Reviewerly/exHarmony huggingface dataset:
Polygon generation and video-to-video translation for time-series prediction

Abstract This paper proposes an innovative method for time-series prediction in energy-intensive industrial systems characterized by highly dynamic non-linear operations. The proposed method can capture the true distributions of the inputs and outputs of such systems and map these distributions using polygon generation and video-to-video translation techniques. More specifically, the time-series data are represented as polygon streams (videos), then the video-to-video translation is used to transform the input polygon streams into the output ones. This transformation is tuned based on a model trustworthiness metric for optimal video synthesis. Finally, an image processing procedure is used for mapping the output polygon streams back to time-series outputs. The proposed method is based on cycle-consistent generative adversarial networks as an unsupervised approach. This does not need the heavy involvement of the human expert who devotes much effort to labeling the complex industrial data. The performance of the proposed method was validated successfully using a challenging industrial dataset collected from a complex heat exchanger network in a Canadian pulp mill. The results obtained using the proposed method demonstrate better performance than other comparable time-series prediction models. This allows process operators to accurately monitor process key performance indicators (KPIs) and to achieve a more energy-efficient operation.
Improved RRT-Based Moving Path Planning Algorithm for Teaching Reform and Innovation in Western Orchestral Ensemble Classes in Colleges and Universities

Western orchestral instruments in colleges and universities, as important instruments, occupy a central place in many bachelor’s degree programs in music education. Music teaching reform and innovation are central to the creative wisdom of music teachers, their musical upbringing, and the lived experience of their students. For teachers, the process of pedagogical reform and innovation is the process by which teachers realize their own pedagogical ideals. Teachers must first be motivated; in the absence of motivation, any advanced teaching methods will become pale in comparison and lose their value and usefulness. Therefore, the construction of teaching activities requires teachers to take responsibility for the education and support of students, to align their teaching goals with the development of each student, to make the students’ feelings closely connected to them, and to make them feel their value in the learning process. Starting from teaching western orchestral instrument ensembles in colleges and universities, this thesis uses the research method of improving RRT’s moving path planning algorithm to explain and analyze some problems that arise in teaching western orchestral instrument ensembles in colleges and universities and puts forward constructive suggestions and recommendations.
Schorl-1A from Langesundsfjord (Norway)

A crystal fragment of schorl from Langesundsfjord (Norway), showing a zonation with a biaxial optic behavior in the rim, was studied by electron microprobe analysis, single-crystal X-ray diffraction, Mössbauer, infrared and optical absorption spectroscopy and optical measurements.Measured 2V x is 15.6°.We concluded that biaxial character of the sample is not due to internal stress because it cannot be removed by heating and cooling.Diffraction data were refined with a standard R3m space group model, with a = 16.0013(2)Å, c = 7.2263(1) Å, and with a non-conventional triclinic R1 space-group model keeping the same hexagonal triple cell (a = 16.0093(5)Å, b = 16.0042(5)Å, c = 7.2328(2) Å, α = 90.008(3)°,β = 89.856(3)°,γ = 119.90(9)°),yielded R all = 1.75% (3136 unique reflections) vs. R all = 2.53% (17342 unique reflections), respectively.The crystal-chemical analysis resulted in the chemical formula X (Na 0.98 K 0.01 ☐ 0.01 ) Σ1.00 Y (Fe 2+ 1.53 Al 0.68 Mg 0.35 Ti 0.20 Fe 3+ 0.20 Mn 0.02 V 0.01 Zn 0.01 ) Σ3.00 Z (Al 5.10 Fe 2+ 0.50 Mg 0.40 ) Σ6.00 (Si 6 O 18 )(BO 3 ) 3 (OH) 3 [(OH) 0.39 F 0.22 O 0.39 ] Σ1.00, which agrees well in terms of calculated site-scattering (X 10.9 epfu, Y 63.7 epfu, Z 83.7 epfu) and refined site-scattering (X 11.4 epfu, Y 63.4 epfu, Z 83.6 epfu).About 0.19 apfu Fe 2+ is at the Z sites in the R1 model that showed that one out of six independent Z sites (Zd) has higher refined site scattering [15.5 eps vs. mean 13.7(2) eps for the other five sites] and larger mean bond length [1.969 Å vs. 1.927(6)Å for the other five sites] and larger octahedral angle variance [53° vs. 42(3)°].All these features support local order of Fe 2+ at the Zd site.Optical absorption spectra also show evidence of Fe 2+ at the Z sites.The elongation of the Zd-octahedron is along a direction that forms an angle of ca.73° with a unit-cell edge and is coincident with the direction of the γ-refraction index.All these data support the triclinic character of the structure of the optically biaxial part of the tourmaline sample from Langesundsfjord and provide evidence that even in the presence of excellent statistical agreement factors from excellent X-ray diffraction data, the lowering of symmetry due to cation ordering may have been overlooked in many other tourmaline samples in the absence of a check of the optical behaviour.According to the nomenclature rules, the studied triclinic schorl, should be named schorl-1A.
TSSA: Task structure-aware scheduling of energy-constrained parallel applications on heterogeneous distributed embedded platforms

