Reviewerly/exHarmony huggingface dataset:
Error Compensation for Long Arm Manipulator Based on Deflection Modeling and Neural Network

Long arm manipulators are designed to work in special conditions including aviation, engineering, and other scenarios that require a large span operation. However, since the long arm will cause a large flexible error, the manipulators maintain large terminal absolute error and difficulty in control. In addition, testing in a real machine is time and economic consuming, and obtaining enough data is untoward. Under such conditions, this paper proposes an error compensation method for a long arm manipulator combining deflection error modeling and neural network, using a specially designed long arm manipulator. By using this method, better results are achieved than traditional error modeling alone since non-traceable errors are also compensated for. The result is also better than neural network compensation alone since in the case of less training data preferable results can still be achieved.
The 3D Facemask Recognition: Minimization for Spreading COVID-19 and Enhance Security

Nowadays, the world suffers from a terrible problem with COVID-19. Some researchers worked for a facemask detection biometric system by using Residual Networks such as Resnet50 or Resnet100. This research focuses on depreciating the transmission of Corona Virus Disease (COVID) from person to person. To wear a facemask plays an excellent role in not spreading COVID to the community. Moreover, to enhance social life, automatic facemask detection and facemask recognition systems are needed. Additionally, this system is helpful for security enhancement through face recognition. Here, the research for developing and implementing a biometric system for banks, offices, universities, and other public areas. This system helps in detecting the facemask for wearing by the human With Artificial Intelligence (AI), Deep Learning (DL), and Digital Image Processing technologies, this system allows the person to enter inside or stay outside because of not wearing a facemask. Besides designing and implementing a new model, the authors will discuss pre-defined the VGG16 deep learning model of CNN (Convolution Neural Network). Further, conclude the performance of both CNN models. This paper proposes a Facemask Detection model for solving the problem of manual care. This proposed model is a combination of most trending technologies such as AI, DL, and Image processing. In the future, describe this newly implemented model by appending Gaussian noise with images and make the system more reliable for actual use.
The application framework of blockchain technology in higher education based on the smart contract

With the rapid development of blockchain, the two essential concepts of digital innovation, namely Smart Contract and consensus mechanism algorithm and regional sharing, have begun to trigger changes in higher education worldwide. This paper aims to establish a new framework for applying blockchain in higher education. The content of this framework includes (1) unified mining; (2) the establishment of a consensus mechanism with sake proof as to the core in the block; (3) the use of smart contracts for course bidding and student team tasks; (4) Anonymous participation; (5) Exchange of tokens and credits.
Security of the Medical Images using Different Chaos map Techniques

Nowadays Internet is used for faster transmission of valuable information. This information may be text, image, and video anything. With the advancement of computer and biomedical innovations, medical JPEG images contain the patient’s individual data and the security of the private data pulls in incredible consideration. The progression of program and correspondence advancements has made images act significant parts in the domain of telediagnosis, tele-surgery, and so forth. Simultaneously, such signs of progress provide new intends to process clinical pictures; it additionally expands security concern as far as confidentiality, integrity and availability. This paper provides an overview of the various approaches currently in use and then goes on to develop a new mechanism for the same using various Chaos maps techniques in order to provide more integrityand security to the image by making the keyspace large enough towithstand brute force attacks.
Designing Intelligent Marine Framework Based on Complex Adaptive System Principles

The increasing dependency in the maritime transportation platforms brings into the spot the reliability aspect in container terminals (CT). In this paper agent’s technology integrated with decision support process in CT complex system. It is used to build intelligence in the containers as to manage the risk at its different levels. In this paper we address the containers control as a system where decision making is distributed among the various components especially the containers carrying dangerous materials. The multi-agent modeling is well suited to represent the complexity as a distributed system. The proposed model based on a complex process which targeting the highly risked containers based on multi agents’ systems and intelligent environment called CTRMS: Container Terminal Risk Management System. Diversity of communities and emergence play a major role in this approach. In this paper we define the CT environment as complex adaptive system (CAS) with respect to some CAS properties and mechanisms as aggregation and tagging in CT. The originality of our approach lies in the proposed new mechanisms for reasoning and coordination of agents in which we put special emphasis on the mechanisms of decision making for dynamic and autonomous system components.
Compressing audio signals with interactive and cloud-based learning material—a workshop for high-school students

