Reviewerly/exHarmony huggingface dataset:
More data or more parameters? Investigating the effect of data structure on generalization.

One of the central features of deep learning is the abilities of neural networks, which seem to improve relentlessly with over-parametrization. In this work, we investigate how properties of data impact the test error as a function of the number of training examples and number of training parameters; in other words, how the structure of data shapes the generalization phase space. We first focus on the random features model trained in the teacher-student scenario. The synthetic input data is composed of independent blocks, which allow us to tune the saliency of low-dimensional structures and their relevance with respect to the target function. Using methods from statistical physics, we obtain an analytical expression for the train and test errors for both regression and classification tasks in the high-dimensional limit. The derivation allows us to show that noise in the labels and strong anisotropy of the input data play similar roles on the test error. Both promote an asymmetry of the phase space where increasing the number of training examples improves further than increasing the number of training parameters. Our analytical insights are confirmed by numerical experiments involving fully-connected networks trained on MNIST and CIFAR10.
A note on detection of sports action based on temporal cycle consistency learning

This paper presents a method for action detection based on Temporal Cycle Consistency(TCC) Learning. The proposed method realizes the action detection of flexible length segments based on a frame-level action prediction technique. We enable calculation of similarities for spatio-temporal features based on TCC to detect target actions from input videos. Finally, our method determines temporal segments by smoothing the frame-level action detection result. Experimental results show the validity of the proposed method.
Autonomous Flight through Cluttered Outdoor Environments Using a Memoryless Planner

This paper introduces a collision avoidance system for navigating a multicopter in cluttered outdoor environments based on the recent memory-less motion planner, rectangular pyramid partitioning using integrated depth sensors (RAPPIDS). The RAPPIDS motion planner generates collision-free flight trajectories at high speed with low computational cost using only the latest depth image. In this work we extend it to improve the performance of the planner by taking the following issues into account. (a) Changes in the dynamic characteristics of the multicopter that occur during flight, such as changes in motor input/output characteristics due to battery voltage drop. (b) The noise of the flight sensor, which can cause unwanted control input components. (c) Planner utility function which may not be suitable for the cluttered environment. Therefore, in this paper we introduce solutions to each of the above problems and propose a system for the successful operation of the RAPPIDS planner in an outdoor cluttered flight environment. At the end of the paper, we validate the proposed method's effectiveness by presenting the flight experiment results in a forest environment. A video can be found at this http URL
Weighted Sparse and Lightweight Spanners with Local Additive Error.

An \emph{additive $+\beta$ spanner} of a graph $G$ is a subgraph which preserves shortest paths up to an additive $+\beta$ error. Additive spanners are well-studied in unweighted graphs but have only recently received attention in weighted graphs [Elkin et al. 2019 and 2020, Ahmed et al. 2020]. This paper makes two new contributions to the theory of weighted additive spanners. For weighted graphs, [Ahmed et al. 2020] provided constructions of sparse spanners with \emph{global} error $\beta = cW$, where $W$ is the maximum edge weight in $G$ and $c$ is constant. We improve these to \emph{local} error by giving spanners with additive error $+cW(s,t)$ for each vertex pair $(s,t)$, where $W(s, t)$ is the maximum edge weight along the shortest $s$--$t$ path in $G$. These include pairwise $+(2+\eps)W(\cdot,\cdot)$ and $+(6+\eps) W(\cdot, \cdot)$ spanners over vertex pairs $\Pc \subseteq V \times V$ on $O_{\eps}(n|\Pc|^{1/3})$ and $O_{\eps}(n|\Pc|^{1/4})$ edges for all $\eps > 0$, which extend previously known unweighted results up to $\eps$ dependence, as well as an all-pairs $+4W(\cdot,\cdot)$ spanner on $O(n^{7/5})$ edges. Besides sparsity, another natural way to measure the quality of a spanner in weighted graphs is by its \emph{lightness}, defined as the total edge weight of the spanner divided by the weight of an MST of $G$. We provide a $+\eps W(\cdot,\cdot)$ spanner with $O_{\eps}(n)$ lightness, and a $+(4+\eps) W(\cdot,\cdot)$ spanner with $O_{\eps}(n^{2/3})$ lightness. These are the first known additive spanners with nontrivial lightness guarantees. All of the above spanners can be constructed in polynomial time.
Wargaming in Cyber Security Education and Awareness Training