Heterogeneous distributed embedded systems are widely used because they can significantly improve the computing performance of parallel applications in embedded systems. Since such systems are limited by both system energy consumption and schedule length, further scheduling optimization design is required. To achieve a balance between computing performance and energy consumption, the task scheduling problem under energy constraints has attracted great attention from researchers in recent years. However, state-of-the-art algorithms ignore the impact of the task structure of the application on scheduling performance. In this study, a structure-aware task scheduling strategy is proposed for the parallel application scheduling problem in heterogeneous distributed embedded systems. Specifically, the structure of the application is considered a factor affecting the algorithm. Meanwhile, to reduce the schedule length of the application and avoid pessimistic energy allocation, this study proposes an improved weighted energy pre-allocation algorithm. Experimental results demonstrate that the task structure-aware algorithm can decrease the scheduling length for energy consumption-constrained parallel applications. Compared with the MSLECC algorithm, the task scheduling length of the task structure-aware algorithm in the Gaussian Elimination transform can be shortened by 42.39%.
Typomorphic Characteristics of Gold-Bearing Pyrite and Its Genetic Implications for the Fang’an Gold Deposit, the Bengbu Uplift, Eastern China

The Fang’an quartz-vein gold deposit is located in the eastern part of the Bengbu uplift. The eastern part of the Bengbu uplift is considered to be the western extension of the Zhaoyuan gold mineralization zone in the Jiaodong area of Shandong Province and has huge mineralization potential. The Fang’an deposit was a newly discovered, small-sized gold deposit, and the research in the area is relatively weak. In this study, samples of quartz-vein type ore were collected from the ZK141, ZK1549, and ZK1665 drill holes of the Fang’an gold deposit. Based on the study of the geological characteristics, the major and trace elements of pyrite in different stages were analyzed by electron probe microanalyzer (EPMA), to explore the compositional characteristics of pyrite, the occurrence of gold, and the source of ore-forming fluid. The studies indicate that the deposit experienced four ore-forming stages: the quartz stage, the quartz-pyrite stage, the polymetallic sulfide stage, and the carbonate stage. The pyrites are grouped into three stages, corresponding to the first three ore-forming stages. The EPMA analyses showed that the major elements of pyrite were high Fe and low S, indicating that the formation was hydrothermal. The high content of Ni indicated that the metallogenic materials were derived from between the mantle and the lower crust. The general Co/Ni ratio of &gt;1, with an average of 5.34, indicated that the ore-forming fluid was derived from the magmatic–hydrothermal fluid and wall rock. The Fang’an gold deposit mainly contains nano-gold.
A Prospect-Theory-Based Operation Loop Decision-Making Method for Kill Web

