Reviewerly/exHarmony huggingface dataset:
The Machine Learning Algorithm for Solving the Problem of Generating Recommendations for Goods and Services

The article proposes an unsupervised machine learning algorithm for assessing the most possible relationship between two elements of a set of customers and goods / services in order to build a recommendation system. Methods based on collaborative filtering and content-based filtering are considered. A combined algorithm for identifying relationships on sets has been developed, which combines the advantages of the analyzed approaches. The complexity of the algorithm is estimated. Recommendations are given on the efficient implementation of the algorithm in order to reduce the amount of memory used. Using the book recommendation problem as an example, the application of this combined algorithm is shown. This algorithm can be used for a “cold start” of a recommender system, when there are no labeled quality samples of training more complex models.
THE ROLE OF INSTITUTIONAL EDUCATION IN TEACHING PERSONAL SECURITY ON THE INTERNET - WHOSE DUTY IS IT TO EXPLAIN THE DANGERS OF PRIVATE CONTENT ABUSE?

Information about the paper titled "THE ROLE OF INSTITUTIONAL EDUCATION IN TEACHING PERSONAL SECURITY ON THE INTERNET - WHOSE DUTY IS IT TO EXPLAIN THE DANGERS OF PRIVATE CONTENT ABUSE?" at IATED Digital Library
Development of lures moving irregularly

By applying the technology of the fluid element, a lure that randomly moves from side to side and up and down was developed. By aerial shooting of the behavior with a drone and analyzing the image, it was confirmed that the course progressed while changing the trajectory greatly to the left and right and up and down. As a result of the trial fishing of the lures developed in Lake Yamanaka and Lake Sagami, a black bass about 38 cm was caught in each lake.
Strategical of visual, graphic and symbolic communication

An ability to speak clearly, eloquently and effectively has been recognized as the hallmark of an educated person, since the beginning of recorded history. Communication is essential for the socialization process to transpire. Its implications are created more because of the lack of proper teacher’s information, during their studies or the new century inventions and requirements. Communication in education process includes communication issues of education as effective communication, all dimensions of interpersonal communication, types of communication, obstacles for effective communication, group’s communication and listening skills, verbal and nonverbal communication (from pupils and teachers perspective), strategies for guiding debates and presentation. In this case it can be understood also other factors which influence the education communication process, which are discussed also for creating and developing new effective communication towards education process. This paper explores also the visual, graphic and symbolic communication strategies on which are involved too also the characteristics of each type of communication, their positive sides, negative sides, weaknesses, opportunities, advantages and disadvantages in general. In accordance with that during the elaboration of this paper’s topic we have talked also for the types of communication in classroom, which must be used from teachers, their ways of use in teaching process. Expect this we tried to give some examples for the right way of communication, during some lessons described in some rules. Teacher’s messages are very important for the pupil’s state as they try to establish their knowledge and information to the pupils. For pupils the voice of teacher is an order to them, not because they feel afraid from him, but because of the security and trust felt from him. This shows a lot to us that the communication it’s like a door of success depended more from the teacher’s creativity and his organization too, how he organizes and plans his work.
Analisis Survival pada Data Kejadian Bersama Menggunakan Metode Exact Partial Likelihood

Survival data is the data of survival time until the appearance of certain events. In the survival analysis, ties are sometimes found, that is the situation where there are two or more individuals who experience the same event at the same time. There are several methods in estimating the parameters in the case of a ties, one of which is by applying the exact partial likelihood method. The exact method is the most accurate method, which can be applied in estimating Cox regression parameters from traffic accident data of Samarinda City in 2016. Traffic accidents are one of the most deadly events. The four main factors that cause traffic accidents are human factors, vehicles, roads, and weather or environmental factors. The variables used in this study are age, gender, role of victim, driver’s license, vehicle type, time of incident, line of the road, and weather. The results of analysis with the help of Rstudio software showed that the factors whose affect the fatality rate of traffic accident victims of Samarinda City are age and gender. For the age variables concluded that each addition of one year of age of the accident victim, the risk of dying from a traffic accident will also increase 1,0258 times. As for the gender variables concluded that the victim of male sex has a risk of 0.4180 times greater to die due to traffic accidents compared with female victims.
Using Simple Controls

Controls provide the means for users to interact with apps, and they play a crucial role in app design. This chapter focuses on controls that can edit and display a single value. These include the text input, rich text, radio, toggle, slider, rating, and date controls.
Multi-scale information quantification framework in network information system environment

