Reviewerly/exHarmony huggingface dataset:
An IoT-Based Services Infrastructure for Utility-Scale Distributed Solar Farms

Internet of Things (IoT) provides large-scale solutions for efficient resource monitoring and management. As such, the technology has been heavily integrated into domains such as manufacturing, healthcare, agriculture, and utilities, which led to the emergence of sustainable smart cities. The success of smart cities depends on the availability of data, as well as the quality of the data management infrastructure. IoT introduced numerous new software, hardware, and networking technologies designed for efficient and low-cost data transport, storage, and processing. However, proper selection and integration of the correct technologies is crucial to ensuring a positive return on investment for such systems. This paper presents a novel end-to-end infrastructure for solar energy analysis and prediction via edge-based analytics.
Statistical Modelling of Level Difficulty in Puzzle Games

Successful and accurate modelling of level difficulty is a fundamental component of the operationalisation of player experience as difficulty is one of the most important and commonly used signals for content design and adaptation. In games that feature intermediate milestones, such as completable areas or levels, difficulty is often defined by the probability of completion or completion rate; however, this operationalisation is limited in that it does not describe the behaviour of the player within the area. In this research work, we formalise a model of level difficulty for puzzle games that goes beyond the classical probability of success. We accomplish this by describing the distribution of actions performed within a game level using a parametric statistical model thus creating a richer descriptor of difficulty. The model is fitted and evaluated on a dataset collected from the game Lily's Garden by Tactile Games, and the results of the evaluation show that the it is able to describe and explain difficulty in a vast majority of the levels.
Need Analysis on English for Computer and Technique

Practice is as important as the theory in learning a language. A recommended syllabus is needed to use in the institution according to the need of the learners as well. This esearch aims to analyze the necessities, the lacks and wants of the learners in the English for Computer and Technique course. The research method is descriptive with a qualitative model. The survey research using a series of questionnaires and was distributed to 32 respondents from the informatics study program. In collecting the data, the researcher applied questionnaire for the students as the instrument of the research. The results of the needs analysis concluded that students need English to improve their speaking, listening, reading, and writing. material that per the field of science, namely computer science, becomes more interesting and strongly supports the knowledge being studied. The implementation of this research is to prepare an Semester Learning Plan in accordance to the student’s needs and to develop lecturer competence as well as technology and information progress.
Discrediting Strategy in Internet Comments to Publications made by the Mayors of New York and London

This research featured the lingua-personolological aspect of linguistic means and speech techniques connected with the use of discrediting strategy in online comments aimed at discrediting the authorities. The study was based on the methods of continuous sampling and linguistic analysis of official Facebook and Twitter pages of the mayors of New York and London. Lowering proved to be the leading strategy used in comments aimed at discrediting the government. The research revealed the prevailing tactics and techniques of the speech strategy for lowering, e.g. "analysis minus", insult, accusation, etc. Internet users appeared to have an individual style and vocabulary choice that depended on various subjective factors. The comments showed little trust for the government, which makes discrediting the authorities an important contemporary issue.
Demonstrating BB84 Quantum Key Distribution in the Physical Layer of an Optical Fiber Based System

Nowadays, widely spread encryption methods (e.g., RSA) and protocols enabling digital signatures (e.g., DSA, ECDSA) are an integral part of our life. Although recently developed quantum computers have low processing capacity, huge dimensions and lack of interoperability, we must underline their practical significance – applying Peter Shor’s quantum algorithm (which makes it possible to factorize integers in polynomial time) public key cryptography is set to become breakable. As an answer, symmetric key cryptography proves to be secure against quantum based attacks and with it quantum key distribution (QKD) is going through vast development and growing to be a hot topic in data security. This is due to such methods securely generating symmetric keys by protocols relying on laws of quantum physics.
Impact of the COVID-19 pandemic on the expression of emotions in social media