This paper introduces readers to core concepts around cyber wargaming. Wargames can be powerful learning tools, but few wargames exist to teach players about cyber security. By way of highlighting possibilities in this space, the author has developed an original educational tabletop wargame based on the UK National Cyber Security Strategy and deployed the game to a variety of organisations to determine its pedagogic efficacy. Overall, it is found that the game was effective in generating high-engagement participation and clear learning opportunities. Furthermore, there are design lessons to be learned from existing games for those seeking to use wargames for cyber security training and education.
Ludii User Guide

This user guide describes the basic functionality of Ludii -- a general game system that can be used to play a wide variety of games. Ludii is a program developed for the ERC-funded Digital Ludeme Project, in which mathematical and computational approaches are used to study how games were played, and spread, throughout history. Primary features of Ludii include: - A large library of implemented games, which can be played by any combination of human and computer players, and displayed graphically in the application. - A new ludeme-based game description language, which allows for new games to be modelled in more succinct and human-friendly formats than prior game description languages, and run more efficiently. - Implementations of common game-playing algorithms from the Artifficial Intelligence literature, and the ability to write and import custom agents in the application. Ludii primarily contains board game, dice games, card games, puzzles, and other abstract games.
Digital corpora: language teaching and learning in the age of big data

What is it? Using corpora to teach languages is nothing new and, while the term corpus linguistics hails from the 1940s, most language learning before the 20th century adopted a corpus approach – using a series of texts in the language under study as a type of corpus on which to base acquisition. With the advent of widespread computing in the latter half of the 20th century, corpora began to be digitised, rendering interrogation of large amounts of data a much simpler and more appealing prospect. Today, languages in all forms (written, spoken, performed, formal, informal, etc.) are captured all the time through online and digital platforms, apps, etc. meaning that the wealth of language data literally at our fingertips is enormous. This has triggered the development of appropriate tools to explore these vast data sets.
Computer-Based Multi-Layered Monitoring Systems on Textile Manufacturing Processes

Abstract Monitoring is a set of activities carried out to measure the achievement of objectives. One area that requires a monitoring system is the production process, this aims to ensure that production can be controlled and run as it should. Lack of monitoring of production can cause problems. Although there has been a lot of research on the production monitoring system, so far there has not been much mention regarding the provision or sharing of information on the progress of production implementation in more detail between the production department and the parties involved, especially in sharing information handling errors that occur during the production process. This is important because mistakes that occur during production can cause delays in shipping and will have an impact on the valuation or reputation of the company. The results obtained from the implementation of the system based on user acceptance testing is getting a level of acceptance of 81.5% of 42 test scenarios for 4 types of users. Implementing a production monitoring system can help in monitoring production. More interesting studies can be further carried out especially in the area of artificial intelligence for monitoring production systems.
Patent pending: the law on AI inventorship

Thaler v The Comptroller-General of Patents, Designs and Trade Marks [2020] EWHC 2412 (Pat) The High Court of England and Wales recently decided that an artificial intelligence (AI) system cannot be named as inventor on a patent application within the meaning of the Patents Act 1977, simply because an AI is not a person—but with the UK Intellectual Property Office ("UKIPO") calling for views on AI inventorship, could a change in law be on the horizon? There is no existing legislation or case law on AI inventorship in the UK, so the judge in this case was required to turn to the Patents Act 1977 (the ‘Act’), and case law on inventorship generally, in order to determine whether the law currently permits an AI to be named as inventor on a patent application. Section 7(3) of the Act defines the ‘inventor’ as ‘the actual deviser of the invention’. The...
Complex System for Earthquake Prediction, Warning and Local Assessment of Seismic Events

