Reviewerly/exHarmony huggingface dataset:
Probabilistic Diagnostic Tests for Degradation Problems in Supervised Learning

Several studies point out different causes of performance degradation in supervised machine learning. Problems such as class imbalance, overlapping, small-disjuncts, noisy labels, and sparseness limit accuracy in classification algorithms. Even though a number of approaches either in the form of a methodology or an algorithm try to minimize performance degradation, they have been isolated efforts with limited scope. Most of these approaches focus on remediation of one among many problems, with experimental results coming from few datasets and classification algorithms, insufficient measures of prediction power, and lack of statistical validation for testing the real benefit of the proposed approach. This paper consists of two main parts: In the first part, a novel probabilistic diagnostic model based on identifying signs and symptoms of each problem is presented. Thereby, early and correct diagnosis of these problems is to be achieved in order to select not only the most convenient remediation treatment but also unbiased performance metrics. Secondly, the behavior and performance of several supervised algorithms are studied when training sets have such problems. Therefore, prediction of success for treatments can be estimated across classifiers.
LibGuides: Open Access: Copyright-Free Sounds

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Current State Of Using Information And Communication Technologies In Higher Education In The Context Of Implementation Of Multi-Level Professional Education

The introduction of information and communication technologies in the learning process in the modern Russian higher education in General and, in particular, foreign languages due to the complex economic and socio-educational factors: the globalization of the economy, openness of Russian society, the increasing exchange of information at all levels and expanding intercultural communication, development of didactic potential of ICT, increasing public quality requirements of domestic education. Current State Of Using Information And Communication Technologies In Higher Education In The Context Of Implementation Of Multi-Level Professional Education
Recognizing Semantic Relations: Attention-Based Transformers vs. Recurrent Models

Automatically recognizing an existing semantic relation (such as "is a", "part of", "property of", "opposite of" etc.) between two arbitrary words (phrases, concepts, etc.) is an important task affecting many information retrieval and artificial intelligence tasks including query expansion, common-sense reasoning, question answering, and database federation. Currently, two classes of approaches exist to classify a relation between words (concepts) X and Y: (1) path-based and (2) distributional. While the path-based approaches look at word-paths connecting X and Y in text, the distributional approaches look at statistical properties of X and Y separately, not necessary in the proximity of each other. Here, we suggest how both types can be improved and empirically compare them using several standard benchmarking datasets. For our distributional approach, we are suggesting using an attention-based transformer. While they are known to be capable of supporting knowledge transfer between different tasks, and recently set a number of benchmarking records in various applications, we are the first to successfully apply them to the task of recognizing semantic relations. To improve a path-based approach, we are suggesting our original neural word path model that combines useful properties of convolutional and recurrent networks, and thus addressing several shortcomings from the prior path-based models. Both our models significantly outperforms the state-of-the-art within its type accordingly. Our transformer-based approach outperforms current state-of-the-art by 1–12% points on 4 out of 6 standard benchmarking datasets. This results in 15–40% error reduction and is closing the gap between the automated and human performance by up to 50%. It also needs much less training data than prior approaches. For the ease of re-producing our results, we make our source code and trained models publicly available.
Explainable Robotics applied to bipedal gait development

Explainability is becoming an important topic in artificial intelligence (AI). A well explainable system can increase the trust in the application of that system. The same holds for robotics where the walking gait controller can be some AI system. We will show that a simple and explainable controller that enables an energy efficient walking gait and can handle uneven terrains, can be developed by a well structured design method. The main part of the controller consist of three simple neural networks with 4, 6 and 8 neurons. So, although creating a stable and energy efficient walking gait is a complex problem, it can be generated without some deep neural network or some complex mathematical model.
Research on Technological Innovation Ability Cultivation for Private College Student

