Reviewerly/exHarmony huggingface dataset:
Forecasting and Decision–Making using Feature Vector Analysis (FEVA)

A multiple set of discrete time series can be visualized as a set of tracks running along together. If the data points are connected by a wire diagram then we have a surface. This surface is fuzzy (influenced by error) and dynamic, in that interrelationships (transient and permanent) are pro pogated through time. Our focus is on procedures which can help detect fundmental patterns and time-varying relationships on such a surface. This paper proposes a new approach called FEVA, feature vector analysis, where the key idea is to replace each scalar data point with a vector of information which represents what the point Sees” around itself. The main processing unit consists of a clustering module to detect patterns in the enhanced (feature vector) data set, and a neural net simulator to estimate flexible functional relationships. The cluster analysis results are presented graphically using pattern–grids and the neural net simulator builds a decision surface. These two visual objects can be used to perform both forecasting and “what-if” analyses. A small but real illustrative example involving stock market information is used to demonstrate the FEVA approach.
Evaluating the Impact and Reach of Biogeochemical Cycles

A new book examines flow of the elements in the biosphere from biological drivers to human influences, and explores the analytical and computational methods used to access biogeochemical cycles.
GAMES IN BUSINESS EDUCATION

The article shows the role of games (gamification) in increasing the effectiveness of teaching students and listeners, as well as increasing their involvement in this process. The essence of the game, its types and effects obtained as a result of its use in the educational process are revealed. The authors identified the stages of creating this learning tool and the key skills and abilities necessary for training games. The study conducted by the authors on the basis of conducting games among students during training is described, the results are given.
Underreporting of errors in NLG output, and what to do about it

We observe a severe under-reporting of the different kinds of errors that Natural Language Generation systems make. This is a problem, because mistakes are an important indicator of where systems should still be improved. If authors only report overall performance metrics, the research community is left in the dark about the specific weaknesses that are exhibited by `state-of-the-art' research. Next to quantifying the extent of error under-reporting, this position paper provides recommendations for error identification, analysis and reporting.
Outlier: Enabling Effective Measurement of Hypervisor Code Integrity With Group Detection

Virtualization brings the benefits of utilization and scalability to the multi-tenant cloud platforms. However, the hypervisor, as one of the foundations in virtualization, is challenging to survive under various malicious attacks due to its large attack surface. This article presents a novel group detection framework for the hypervisor code integrity, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Outlier</i> . In an Outlier group, each host contains two parts. One is a distributed detection protocol, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co-protocol</i> , and the other is a detection interface, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Checker</i> . The Co-protocol constructs trust for the integrity detection within an Outlier group. With the Co-protocol, each Checker conducts reliable integrity detection on the hypervisor code, and then the potential “outlier” host is perceived. We implement our Outlier prototype on the Xen hypervisor and evaluate its overhead. Experiments show that the introduction of the Outlier has few impacts on the performance of the virtualized systems.
EYE WITNESS MESSAGE IDENTIFICATION ON FOREST FIRES DISASTER USING CONVOLUTIONAL NEURAL NETWORK

Social media, one of which is Twitter, is a medium for disseminating information that is growing rapidly at this time. The advantage of Twitter which has such a huge impact is its speed in spreading news and information that is happening. One of the information that is often reported through social media is information about natural disasters. Therefore, a lot of research on sensor social networks has been carried out by researchers using data from social media with the aim of obtaining valid data for the disaster emergency response process. In this study, the classification of eye witness messages for forest fires was carried out using Convolutional Neural Network and feature extraction Word2Vec with dimensions of 100. Twitter data used amounted to 3000 data and divided into 3 classes, namely eyewitnesses, non-eyewitnesses, and unknowns. The research was conducted to determine the accuracy performance obtained from testing using several types of configurations hyperparameter. Based on the results of the tests carried out, the best accuracy value was 81.97%.
Approximations of Belief Functions Using Compositional Models