A complex system for zonal earthquake prediction, warning, and local assessment of seismic events has been designed, performed, implemented, and experimented/validated. The system was designed to ensure simultaneously: the reception of warning signals following earthquakes with the epicentre on a radius of 1000 km; acquisition of local precursor data for a possible prediction of seismic events with the epicentre in the perimeter of the targeted locality and/or improvement of the database in the field of Earth physics purchased and processed centrally at the national seismic dispatcher; acquisition of data on the intensity of local seismic movements, based on which, when a predetermined threshold considered dangerous is exceeded, a real-time action order is issued for the protection of high-risk equipment and installations in operation. The realized system is structured on the national seismic dispatcher DSN (with the role of seismic data acquisition from the territory) connected by a bidirectional communication system with a local dispatcher DL which is provided with a system for acquiring and storing local seismic data (vibration detector 3D and temperature transducer mounted in a 40 m deep drilled well, radon detector and associated parameters: temperature, pressure, and humidity of the air mounted at the mouth of the drilled well). The implemented system is able, through the specialized software implemented, to take over the warning signals received from the national seismic dispatcher, to process the locally acquired data, and after the local validation of the seismic event to issue real-time action command (when exceeding values of pre-established major risk threshold) of the protections of high-risk installations in operation in the targeted perimeter. The experimentation/validation of the system, of the interconnection networks, and of the specialized software of the implemented application was done both by continuously recording the local seismic parameters, verifying the communication between DSN and DL, and by taking two warnings regarding seismic events produced (on 30.10.2020  Mw = 7, Greece and on 22.10.2020, at 20:22 hours, ML = 4 R, Vrancea, RO). By processing the data recorded during these events, the speeds of seismic waves in the respective directions were calculated. Thus, for the event of 30.10.2020 Greece, a speed of seismic waves of 7,418 km/second was determined and for the event from 22.10.2020 Vrancea, at 20:22 hours, it was calculated that the secondary waves are moving with 12,686 km/second and the surface seismic waves with 5,063 km/second. Following the analysis/comparison of acceleration intensities with the pre-set threshold level recorded locally for potentially dangerous events, it was found that these events were felt in Râmnicu Vâlcea at a level below the pre-set danger threshold and consequently, the specialized software of the application did not generate a control signal for actuating the protection of high-risk equipment in operation.
Text-Independent Speaker Identification using Mel-Frequency Energy Coefficients and Convolutional Neural Networks

Automatic Speaker Identification (ASI) is a biometric technique, which had achieved reliability in real applications, with standard feature extraction methods such as Linear Predictive Cepstral Coefficients (LPCC), Perceptual Linear Prediction (PLP), and modeling methods such as Gaussian mixture model (GMM), etc. However, the success of these manual approaches was quickly hampered by the emergence of big data, and the inability of scientists to manipulate large amounts of data, which led researchers to move towards automatic methods such as deep neural networks. In this work, a Convolutional Neural Network (CNN) is suggested for speaker identification in text-independent mode. Mel-Frequency Energy Coefficients (MFEC) method was used for extracting the characteristics of audio signals and the obtained coefficients were injected into the convolutional neural network model for classification (identification). In addition, a comparison was made between the proposed method and the existing traditional methods. Experimental results show that the proposed structure resulted in a speaker identification rate of 97.89%, which is much higher than the rates obtained in the old state of the art methods.
Pengaruh Literasi Ekonomi, Status Sosial Ekonomi Orang Tua dan Modernitas terhadap Perilaku Konsumsi Peserta Didik

The study aims to know the impact of economic literacy, the social status of parents and modernity on consumption behavior students class XI IPS SMA Negeri 1 Selayar. The study was categorized as quantitative work with a population of 96 students, and a samples of 96 students. Data collection is done with documentation and questionnaire technique. The analysis technique used is a descriptive analysis technique. Research shows that: (1) Economic literacy does not have a positive and significant impact on consumption behavior students class XI IPS SMA Negeri 1 Selayar. (2) The social status of parents has a negative and significant impact on consumption behavior students class XI IPS SMA Negeri 1 Selayar. (3) Modernity has a positive and significant impact on consumption behavior students class XI IPS SMA Negeri 1 Selayar.
PAC-learning gains of Turing machines over circuits and neural networks

A caveat to many applications of the current Deep Learning approach is the need for large-scale data. One improvement suggested by Kolmogorov Complexity results is to apply the minimum description length principle with computationally universal models. We study the potential gains in sample efficiency that this approach can bring in principle. We use polynomial-time Turing machines to represent computationally universal models and Boolean circuits to represent Artificial Neural Networks (ANNs) acting on finite-precision digits. Our analysis unravels direct links between our question and Computational Complexity results. We provide lower and upper bounds on the potential gains in sample efficiency between the MDL applied with Turing machines instead of ANNs. Our bounds depend on the bit-size of the input of the Boolean function to be learned. Furthermore, we highlight close relationships between classical open problems in Circuit Complexity and the tightness of these.
Diabetes and Heart Disease Identification System Using Iris on the Healthcare Kiosk

Abstract Data reported by World Life Expectancy said, as many as 9.89% of Indonesia’s population died due to suffering from heart disease and as much as 7.18% due to suffering from diabetes. The heart and pancreas are very important organs for the human body. The heart has a function to flow blood to all parts of the body. While the pancreas is the organ responsible for regulating insulin levels. Organs in the human body can also be damaged so that it can inhibit the work process of these organs. Damage to the pancreas causes diabetes. To find out whether a person has diabetes or heart disease, it is necessary to carry out time-consuming and expensive laboratory tests. In this study, we propose an identification system for diabetes and heart disease using irises on Healthcare Kiosk. The method used in detecting diseases of the body through the iris is called iridology. This identification system will be in the form of a desktop application that can be used at Healthcare Kiosk. The stages carried out in this study were photographing the patient’s left eye using a special camera called an Eyeronec, target-based cropping, preprocessing, auto-cropping using integral projection, auto-cropping to remove sclera, taking pancreatic and heart ROI, feature extraction and classification. Auto cropping shows results 60% successful, 33% scant, and 7% failed. The classification process was carried out by training 31 training data that was labeled normal or abnormal by iridology experts. In this study, the system testing accuracy was 83.87% for diabetes and 80.65% for heart disease.
WITHDRAWN: Combined approach for mining high quality interesting association rules