The innovative education is the trend of China's higher education reform.The implementation of innovation education in China's colleges meets the requirement of the times, the construction of an innovative country in China, and the prosperity of the nation.Based on the theoretical basis of innovation education for college students, this paper analyses the necessity and feasibility of innovation education for college students in private colleges.Taking the Department of Electronic Information Engineering in Guangzhou College of Technology and Business for example, this paper expounds its innovation education system in detail.Combining innovative spirit in education, this paper discusses the methods and experiences of cultivation innovative ability in private college.The research further enriches the research of innovation education in China and provides a reference for the practice of innovation education in private colleges, which has important theoretical and practical significance.
A tie-point zone group compaction schema for the geolocation data of S-NPP and NOAA-20 VIIRS SDRs to reduce file sizes in memory-sensitive environments

The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) disseminates weather and climate-related satellite data to its users via satellite broadcast. As part of this, the EUMETSAT Advanced Retransmission Service (EARS) provides Sensor Data Records (SDR) from the United States satellites Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20. Due to bandwidth constraints and for cost-reduction reasons for the upload to the disseminating satellite, a near-lossless compaction method has been developed for the geolocation data of the data capturing satellites’ Visible Infrared Imager Radiometer Suite (VIIRS) instrument. The geolocation data are compacted by storing only the data representation for so called tie-points which form tie-point zones and are grouped in tie-point zone groups. Compression factors of 120 for M-Band, 479 for I-Band, and 154 for Day/Night Band, not using HDF5 internal compression on neither the input nor the output, and 56 for M-Band, 210 for I-Band, and 89 for Day/Night Band, using HDF5 internal compression on both the input and output, respectively, are reached. The compaction process introduces an error of about 1 ​m RMSE for the position in latitude/longitude and of about 0.001° RMSE for the angular data. The concept of the tie-point zones and the quadratic interpolation schema with coefficients analytically derived from the scanning geometry forms the main elements of the method presented. The full algorithm and mathematical background needed are presented, and the algorithm can be highly parallelized. The method can likely be applied to any cross-track scanning sensor.
Wagner, Doric Isabel - Biographical entry - Index to the AMA archive

Wagner, Doric Isabel - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Aspect-Based Sentiment Analysis Using Smart Company and Hotel Aspect Review Data

Digital sources such as smart applications opinions and online feedback statistics are crucial resources to be seeking for customers’ remarks and input. However, the reviews are often disorganized, leading to difficulties in information navigation and knowledge acquisition. The aforementioned problem is overcome by generating aspect-sentiment based embedding for the hotels and companies by looking into reliable reviews of them. The important product aspects are identified based on two observations: 1) the important aspects are usually commented on by a large number of consumers and 2) consumer opinions on the important aspects greatly influence their overall opinions. Aspect frequency and the influence of consumer opinions given to each aspect over their overall opinions are identified for hotel reviews whereas for company reviews approach adopts language processing techniques, policies, and lexicons to address several sentiment evaluation challenges, and convey summarized results. Moreover, aspect ranking achieve significant performance improvements, which demonstrate the capacity of aspect ranking in facilitating real-world applications.
EU Ethical Charter on the Use of Artificial Intelligence in Judicial Systems with a part of the law being established on Blockchain as a trojan horse anti-counterfeiting in a Global perspective

In 2018 the use of artificial intelligence (AI) algorithms in European judicial systems remains primarily a private-sector commercial initiative aimed at insurance companies, legal departments, lawyers and individuals. The European Commission for the Eﬃciency of Justice (CEPEJ) of the Council of Europe in the 4 of the december 2018 has adopted the first european text setting out ethical principles relating to the use of artificial intelligence (AI) in judicial systems. The Charter provides a framework of principles that can guide policy makers, legislators and justice professionals when they grapple with the rapid development of AI in national judicial processes. Aside from the benefits for copyright traceability and management, blockchain can also become a central means for protecting digital assets and provide the rightholder with the ability to pursue civil and criminal remedies. The Anti-Counterfeiting Blokathon Forum on 2018 takes forward its works on using blockchain to cocreate the future EU anti-counterfeiting infrastructure. Could blockchain live up to the challenge? Any future anti-counterfeiting blockchain solution must comply with Regulation (EU) No 608/2013 of the European Parliament and of the Council of 12 June 2013 concerning customs enforcement of intellectual property rights, and with Ethical Charter made from CEPEJ in the 2018.
A Few Topical Tweets are Enough for Effective User-Level Stance Detection

