Reviewerly/exHarmony huggingface dataset:
Interception wideband FM signals with RTL-SDR

This article presents a Python program to receive and demodulate FM signals, which is based on software-defined radio (SDR). RTL-SDR receiver R820T2 connects to a computer via a USB port and can receiving 100KHz - 1.7GHz electromagnetic signals. The antenna is included with RTL-SDR. The receiver circuit is represented by a threshold detector with signal accumulation. If the signal level is higher than the specified value in the program, the frequency is memorized, and the signal is written to the file. In the experiment the signal was searched in two frequency bands. Radio stations were found, the signal from each was recorded in a separate file. In all recording's speech is clearly audible. Using a broadband antenna, changing the algorithm for receiving signals in a wider frequency band and with other modulations we can improve the recording quality. The power spectral density (PSD) of signals, algorithm of the program and demodulated signal spectrum are presented.
The Use of Android-Based Mobile Learning Application to Comprehend Local-Based Descriptive Text

The rapid development of technology today has greatly helped facilitate human affairs in all aspects of life, including the activities of reading. Reading is important to penetrate the world window. Through reading, knowledge and information needed can be obtained. In this era of technological advances, a shift in students' reading interest can be made through the world of the internet. This research aims to obtain a detailed description of the use of Android-based mobile learning applications in teaching reading local-based descriptive texts for SMP students. This research used descriptive-qualitative method with online interviews, observation and documentation as the research instruments. Data analysis was done based on the theory of Miles and Hubberman (1992). The result shows that in this crucial situation of pandemic Covid 19 the Android-based mobile learning applications support the online teaching and learning process of reading. The local-based descriptive text teaching materials are provided in the form of an android application of Descriptive Text. In addition, the teacher also explains the learning objectives and the steps for learning local-based descriptive texts using the Android-based mobile learning application.
New applications of an oversampling method based on generative adversarial networks

This paper presents new applications of a novel oversampling method based on Generative Adversarial Networks (GAN). Two challenging applications are approached: classification of stages of a neuropsychological test (Barcelona test) using electroencephalographic (EEG) data from epileptic patients; and classification of sleep stages using electrocardiographic (ECG) data from apnea patients. The results of the GAN-based method are compared with the commonly used interpolation-based method synthetic minority oversampling technique (SMOTE). The results show the superiority of the GAN method over SMOTE to improve the probability of error of the two-class classification proposed applications.
Incentivizing Participation to Distributed Neural Network Training

During the last years a vast number of online sensors continuously generate data that can be utilized to create novel deep learning applications. Training very large models requires enormous processing power; thus, the evident way to follow is to lease the power of a corporate data center. But the diffusion of Artificial Intelligence to an always increasing number of human activities, constantly attracts new researchers who wish to train and test their models. Our work on LEARNAE is a proposal for a purely distributed neural network training, based on a peer-to-peer and permissionless architecture. LEARNAE allows individual researchers to join forces, in order to collaboratively train a model. The process utilizes modern Distributed Ledger Technology and it is fully democratized, prioritizing decentralization, fault tolerance and privacy. In this paper we add another piece to the puzzle: A method for incentivizing peers to participate to the training swarm, even if they don’t have any interest in the produced neural network. This is achieved by embedding a reward subsystem to LEARNAE; thus, peers who contribute to teamwork can receive a proportional digital payment.
Study on Environmental Performance Evaluation Model of Coal Mine Using Balanced Scorecard and Ubiquitous Computing

Abstract Based on the analysis of the current situation of environmental performance evaluation of coal enterprises in China, this paper uses the Balanced Scorecard as the basic framework to comprehensively evaluate environmental performance from the dimensions of environmental finance, internal environmental management, stakeholders and learning and growth. Set up a number of indicators in each dimension, use AHP to determine the weights of indicators at all levels, and test the rationality of index weights through consistency, and establish a comprehensive environmental performance evaluation system.
Adjusting Post-processing Approach for Very Short-Term Solar PV Power Forecasts

The solar energy resources are rapidly increase their shares into the generation fleet of electric power systems. Therefore, accurate solar power forecasts is a vital requirement for an economical, reliable and stable operation of these power systems. In this paper a post-processing adjusting approach is implemented to adjust and improve the intra-hourly forecasts of the PV solar power output for three stations with different climate patterns in the United States. Thus, very short-term forecasts of multiple horizons are generated by several individual models, and then these diverse forecasts are post-processed using the adjusting approach. The performance of the adjusting approach is evaluated by adopting the out-of-sample test for intra-hour forecasts of different temporal and spatial resolutions over a year. The evaluation is also conducted with a variety of performance metrics. The results indicate that the forecasts from the adjusting approach have the highest accuracy.
Pragma-linguistic and stylistic features of the English online movie review