The network information system surrounding information acquisition, information interaction, and information utilization is the core element of the current information environment, and the measurement of information is one of the cores of the research on the network information system. This article quantitatively calculates different levels of information from the perspective of effectiveness evaluation, maps the information measurement issues in the physical domain, system domain, and system domain to the equipment performance, system effectiveness domain and system effectiveness, and quantitatively calculates the information from the perspective of application effects, get the multi-scale measurement framework of information measurement. Through this framework, multi-scale quantitative calculations can be carried out on the information application capabilities of different levels of network information systems, providing support for equipment construction, capability evaluation, and optimized applications.
On Bounds for Ring-Based Coding Theory

Coding Theory where the alphabet is identified with the elements of a ring or a module has become an important research topic over the last 30 years. Such codes over rings had important applications and many interesting mathematical problems are related to this line of research. It has been well established, that with the generalization of the algebraic structure to rings there is a need to also generalize the underlying metric beyond the usual Hamming weight used in traditional coding theory over finite fields. This paper introduces a new weight, called the overweight, which can be seen as a generalization of the Lee weight on the integers modulo $4$. For this new weight we provide a number of well-known bounds, like a Plotkin bound, a sphere-packing bound, and a Gilbert-Varshamov bound. A further highlight is the proof of a Johnson bound for the homogeneous weight on a general finite Frobenius ring.
Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving

Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous driving. However, the low sample efficiency and difficulty of designing reward functions for DRL would hinder its applications in practice. In light of this, this paper proposes a novel framework to incorporate human prior knowledge in DRL, in order to improve the sample efficiency and save the effort of designing sophisticated reward functions. Our framework consists of three ingredients, namely expert demonstration, policy derivation, and reinforcement learning. In the expert demonstration step, a human expert demonstrates their execution of the task, and their behaviors are stored as state-action pairs. In the policy derivation step, the imitative expert policy is derived using behavioral cloning and uncertainty estimation relying on the demonstration data. In the reinforcement learning step, the imitative expert policy is utilized to guide the learning of the DRL agent by regularizing the KL divergence between the DRL agent's policy and the imitative expert policy. To validate the proposed method in autonomous driving applications, two simulated urban driving scenarios (unprotected left turn and roundabout) are designed. The strengths of our proposed method are manifested by the training results as our method can not only achieve the best performance but also significantly improve the sample efficiency in comparison with the baseline algorithms (particularly 60\% improvement compared to soft actor-critic). In testing conditions, the agent trained by our method obtains the highest success rate and shows diverse and human-like driving behaviors as demonstrated by the human expert.
SCHOOL ONLINE E-LEARNING PLATFORMS IN THE TRAINING OF FUTURE TEACHERS OF MATHEMATICS AND COMPUTER SCIENCE

The strategy for the development of the information society in the Russian Federation for 2017-2030 sets one of the most important tasks – to form the national technological platforms for online education, to include various levels of online courses and educational platforms in the educational process. The formation of the digital learning environment of the school is necessary to improve the quality and accessibility of education, to meet the students’ needs in obtaining new competencies.The process of digitalization of the modern school and the tasks of the National project "Education" and the Federal project "Digital educational environment" demand qualified teachers. A modern University graduate should have professional competencies according to the goals of education development and school digitalization.Training bachelors of pedagogical direction, future teachers of mathematics and informatics, at the Institute of Mathematics and Information Technology of the PetrSU is carried out in the conditions of active use of distance learning technologies in the educational process and also in the conditions of active use of e-learning and blended learning in a wide range of disciplines.While studying different disciplines of the curriculum of the "Pedagogical education" direction, students also study the essence of these technologies and their application in school education.The curriculum and the training programs are updated by including special disciplines and courses, such as "Distance learning technologies", "Modern tools for evaluating learning outcomes" and others. And also, by changing the content of traditional basic disciplines in the context of digital transformation of schools: "Methods of teaching Informatics", "Information and communication technologies in education» and others.The research of educational and functional capabilities of school electronic online platforms, such as "Russian electronic school", "YaKlass", "SkySmart. Interactive notebook", defines the main directions and the ways to integrate online platforms into the learning process of future teachers of mathematics and informatics on the basis of special disciplines of subject-methodical training. Generalization of students' learning experience in the basic course of the curriculum "Methods of teaching Informatics" with the use of school online platforms proves the effectiveness of this tool.School electronic educational online platforms are an effective means of teaching students, they help to establish the relationship between the theory and methodology of teaching the subject and the practice of studying it in the context of digitalization of school education. The use of educational online platforms help future teachers of mathematics and informatics to get ready to utilize online teaching in their professional activity, to form professional competencies in the context of digital transformation of school education.
JDSR-GAN: Constructing A Joint and Collaborative Learning Network for Masked Face Super-Resolution