In the military field, decision making has become the core of the new operational concept, known as the “kill web”. Although the theory of kill web has been widely recognized by many countries, the decision-making methods for the kill web are still in the early stage. Therefore, there is a need for a new decision-making method for the kill web. Firstly, different from the traditional scheme decision, the kill web is a complex system. The method of complex network provides a new perspective on complex systems, so the kill web was modeled based on complex network. Secondly, the kill web relies on artificial intelligence to provide decision-makers with operation loop solutions, and then decision-makers rely on the experience to make a final decision. However, the current decision-making methods only consider one of the intelligent and human decision-making methods, while the kill web needs to consider both. Hence, we combined intelligent decision making with human decision making through multi-objective optimization and the prospect theory. Finally, we designed a nondominated sorting ant colony genetic algorithm-II (NSACGA-II) to solve large-scale problems, since the kill web is a large-scale system. In addition, an illustrative case was used to verify the feasibility and effectiveness of the proposed model. The results showed that, compared with other classical multi-objective optimization algorithms, the NSACGA-II is superior to other superior algorithms in terms of the hypervolume (HV) and spacing (SP), which verifies the effectiveness of the method and greatly improves the quality of commanders’ decision-making.
Algorithms for Reasoning in a Default Logic Instantiation of Assumption-Based Argumentation1

Assumption-based argumentation (ABA) is one of the most-studied formalisms for structured argumentation. While ABA is a general formalism that can be instantiated with various different logics, most attention from the computational perspective has been focused on the logic programming (LP) instantiation of ABA. Going beyond the LP-instantiation, we develop an algorithmic approach to reasoning in the propositional default logic (DL) instantiation of ABA. Our approach is based on iterative applications of Boolean satisfiability (SAT) solvers as a natural choice for implementing derivations as entailment checks in DL. We instantiate the approach for deciding acceptance and for assumption-set enumeration in the DL-instantiation of ABA under several central argumentation semantics, and empirically evaluate an implementation of the approach.
HUMAN ABNORMAL ACTIVITY RECOGNITION

This paper brings forward one amongst the foremost significant applications of human suspicious activity recognition that is termed as anomaly detection. A key concern of any society today is providing safety to an individual. The main reason behind this concern is due to the constantly increasing activities causing threats, starting from deliberate ferocity to an injury caused through an accident. Simple installation of a traditional closed circuit television(CCTV) is not sufficient as it requires a person to continuously stay alert and monitor the cameras, which is quite inefficient. This call for the requirement to develop an security system which is fully automated system that recognizes anomalous activities in real time and brings instant help to the victims. Hence we proposed a system which will examine and detect the suspicious human action from real-time CCTV footage with help of machine learning techniques and generates the alert if the abnormal activity is occurred. The method is implemented on the dataset containing both normal and anomaly activity and experiment has shown better results.
Estimating the Quality of Experience of Immersive Contents

Recent technology advancements have driven the production of plenoptic devices that capture and display visual contents, not only as texture information (as in 2D images) but also as 3D texture-geometric information. These devices represent the visual information using an approximation of the plenoptic illumination function that can describe visible objects from any point in the 3D space. Depending on the capturing device, this approximation can correspond to holograms, light fields, or point clouds imaging formats. Naturally, the success of immersive applications depends on the acceptability of these formats by the final user, which ultimately depends on the perceived quality of experience. Several subjective experiments have been performed with the goal of understanding how humans perceive immersive media in 6 Degree-of-Freedom (6DoF) environments and what are the impacts of different rendering and compression techniques on the perceived visual quality. In this context, an open area of research is the design of objective methods that estimate the quality of this type of content. In this talk, I describe a set of objective methods designed to estimate the quality of immersive visual contents -- an important aspect of the overall user quality of experience. The methods use different techniques, from texture operators to convolution neural networks, to estimate quality by also taking into consideration the specificities of the different formats. Finally, I discuss some of the exciting research challenges in the area of realistic immersive multimedia applications.
Two-Layer Security Algorithms to Prevent Attacks on Data in Cyberspace

