with a modified state of the art neural adaptive feature extractor to achieve improved test-time classification accuracy using unlabelled data. We evaluate our method on transductive few-shot learning tasks, in which the goal is to jointly predict labels for query (test) examples given a set of support (training) examples. We achieve new state of the art performance on the Meta-Dataset and the mini-ImageNet and tiered-ImageNet benchmarks.
Education Informationization

Starting from the policy of education informatization, this paper firstly analyzes the differences between the 1.0 and 2.0 versions of education informatization, indicating that technology and education are gradually deeply integrated. Secondly, the future development prospect of the industry is predicted based on the continuous growth of financial funds for education and the large demand of value-added services on the customer side of education informatization. In addition, this paper deeply studies the industry chain and market participants of education informatization, and finds that the competition pattern is highly dispersed, and there will be integration opportunities in the future market. Finally, the author believes that the future development trend of educational informatization will be marked by big data application, platform and content digitalization.
Robustness of software

In the English civil court case Bates v Post Office Limited, the properties of the Post Office Horizon transaction-processing system were investigated and argued. Anthony de Garr Robinson QC for the Post Office defined “robustness” of the Horizon software-based system. The concept of robustness was at the core of the defendant’s argument, which was that Horizon was “robust”, if not infallible. This article sets out that the vocabulary deployed by Mr de Garr Robinson is not used in this way in computing, whether or not it is conceptually clear.&#x0D; Index words: software, electronic documents, electronic evidence, computer evidence, robust
Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.
Exact Design Space Exploration Based on Consistent Approximations

The aim of design space exploration (DSE) is to identify implementations with optimal quality characteristics which simultaneously satisfy all imposed design constraints. Hence, besides searching for new solutions, a quality evaluation has to be performed for each design point. This process is typically very expensive and takes a majority of the exploration time. As nearly all the explored design points are sub-optimal, most of them get discarded after evaluation. However, evaluating a solution takes virtually the same amount of time for both good and bad ones. That way, a huge amount of computing power is literally wasted. In this paper, we propose a solution to the aforementioned problem by integrating efficient approximations in the background of a DSE engine in order to allow an initial evaluation of each solution. Only if the approximated quality indicates a promising candidate, the time-consuming exact evaluation is executed. The novelty of our approach is that (1) although the evaluation process is accelerated by using approximations, we do not forfeit the quality of the acquired solutions and (2) the integration in a background theory allows sophisticated reasoning techniques to prune the search space with the help of the approximation results. We have conducted an experimental evaluation of our approach by investigating the dependency of the accuracy of used approximations on the performance gain. Based on 120 electronic system level problem instances, we show that our approach is able to increase the overall exploration coverage by up to six times compared to a conservative DSE whenever accurate approximation functions are available.
Embedding Directed Graphs in Potential Fields Using FastMap-D

Embedding undirected graphs in a Euclidean space has many computational benefits. FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs. However, Euclidean distances are inherently symmetric and, thus, Euclidean embeddings cannot be used for directed graphs. In this paper, we present FastMap-D, an efficient generalization of FastMap to directed graphs. FastMap-D embeds vertices using a potential field to capture the asymmetry between the to-and-fro pairwise distances in directed graphs. FastMap-D learns a potential function to define the potential field using a machine learning module. In experiments on various kinds of directed graphs, we demonstrate the advantage of FastMap-D over other approaches.
Analyzing Bio-Geo-Chemical Factors in Relation to Land Use Trends on Basalt Terrain in Dong Nai and Nearby Areas

Basalt terrain is widely distributed in the Central Highlands and Southeast of Vietnam. Currently, land use policies on basalt types are not really suitable, affecting economic growth and creating negative impacts on the environment, especially soil and water pollution. This study carried out the assessment of bio-geochemical conditions in three types of basalt terrain, using 16 remote sensing indices. The relationship between these indices is analyzed based on correlation matrix and structural equation model (SEM). The results show different land use management trends across the three basalt types. The results encourage the land-use management to (i) expand and maintain the protected forests on Miocene basalt; (ii) develop agriculture on the Pliocene - Pleistocene basalt terrain; and (iii) improve soil quality and ecotourism on late Pleistocene basalt terrain.
Application of BIM Technology in Port Breakwater Construction