Abstract Undeniably omnipresent in our everyday lives, electronic signal compression is a classical example that illustrates the ubiquity of mathematics in high-end technology and sciences. When dealing with piles of files, it is essential that each file takes up as little storage space as possible while maintaining quality. The cloud-based—available under a Creative Commons licence—workshop that is described in the following paper addresses this trade-off between quality and compression rate based on the example of audio signals. Here, students use tools from linear algebra to compress music according to their own hearing model which they developed previously. More specifically, they use the basis change (i.e. the Fourier transform) between the time-dependent and frequency-dependent representations of sounds to remove those frequencies from an instrumental piece that are outside of their hearing model. Such mathematical tools may seem out of reach for high-school students, but we demonstrate that they are indeed suited for developing solutions with minimal help.
A New Challenge: Approaching Tetris Link with AI

Decades of research have been invested in making computer programs for playing games such as Chess and Go.This paper introduces a board game, Tetris Link, that is yet unexplored and appears to be highly challenging.Tetris Link has a large branching factor and lines of play that can be very deceptive, that search has a hard time uncovering.Finding good moves is very difficult for a computer player, our experiments show.We explore heuristic planning and two other approaches: Reinforcement Learning and Monte Carlo tree search.Curiously, a naive heuristic approach that is fueled by expert knowledge is still stronger than the planning and learning approaches.We, therefore, presume that Tetris Link is more difficult than expected.We offer our findings to the community as a challenge to improve upon.
Design and Implementation of Protective Relays Benches

Inverse Time Overcurrent, Differential and Directional Overcurrent relays are some of the types of protective devices used for the protection of electrical power systems. Undergraduate students, studying protection of power system courses, can benefit greatly if given the opportunity to work with these relays in their labs. To provide a small setup for demonstrating the working of overcurrent, differential, and directional overcurrent relays, two test benches have been designed and implemented. The first bench consists of electromechanical inverse time overcurrent relays and microprocessor-based feeder protection relays. The second bench consists of two independent circuits to demonstrate the working of differential and directional overcurrent protection devices. The first circuit in this bench is implemented to show the operation of a microprocessor based differential protection relay. The second circuit is intended to show the working of electromechanical and microprocessor based directional overcurrent protection relays.
Capturing the Spread of Hate on Twitter Using Spreading Activation Models

Hate speech is a prevalent and pervasive phenomenon on social media platforms. Detecting hate speech and modelling its spread is a significant research problem with practical implications. Though hate speech detection has been an active area of research, hate spread modelling is still in its nascent stage. Prior works have analyzed the hateful users’ social network embedding, hateful user detection using belief propagation models, and the spread velocity of hateful content. However, these prior works fail to factor in the multiple hateful forms (such as hate against gender, race and ethnicity) and the temporal evolution of hate spread, limiting their applicability. We take a holistic approach wherein we model the spread of hate as a single form and fine-granular spread of hateful forms. We extend the traditional spread and activation (SPA) model to capture the spread of hate and its forms. We use SPA to model, the spread of hate as one single form while TopSPA captures the spread of multiple hate forms. We also propose ways to detect hateful forms by using latent topics present in hateful content. We empirically demonstrate our approach to a dataset from Twitter that contains ample hate speech instances along with users labelled as hateful or not.
The In-Robot Network Structure of Humanoid Robot for Burst Traffic Data Situations

According to the basic concept of the In-Robot Network(IRN), an IRN structure that can handle burst data is proposed. According to the existing research on humanoid robots, it can be seen that the current humanoid robots have a high degree of freedom of joints and carry various sensors at the same time to form a huge IRN network. It can be known from existing research that most of the current humanoid robots operate according to preset scenarios and plans. If a special situation occurs, the current IRN structure of a humanoid robot cannot handle a large amount of burst data in a special situation. Therefore, based on the in-vehicle network (IVN) and time-sensitive network (TSN) concepts, an IRN structure that can handle the data generated in the above-mentioned emergencies is proposed in this article. In this IRN structure, the Domain Control Unit (DCU) is used to detect the status of the data stream in each different area to distinguish whether an emergency occurs. Also, according to different emergencies, use different ways to deal with the emergent data. Meanwhile, it is verified by the OMNet++ network simulation tool.
Protected core networking: communications challenges in tactical environments