The ratio at which the number of users is increasing in cloud computing, the same as the number of data destruction, has doubled. Various algorithms have been designed to secure cloud servers to prevent an attacker from attacking, but they cannot protect the data. With the help of cryptography, data are converted into a format that is difficult for an attacker to understand, and these data can be broadcasted in a reliable format. If the attacker obtains access to the cryptographic algorithm, then the attacker can easily decrypt the data, but if the data are encrypted with the help of the key, it is difficult for the attacker to decrypt the data without the key. When the data are encrypted with the same key at different times, there is a possibility to obtain the actual key with the help of the Kasiski test. Different researchers have encrypted the data with the help of the same key or have developed an algorithm by combining different algorithms, which is a problem. This paper developed an efficient algorithm that cannot be broken easily. Even if an attacker obtains access to the algorithm, the attacker cannot identify the keys derived from that text, and the key only works in the text from which it is generated, which is the novelty of this paper. Data are secured in two different layers. In Layer-1, the proposed algorithm implements plaintext and a static key to obtain the encrypted text and a Vigenère key. In Layer-2, the encrypted text and the Vigenère key are implemented on the Vigenère cipher algorithm to obtain the ciphertext. At the end of the paper, a comparison of different papers is made with the proposed paper, and a conclusion is made based on different techniques and results.
Recognition of the Impact of Artificial Intelligence on Patent Law of the European Union

Artificial intelligence has been paving the way for revolutionizing science, technology and production worldwide, and at the same time entailing changes in traditional legal relations – patent law is not outside the scope of influence of artificial intelligence. Questions are being raised about the increasing patent protection system such as the object of an invention, the subject of an invention or the criteria for evaluating patentability. The article provides an insight into the experience of the European Union patent law in response to the effects of artificial intelligence, thereby proposing some recommendations for Vietnam.
Identifying optimal message embedding location in audio steganography using generative adversarial networks

Audio steganography (AS) uses the auditory redundancy of the human ear to conceal the hidden message inside the audio track. In recent studies, deep learning-based steganalysis has swiftly revealed AS by extracting high-dimensional stego acoustic features for categorization. There is still an opportunity for improvement in the current audio steganography required for managing communication confidentiality, access control and data protection. The main objective of this research is to improving the data protection by identifying the data embedding location in the audio. Generative Adversarial Network-based Audio Steganography Framework (GAN-ASF) is presented in this study, and it can automatically learn to provide better cover audio for message embedding. The suggested framework's training architecture comprises a generator, a discriminator, and a steganalyzer learned using deep learning. The Least Significant Bit Matching (LSBM) message embedding technique encrypts the secret message into the steganographic cover audio, which is then forwarded to a trained steganalyzer for misinterpretation as cover audio. After performing the training, stenographic cover audio has been generated for encoding the secret message. Here, Markov model of co-frequency sub images to generate the best cover frequency sub-image to locate an image's hidden payload. Steganographic cover audio created by GAN-ASF has been tested and found to be of excellent quality for embedding messages. The suggested method's detection accuracy is lower than that of the most current state-of-the-art deep learning-based steganalysis. This payload placement approach has considerably increased stego locations' accuracy in low frequencies. The test results GAN-ASF achieves a performance ratio of 94.5 %, accuracy ratio of 96.2 %, an error rate of 15.7 %, SNR 24.3 %, and an efficiency ratio of 94.8 % compared to other methods.
A System with Minimal Redundancy for Intelligent Prediction Management