Hua, X., 2020. Application of BIM technology in port breakwater construction. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 548–551. Coconut Creek (Florida), ISSN 0749-0208.Aiming at the problem of poor load-bearing performance of island dike structure in the traditional construction method of port breakwater, a construction method of port breakwater based on BIM technology is designed. The concrete types of port breakwater engineering choose the structure scheme of breakwater engineering, including square caisson, open-hole square caisson, round caisson, double-layer cylinder caisson, slope, steel pipe pile, t-type structure, comb type. According to the choice of the breakwater project structure plan, as well as the breakwater project all work time node and the logical relationship between the work of the port breakwater project construction plan. Construction of port breakwater project based on BIM technology, construction of port breakwater project with BIM core design modeling software, construction of port breakwater project model, and then use BIM technology to carry out construction of port breakwater project, and realize construction of port breakwater project. The experimental results show that the load-bearing performance of the proposed method is better than that of the traditional method.
EmpTransfo: A Multi-Head Transformer Architecture for Creating Empathetic Dialog Systems.

Understanding emotions and responding accordingly is one of the biggest challenges of dialog systems. This paper presents EmpTransfo, a multi-head Transformer architecture for creating an empathetic dialog system. EmpTransfo utilizes state-of-the-art pre-trained models (e.g., OpenAI-GPT) for language generation, though models with different sizes can be used. We show that utilizing the history of emotions and other metadata can improve the quality of generated conversations by the dialog system. Our experimental results using a challenging language corpus show that the proposed approach outperforms other models in terms of Hit@1 and PPL (Perplexity).
Visual Kinship Recognition: A Decade in the Making

Kinship recognition is a challenging problem with many practical applications. With much progress and milestones having been reached after ten years since pioneered - it is now that today we are able to survey their research and create new milestones. We list and review the public resources and data challenges that enabled and inspired many to hone-in on one or more views of automatic kinship recognition in the visual domain. The different tasks are described in technical terms and syntax consistent across the problem domain and the practical value of each discussed and measured. State-of-the-art methods for visual kinship recognition problems, whether to discriminate between or generate from, are examined. As part of such, we review systems proposed as part of a recent data challenge held in conjunction with the 2020 IEEE Conference on Automatic Face and Gesture Recognition. We establish a stronghold for the state of progress for the different problems in a consistent manner. We intend for this survey will serve as the central resource for work of the next decade to build upon. For the tenth anniversary, demo code is provided for the various kin-based tasks. Detecting relatives with visual recognition and classifying the relationship is an area with high potential for impact in research and practice.
Duty to Warn in Strategic Games

The paper investigates the second-order blameworthiness or duty to warn modality one coalition knew how another coalition could have prevented an outcome''. The main technical result is a sound and complete logical system that describes the interplay between the distributed knowledge and the duty to warn modalities.
Understanding Privacy-Utility Tradeoffs in Differentially Private Online Active Learning

We consider privacy-preserving learning in the context of online learning. Insettings where data instances arrive sequentially in streaming fashion, incremental trainingalgorithms such as stochastic gradient descent (SGD) can be used to learn and updateprediction models. When labels are costly to acquire, active learning methods can beused to select samples to be labeled from a stream of unlabeled data. These labeled datasamples are then used to update the machine learning models. Privacy-preserving onlinelearning can be used to update predictors on data streams containing sensitive information.The differential privacy framework quantifies the privacy risk in such settings. This workproposes a differentially private online active learning algorithm using stochastic gradientdescent (SGD) to retrain the classifiers. We propose two methods for selecting informativesamples. We incorporated this into a general-purpose web application that allows a non-expert user to evaluate the privacy-aware classifier and visualize key privacy-utility tradeoffs.Our application supports linear support vector machines and logistic regression and enablesan analyst to configure and visualize the effect of using differentially private online activelearning versus a non-private counterpart. The application is useful for comparing theprivacy/utility tradeoff of different algorithms, which can be useful to decision makers inchoosing which algorithms and parameters to use. Additionally, we use the application toevaluate our SGD-based solution and to show that it generates predictions with a superiorprivacy-utility tradeoff than earlier methods.
Klasifikasi Kematangan Stroberi Berbasis Segmentasi Warna dengan Metode HSV