The paper deals with linguo-pragmatic and semantic-stylistic characteristics of the English-online movie review. Internet communication provides a fertile ground for the development of the modern genre studies due to the emergence and development of digital genres, or Internet genres. The amateur online movie review is one of the most widespread Internet genres due to its high degree of the audience-oriented intention, focus on the wide range of readers, as well as the dialogical nature of communication. The aim of this paper is to reveal and systematize pragmatic and stylistic parameters of the English amateur online movie review. The empirical study is based upon the movie reviews taken from the IMDb website. The analysis of online movie reviews has revealed that their pragmatic function is determined by the use of the wide range of linguistic and stylistic evaluative means. In most cases the ascription of explicit evaluative characteristics to the film and its components in the text of the amateur online movie review is done with the help of the evaluative language. The important finding is that the creation of perlocutionary effect and the attribution of dialogization and intimization to the speech act are created by the use of personal pronouns of the first and second person singular, imperatives, appeals, question-and-answer structure. We have come to the conclusion that various lexical and syntactic means of expressiveness belong to stylistic markers of online movie review carrying evaluative, emotional and emphatic connotations. All the analysis conducted allows us to conclude that online movie review beside the initial function of informing also performs perlocutionary force.
A Few Methodological Clarifications

Design for flexibility requires some methodological clarifications in terms of underlying models. The SFAC model (Structure/Function vs. Abstract/Concrete) provides an abstract and concrete articulation between the structure and function of system. A sociotechnical system cannot be studied, modeled, designed and developed if the distinction and complementarity of cognitive and physical functions, which characterize it, are not well mastered. The NAIR (Natural/Artificial vs. Cognitive/Physical) model rationalizes this distinction to support human-centered design. The AUTOS pyramid (Artifact, User, Task, Organization and Situation) is an extension of the TOP model (Technology, Organization and People). SFAC, NAIR and AUTOS models and frameworks enable a design team to rationally state design problems toward enhanced HSI.
An Optimized Apriori Algorithm Applied to Student Achievement Data

With the popularization of compulsory education and the increase in demand for high-quality talents, higher education has gradually become a popular education. The rapid increase in the number of students in colleges and universities has generated a lot of teaching information. How to make full use of these data to facilitate the school’s overall management of students has become the direction of our research. In this paper, by improving the Apriori algorithm, the evaluation results of the 15-year undergraduate students of the computer department of Qinghai University are analyzed. The algorithm sets the pre- or post-appearances of the association rules in the course performance evaluation, filters out the effective rule set in the data set, and mines the association rules between courses accordingly. These rules can provide auxiliary reference for teaching staff, and can also help students find their own course warning information in a timely manner. We also designed to improve the operating efficiency of the algorithm by generating a matrix of course evaluation items. The relationship between courses can be found in the association rules generated by the optimized apriori algorithm. The results of Data Structure and Algorithm in professional basic courses will affect the results of Parallel Computing in professional elective courses. Discrete mathematics is directly related to the network offensive and defensive experimental technology of professional elective courses. The learning degree of probability theory and mathematical statistics and advanced mathematics I is directly related to the learning degree of algorithm design. The optimized Apriori algorithm improves the operating efficiency by 87.5%.
Overview of the SOURCe project as an open educational resource

In this paper, we present an overview of the functionalities of the SOURCe Project, as it has evolved in a span of eight years after its creation in 2012. The SOURCe Project currently includes the search engine for the Searchable Online French-Greek Parallel Corpus for the University of Cyprus (SOURCe), the Pencil (an alignment tool), the Exercises, the Synonyms and the Library. The collection of corpora and tools are designed to be freely available as language processing resources for language and translation teachers and learners, but also for translators. Furthermore, we discuss how its applications can form an important part of effective learning resources. This paper focuses on the development and construction of the SOURCe Project, which is mainly based on a set of parallel corpora. The design, the content, and the availability of these corpora aim to serve the needs of teachers and students of French as a foreign language (other languages may be also added in the future) and also to facilitate future linguistic research. Our approach is a corpus linguistics one, undertaken from the perspective of language acquisition and translation studies.
Google v. Oracle: Copying Declaring Code Is Fair Use