This paper proposes a predictive redundancy method for management, which is used for intelligent prediction management, which can effectively improve the real-time online reliability of agents. This method changes the multi-redundant post-processing scheme of traditional system and adopts single-redundant real-time pre-switching. Through a large number of experiments, the feasibility of the system is verified, and the system can be built as a redundant fault-tolerant system. The redundant fault-tolerant system has good generalization and can be implemented in the scene. A redundant module that is about to fail is used, including: the proposed redundant module that continues to decline in health, a redundant module that rapidly decreases in health at a certain time, and a redundant module that is always in a sub-healthy state. After the health assessment, you can enter the early warning model and observe whether the early warning model can issue a health warning, which can verify the effectiveness of the early warning model.
UAV swarm collaborative coverage control using GV division and planning algorithm

Abstract Unmanned aerial vehicle (UAV) swarm coverage is one of the key technologies for multi-UAV cooperation, which plays an important role in collaborative investigation, detection, rescue and other applications. Aiming at the coverage optimisation problem of UAV in the target area, a collaborative visual coverage control method under positioning uncertainty is presented. First, the visual perception area with imprecise localisation, UAV model and sensor model are created based on the given task environment. Second, a regional division algorithm for the target task area is designed based on the principle of Guaranteed Voronoi (GV) diagram. Then a visual area coverage planning algorithm is designed, in which the task area is allocated to the UAV according to the corresponding weight coefficient of each area, and the input control law is adjusted by the expected state information of the UAV, so that the optimal coverage quality target value and the maximum coverage of the target area can be achieved. Finally, three task scenarios for regional division and coverage planning are simulated respectively, the results show that the proposed area coverage planning algorithm can realise the optimal regional distribution and can obtain more than 90% coverage in different scenarios.
Detecting Cyberbullying in Roman Urdu Language Using Natural Language Processing Techniques

Nowadays, social media platforms are the primary source of public communication and information. Social media platforms have become an integral part of our daily lives, and their user base is rapidly expanding as access is extended to more remote locations. Pakistan has around 71.70 million social media users that utilize Roman Urdu to communicate. With these improvements and the increasing number of users, there has been an increase in digital bullying, often known as cyberbullying. This research focuses on social media users who use Roman Urdu (Urdu language written in the English alphabet) to communicate. In this research, we explored the topic of cyberbullying actions on the Twitter platform, where users employ Roman Urdu as a medium of communication. To our knowledge, this is one of the very few studies that address cyberbullying behavior in Roman Urdu. Our proposed study aims to identify a suitable model for classifying cyberbullying behavior in Roman Urdu. To begin, the dataset was designed by extracting data from twitter using twitter's API. The targeted data was extracted using keywords based on Roman Urdu. The data was then annotated as bully and not-bully. After that, the dataset has been pre-processed to reduce noise, which includes punctuation, stop words, null entries, and duplication removal. Following that, features are extracted using two different methods, Count-Vectorizer and TF-IDF Vectorizer, and a set of ten different learning algorithms including SVM, MLP, and KNN was applied to both types of extracted features based on supervised learning. Support Vector Machine (SVM) performed the best out of the implemented algorithms by both combinations, with 97.8 percent when implemented over the TF-IDF features and 93.4 percent when implemented over the CV features. The proposed mechanism could be helpful for online social apps and chat rooms for the better detection and designing of bully word filters, making safer cyberspace for end users.
CSI gesture recognition method based on lightweight deep network

Aiming at the problems that the existing gesture recognition technology needs video devices or sensors, is easily affected by ambient light and has high deployment cost, this paper proposes a gesture recognition method based on WIFI signal and lightweight depth network. The amplitude and phase of channel state information are extracted from WIFI signal, the amplitude is filtered, and the phase is unwrapped and linearly transformed. Use normalization and interpolation to transform CSI signals into RGB pictures. A lightweight depth network model based on MobileNet_V2 is used to classify RGB pictures with combined amplitude and phase, and the width factor &alpha;=0.5 is changed to reduce the parameters of the model. The channel and spatial attention module are embedded in the inverted residual structure to improve the classification accuracy. Experimental results on public data sets show that the average accuracy rate of this method for recognizing six common push-pull gestures reaches 96.4%. The amount of calculation is 30% of the basic model, and the classification accuracy is improved by 1.3%.