For applications to practical problems, the paper proposes to use the approximations of belief functions, which simplify their dependence structure. Using an analogy with probability distributions, we represent these approximations in the form of compositional models. As no theoretical apparatus similar to probabilistic information theory exists for belief functions, the problems arise not only in connection with the design of algorithms seeking the optimal approximations but even in connection with a criterion comparing two different approximations. With this respect, the application of the analogy with probability theory fails. Therefore, the paper suggests the employment of simple heuristics easily applicable to real-life problems.
Non-common Field of View 2D Full-Field Digital Image Correlation Method Based on Space Conversion Calibration Method

This paper introduces a full-field, 2D, non-contact displacement and deformation measurement method under non-common field of view based on the space conversion calibration method. Through the spatial constraint relationship of adjacent cameras in the camera array, the spatial conversion relationship of the cameras in the non-common field of view is established to complete the global calibration. In order to verify the effectiveness and accuracy of the method, three cameras were used to measure the displacement under the common field of view, and then four cameras were used to build the non-common field of view. At the same time, pictures were taken before and after the deformation of the object. 2D Digital Image Correlation was used to measure the two-dimensional deformation field of the object surface and coordinate splicing was conducted according to the spatial transformation calibration. Experimental results show that this method can accurately and conveniently measure the deformation of objects in non-common field of view.
Cyber Racism Detection Using Bidirectional Gated Recurrent Units and Word Embeddings1

Racism is an unequal treatment based on race, color, origin, ethnicity or religion. It is often associated with rejection, inequality, and value judgment. A racist act, whether conscious or unconscious, goes beyond insult and aggression and leaves a devastating psychological effect on the victim. Although almost all laws around the world punish racist acts and speech, racist messages are on the rise on social networks. As a result, there is a strong need for reliable and accurate detectors of racist comments to identify the offenders and take appropriate punitive action against them. In this paper, we propose a model for the detection of racist statements in text messages by Bidirectional Gated Recurrent Units. For the word representation, we use different word embedding techniques, namely Word2Vec and GloVe. We show that this combination works well and provides a good level of detection. At the end of our study, we will suggest new horizons to improve the quality of our model.
Implementation of a competence-based approach in the process of teacher's project activities

The purpose of the article is to study the competence-based approach in the process of project activity of the teacher. The research methodology is formed from the most frequently used methods and principles of scientific and theoretical research. In addition, the article uses the following methods: general scientific; methods of theoretical analysis; specific scientific methods. Modern society requires the upbringing of independent, proactive, responsible citizens who are able to effectively interact in the implementation of social, industrial and economic tasks. The fulfillment of these tasks requires the development of personal qualities and creative abilities of a person, the ability to independently acquire new knowledge and solve problems and navigate in the life of society. The result of the research is the identification of key aspects of the implementation of the competence-based approach in the process of project activities of the teacher.
The Importance of School Libraries in Open Schooling, Sustainable Development, and Outdoor Learning

This paper reports the preliminary results of a 2020/2021 research project about open schooling, Sustainable Development Goals (SDGs), and outdoor learning in Europe and the role of school libraries in implementation and development of Open Education Resources (OER). School librarians support learners who have been unable to access schooling, but also the learners who are in school, but are not learning effectively. School librarians as Information and Communication Technology (ICT) and educational experts have all important skills to support teachers and students in development and using an OER. Innovative distance learning needs to be established for monitoring, evaluation, quality assurance, and continuous improvement. The SDGs could be achieved only if local communities ensure open access to education.
CNN BLSTM Joint Technique on Dynamic Shape and Appearance of FACS

Facial recognition is a process where we can identify or verify a person from digital image or videos and is used in ID verification services , protecting law enforcement ,preventing retail crime etc. Past work on automatic analysis of facial expression focuses on detecting the facial expression and exploiting the dependencies among AU’s. But, spontaneous detection of facial expression depending on various factors such as shape, appearance and dynamics is very difficult. Joint learning of shape , appearance and dynamics is done by a deep learning technique.This includes a convolutional neural networks and bidirectional long short term memory(CNN-BLSTM). This combination of CNN-BLSTM excels the modeling of temporal information. FERA2015 dataset achieves the state of art.
Timing Analysis of Algorithm Substitution Attacks in a Post-Quantum TLS Protocol