Association rule mining is an essential data mining job used for mining relationships present within the data. Support - confidence framework is traditionally used for mining Association Rules. But the limitation is huge number of Association Rules are generated, which may be uninterested to the user. So, measures used for filtering Association Rules have been proposed in literature. They are classified into Subjective & objective. Subjective measures takes into account the user’s knowledge and his/her interest in pattern analysis, where as objective measures depends on structure of the pattern and the input data. Despite of advantages of subjective and objective measures, both the measures has limitations. In case of subjective measures, user’s can feel difficulty in expressing interestingness during pattern evaluation, where as in some cases objective measures may not generate really interesting rules. Hence an approach is proposed which generates interesting high quality association rules, which takes into account the advantages of existing measures.
Improved camera color accuracy in the presence of noise with a color prefilter

It is possible to achieve improved color accuracy with a color camera by placing a color filter in front of the camera. Unfortunately, the color filter will block some of the light entering the camera, which will result in additional noise in the recorded data. This paper provides an initial investigation into finding an optimal solution to the filter design, in the presence of noise.
Research and design of virtual simulation experiment learning interaction for embodied cognition

At present, virtual simulation experiment is commonly used in teaching practice, greatly enriching students' learning experience. However, students still face the problems of less interactive experience, poor participation and a low degree of freedom in the process of interaction through virtual simulation experiment. With the popularity of virtual reality technology, somatosensory interaction and other new human-computer interaction technology, the innovative learning method of learning with physical experience has attracted extensive attention in academic circles. Based on the theory of embodied cognition, this paper constructs a virtual simulation experiment interaction model based on the theory of embodied cognition. The purpose of this model is to emphasize the embodiment of cognition and the embeddedness of cognitive environment, enhance the immersion of students, effectively stimulate students' interest in learning, and realize the dynamic interaction among students, learning resources and teachers in virtual environment The construction and development of the real experimental resources can be used for reference.
Practical Quantum Secure Direct Communication

Quantum secure direct communication (QSDC) transmits information securely through a quantum channel, distinct among various quantum communications. Proposed by me and Liu 20 years ago, QSDC has now been developed to a stage that is ready for practical application. QSDC advances classical communication of reliable communication over a noisy channel, which is guaranteed by Shannon's theory, to a higher plane of both secure and reliable communication over a noisy channel even in the presence of eavesdropping. Here we briefly introduce the basic principles, the techniques for practical applications.
Application of Fuzzy C-Means Clustering and Semantic Ontology in Web Query Session Mining for Intelligent Information Retrieval

Information retrieval based on keywords search retrieves irrelevant documents because of vocabulary gap between document content and search queries. The keyword vector representation of web documents is very high dimensional, and keyword terms are unable to capture the semantic of document content. Ontology has been built in various domains for representing the semantics of documents based on concepts relevant to document subject. The web documents often contain multiple topics; therefore, fuzzy c-means document clustering has been used for discovering clusters with overlapping boundaries. In this paper, the method is proposed for intelligent information retrieval using hybrid of fuzzy c-means clustering and ontology in query session mining. Thus, use of fuzzy clusters of web query session concept vector improve quality of clusters for effective web search. The proposed method was evaluated experimentally, and results show the improvement in precision of search results.
DIGITAL PROJECTS NATIONAL LIBRARY UKRAINE NAME V. I. VERNADSKY IN THE INFORMATION SPACE

The article presents ongoing projects that have been introduced by the V. I. Vernadskyi National Library of Ukraine, namely: the national information resources «Ukrainika» and «Scientific Electronic Library», the electronic library of special periodicals «Scientific Periodicals of Ukraine», and the abstract database «Ukrainika» scientific», information portal «Science of Ukraine: access to knowledge» and others. It has been argued that only the implementation of joint programs and projects is a reliable current source for the world community on the results of modern research by Ukrainian scientists, a means of promoting the latest areas of scientific knowledge, presentations of scientific schools, teams, scientific and educational institutions.