Stance detection entails ascertaining the position of a user towards a target, such as an entity, topic, or claim. Recent work that employs unsupervised classification has shown that performing stance detection on vocal Twitter users, who have many tweets on a target, can yield very high accuracy (+98%). However, such methods perform poorly or fail completely for less vocal users, who may have authored only a few tweets about a target. In this paper, we tackle stance detection for such users using two approaches. In the first approach, we improve user-level stance detection by representing tweets using contextualized embeddings, which capture latent meanings of words in context. We show that this approach outperforms two strong baselines and achieves 89.6% accuracy and 91.3% macro F-measure on eight controversial topics. In the second approach, we expand the tweets of a given user using their Twitter timeline tweets, and then we perform unsupervised classification of the user, which entails clustering a user with other users in the training set. This approach achieves 95.6% accuracy and 93.1% macro F-measure.
Semantic Path-Based Learning for Review Volume Prediction

Graphs offer a natural abstraction for modeling complex real-world systems where entities are represented as nodes and edges encode relations between them. In such networks, entities may share common or similar attributes and may be connected by paths through multiple attribute modalities. In this work, we present an approach that uses semantically meaningful, bimodal random walks on real-world heterogeneous networks to extract correlations between nodes and bring together nodes with shared or similar attributes. An attention-based mechanism is used to combine multiple attribute-specific representations in a late fusion setup. We focus on a real-world network formed by restaurants and their shared attributes and evaluate performance on predicting the number of reviews a restaurant receives, a strong proxy for popularity. Our results demonstrate the rich expressiveness of such representations in predicting review volume and the ability of an attention-based model to selectively combine individual representations for maximum predictive power on the chosen downstream task.
CONTINUOUS EXPERTS COACHING TO INCREASE THE COMPETENCE OF PARTICIPANTS OF THE INCLUSIVE PROCESS IN EDUCATIONAL INSTITUTIONS

In the article, the authors analyze ways to increase the professional competence of teachers of multidisciplinary IEP teams. This allows the conclusion that that the use of coaching is appropriate for mastering support specialists with inclusive technologies, as well as the ability to work in a team interaction mode. Despite the obvious advantages of teamwork, in Ukraine this format of work is still not mastered in an inclusive educational environment. In the article the authors present the structure of the training program based on the team management model for significant achievements (R. Hackman). We conducted a training program for 10 IEP teams, aimed at increasing the level of team interaction and inclusive competence of specialists. Formative influence was done directly during the professional activity of teachers and was accompanied by prolonged expert coaching. In their study, the authors applied a special design scheme for teamwork. This scheme was concretized in the fact that the participants of the IEP teams used the experience gained in the training sessions in the practice of their professional activity, critically comprehended and analyzed their new experience, sought to comprehend the factors of both success and failure. Together with the coordinator and experts, all participants of the IEP teams developed rules for the team, established feedback, sought to influence the focus on mastering the modern approaches of the inclusive process at the level of the entire educational institution. In turn, this maintained the necessary level of motivation and joint intentions to introduce a competently constructed educational environment. To assess the state of inclusive competence formation before and after the training program, we used teacher self-assessment method «Professional Development Tool for Improving the Quality of Practice in primary school». As a result, all teachers have achieved a significant increase in the effectiveness of interdisciplinary support for children with special needs, which have reflected in the positive changes that have occurred in teachers at the professional, interpersonal and personal levels.
Dualism in Topical Relevance

There are several concepts whose interpretation and meaning is defined through their binary opposition with other opposite concepts. To this end, in this paper we elaborate on the idea of leveraging the available antonyms of the original query terms for eventually producing an answer which provides a better overview of the related conceptual and information space. Specifically, we sketch a method in which antonyms are used for producing dual queries, which can in turn be exploited for defining a multi-dimensional topical relevance based on the antonyms. We motivate this direction by providing examples and by conducting a preliminary evaluation that shows its importance to specific users.
The visible file system