Protected Core Networking (PCN) concepts are seen as key elements to securely automate the deployment and management of federated coalition networks. However, the extension of PCN principles to the tactical domain is not trivial. Indeed, it encompasses challenges that contest the notion that PCN and its interfaces are agnostic to the communications bearer. In this paper, we address the tactical environment idiosyncrasies, the particularities of its communications and their ability to support PCN concepts. Complementary, we present architectural options for integrating PCN into the tactical and discuss their open challenges and ways forward.
Time Series Data Processing Algorithm in Deep Water Drilling

The machine learning technology can be applied to the drilling data of deep water. The drilling data is an internally associated multi -dimensional time series data set, which contains a certain amount of abnormal data that greatly affect the data mining process. Because of the high dimension and large scale in drilling data set, existing detection algorithms perform poorly in drilling data sets. To achieve more effective outlier detection, we propose a data processing algorithm based on fusion outlier detection method. Firstly, Isolation Forest, Elliptic Envelope and Local Outlier Factor are used to detect outliers, judge the abnormal data in different conditions, which are weighted to judge and remove the outliers. Secondly, Savitzky-Golay(SG) filter is used to smooth the data, which eliminates the burrs in the data and get clean time series data. Finally, the proposed algorithm is tested in the real drilling data sets. Compared with existing methods, the experiments show that the proposed algorithm can achieve better performance, the RMSE and MAE values are 0.454 and 0.361, respectively.
Syntactic Analysis of Sentences Containing Arabic Psychological Verbs

In the Arabic language, all the structures of simple verbal sentences have the same main components: the predicate (al-mosnad, Open image in new window ), the subject (al-mosnad 'ilayh, Open image in new window ) that are mandatory in the Arabic sentence, and the complement (al-fodla, Open image in new window ) to reach the meaning of the sentence. In this paper, we will expand and improve our previous work of identification and classification of Arabic psychological verbs through lexicon-grammar tables [], to be able to realize a syntactic analyzer of sentences containing Arabic psychological verbs using the NooJ platform. In this regard, we will use the dictionary with about 400 verb entries, containing all the lexical, syntactic, semantic, and transformational information of these verbs, which will facilitate the realization of our analyzer. Then, we will adapt to our needs, the simple sentences analyzer [, ], to parse the sentences containing Arabic psychological verbs regardless of the sentence components order. The parser is tested on many texts, and the results were satisfactory.
Joint feature and task aware multi-task feature learning for Alzheimer’s disease diagnosis

Alzheimer’s disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years. There have been efforts to identify the risk of developing AD in its earliest time. Recently, multi-task feature learning (MTFL) methods with sparsity-inducing $\ell_{2,1}$-norm have been widely studied to select a discriminative feature subset from MRI features. However, they ignore the complex relationships among imaging markers and among cognitive outcomes. Constructing the relationships with simple Pearson correlation coefficient may degrade model generalizability. To better capture the complicated but more flexible relationship between the cognitive scores and the neuroimaging measures, we propose a two-stage framework to jointly learn the structure within the feature correlation as well as within the task correlation. Moreover, we propose a dual graph regularization to encode the learned correlation structure. It is able to guide the training procedure of MTFL by incorporating both the inherent correlations. Extensive results on benchmark datasets show that for the proposed FTSMTFL model trained with the dual graph regularization, the proposed joint training framework outperforms existing methods and achieves state-of-the-art cognitive prediction performance of AD.
Kernel-based data transformation model for nonlinear classification of symbolic data

Symbolic data are usually composed of some categorical variables used to represent discrete entities in many real-world applications. Mining of symbolic data is more difficult than numerical data due to the lack of inherent geometric properties of this type of data. In this paper, we use two kinds of kernel learning methods to create a kernel estimation model and a nonlinear classification algorithm for symbolic data. By using the kernel smoothing method, we construct a squared-error consistent probability estimator for symbolic data, followed by a new data transformation model proposed to embed symbolic data into Euclidean space. Based on the model, the inner product and distance measure between symbolic data objects are reformulated, allowing a new Support Vector Machine (SVM), called SVM-S, to be defined for nonlinear classification on symbolic data using the Mercer kernel learning method. The experiment results show that SVM can be much more effective for symbolic data classification based on our proposed model and measures.
Particularities of Sync Monitoring in FPGA Components of Safety-Related Systems

