Reviewerly/exHarmony huggingface dataset:
Fixed and adjusted optimal tilt angle of solar panels in three cities in Albania

In this paper, we are concerned about optimal tilt angle choice of solar panels installation in three cities in Albania. The optimal tilt angle of solar panels is a crucial parameter for a high performance of solar energy systems. Solar energy systems can be tracking systems or fixed systems, fixed tilt angle determination and adjustable tilt angle evaluation of solar panels is an important issue to solve. In this study, Bernard-Menguy-Schwartz (BMS) model is used to determine the yearly and seasonally optimum tilt angle of the solar panels in three cities in Albania. The determination of the optimum tilt angle is made considering the fact that the solar energy reaching the solar panels has maximum value for optimum tilt angle. The yearly optimum tilt angle of the solar panels for the cities: Tirana (41.3275° N, 19.8187° E), Kuksi (42.0807° N, 20.4143° E) and Vlora (40.4661° N, 19.4914° E) are respectively 38o, 39o and 37o. It should be noted that, the yearly optimal tilt angle of solar panels based on the above mathematical model is almost equal to the latitude of the location. A suggestion of seasonably adjustable tilt angle is made. In addition, seasonally or adjusted tilt angles of solar panels were compared with yearly optimal tilt angle.
Analysis of Chinese Image Discourse Based on Crawler Algorithms

The study explores the construction of China's image in the context of a major public health event such as the Newcastle pneumonia epidemic by crawling social media data in China during the epidemic prevention and control period. The study argues that, in the era of new media, especially self-media, the image of China in Chinese social media during the prevention and control of the Newcastle pneumonia epidemic presents multiple types of images, ranging from a “dual” image of those fighting and preventing the epidemic in their home country and their country of residence to a “dual” image of those being questioned and even those being “questioned” in their home country and their country of residence. “It is worth noting that in March 2020, the number of people who have been affected by the epidemic in their home countries will be increased to 3,000. It is worth mentioning that in mid to late March 2020, Chinese people in China had a serious negative image crisis, but it was quickly repaired through mechanisms such as government “stabilisers,” mainstream media “gatekeepers,” and audience “self-purification.” One of the lessons is that image repair relies on the “stabiliser,” the “gatekeeper” of mainstream media, and the “self-purification” of the audience. One of the lessons is that image repair relies on the triple factor of “real society—audience” working together. The second lesson is that while the open Internet environment can generate a lot of exaggerated and false information; at the same time, audiences, including Chinese people, can remove misunderstandings and bridge differences through active voices and debates and activate rational and healthy positive energy. The epidemic is a “danger” and an “opportunity” for China’s image.
Construction of an Intelligent Arrangement Model for Vocal Music Based on HPC Cluster Programming

We model in this paper the HPC cluster programming structure and perform a multidimensional computational analysis to design a vocal intelligent arrangement model based on HPC cluster programming using brilliant vocal arrangement. This paper proposes the construction scheme of the HPC interconnection simulation environment based on the full investigation of the research related to the construction of a supercomputer simulation environment at home and abroad. The rapid development of HPC technology has promoted the study of automatic music generation technology, which effectively avoids the traditional machine learning method of music. The rapid growth of HPC technology has facilitated the research of automated music generation techniques, which effectively avoids the overreliance of conventional machine learning methods on music rules and artificial design features. It achieves better results in complex music generation tasks. The key features of intelligent music arrangement are designed and implemented by summarizing the principles of converting paintings to music and then creating the rules of converting images to music and the corresponding relationships based on the theoretical basis. After the conversion rules are clarified, the specific design is carried out according to the computing process of the genetic algorithm. At the same time, the framework of the arrangement is combined with children’s aesthetic psychology; the evolutionary adaptation function is developed in combination with music theory, which finally ensures the motility of the generated music.
AI CHATBOT USING DIALOG FLOW

A chatbot is a programme that uses Artificial Intelligence to provide human conversation (AI). Chatbots are intended to serve as VIRTUAL ASSISTANTS (VA). They themselves provide one platform for the online promotion of Products and Services. All Higher Educational Institutes provide comprehensive information to students via their websites, which permit the use of social networks such as Facebook, WhatsApp, but also College websites. Overall, searching functionality is required in any website to search for any information, and it includes Social Networking sites Applications such as Facebook and Snapchat regular response are utilised. As a result, Chatbot is both an effective auto-response system and an instant messaging platform. In this paper, AICMS, an AI-based CollegeBot management platform for professional Engineering colleges, provides auto-response to college queries about the college's basic information, class schedules, and academic examination schedules. The system can handle a large number of questions about subjects and placements. The AICMS system is built with Dialogflow, which is supported by the Google API. AI is running as a messenger on Facebook, taking input in the form of text and voice and responding in the form of text and voice. It responds quickly and accurately to student and staff inquiries in an interactive manner.
Performance analysis of different DCNN models in remote sensing image object detection