Not all code is created equal—at least not in the majority’s view in Google v. Oracle, handed down on April 5th. In this landmark 6-2 decision, the Supreme Court held that Google’s verbatim reproduction of approximately 11,500 lines of computer programming code from Oracle’s Java library was fair use, and so did not violate Oracle’s copyright.After Google v. Oracle, Sun may as well change its slogan to: write once, copy anywhere. Given the prolific use of APIs, programmers, developers, and others who want to use their acquired knowledge and experience with software interfaces in subsequent platforms would rejoice in the Court’s decision. They can now use APIs without worry. So long as a party can demonstrate that it has not made use of the “expressive” implementing code, and that its use has furthered the development of new products, then it may well succeed under a fair use defense following this precedent, even if the volume of code copied is significant. This opens the door to a much freer exchange of coding packages, making new product development faster and easier. One would expect to see more copying, more cases, and more claims of fair use. Lower courts will doubtlessly ponder this decision’s implications as they apply it to the never-ceasing tide of fights over the limits of the reuse and redeployment of software code.On the other hand, software creators may have lost what little protections they have on their software, which may—counter to the Court’s intent—result in less software innovation. The Court has somewhat sidelined declaring code as far as copyright protection is concerned, thereby de-incentivizing further development of code with perhaps better functional and organizational capacity than Java’s API because of the possibility of its “fair-use” by another company at zero cost or repercussion. Further, the Court may have declared that mobile platforms are both a distinct programming entity and a distinct market from desktops and laptops. Whether you side with the majority or dissent here depends, in part, on how literally you take the saying that a smartphone is like “a computer in your pocket.” In future cases where transformative use or market interference is at issue, this opinion’s strong language may serve as precedent for parties to argue that mobile technology is in a realm on its own. In any case, under this holding, copying the very building blocks of the Java API is, as it was, fair game.
Fingerprint Feature Recognition of Power Amplifier Based on One-dimensional Convolutional Neural Network

Traditional communication station recognition usually needs to manually extract features and then use pattern recognition for classification. There are problems of complex preprocessing and difficult feature extraction. Power amplifiers are the core components of shortwave radio stations, and their fingerprint features can be used as radio stations. For the characteristics of individual recognition, a method of power amplifier fingerprint recognition based on one-dimensional convolutional neural network (1D-CNN) is proposed. The characteristic is that it can directly learn features from the original signal and finally complete the classification and recognition. By establishing a power amplifier model, the shortwave signal can obtain fingerprint characteristics. A four-layer 1D-CNN network is used to extract signal features, the network parameters are optimized through experiments, and finally the Softmax classifier is used for classification and recognition. A 98% recognition rate was obtained through experiments, which verified the effectiveness of the method.
Digital technologies in modern higher educational space: analysis of risks and threats

At present, Russia faces a strategic task related to building a course aimed at a breakthrough in the scientific, technological and socio-economic development of the country. This development is inextricably linked with the digital economy, which, in turn, is impossible without the digitalization of education. The essence of digitalization of education is to achieve high performance in the educational process using digital technologies. It is noted, that within the framework of the digital transformation of education, the content of education, methods, forms of educational work, forms of assessing the results achieved by students are changing. On the one hand, this process is aimed at developing a digital educational environment in order to improve the educational results of each student, but on the other hand, when using digital technologies in the higher educational space, the following risks arise: the risk of dehumanization, the risk of spiritual lumpenization, the threat to the fundamental component of traditional education caused by the promotion of “educational services”, the risk of loss of cognitive competencies, the risk of technologization and robotization, the deepening crisis of intellectual culture, the threat to the creative development of the individual, the risk of social exclusion.
Knowledge as an Epistemological and Logical Concept

This chapter is devoted to the analysis of the concept of ‘knowledge’Knowledge both taken as an abstract notion and in its applications in Epistemic LogicEpistemic Logic (EL). It starts with a review of the epistemological theories that have been developed since its inception. Afterwards it analyses the different interpretations that have been made of this concept in Epistemic LogicEpistemic Logic (EL) and its contemporary developments, highlighting the difference between ‘implicit’ and ‘explicit’ knowledgeKnowledge and the strategies for drawing this distinction. Finally, it offers a new understanding of these notions, which supports the correlation between human knowledgeKnowledge and ‘Explicit Aware Knowledge’ (EAK)Explicit Aware Knowledge (EAK), the central notion of the theoretical framework: the EAK-SchemaEAK-Schema.
Simplified security learning using vertically partitioned data with IoT