With the growing importance of preventing the COVID-19 virus, face images obtained in most video surveillance scenarios are low resolution with mask simultaneously. However, most of the previous face super-resolution solutions can not handle both tasks in one model. In this work, we treat the mask occlusion as image noise and construct a joint and collaborative learning network, called JDSR-GAN, for the masked face super-resolution task. Given a low-quality face image with the mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some comparable methods which perform the previous two tasks separately.
Strategizing Reading in a Digital World

Chapter 9 considers how reading practices are embedded in broader societal trends, especially those involving digital technology. One important development is a move from the durable to the ephemeral. This transformation is reflected in progressively choosing experiences over possessions. It also surfaces in acquiring digital over physical versions of possessions, even though users judge the physical as more valuable. Growing use of digital technologies may be leading readers using print to adopt the mindset typically associated with reading digitally. We have seen from the research that digital reading is often shallow and done quickly, frequently while multitasking. Is this the fate of print reading as well? Already some readers report print reading is boring, compared with the potential entertainment opportunities with digital. The chapter closes with suggestions for both print and digital reading in a digital world, including Maryanne Wolf’s model of biliteracy.
Meta-Learning with MAML on Trees

In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related. Sharing information between unrelated tasks might hurt performance, and it is unclear how to transfer knowledge across tasks with a hierarchical structure. Our research extends a model agnostic meta-learning model, MAML, by exploiting hierarchical task relationships. Our algorithm, TreeMAML, adapts the model to each task with a few gradient steps, but the adaptation follows the hierarchical tree structure: in each step, gradients are pooled across tasks clusters, and subsequent steps follow down the tree. We also implement a clustering algorithm that generates the tasks tree without previous knowledge of the task structure, allowing us to make use of implicit relationships between the tasks. We show that the new algorithm, which we term TreeMAML, performs better than MAML when the task structure is hierarchical for synthetic experiments. To study the performance of the method in real-world data, we apply this method to Natural Language Understanding, we use our algorithm to finetune Language Models taking advantage of the language phylogenetic tree. We show that TreeMAML improves the state of the art results for cross-lingual Natural Language Inference. This result is useful, since most languages in the world are under-resourced and the improvement on cross-lingual transfer allows the internationalization of NLP models. This results open the window to use this algorithm in other real-world hierarchical datasets.
A novel method for generating structured illumination of three-dimensional shape measurement using phase modulation

With the development of science and technology and industrial production, the application of three-dimensional shape measurement technology in the fields of machine vision, industrial monitoring, mechanical engineering and medical testing has become increasingly widespread. The optical three-dimensional shape measurement technology is a method that uses a series of optical methods to obtain the three-dimensional information of the measured object, and the structured light measurement method is a widely used measurement method. In the structured light measurement method, the generation of fringe patterns is a very important link. Here, we use holographic method to generate a fringe pattern whose period and phase can be easily modulated. First, a black and white fringe pattern with a certain spatial frequency is generated according to the cosine structured light period and phase. The spatial frequency of the black and white fringe is determined by the spatial frequency of the structured light to be generated and the magnification of the projection system. A prism phase that can cause lateral movement is applied to the black part of the black and white stripes, so that the light incident on the black area deviates from the optical axis to the first order of diffraction, while the light incident on other parts (without being modulated) continues to travel along the optical axis. In this way, the deviated beam is bright and dark, and finally structured illumination is obtained. Theories and experiments verify the effectiveness of the method. The method to generate fringe patterns is simple, fast, accurate and can be conveniently controlled, and can be well applied to three-dimensional shape measurement technology.
Generating Adversarial Computer Programs using Optimized Obfuscations