Classification of Strawberry Maturity Based on Color Segmentation using HSV Method. Manual fruit maturity classification has many limitations because it is influenced by human subjectivity. Hence, the application of digital image processing and artificial intelligence becomes more effective and efficient. This study aims to create a classification system that automatically divides strawberry maturity into three categories, namely not ripe, half-ripe, and ripe. The process of identifying the level of fruit maturity is based on the color characteristics Red, Green, Blue (RGB) value of the image. The method used for color segmentation is Hue, Saturation, Value (HSV) and for the classification of strawberry maturity using the Multi-Class Support Vector Machine (SVM) algorithm with a Radial Basic Function (RBF) kernel. Strawberry image data was retrieved using the Logitech C920 camera. The dataset consisted of 158 images of strawberries. The results showed that the classification of strawberry maturity using the multi-class SVM algorithm with kernel parameters RBF cost (C) = 10 and gamma (γ) = 10-3 produced the highest accuracy of 97%.
Wearable Medical Device for Remote Monitoring the Health of Elderly People at Home

The design, fabrication and testing of a wearable medical device for remote monitoring the health of elderly people at home are presented in this paper. The proposed wearable medical device comprises a wide range of sensors, innovative sensor fusion algorithms and flexible electrodes that make possible to continuously run analyses such as hydration assessment, stress assessment, body temperature assessment, fall detection and heart monitoring. In-vivo measurement results showed the excellent performance of this wearable medical device in real-life scenarios.
Virtual and augmented reality in neuroscience

Neuroscience encompasses the interpretive layers of a person with the environment, implemented as the central and peripheral nervous systems. In neuroscientific research, normal and abnormal neurophysiological function is studied for diagnosis, or altered through treatment by affecting paired or evoked responses from environmental sensory input. With the advent of virtual reality (VR) and augmented reality (AR), as human-computer interaction (HCI) modalities of highly immersiveness and malleability, a new avenue for previously unfeasible environmental input has been realized. This chapter explores the potential of VR/AR in the axes that these modalities are currently being deployed in neuroscience. Virtual reality environments (VREs) are implemented as alternative customizable platforms for brain-computer interface (BCI) applications but are also exploited in neurorehabilitation paradigms. In medical neurosciences, VR/ AR have proven added value in the visualization of the nervous system both for operative enhancement and for surgical training. Additionally, in medical education there is a strong case to be made in favor of the “virtual lab” (VL) for neuroanatomy offering added value to teaching of the complexity of the neural structures and networks. Furthermore, the controllability of VREs and VR/AR render them excellent candidates for studying cognition through event related potentials (ERPs). Finally, we explore the combined role of VR/AR and affect in educational and internal learning processes, aiming toward an integrated sensor immersion ecosystem.
Building an Artificial Conscience: Prospects for Morally Autonomous Artificial Intelligence