Abstract In recent years, deep learning, especially deep convolutional neural networks (DCNN), has made great progress. Many researchers use different DCNN models to detect remote sensing targets. Different DCNN models have different advantages and disadvantages. In this paper, we use YoloV4 as the detector to “fine-tune” various mainstream deep convolutional neural networks on two large public remote sensing data sets−LEVIR data set and DOTA data set to compare the advantages of various networks. This paper analyzes the reasons why the effect of “fine-tuning” convolutional neural networks is sometimes not good, and points out the difficulties of object detection in optical remote sensing images. To improve the detection accuracy of optical remote sensing targets, in addition to “fine-tuning” convolutional neural network, we also provide a variety of adaptive multi-scale feature fusion methods to improve the detection accuracy. In addition, for the large number of parameters generated by deep convolutional neural network, we provide a method to save storage space.
Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network

The convolutional neural network (CNN) is a technique that is often used in deep learning. Various models have been proposed and improved for learning on CNN. When learning with CNN, it is important to determine the optimal parameters. This paper proposes an optimization of CNN parameters using logarithm decreasing inertia weight (LogDIW). This paper is used two datasets, i.e., MNIST and CIFAR-10 dataset. The MNIST learning experiment, the CIFAR-10 dataset, compared its accuracy with the CNN standard based on the LeNet-5 architectural model. When using the MNIST dataset, CNN's baseline was 94.02% at the 5th epoch, compared to CNN's LogDIWPSO, which improves accuracy. When using the CIFAR-10 dataset, the CNN baseline was 28.07% at the 10th epoch, compared to the LogDIWPSO CNN accuracy of 69.3%, which increased the accuracy.
Data Storage Security in Cloud Computing Using AES Algorithm and MD5 Algorithm

Abstract: The proposed method uses cryptographic techniques to maintain security as security is a critical issue for the cloud, and keeping secure data is a major concern today. Overload due to data duplication is a new challenge for clouds. This article presents the data a download algorithm based on MD5 and AES. Because MD5 more secure than other hashing algorithms, goal Using MD5 algorithm for duplicating data and bringing data to server after uninstalling duplicate copy server. Purpose of use AES security is encrypting data and storing it securely on a server. Update the hash of a newly uploaded file using AES. Results and provides a comparison of several hashing algorithms, showing that MD5 is much safer and takes less time than others. The basic method, which does not use duplicate testing, uses additional storage space and takes longer. The main goal storing useful data in the cloud. Keywords: Cloud Computing, Security, AES Algorithm, MD5 Algorithm
Tangible VR Lab: Studying User Interactionin Space and Time Morphing Scenarios

Virtual Reality (VR) offers an interesting interface for exploring changes in space and time that otherwise could be difficult to simulate in the real world. It becomes possible to distort the virtual world by increasing or diminishing distances, as well as to play with time delays. In this way, it is possible to create different spatiotemporal conditions to study different interaction techniques to analyse which ones are more suitable for each task.
Subjective Trusts for the Control of Mobile Robots under Uncertainty

The paper deals with the methods of handling imperfect information and the control of mobile robots acting under uncertainty. It is assumed that the robots act in autonomous regime and are controlled internally without referring the externally defined probabilities of the states and actions. To control the activity of the robots, we suggest the novel multi-valued logic techniques based on the recently developed measures, known as the subjective trusts. In addition to specification of the robots' movements, such a technique allows for direct definition of the robots' swarming using the methods of artificial neural networks with mobile neurons. The suggested methods are verified by numerical simulations and running examples. The resulting framework forms a basis for processing non-probabilistic uncertainties and making individual decisions with imperfect information.
The Innovation of Computer Network Cloud Computing Talent Training Mode under the Background of 1+X Course Certificate Integration

The changes in the social development environment have put forward new requirements for talent training in higher vocational colleges. This paper analyzes the problems faced in the current talent training process, and proposes a talent training model of "integration of job, course, certificate and competition", which integrates the real project of the enterprise, the ability requirements of vocational qualification certification and vocational skills competition into the curriculum. It is of great significance to improve the quality of cloud computing talents training for computer network technology majors and to accelerate the construction of a modern vocational education system so that students have skills that match the needs of future occupations.
Multilevel K-Means Density Based Flow Clustering Algorithm for Data Streams