Machine learning (ML) models that learn and predict properties of computer programs are increasingly being adopted and deployed. These models have demonstrated success in applications such as auto-completing code, summarizing large programs, and detecting bugs and malware in programs. In this work, we investigate principled ways to adversarially perturb a computer program to fool such learned models, and thus determine their adversarial robustness. We use program obfuscations, which have conventionally been used to avoid attempts at reverse engineering programs, as adversarial perturbations. These perturbations modify programs in ways that do not alter their functionality but can be crafted to deceive an ML model when making a decision. We provide a general formulation for an adversarial program that allows applying multiple obfuscation transformations to a program in any language. We develop first-order optimization algorithms to efficiently determine two key aspects -- which parts of the program to transform, and what transformations to use. We show that it is important to optimize both these aspects to generate the best adversarially perturbed program. Due to the discrete nature of this problem, we also propose using randomized smoothing to improve the attack loss landscape to ease optimization. We evaluate our work on Python and Java programs on the problem of program summarization. We show that our best attack proposal achieves a $52\%$ improvement over a state-of-the-art attack generation approach for programs trained on a seq2seq model. We further show that our formulation is better at training models that are robust to adversarial attacks.
Correction to: Ockham’s Semantics of Real Definitions

The original version of this chapter was inadvertently published without the following acknowledgement.
TRAINING DATA SAMPLING FOR CONVENTIONAL NEURAL NETWORKS CONFIGURING

The problem of generating training data for setting up the convolutional neural networks is considered, which is of great importance in the construction of intelligent medical diagnostic systems, where due to the lack of elements of the training sample, it is proposed to use the approaches of artificial data multiplication based on the initial training sample of a fixed size for the image processing (the results of the ultrasound, CT and MRI). It shows that the increase of the training sample resulted in less informative and poor quality elements, which can introduce extra errors in the goal achievement. To eliminate this situation the algorithm for assessing the quality of a sample element with the subsequent removal of uninformative elements is proposed.
Evolution of the YouTube Personas Related to Survival Horror Games

The indie survival horror game genre has given rise to some of the most famous game streamers on YouTube, especially titles like Amnesia: The Dark Descent (Frictional Games 2010), Slender: The Eight Pages (Parsec Productions 2012), and Five Nights at Freddy’s (Scott Cawthon 2014). The games are strongly focused on horror tropes including jump scares and defenceless protagonists, which lend them to displays of overemphasised emotional reactions by YouTubers, who use them to build their online personas in a certain way. This paper retraces the evolution of the relationship between horror games and YouTube personas, with attention to in-game characters and gameplay mechanics on the one hand and the practices of prominent YouTube personas on the other. It will show how the horror game genre and related media, including “Let’s play” videos, animated fanvids, and “creepypasta” stories have influenced prominent YouTuber personas and resulted in some changes in the common processes of persona formation on the platform.
Passport tool fails with dark skin

The UK is still using a racially biased photo checker despite an update being available
Selfish Mining Attacks Exacerbated by Elastic Hash Supply

Several attacks have been proposed against Proof-of-Work blockchains, which may increase the attacker's share of mining rewards (e.g., selfish mining, block withholding). A further impact of such attacks, which has not been considered in prior work, is that decreasing the profitability of mining for honest nodes incentivizes them to stop mining or to leave the attacked chain for a more profitable one. The departure of honest nodes exacerbates the attack and may further decrease profitability and incentivize more honest nodes to leave. In this paper, we first present an empirical analysis showing that there is a statistically significant correlation between the profitability of mining and the total hash rate, confirming that miners indeed respond to changing profitability. Second, we present a theoretical analysis showing that selfish mining under such elastic hash supply leads either to the collapse of a chain, i.e., all honest nodes leaving, or to a stable equilibrium depending on the attacker's initial share.
Dependency Graph-to-String Statistical Machine Translation

We present graph-based translation models which translate source graphs into target strings. Source graphs are constructed from dependency trees with extra links so that non-syntactic phrases are connected. Inspired by phrase-based models, we first introduce a translation model which segments a graph into a sequence of disjoint subgraphs and generates a translation by combining subgraph translations left-to-right using beam search. However, similar to phrase-based models, this model is weak at phrase reordering. Therefore, we further introduce a model based on a synchronous node replacement grammar which learns recursive translation rules. We provide two implementations of the model with different restrictions so that source graphs can be parsed efficiently. Experiments on Chinese--English and German--English show that our graph-based models are significantly better than corresponding sequence- and tree-based baselines.