Snowden's revelations about mass surveillance brought to public attention devastating attacks on cryptographic algorithm implementations. One of the most prominent subsets of these attacks is called Algorithm Substitution Attacks (ASA), where a subverted implementation leaks sensitive information. Recently, it has been proposed to modify TLS implementations to use Post-Quantum Cryptography (PQC). In this paper, we propose and analyze ASA in two PQC schemes that can be used in TLS. We attacked the Kyber Key Encapsulation Mechanism (KEM) and Falcon Signature and successfully deployed them in a TLS implementation. Results show that timing analysis can distinguish our Falcon subversion, but it is not enough to detect our attacks deployed in TLS.
Research on multi-channel data acquisition system of production index information based on genetic algorithm

The multi-channel data acquisition system is the key link of the industrial computer tomography (ICT) in image processing. How to realise the real-time data acquisition and storage of these huge data content has become an urgent problem for ICT. Based on this, a multi-channel data acquisition system of production index information based on genetic algorithm is proposed. This paper introduces the design of the basic structure of the multi-channel data acquisition system of production index information, expounds the hardware design and software design of the multi-channel data acquisition system, and gives the design and implementation scheme of the system. It is confirmed by experiments that the multi-channel data acquisition system based on genetic algorithm designed in this paper has high acquisition accuracy. It is particularly excellent, especially in the same sampling rate, compared with the traditional multi-channel data acquisition system of production index information. Through further analysis, it is found that the trigger mode of the multi-channel data acquisition system based on genetic algorithm can significantly reduce the loss rate of hardware resources compared with the traditional multi-channel data acquisition system.
Prediction and Analysis of Amazon User Behavior Based on Long Short-Term Memory and Manual Feature

The user preference information implicits that the user behavior sequence has an important impact on the click-through rate estimation. Current research on click-through rate estimation mostly designs models from the perspective of low- order and high-order interactions of modeling features, which is difficult to fully explore and utilize user preference information implied in the user’s historical behavior sequence. This article reviews and summarizes the results of previous research and exploration on the click-through rate estimation problem. On this basis, this article proposes a click-through rate prediction model based on user preference networks, modeling user preferences. Two Experiments on the two click-through rate prediction models proposed in this paper based on user preference networks are conducted based on the international public data set. The final experimental results show that the model proposed in this paper can fully mine user preference information hidden in user behavior sequences and, as a result, obtain more effective results. User preference indicates that compared to other comparative click-through rate estimation models, it has achieved better performance in the relative improvement of AUC and models.
Seven C’s Communication Skills Problems in Writing Business Letter of English Major Undergraduate Students

This article examines the students' low ability and the degrees of their difficulties in writing business letters.The identified problems were based on the indicators of Seven C's communication skills.Students had low skills in writing opening, body, and closing paragraphs of business letters.Questionnaires and tests were the main instruments.There were 62 students from two classes from different Universities who followed the tests.The findings showed that a) completeness students had some lack of organization.A few of them provided the necessary information and answered the question asked.b) Conciseness, they had a little problem eliminating a wordy expression, c) Consideration, they had little or no attempt at connectivity, d).Concreteness, the students had a problem in emphasizing positive, pleasant facts.They sometimes had limited vocabulary, and frequent errors hinder the expression of ideas, e) Clarity, they had a few chose the imprecise, not-concrete, and familiar words.They had little or no attempt at connectivity, f).Courtesy, students had difficulties in using expressions that show respect.g).Correctness, they still kept having a big problem in using the right level of a language.Simply, they made errors of grammar or word order very frequently; the reader often has to rely on their interpretation.The results of students' responses frequencies questionnaire toward the difficulties in writing business letters based on the Seven C's communication skills indicators showed that most of them really agreed that the Seven C's were very difficult to implement in writing.
Strangely mined bitcoins: Empirical analysis of anomalies in the bitcoin blockchain transaction network