In digital forensics one sometimes needs to look at a file system without filtering. One may be interested in seeing items that have been deleted, or see how deletion is done in a particular file s...
LibGuides: Stay Safe, Stay Informed, Stay Entertained: eContent: Books/Musics/Movies & More

Use this guide to stay informed, find out about Library programs, download econtent and take some time for yourself.
Accelerating a neural network with optimised memory management using CUDA

Training a neural network to classify objects in a large dataset entails heavy computation which presents a potential bottleneck and drawback to the usage of such a structure. Improvements to neural network training can be achieved by utilising the Compute Unified Device Architecture (CUDA) programming model. The main problem is decomposed into a large number of sub-problems which are solved independently on a CUDA-enabled GPU via the CUDA framework. In this paper, we use CUDA and C++ to reduce the training time of a neural network. Most of the simple repetitive calculation is moved from the CPU to the GPU with a limitation on the amount of communication between these components. Our parallel neural network is trained 12, 50, and 63 times faster than the corresponding CPU implementation for the small, medium, and large datasets, respectively. We also show that, contrary to related work, training on one GPU is more efficient than the use of multiple smaller GPUs. This was achieved by using an Nvidia GeForce GTX 660 with 960 CUDA cores and an Intel Core i7- 3630QM 2.40 GHz CPU.
New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert Finding

The scientific literature is growing faster than ever. Finding an expert in a particular scientific domain has never been as hard as today because of the increasing amount of publications and because of the ever growing diversity of expertise fields. To tackle this challenge, automatic expert finding algorithms rely on the vast scientific heterogeneous network to match textual queries with potential expert candidates. In this direction, document network embedding methods seem to be an ideal choice for building representations of the scientific literature. Citation and authorship links contain major complementary information to the textual content of the publications. In this paper, we propose a benchmark for expert finding in document networks by leveraging data extracted from a scientific citation network and three scientific question &amp; answer websites. We compare the performances of several algorithms on these different sources of data and further study the applicability of embedding methods on an expert finding task.
Mobile applications to obtain minimum cost feed mixes

In this study, ration preparation software to minimize the cost of feed for ruminant livestock such as cattle, sheep, and goats for both milk and meat yield was developed for Web- and Android-based systems using genetic algorithms. To maximize accessibility on PCs, smartphones, and tablet PCs, we used Web- and Android-based software to find cheaper feed mixes that satisfy the nutritional requirements of ruminants. With this novel system, farmers and scientists can obtain low-cost feed mixes via the Web or smartphones, regardless of time or location. This application is useful for feed producers and farmers because they can use this software from any location and at any time. Users can input their new feed resources for preparing rations
ANALISIS KONTRIBUSI ALOKASI DANA PENDIDIKAN TERHADAP PROGRAM PENINGKATAN MUTU PENDIDIKAN ANAK USIA DINI (PAUD) PADA DINAS PENDIDIKAN KABUPATEN KEBUMEN

This research intends to know how much contribution of funding allocation to the program improving quality of early childhood education. This is based on the program of improving quality of early childhood education is closely related to the allocation of funds provided. Starting from the problems,the purpose of this research are to know how much contribution and effectiveness of funding allocation to the program improving quality of early childhood education.&#x0D; The method used in this research is descriptive research method with quantitative approach. While the data collection method used are observation, questionnaire and documentation.&#x0D; The result of this reseach concluded that contribution of funding allocation to the program improving quality of early childhood education included in the medium (enough) category with the percentage of 46,69% each year. While the effectiveness of the quality improvement program of early childhood with the percentage of 82,6% included in the very effective category.
The EU Commission publishes a package of initiatives on Europe's digital future to create legislation on AI and big data

The New EU AI and Data Strategy and what it Means for EU Antitrust Reform* On February 19, the European Commission (EC) published a package of initiatives (the AI and Data Package) on Europe's…
Методика пространственно-временного анализа электрической активности головного мозга

In the framework of this work a new method based on continuous wavelet transform was proposed for analyzing the spatio-temporal dynamics of brain activity patterns. We described the example of this method application for the analysis of brain electrical activity signals. It is shown that this method has the ability to visually detect the occurrence and spatial dynamics of frequency patterns.