Reviewerly/exHarmony huggingface dataset:
End-to-end learning of representative PV capacity factors from aggregated PV feed-ins

&amp;lt;p&amp;gt;Energy system models rely on accurate weather information to capture spatio-temporal characteristics of renewable energy generation. Whereas energy system models are often solved with high abstraction of the actual energy system, meteorological data from reanalysis or satellites provides rich gridded information of the weather. The mapping from meteorological data to renewable energy generation usually relies on major assumptions as for solar photovoltaic energy the photovoltaic module parameters. In this study, we show that these assumptions lead to large deviations between reported and estimated energy as shown in case of photovoltaic energy feed-ins in Germany. To decrease these deviations, we propose a novel gradient-based end-to-end framework which is able to learn local representative photovoltaic capacity factors from aggregated reported transmission system operator feed-ins. As part of the end-to-end framework, we compare physical and neural network model formulations to obtain a functional mapping from meteorological data to photovoltaic capacity factors. We show that all developed methods have better performance than commonly used reference methods. The neural network shows remarkable success to predict the aggregated Transmission System Operator photovoltaic energy feed-ins leading to an accurate, unbiased prediction model. However, choosing the neural network model is not always the strictly preferred choice as it depends on the use case: Operational use cases may decide for the neural network implementation due to its higher accuracy whereas academic settings prefer the physical model due to its high interpretability and transferability. In this talk, we discuss the development of the end-to-end framework which we believe is highly relevant due to its energy-meteorological and methodological contributions.&amp;amp;#160;&amp;lt;/p&amp;gt;
Comparison of image processing methods for tensile test

This work deals with the comparison of the used methods of video extensometry in the static tensile test. In the beginning, methods of video extensometry and comparison with laser extensometry are recommended, then experimental measurements of measuring the evaluation of static tensile test using commonly available industrial cameras and comparing the accuracy of our algorithm with industrial professional technologies are described.
Theory and Methodology of Value-Sensitive Design: Critical Analysis

The paper is devoted to Value Sensitive Design (VSD). This approach, widely presented in foreign scientific and specialized literature, has not been reflected in the Russian-language discourse. The authors of the study consider the theoretical foundations and methodological tools of VSD, clarify the conceptual apparatus, and also carry out a critical analysis of some of its main provisions (in particular, the question is raised about the absence of a philosophical theory of values, the eclecticism of the modern approach, insufficiently developed methods of cooperation with engineers, designers and potential stakeholders). VSD is viewed in the context of technology assessment and socially responsible innovation. We are talking about a diverse set of practices for the rational shaping of technologies, taking into account the values of society, suggesting a more active and conscious involvement of ordinary citizens (not experts) in the discussion of issues related to the development and design of technologies. An understanding of values with an emphasis on ethics and morality raises the question of achieving a balance between competing values and choosing the desired values, taking into account the diversity of interests of direct and indirect stakeholders. In this regard, two main goals of the VSD stand out. On the one hand, it involves identifying and critically analyzing desirable or limiting values that have been (intentionally or unintentionally) inscribed into existing technologies. On the other hand, VSD offers practical recommendations on how to deliberately and purposefully inscribe socially approved values into the design of new equipment, software, databases, and algorithms. Finally, the article defines the directions of possible applied research: (a) the development of the theory and methodology of change management through structuring the change management process according to the hierarchical principle of interaction, as well as identifying persons and groups that are affected by organizational changes and how; (b) fundamental aspects of human-machine interaction, in particular, human-robot interaction (HRI), which is of great practical importance for the design and production of socially responsible service robotics.
Deepfakes—A Looming Threat to Our Society

Deepfake, which is a portmanteau of “deep learning” and “fake,” refers to the artificial intelligence–based human image synthesis technique that is used to combine and superimpose existing images and videos onto source images or videos. Simply stated, deepfakes are created by superimposing the face of one person onto the body of another person, thereby manipulating the reality of what the viewer perceives. This type of fakery goes beyond the usual kinds of image and video manipulation tactics that we have witnessed in the past. And, it is far more consequential. In this chapter, we will examine the origin and history of deepfakes, study what makes them dangerous, and also discuss the various strategies that have been developed so far for the detection of such fakery. A novel deepfake detection strategy is also presented.
Smart offload chain: a proposed architecture for blockchain assisted fog offloading in smart city