Discussions of ethics and Artificial Intelligence (AI) usually revolve around the ethical implications of the use of AI in multiple domains, ranging from whether machine learning trained algorithms may encode discriminatory standards for face recognition, to discussions of the implications of using AI as a substitute for human intelligence in warfare. In this chapter, I will focus on one particular strand of ethics and AI that is often neglected: whether we can use the methods of AI to build or train a system which can reason about moral issues and act on them. Here, I discuss (1) what an “artificial conscience” consists of and what it would do, (2) why we collectively should build one soon given the increasing use of AI in multiple areas, (3) how we might build one in both architecture and content, and (4) concerns about building an artificial conscience and my rejoinders. Given the increasing importance of artificially intelligent semi- or fully autonomous systems and platforms for contemporary warfare, I conclude that building an artificial conscience is not only possible but also morally required if our autonomous teammates are to collaborate fully with human soldiers on the battlefield.
UNIVERSAL COMPETENCES IN THE EDUCATIONAL PROCESS OF A MODERN UNIVERSITY

The article analyzes the problems of transition of higher education institutions of the Russian Federation to the educational standards of the new generation, which introduce the concept of universal competencies into the educational process. In the process of mastering these competencies, there is a problem of defining the indicators of competence achievement in the form of generalized characteristics that reveal the structure of competence in the process of actions that can be performed by a graduate who mastered this competence to some extent. The article examines the historical experience of the introduction of the concept of competence in the educational process of European universities that joined the Bologna Agreement. The proposals of a number of research centers of Russia to determine the list of indicators of competence achievement are studied. Some examples of formation of universal competences in SSUGT are considered. A number of problems of introduction of educational standards of new generation to educational process of University are designated.
Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)

Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning (CBR) system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance.
Offensive Language Recognition in Social Media

This article proposes an approach to solving the problem of multiclassification within the framework of aggressive language recognition in Twitter.At the stage of preprocessing external data is added to the existing dataset, which is based on information in the links in dataset.This made it possible to expand the training dataset and thereby to improve the quality of the classification.The model created is an ensemble of classical machine learning models included Logistic Regression, Support Vector Machines, Naive Bayes models and a combination of Logistic Regression and Naive Bayes.The obtained value of macro F1-score for one of the experiments achieved 0.61, which exceeds the state-of-art published value by 1 percentage point.This indicates the potential value of the proposed approach in the field of hate speech recognition in social media.
Machine Learning Models for Cancer Type Classification with Unstructured Data

Machine learning (ML) techniques have been used to classify cancer types to support physicians in the diagnosis of a disease. Usually, these models are based on structured data obtained from clinical databases. However valuable information given as clinical notes included in patient records are not used frequently. In this paper, an approach to obtain information from clinical notes, based on Natural Language Processing techniques and Paragraph Vectors algorithm is presented. Moreover, Machine Learning models for classification of liver, breast and lung cancer patients are used. Also, a comparison and evaluation process of chosen ML models with varying parameters were conducted to obtain the best one. The ML algorithms chosen are Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP). Results obtained are promising and they show the best model for classification is the MLP model with aprecision 0.89 and f1-score 0.87, although the difference in precision between models is minimal (0.02).
On the Language of Nested Tuple Generating Dependencies

During the past 15 years, schema mappings have been extensively used in formalizing and studying such critical data interoperability tasks as data exchange and data integration. Much of the work has focused on GLAV mappings, i.e., schema mappings specified by source-to-target tuple-generating dependencies (s-t tgds), and on schema mappings specified by second-order tgds (SO tgds), which constitute the closure of GLAV mappings under composition. In addition, nested GLAV mappings have also been considered, i.e., schema mappings specified by nested tgds, which have expressive power intermediate between s-t tgds and SO tgds. Even though nested GLAV mappings have been used in data exchange systems, such as IBM’s Clio, no systematic investigation of this class of schema mappings has been carried out so far. In this article, we embark on such an investigation by focusing on the basic reasoning tasks, algorithmic problems, and structural properties of nested GLAV mappings. One of our main results is the decidability of the implication problem for nested tgds. We also analyze the structure of the core of universal solutions with respect to nested GLAV mappings and develop useful tools for telling apart SO tgds from nested tgds. By discovering deeper structural properties of nested GLAV mappings, we show that also the following problem is decidable: Given a nested GLAV mapping, is it logically equivalent to a GLAV mapping?