Two approaches to monitoring synchronization in FPGA components of safety-related systems are compared: logical and power-oriented. Logical on-line testing detects errors in the results of calculations and operates within the logical checkability of the circuit, i.e. when a fault manifests itself as an error. The power-oriented monitoring of synchronization reacts to disruptions in case of a significant change in the circuit's energy parameter, or equivalently its current consumption. Power-oriented checkability determines the possibilities of such monitoring in the area located below the consumption current with zero activity of information signals. The synchronization monitoring responses follow from the features of safety-related systems, which are designed to operate in two modes: normal and emergency. As a rule, in normal mode, information signals change slightly, often at the noise level. This reduces the logical checkability of the circuits and brings the current consumption closer to the level of zero activity of information signals, significantly increasing the power-oriented checkability. Experiments are carried out comparing the efficiency of logical and power-oriented synchronization monitoring in FPGA components of safety-related systems in conditions of reduced activity of information signals.
Anomalous Event Detection in Crowd Scenes using Histogram of Optical Flow and Entropy

This work proposes an algorithm that makes advances in anomaly detection in a crowded video. The proposed model uses optical flow magnitude, orientation, and entropy to capture anomalies in a given crowded video. Assuming that the video is recorded from a stationary camera, a feature descriptor, Histogram of Optical Flow and Entropy (HOFE) that uses spatial and temporal information, is introduced. An SVM classifier is used to classify anomalous frames from the extracted features. From the conducted experiments, it is found that the performance of the model is superior to other existing methods.
Composite Evolutionary GAN for Natural Language Generation with Temper Control

Natural Language Generation (NLG) is a fundamental problem in NLP. The basic approach for NLG is Maximizing the Likelihood Estimation (MLE). However, MLE is blamed for exposure bias. To tackle with the problem, Generative Adversarial Networks (GAN) based methods are proposed, which use the adversarial structure to overcome the problem. Although the reinforcement learning framework in GAN makes NLG available, non-eliminated problems such as sparse reward and intensive computations still exist. Gumble-Softmax relaxation is an alternative approach to the reinforcement learning framework to deal with discrete data. The dynamic temperature of relaxation is essential to control the model preference between quality and diversity. In this paper, we propose CEGAN, which uses an evolutionary strategy to help the generators finding the most suitable temperature during training. Besides, CEGAN uses composite discriminators to make full use of samples and embedding from generator, which is able to provide more informational signals back to generator. We conduct extensive experiments to verify the superiority of our model. The results show that CEGAN achieves higher performance compared with baselines and makes a good trade-off between quality and diversity.
Graph-Based Classification With Multiple Shift Matrices

Due to their effectiveness in capturing similarities between different entities, graphical models are widely used to represent datasets that reside on irregular and complex manifolds. Graph signal processing offers support to handle such complex datasets. In this paper, we propose a novel graph filter design method for semi-supervised data classification. The proposed design uses multiple graph shift matrices, one for each feature, and is shown to provide improved performance when the feature qualities are uneven. We introduce three methods to optimize for the graph filter coefficients and the graph combining coefficients. The first method uses the alternating minimization approach. In the second method, we optimize our objective function by convex relaxation that provides a performance benchmark. The third method adopts a genetic algorithm, which is computationally efficient and better at controlling overfitting. In our simulation experiments, we use both synthetic and real datasets with informative and non-informative features. Monte Carlo simulations demonstrate the effectiveness of multiple graph shift operators in the graph filters. Significant improvements in comparison to conventional graph filters are shown, in terms of average error rate and confidence scores. Furthermore, we perform cross validation to show how our approach can control overfitting and improve generalization performance.
A Novel Certificateless Multi-signature Scheme over NTRU Lattices

With the help of multi-signature technology, multiple signatures on the same message could be compressed into one compact signature, which is efficient for financial applications such as blockchain for saving storage space and transmission bandwidth costs. Due to the advantage of no certificate and no escrow feature, certificateless signature is widely used in many applications since its introduction in 2003. Unfortunately, traditional signature schemes may face serious security threats with the advent of quantum computers as their underlying security assumptions (RSA or discrete logarithm problems) may no longer hold anymore. Lattice-based cryptography is considered to be quantum secure. Therefore, we propose a novel certificateless multi-signature (CLMS) scheme over NTRU lattices in this paper, which is provably secure in the random oracle model based on the ring version of the short integer solution assumption (Ring-SIS). To the best of our knowledge, our scheme is the first lattice-based CLMS scheme. Compared with closely related works in the literature, our scheme is based on certificateless cryptography, which not only solves the key escrow problem, but also relieves the certificate management problem effectively.