Blockchain enables smart contract for secure data transfer by which fog offloading servers can have trustworthy access control to work with data execution. When cloud is used for handling requests from mobile users, the attacker may perform denial of service attack and the same is possible at fog nodes and the same can be handled with the help of blockchain technology. In this paper, smart city application is discussed a use case study for blockchain based fog computing architecture. We propose a novel offload chain architecture for blockchain-based offloading in internet of things (IoT) networks where mobile devices can offload their data to fog servers for computation by an access control mechanism. The offload chain model using deep reinforcement learning (DRL) is proposed to improve the efficiency of blockchain based fog offloading amongst existing models.
Non-elitist Selection Can Improve the Performance of Irace

Modern optimization strategies such as evolutionary algorithms, ant colony algorithms, Bayesian optimization techniques, etc. come with several parameters that steer their behavior during the optimization process. To obtain high-performing algorithm instances, automated algorithm configuration techniques have been developed. One of the most popular tools is irace, which evaluates configurations in sequential races, making use of iterated statistical tests to discard poorly performing configurations. At the end of the race, a set of elite configurations are selected from those survivor configurations that were not discarded, using greedy truncation selection. We study two alternative selection methods: one keeps the best survivor and selects the remaining configurations uniformly at random from the set of survivors, while the other applies entropy to maximize the diversity of the elites. These methods are tested for tuning ant colony optimization algorithms for traveling salesperson problems and the quadratic assignment problem and tuning an exact tree search solver for satisfiability problems. The experimental results show improvement on the tested benchmarks compared to the default selection of irace. In addition, the obtained results indicate that non-elitist can obtain diverse algorithm configurations, which encourages us to explore a wider range of solutions to understand the behavior of algorithms.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS ANDROID PADA MATERI ATURAN SINUS DAN COSINUS DI MASA PANDEMI

This study aimed to develop android-based interactive multimedia and analyze student responses to android-based interactive multimedia in mathematics. The research method used is Research and Development, with 7 out of 10 steps of Borg and Gall development, namely: (1) Research and data collection, (2) Planning, (3) Initial product development, (4) Initial product trial, (5) Product improvement, (6) Field trial, (7) Completion of field test results. The sample was taken randomly, namely class X. IPS 1 MA Assa'adah Jamanis, totaling 31 students. Due to gadget limitations, only 25 students can join. Data was collected using interviews, documentation studies, questionnaires (for media experts, material experts, and students), and tests (pre-test and post-test). Data analysis techniques are calculating and recapitulating the questionnaire, calculating the percentage of eligibility from the validators, converting the average score that has been obtained into qualitative values according to the assessment criteria, attractiveness analysis, and effectiveness analysis. The learning media developed is very feasible based on the validation of media experts and material experts. Media can be used in the learning process to help students understand the sine and cosine rule learning material. In addition, the use of learning media can also improve student learning outcomes during the pandemic.
Time efficient joint optimization federated learning over wireless communication networks

Artificial intelligence (AI) has made a profound impact on our daily life. The 6th generation mobile networks (6G) should be designed to enable AI services. The native intelligence is introduced as an important feature in 6G. 6G native AI network is realized by the philosophy of federated learning (FL) to ensure data security and privacy. Federated learning over wireless communication networks is treated as a potential solution to realize native AI. However, introducing FL in the 6G will lead to expansive communication cost and unstable FL convergence with unreliable air interface. In this paper, we propose a solution for FL over wireless networks and analyze the training efficiency. To make full use of the advantages of the proposed network, we introduce a communication-FL joint optimization (CFJO) algorithm by jointly considering the effects of uplink resource, energy consumption and latency constraints. CFJO derives a transmission strategy with resource allocation and retransmissions to reduce the wireless transmission interruption probability and model upload latency. The simulation results show that CFJO significantly improves the model training efficiency and convergence performance with lower interruption probability under the latency constraint.
Decryptable Attribute-Based Keyword Search on E-Health Cloud

Cloud computing provides lot of benefits to enterprises to offload their data and software services to cloud saving them lot of money that has to be spent on infrastructure setup cost. Enterprises wanted to offload their data to cloud and save on their infrastructure cost. But when offloading the data security and privacy is a important concern. In this work, we focus on the search on encrypted data and provide a effective solution for the search. Searchable symmetric encryption (SSE) allows retrieval of the encrypted data over cloud. We formulate the privacy issue from the aspect of similarity relevance and scheme robustness. We observe that serve-side ranking based on order-preserving encryption (OPE) inevitably leaks data privacy. To eliminate the leakage, we propose a secure-channel free ciphertext-policy decryptable attribute-based keyword search (CP-DABKS) scheme on eHealth cloud in the Internet of Things (IoT) platform. Additionally in CP-DABKS, we employ a vector space model and homomorphic encryption. The vector space model helps to provide sufficient search accuracy, and the homomorphic encryption enables users to involve in the ranking while the majority of computing work is done on the server side by operations only on ciphertext.
Cyber Pandemic: Roots and Solution