In the age of social media, every second thousands of messages are exchanged. Analyzing those unstructured data to find out specific emotions is a challenging task. Analysis of emotions involves evaluation and classification of text into emotion classes such as Happy, Sad, Anger, Disgust, Fear, Surprise, as defined by emotion dimensional models which are described in the theory of psychology (www 1; Russell, 2005). The main goal of this paper is to cover the COVID-19 pandemic situation in India and its impact on human emotions. As people very often express their state of the mind through social media, analyzing and tracking their emotions can be very effective for government and local authorities to take required measures. We have analyzed different machine learning classification models, such as Naïve Bayes, Support Vector Machine, Random Forest Classifier, Decision Tree and Logistic Regression with 10-fold cross validation to find out top ML models for emotion classification. After tuning the Hyperparameter, we got Logistic regression as the best suited model with accuracy 77% with the given datasets. We worked on algorithm based supervised ML technique to get the expected result. Although multiple studies were conducted earlier along the same lines, none of them performed comparative study among different ML techniques or hyperparameter tuning to optimize the results. Besides, this study has been done on the dataset of the most recent COVID-19 pandemic situation, which is itself unique. We captured Twitter data for a duration of 45 days with hashtag #COVID19India OR #COVID19 and analyzed the data using Logistic Regression to find out how the emotion changed over time based on certain social factors. Keywords: classification, COVID-19, emotion, emotion analysis, Naïve Bayes, Pandemic, Random Forest, SVM.
Digital Reading Practices as a Factor in Identity Formation

The article is devoted to studying the influence of modern digital opportunities related to books and reading on the formation of a sense of belonging to the cultural and national community.Reading practices are seen as repetitive joint actions that are part of the formation of national identities.Representation in the national culture of the body of significant texts and the development of literary and reading infrastructure are seen as an important component of the cultural field.Modern information technologies intensify such processes and provide additional tools for them.Mobile applications are considered one of the tools of wide and scientifically supported access to significant national texts.The conclusion is substantiated the influence of the latest reading practices, on the formation of national identities as tools for involving individuals in the processes of shared experience and understanding of culture.
Additional education of schoolchildren in physics at the Children's University

The article describes physics projects created by students as future physics teachers to organize classes on the educational platform of the Children's University. The study was aimed at examining the performance of physics students through physics projects. The advantages of the Labster 3D virtual reality learning environment for motivating students to study physics are also considered. Generally, the results of the study showed an increase in interest among primary school students in STEM education and science. The study involved children from 10 to 12 years old in the amount of 250 schoolchildren in the city of Kokshetau(Kazakhstan). The article used methods such as mixed methods, questionnaires, and semi-structured interviews for children. The Likert scale questions allowed the analysis using descriptive statistics. Open-ended questions and data from the interviews were classified using content analysis and analytically interpreted through the theory of the development of cognitive interest in children. Children were provided by links to short 10-minute educational videos posted on Youtube video hosting called FIZMAT KSU While they were working remotely. During distance learning, children used different forms of virtual laboratory work. The results and conclusions of the study revealed the followings: visiting the Children's University increases children's interest in STEM - education and science, as well as the participation of future physics teachers in the activities of the Children's University forms the skills of organizing informal forms of work with children.
Q-SegNet: Quantized deep convolutional neural network for image segmentation on FPGA

One of the important tasks in the area of computer vision is semantic segmentation. The implementation of a semantic segmentation system in an embedded platform is a fruitful idea. But due to the limitations of embedded ability, it becomes a tough task. In this article, we proposed a novel and practical architecture i.e. quantized deep convolutional neural network for image segmentation (Q-SegNet). This architecture will be implemented on an FPGA device, which allows reducing the parameter size of the original architecture. Hence the required power also reduces. Thus, this paper proposed a high performance deep learning processor unit (DPU) based accelerator for Semantic segmentation neural network. This research is quite suitable for robot vision in an embedded platform and the segmentation accuracy is up to 89.60% on average. Notably, the proposed faster architecture is ideal for low power embedded devices that need to solve the shortest path problem, path searching, and motion planning, in the ADAS and Robot.
Cloud infrastructure for public health blockchain

Inspired by the challenges of keeping secure and private lifelong longitudinal health-related transactions in a distributed and shared, encrypted and access controlled, immutable and rapidly growing blockchain for public health, a cloud infrastructure with machine learning (ML), data analytics, security and privacy considerations has to be designed. This chapter presents how this proposed cloud infrastructure facilitates distribution and sharing of health transactions and discusses how the security and privacy mechanisms work for the public health stakeholders to access and analyse anonymous health data stored in the blockchain in the cloud platform. Also, the immutability and rapid volume growth features of the historical transactional data stored on the public health blockchain provide big data for the health care practitioners, researchers and government to analyse and get insights from. In this regard, those transactional data relationships have to be managed effectively for efficient data processing and data analytics integrated with learning machine models can be adopted to examine the data for sophisticated analyses for health care concerns. This chapter exhibits how ML can be integrated into data analytics and highlights some applications on cloud using Amazon Web Services as an example.
Artificial Intelligence and the Risk for Intuition Decline in Clinical Medicine