The Data stream clustering is an active area of research that has recently emerged with the goal of discovering new knowledge from a large amount and variability of constantly generated data. In this context, different-different algorithm for unsupervised learning that clusters multiple data streams has been proposed by many researchers. There is a need for a more efficient and efficient data analysis method. This paper introduces a multi-level K-Means density-based flow clustering algorithm (MKDCSTREAM) for clustering problems. This approach proposes to view the problem of clustering is a optimization process hierarchy that follow different levels, from unrefined to subtle. In the clustering problem, for the solution first divide the problem in parts, by following different levels to make the first clustering a coarser problem than calculated. Coarse problem clustering is mapped level by level and improves the Clustering the original problem by improving intermediate clustering using the general K-means algorithm. Compare the performance of the hierarchical approach with its single-tier approach using tests with a set of datasets collected from different areas.
The Multidimensional Lexicon of Emojis: A New Tool to Assess the Emotional Content of Emojis

Emerging studies suggest that emojis can make important contributions to the emotional content and meaning of digital messages. Yet researchers currently lack adequate tools to incorporate emojis into their analyses. To address this gap, we used over 3 million Twitter posts from a period of 17 months and emotion ratings provided by 2,230 human raters to develop and validate the Multidimensional Lexicon of Emojis (MLE). This new lexicon consists of 359 common emojis rated on ten emotion and sentiment dimensions. The MLE is an open-access tool that holds promise for those interested in conducting a comprehensive analysis of the emotional content of digital communication that incorporates emojis and goes beyond the dimensions of negativity and positivity.
A Survey on Computationally Efficient Neural Architecture Search

Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs). However, NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS, and training DNNs is computationally intensive. To solve this major limitation of NAS, improving the computational efficiency is essential in the design of NAS. However, a systematic overview of computationally efficient NAS (CE-NAS) methods still lacks. To fill this gap, we provide a comprehensive survey of the state-of-the-art on CE-NAS by categorizing the existing work into proxy-based and surrogate-assisted NAS methods, together with a thorough discussion of their design principles and a quantitative comparison of their performances and computational complexities. The remaining challenges and open research questions are also discussed, and promising research topics in this emerging field are suggested.
Occupational Mobility Promotion Of Vocational Secondary Institution Students

The article is devoted to the problem of the development of occupational mobility of technical college students through the project approach in organizing the educational environment in the context of the current professional education.The article aims to present the experience of developing competencies of future professionals that are in demand in the digital economy, able to work in rapidly changing conditions of institutional relations, to switch to new technologies, able to build not an ordinary line career in one profession, but change qualification and a field of work.The paper reports on theoretical analysis of the notion 'vocational mobility' as the most significant quality of a specialist on the modern labour market, grounds of the structure and content of that category and gives the author's definition of the denoted concept.The authors focus on the identification of interrelation of technical college educational environment and vocational mobility of students, the effectiveness of project approach application in the arrangement of technical college educational environment.The article presents the findings of experimental research into vocational mobility development which provides the data about the level of development of vocational mobility of technical college students and the influence of the educational environment on this process.The project approach described in the article and the considered stages of project activities, applied in the vocational mobility development of technical college students, allow the educational environment to be organized in any vocational education institution taking into account rapidly changing socio-economic conditions and personal characteristics of students.
Dissecting Cloud Gaming Performance with DECAF

Cloud gaming platforms have witnessed tremendous growth over the past two years, with a number of large Internet companies including Amazon, Facebook, Google, Microsoft, and Nvidia publicly launching their own platforms. However, there is an absence of systematic performance measurement methodologies which can generally be applied. In this paper, we implement DECAF, a methodology to systematically analyze and dissect the performance of cloud gaming platforms across different game genres and game platforms. By applying DECAF, we measure the performance of Google Stadia, Amazon Luna, and Nvidia GeForceNow, and uncover a number of important findings such as processing delays in the cloud comprise majority of the total round trip delay (≈73.54%), the video streams delivered by these platforms are characterized by high variability of bitrate, frame rate, and resolution. Our work has important implications for cloud gaming platforms and opens the door for further research on measurement methodologies for cloud gaming.
Seed Schools in Colombia and the Generative Character of Sociotechnical Dissent