Since the pandemic started and a lockdown was announced , we witnessed the unlocking of different solutions switching towards digitization. With an increase in digital devices, internet usage and a decline in the economy globally due to Covid 19, there is a rise in cybercrimes. With technology and online systems in demand, almost the majority of people own at least one of these devices, which include smartphones, personal computers, laptops, tablets etc. People were leaning towards online classes, work, shopping, and had access to the internet for more hours as compared to previous usage records. During this period people were unaware of the possible cyber fraud activities that may harm them, as cybercrime is harder to detect and even more difficult to solve. This paper focuses on finding the most prevalent type of cybercrime, the category of people targeted for cyber fraud, and providing a model of solution for the prevention of the same using computer vision as a core technology for secure access to digital devices and the internet.
Hypothetical reasoning method based on hypothetical cases

The purpose of this article is to present hypothetical reasoning approaches that are mostly based on the expert&#x2019;s mode reasoning. These approaches are used in a variety of domains and by different representations of the expert's reasoning from his initial hypothesis. The objective of this work is to propose a method for understanding and representing an expert's hypothesis in the field of industry. This research paper proposes a hypothetical reasoning method based on hypothetical cases. The proposed method is based on the following steps: First, we propose storing hypothetical cases in a Hypothetical Expert-Case Base (HE-CB) to capture the expert's tacit knowledge from his hypothesis. Second, represent the acquired knowledge as a graph that illustrates the expert's observations. Last, extract tacit knowledge from the graphical representation of the hypothetical cases.
Graphical user interfaces

This chapter describes how apps with graphical user interfaces (or GUIs) can be created using MATLAB. A simple case study of displaying and interacting with some biomedical data is used to illustrate the basics of app creation using the MATLAB App Designer tool. Concepts such as adding components to the GUI, controlling their behavior in response to different events and callback functions are described. The idea of maintaining state between different callback function calls is also covered using properties and helper functions.
Blockchain-Enabled Secure Platforms for Management of Healthcare Data

Massive adoption of technologies like wearable IoT for personal health monitoring, smart health care systems, and digitalization of health records of patients by the hospitals, known as electronic health records (HERs), has led to the generation of huge volumes of sensitive data that is liable to be misused by parties with wrong intentions. These applications are good examples of cyber-physical systems which involve an integration of functionality of physical processes, computing, and networking with built-in feedback mechanisms. Although these systems have many advantages, there have been concerns regarding the security of personal patient data that these applications handle and communicate. There do exist solutions for the security of such data, but they often prove to be too restrictive, and are even patients do not have easy access to them, they are heavily non-reciprocal in the sense that interactive capabilities have not been part of the design for security purposes. The trade-off between data security and ease of access plus interactivity is in favor of too restrictiveness in order to preserve data integrity and avoid unauthorized modifications. Blockchain technology, by design, is suitable for this kind of trustless scenario where data integrity, non-repudiation, ease of access, transactional access to data, and high availability are essential. The volume of this private health data, which contains information about a person’s vital stats like name, medical history, address, clinical notes, laboratory, and imaging examination records, and screening reports, etc., is growing at an exponential rate. There are various parties involved 284in the generation, management, disbursement of this data, therefore there arises a risk of breach of privacy or confidentiality, unauthorized access, or manipulation of this data by any of the involved parties. Blockchain maintains data in a decentralized, transactional, replicated, un-mutable storage units called blocks. Such a platform is a very viable option to manage HERs in a secure, privacy-preserving, trusted yet interactive manner.
Complicated Dynamics of a Delayed Photonic Reservoir Computing System

In this paper, we consider the complicated dynamics of a delay-based photonic reservoir computing system. Since conventional computer architectures are approaching their limit, it is imperative to find new, efficient and fast ways of data processing. Photonic reservoir computing (RC) is a promising way which combines the computational capabilities of recurrent neural networks with high processing speed and energy efficiency of photonics. As the RC system is very promising, we analyze its dynamics so that we can make better use of it. In this paper, we mainly focus on its double Hopf bifurcation. We first analyze the existence of double Hopf bifurcation points. Then we use DDE-BIFTOOL to draw the bifurcation diagrams with respect to two bifurcation parameters, i.e. feedback strength [Formula: see text] and delay [Formula: see text], and give a clear picture of the double Hopf bifurcation points of the system. These figures show stability switches and the existence of double Hopf bifurcation points. Finally, we employ the method of multiple scales to obtain their normal forms, use the method of normal form to unfold and classify their local dynamics. The classification and unfolding of these double Hopf bifurcation points are obtained. Three types of double Hopf bifurcations are found. We verify the results by numerical simulations and find its complicated behavioral dynamics. For example, there exist stable equilibrium, stable periodic and quasi-periodic solutions in distinct regions. The discovered rich dynamical phenomena can help us to choose suitable values of parameters to achieve excellent performance of RC.
Robust adaptive wideband constant beamwidth digital beamforming based on spatial response variation constraint.