Artificial intelligence (AI) is revolutionizing big data analytics. In this issue of The American Journal of Gastroenterology, Ahn et al. introduce the AI-cirrhosis-electrocardiogram score that can grade the electrophysiologic cardiac changes present in patients with cirrhosis. Apart from showing excellent accuracy to identify cirrhosis, the AI-cirrhosis-electrocardiogram algorithm identified a biological gradient and signal reversibility after transplantation. Clinical applicability needs to be determined. Some concerns inherent to the use of AI are discussed, including the need to verify that the quality of data used for machine training is optimal. The black box nature of AI-identified associations is discussed, along with the lack of pathophysiologic coherence allowing intuitive medical reasoning.
Penggunaan Karakter Kontrol ASCII Untuk Integrasi Data Pada Hasil Enkripsi Algoritma Caesar Cipher

&#x0D; &#x0D; &#x0D; Cryptography is a technique or method for securing data from other unauthorized parties. The substitution algorithm is the simplest algorithm and is classified as a classic in the field of cryptography, for example the Caesar cipher algorithm. ASCII code (American Standard Code for Information Interchange) is a code that contains characters that can be processed by a computer. By type, not all ASCII characters can be printed by the computer. Some characters are not printed or illegible as usual. These unreadable characters are called control characters. The control character can be used to improve the performance of the Caesar Cipher algorithm, because it focuses on processing text data. The application of control characters uses a simple method but has a complex solution. The results of this study are in the form of a new method with the Caesar cipher algorithm as a classical cryptographic method or technique and ASCII characters as the basis for the development of the ciphertext performance resulting from the encryption process. In testing this method, the success rate reaches 100% in securing the contents of the document with a sample of 500 letters. While the possibility of solving the ciphertext results is classified as difficult because the control characters of the ciphertext results that are illegible will make the decryption result multiple interpretations.&#x0D; &#x0D; &#x0D;
Trivium Stream Cipher Countermeasures Against Fault Injection Attacks and DFA

Attacks on cryptocircuits are becoming increasingly sophisticated, requiring designers to include more and more countermeasures in the design to protect it against malicious attacks.Fault Injection Attacks and Differential Fault Analysis have proven to be very dangerous as they are able to retrieve the secret information contained in cryptocircuits.In this sense, Trivium cipher has been shown to be vulnerable to this type of attack.This paper presents four different fault detection schemes to protect Trivium stream cipher implementations against fault injection attacks and differential fault analysis.These countermeasures are based on the introduction of hardware redundancy and signature analysis to detect fault injections during encryption or decryption operations.This prevents the attacker from having access to the faulty key stream and performing differential fault analysis.In order to verify the correct operation and the effectiveness of the presented schemes, an experimental system of non-invasive active attacks using the clock signal in FPGA has been designed.This system allows to know the fault coverage for both multiple and single faults.In addition, the results of area consumption, frequency degradation, and fault detection latency for FPGA and ASIC implementations are presented.The results show that all proposed countermeasures are able to provide a fault coverage above 79% and one of them reaches a coverage of 99.99%.It has been tested that the number of cycles for fault detection is always lower than the number of cycles needed to apply the differential fault analysis reported in the literature for the Trivium cipher.
Differential spectra of a class of power permutations with Niho exponents

<p style='text-indent:20px;'>Let <inline-formula><tex-math id="M1">\begin{document}$ m\geq3 $\end{document}</tex-math></inline-formula> be a positive integer and <inline-formula><tex-math id="M2">\begin{document}$ n = 2m $\end{document}</tex-math></inline-formula>. Let <inline-formula><tex-math id="M3">\begin{document}$ f(x) = x^{2^m+3} $\end{document}</tex-math></inline-formula> be a power permutation over <inline-formula><tex-math id="M4">\begin{document}$ {\mathrm {GF}}(2^n) $\end{document}</tex-math></inline-formula>, which is a monomial with a Niho exponent. In this paper, the differential spectrum of <inline-formula><tex-math id="M5">\begin{document}$ f $\end{document}</tex-math></inline-formula> is investigated. It is shown that the differential spectrum of <inline-formula><tex-math id="M6">\begin{document}$ f $\end{document}</tex-math></inline-formula> is <inline-formula><tex-math id="M7">\begin{document}$ \mathbb S = \{\omega_0 = 2^{2m-1}+2^{2m-3}-1,\omega_2 = 2^{2m-2}+2^{m-1}, \omega_4 = 2^{2m-3}-2^{m-1},\omega_{2^m} = 1\} $\end{document}</tex-math></inline-formula> when <inline-formula><tex-math id="M8">\begin{document}$ m $\end{document}</tex-math></inline-formula> is even, and <inline-formula><tex-math id="M9">\begin{document}$ \mathbb S = \{\omega_0 = \frac{7\cdot2^{2m-2}+2^m}3, \omega_2 = 3\cdot2^{2m-3}-2^{m-2}-1, \omega_6 = \frac{2^{2m-3}-2^{m-2}}3, \omega_{2^m+2} = 1\} $\end{document}</tex-math></inline-formula> when <inline-formula><tex-math id="M10">\begin{document}$ m $\end{document}</tex-math></inline-formula> is odd.
IdentiBug: Model-Driven Visualization of Bug Reports by Extracting Class Diagram Excerpts