The blockchain technology introduced by bitcoin, with its decentralised peer-to-peer network and cryptographic protocols, provides a public and accessible database of bitcoin transactions that have attracted interest from both economics and network science as an example of a complex evolving monetary network. Despite the known cryptographic guarantees present in the blockchain, there exists significant evidence of inconsistencies and suspicious behavior in the chain. In this paper, we examine the prevalence and evolution of two types of anomalies occurring in coinbase transactions in blockchain mining, which we reported on in earlier research. We further develop our techniques for investigating the impact of these anomalies on the blockchain transaction network, by building networks induced by anomalous coinbase transactions at regular intervals and calculating a range of network measures, including degree correlation and assortativity, as well as inequality in terms of wealth and anomaly ratio using the Gini coefficient. We obtain time series of network measures calculated over the full transaction network and three sub-networks. Inspecting trends in these time series allows us to identify a period in time with particularly strange transaction behavior. We then perform a frequency analysis of this time period to reveal several blocks of highly anomalous transactions. Our technique represents a novel way of using network science to detect and investigate cryptographic anomalies.
Grammatical Errors on EFL Students’ Conversation Practice: Surface Strategy Taxonomy

The purpose of this study was to analyze grammatical errors on EFL students’ conversation practice. The participants of this study were the fourth term students of Indonesia Institute of Technology and Business, 2020/2021 academic year. The participants taken as the object of this study because after observation in the students’ class found that there were some mistakes on the students’ speech when doing the conversation in English. The population of this study was from English Training class consisted of four parallel classes Semester IV with the total number of the students was 95 students. Purposive sampling technique was used in this study. This study was conducted by using descriptive qualitative research. The data was words, phrases, and sentences which uttered by the students and writing all non-verbal linguistic that students do on their conversation practice. The data was analyzed based on a classification of errors proposed by Dulay, Burt, and Krashen (1982 in Ellis and Barkhuizen, 2005) called Surface Strategy Taxonomy is applied. The errors in students’ speech were classified by using surface structure taxonomy namely misformation, misorder, addition, and omission. The findings of this study show that omission is the most dominant error produced by participants with the percentage 41.30%. It is followed by misformation with total cases 28.26%, addition with total cases 21.73%, and the last followed by misordering with total cases 8.69%.
ANALYSIS OF FACTORS FOR FORECASTING ELECTRIC POWER GENERATION BY SOLAR POWER PLANTS

&#x0D; &#x0D; &#x0D; The new model of the wholesale electricity market in Ukraine causes appearance the market for the day ahead. In this market, the generating company undertakes to supply a certain amount of electricity. So, it is necessary to carry on the most accurate forecast of possible electricity generation by solar power plant (SPP). Generation value depends on certain factors. A brief summary of different influence of parameters on the PV cell performance has been provided. The article analyzes and identifies the factors that should be included in the forecast mathematical model of electricity generation by a solar power plant for a certain short-term period. According to analyzed data from SPP located in the Kyiv region, such parameters are the intensity of solar radiation, temperature and humidity, wind speed, and atmospheric pressure. The degree of influence of these factors on the initial function of electric energy generation were estimated by analyzing the scatter plot diagrams of relationship between parameters and correlation coefficients. Thus, the analysis of the influence of factors on the magnitude of electricity generation allowed to determine the priority of including each of the parameters in the mathematical model of the SPP power forecast. It was established that the influence of certain climate parameters for target function is different in each season. Therefore, in the mathematical model for forecasting electric power generation, it is necessary to take into account seasonality. In addition, the dynamic value change of factors also affects the current magnitude of electricity generation. Moreover, at different times of the year and with different combination of the corresponding values of climatic parameters, this effect may have different magnitudes. Therefore, the data obtained from the last periods before the forecasting should have a greater impact on obtaining the predicted value than the data from previous periods.&#x0D; &#x0D; &#x0D;
Text to Image Translation using Cycle GAN

In the recent past, text-to-image translation was an active field of research. The ability of a network to know a sentence's context and to create a specific picture that represents the sentence demonstrates the model's ability to think more like humans. Common text--translation methods employ Generative Adversarial Networks to generate high-text-images, but the images produced do not always represent the meaning of the phrase provided to the model as input. Using a captioning network to caption generated images, we tackle this problem and exploit the gap between ground truth captions and generated captions to further enhance the network. We present detailed similarities between our system and the methods already in place. Text-to-Image synthesis is a difficult problem with plenty of space for progress despite the current state-of - the-art results. Synthesized images from current methods give the described image a rough sketch but do not capture the true essence of what the text describes. The re-penny achievement of Generative Adversarial Networks (GANs) demonstrates that they are a decent contender for the decision of design to move toward this issue.