In order to solve the problem that the main flap beam of the conventional adaptive beam forming algorithm cannot correctly point to the desired signal direction of the target under the array error, a robust adaptive broadband constant beamwidth digital beamforming method based on spatial response variation constraint is proposed. Firstly, the beamformer with constant beamwidth based on spatial response variation constraints have been designed. Secondly, for the array error, the relationship between the error vector norm between the real steering vector and the assumed steering vector and the array error matrix is derived, and an inequality optimization model is established. Finally, the proposed method is a non-convex problem, which is transformed into a convex programming model through matrix decomposition and the idea of changing elements, and is solved by the convex optimization toolbox. The simulation results show that the proposed method is more robust than some other methods.
Introduction

This collection presents a new outlook on an old problem. Studies of impoliteness, verbal aggression, hate speech, and similar phenomena have been conducted for a significant time now. They can be considered developed research areas with well-established methods, models, and their own bodies of literature. Numerous studies are in print and in progress, diverse aspects are being investigated, and the body of research on language aggression is growing.
Applying blockchain technology in the corporate bond model for default risk assessment under the marketization principle

This work expects to solve the problem that the traditional corporate bond model for default risk assessment has low accuracy and poor data collection and storage robustness. Firstly, this work uses the mature Kealhofer, McQuown, and Vasicek (KMV) model to evaluate the default risk of corporate bonds. Secondly, Blockchain (BC) technology's Information Security (SEC) performance in default risk assessment is studied. Finally, it analyzes the default disposal mechanism of Chinese bond corporates. The Wind database is selected to verify the proposed model. The results show that the proposed BC-based corporate bond model for default risk assessment has a good fitting effect and can meet practical needs. The BC-based data encryption only needs 14.1 ms to obtain a key from an 821-bytes public key. The encryption time and decryption time of the Number Theory Research Unit (NTRU) system are 7.9 ms and 2.9 ms, respectively. Therefore, BC technology shows good performance in data encryption and can improve ISEC. According to the KMV model, corporate bonds have certain default risks. The Distance to Default (DtD) of corporate bonds is 20.8% lower than that of companies. Thus, corporates have higher default risk. Therefore, methods are needed to reduce the default risk and improve the earnings. The finding provides important technical support for effectively reducing the default risk of bond corporates in China and ensuring the healthy development of bond markets.
Görsel Geri Beslemeli Otonom Mobil Kurtarma Robotunun Tasarım, Üretim ve Kontrolü

The construction of a smart autonomous mobile robot system that can operate in an anonymous environment can be difficult. Building an autonomous mobile robot requires collecting data from sensors and cameras in order to recognize the surrounding environment, get knowledge of existing landmarks, and figure out its position and orientation. Many research studies have been carried out in recent years providing algorithms for achieving autonomous motion of mobile robot systems and object detection. The most common platform utilized for robot motion design is ROS (Robot Operating System) and for object detection YOLO (You Only Look Once) is a common choice. Additionally, the autonomous system needs motion planning in a systematic way to avoid obstacles by using LIDAR or other sensing methods generating the feedback signals required for control of motion around occupied spaces. One of the most effective and versatile feedback systems for mobile robot control can be constructed using visual feedback, which relies on a high-resolution camera that processes the image in real-time and the result is fed back to the control system for decision making. Visual feedback can be employed alone without sensors for feedback control of autonomous mobile robots, and it can be used for multiple tasks like path planning, obstacle avoidance, object detection, object recognition in addition to motion control. In this research, results of design and construction of an autonomous mobile rescue robot for finding victims through video data are presented. It is aimed to find victims via applying video as a data collection and feedback generation tool in the autonomous mobile robot control system. The visual feedback and YOLO v3 are used not only for finding victims but also for motion control. Our autonomous mobile robot is designed to move smoothly to the victim's direction and avoids obstacles by using ROS Global Planner. The robot is tested in multiple scenarios in different locations, various obstacle and victim numbers, and satisfactory results with good performances are obtained.