Bug reports are essential software artifacts that describe software bugs using natural language. Bug localization tools can help developers to understand the relation between bug reports and a software system. However, most approaches for localizing bugs work with unstructured textual information from the source codes and bug reports. This paper proposes an approach for locating and visualizing bug reports based on class diagrams representing the overall structural design of a software system. Our approach called IdentiBug takes advantage of deep learning techniques to train our bug localization model to predict connections between a bug report and the system’s class diagram. The result is a ranked list of classes from which we extract and rank a list of class diagram excerpts for assisting the developers during bug documentation and localization.
Local Pyramid Attention and Spatial Semantic Modulation for Automatic Image Colorization

Automatic image colorization is struggling due to its inherent multi-modal nature. Although many learning-based approaches have recently made tremendous progress in this area, they still suffer two limitations i) context confusion and ii) unfaithful color assignment. To cope with such issues, we propose a novel LPA-SSM-Net, which is based on encoder-decoder architecture and mainly consists of two components: the Local Pyramid Attention (LPA) module and the Spatial Semantic Modulation (SSM) module. The LPA strives to enhance the modeling of long-range dependency, leading to less context confusion and color bleeding. In addition, the SSM embeds semantic prior into the original image features via learnable affine transformations to encourage more plausible coloring output. Extensive experiments on benchmark datasets demonstrate the effectiveness of our method, and the results stand for comparable performance to existing methods in terms of quality metrics and qualitative evaluation.
Development of Mobile Learning integrated with Learning Management System (LMS) on Buffer Solutions Topic

This research aims to develop mobile learning integrated with LMS and test its effectiveness motivation learning on the topic of buffer solutions. The research was conducted at SMA AL-HASRA from November 2020 to June 2021. The research and development method by Borg and Gall was used by modifying five stages, needs analysis; product development; validation; product testing; and implementation. Testing used quantitative method with post-test only control group design and analysis using independent sample t-test. The resulting mobile learning medium is called "LarutanPenyangga.apk" which is compatible with Android devices which provides summaries, video animations, kimi , quiz, and application buffer solutions in daily life. The feasibility test for topic and language resulted 82.25% with a reliability 0.975. The feasibility test for media 75.05% with a reliability 0.960. Trial test have very good criteria. The effectiveness test of motivation learning percentage is 68.4% and has a good category. Based on the t-test, obtained t table &lt; t count . It can be concluded that H 0 is rejected and H 1 is accepted, which means that there is a significant positive effect from the use of integrated mobile learning with LMS to increase students' learning motivation on buffer solutions topic.
Network Capacity Estimators Predicting QoE in HTTP Adaptive Streaming

The aim of adaptive HTTP streaming technology is preserving the best possible video streaming quality for viewers in heterogeneous network conditions. This can be achieved by making multiple quality versions of the video available. Switching between versions during playback should be imperceptible and fluent. The decision about quality-level switching is typically based on network capacity estimation and buffer occupancy, which predict the risk of stalling. Since quality-level switching and stalling are directly evident to the user, they are often classified as influence factors of quality of experience (QoE). In this paper, we observe different network capacity estimators and buffer behavior in limited network conditions and study how the estimators predict QoE. The challenges of variable bitrate (VBR)-encoded video are considered. We also propose two new estimators to predict QoE. One compares segment fetch time to segment playback time, while the other explores the difference of throughput and average download rate. As segment duration may influence HTTP adaptive streaming (HAS) playback in unstable conditions, the findings are tested with four segment lengths. Moreover, streaming quality is analyzed in a testbed using two popular web players to reveal possible effects of the players’ features.