In STS, dissent has typically been understood as resistance, a way of saying “no” that is intended to generate change in discourses, material systems, and forms of law and rule that would then govern those who are aggrieved. Using the case of La Red de Semillas Libres de Colombia (RSL) [Network of Free Seeds of Colombia], this paper builds on another approach most visible in feminist, decolonial, and indigenous STS that examines rebuilding rather than direct resistance. Drawing from interviews and participant observation, we show that RSL’s practices of “generative dissent” are temporally distinctive in three ways. Firstly, RSL’s emphasize knowledge-making in the present, they address bio- and social tempos situated in understandings of both the past and the future. Secondly, RSL re-form the bios revitalizing emotional and social capacities. And finally, they reshape human and more-than-human worlds by invigorating biosocial kinships and university-community epistemic ties.
Using Blockchain to Ensure the Integrity of Digital Forensic Evidence in an IoT Environment

Digital forensics deals with digital evidence.Digital forensics is the study of data detection, acquisition, processing, analysis, and reporting.Encouraging the use of digital forensics in law enforcement investigations.With digital forensics, you can find out what data was taken and how it was copied or spread.Some hackers purposefully destroy data to harm their targets.In other cases, malicious software or hacker involvement can accidentally corrupt vital data.Digital forensics faces challenges of security and integrity.IoT devices can collect digital forensic evidence in an IoT setting, putting cybercrime agencies at danger owing to security and integrity.Many studies have been done recently to improve IoT based digital forensics integrity and security, but researchers face the risk of confidentiality.Recent research shows that digital forensics still faces manipulation and security issues.So a clever and effective approach is needed that not only protects security and integrity but also anticipates threats.So we propose an intelligent and effective solution based on Blockchain and Hashing algorithms.We will store the data collected from IoT devices into Blockchain.Anomalies in the evidence and transactions will be predicted using Machine Learning boosted models.So the proposed model works well because it can predict attacks early on.
A Theoretical Framework for Inference Learning

Backpropagation (BP) is the most successful and widely used algorithm in deep learning. However, the computations required by BP are challenging to reconcile with known neurobiology. This difficulty has stimulated interest in more biologically plausible alternatives to BP. One such algorithm is the inference learning algorithm (IL). IL has close connections to neurobiological models of cortical function and has achieved equal performance to BP on supervised learning and auto-associative tasks. In contrast to BP, however, the mathematical foundations of IL are not well-understood. Here, we develop a novel theoretical framework for IL. Our main result is that IL closely approximates an optimization method known as implicit stochastic gradient descent (implicit SGD), which is distinct from the explicit SGD implemented by BP. Our results further show how the standard implementation of IL can be altered to better approximate implicit SGD. Our novel implementation considerably improves the stability of IL across learning rates, which is consistent with our theory, as a key property of implicit SGD is its stability. We provide extensive simulation results that further support our theoretical interpretations and also demonstrate IL achieves quicker convergence when trained with small mini-batches while matching the performance of BP for large mini-batches.
Neurodynamic model for creative cognition of relational networks with even cyclic inhibition

The purpose of this work is study of the neurodynamic foundations of the creative activity of the brain. Modern AI systems using deep neural network training require large amounts of input data, high computational costs and long training times. On the contrary, the brain can learn from small datasets in no time and, crucially, it is fundamentally creative. Methods. The study was carried out through computational experiments with neural networks containing 5 and 7 oscillatory layers (circuits) trained to represent abstract concepts of a certain class of animals. The scheme of neural networks with even cyclic inhibition (ECI networks) contains only bilateral inhibitory connections and consists of two subnets: a reference noncoding network, which is an analogue of the default brain mode neural network, and the main information network that receives time sequences of environmental signals and contextual inputs. After training, the reading of the population phase codes was performed with a simple linear decoder. Results. Conceptual learning of the network leads to the generation of a number of spatial abstract images that are distinguished by the most pronounced features of the relevant line of animals. In computational experiments, a wide set of isomorphic representations of concepts was obtained through: a) transformations of image spaces in a wide range of time scales of the training input signal flow, b) internal regulation of the time scales of mental representations of concepts, c) confirmation on the model of the dependence of psychological proximity of concepts on semantic distance; d) calling from memory (decoding) distributed groups of neurons of animal concepts, which the network has not been trained in. Conclusion. This paper shows for the first time how, using a small set of event input data (a sequence of 4 CCW and 2 CW signals) and very limited computational resources, ECI networks exhibit creative cognitions based on relational relationships, conceptual learning and generalization of knowledge.