Edge (or fog) computing is known as a method for improving the conventional cloud system. The basic idea is to consider a system that places edges (servers) between the cloud and the terminals (things). Then, how should machine learning be realized on the edge system? Fast and secure learning methods are desired for machine learning. Secure systems using distributed processing have attracted attention. SMC (Secure Multiparty Computation) is one of the typical models to realize secure learning. Horizontally and vertically partitioned data are known for SMC. The latter is a method consisting of dividing the dataset into element-separated subsets. It is desired to develop a method for directly executing learning using element-separated subsets. Vertically partitioned data (VPD) is considered to be a data structure that realizes such learning. In the previous papers, we proposed learning methods for BP (Back Propagation) and NG (Neural Gas) using VPD. There, we did not consider about the amount of data transferred between servers. In this paper, simplified learning methods that eliminate wasteful data transfer compared to the method in the previous papers are proposed, and its effectiveness is shown. That is, the data transfer from the central server to each server was reduced to 1/L, where L is the number of training data.
On-the-fly Estimation of Task Difficulty for Item-based Adaptive Online Learning Environments

There are several methods aimed at assessing assignment difficulty and learner ability that originate to a great extent from the area of item response theory (IRT). Computational demands have been defined as the main hurdle in the operational usage of these models within adaptive online learning environments. Therefore, alternative methods of difficulty estimation have been analyzed. The aim of this study is to present and compare several methods of estimating the difficulty of assignments for adaptive item-based online learning environments. We compare the following methods of estimating task difficulty: learner feedback, proportion correct, and two rating algorithms: Elo and Glicko with reference values provided by the IRT graded response model. The data originates from an item-based online programming course consisting of 76 programming tasks of various difficulty, solved by 299 users. There have been 50055 attempts recorded in total, for which more than 300000 unit tests have been executed. Learners have provided their opinion on the difficulty of tasks and have generated 2558 ratings. The highest correlation has been observed for the Glicko rating algorithm (0.959), followed by proportion correct (0.943) and the Elo rating algorithm (0.927). The lowest value (0.827) has been observed for the learner feedback method. We conclude, that in the context of an adaptive online learning environment, where the trade-off between operability and accuracy needs to be accepted, the Glicko rating algorithm may be chosen as a fast method for task difficulty estimation.
Customized Services Using Voice Assistants

Internet of Things (IoT) devices can be defined as a collection of computing devices that communicate and transfer data with one another. As the popularity of IoT devices increases, users want to maximize the functionality of their devices. Voice-enabled IoT devices serve and assist the user by performing functions like playing music, controlling lights, setting alarms and reminders, and much more. The popularity of these devices has grown, and they appeal to consumers because of the available accessories that can be purchased and connected to, such as smart lights, smart shades, etc. These smart accessories can connect to devices such as the Google Home or the Amazon Echo, allowing the user to control multiple common house functions with their voice. This paper demonstrates how to control non-smart LED lights using voice commands with smart home devices. A service known as IFTTT, "If This, Then That", is utilized to add custom commands to two smart speakers. This paper shows how accessible and simple it is for an individual to control a non-smart device using voice commands, while shedding light on how the use of VPAs can aid people with disabilities. The security risks and threats of using IFTTT are addressed.
Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration–Exploitation Dilemma in Reinforcement Learning

A fundamental problem in reinforcement learning is the exploration–exploitation dilemma: a search problem that entails sufficiently investigating the possible action choices and exploiting those that work well for certain contexts. Few exploration mechanisms, however, provide expected performance guarantees for a given search amount. Here, we show that this dilemma can be addressed and the expected agent performance quantified by optimizing Stratonovich’s value of information. The value of information is an information-theoretic criterion that specifies the greatest increase in rewards, from the worst case, subject to a certain uncertainty amount. In the context of reinforcement learning, uncertainty is quantified by a constrained mutual dependence between random variables. When the mutual dependence between the random variables go to zero, agents tend to exploit its acquired knowledge about the environment; little to no improvements in policy performance are obtained in this case. As the mutual dependence increases, a great amount of exploration is permitted and the policy can converge to the global-best action-selection strategy. Optimizing the value of information yields action-selection update strategies that, in the limit, is theoretically guaranteed to uncover the optimal policy for a given mutual dependence amount. We show that, in a finite number of episodes, the value of information yields policies that outperform conventional exploration mechanisms for both single-state and multi-state, multi-action environment abstractions based on Markov decision processes.