Reviewerly/exHarmony huggingface dataset:
Short-time prediction of chaotic laser using time-delayed photonic reservoir computing

&lt;sec&gt;Prediction of chaotic laser has a wide prospect of applications, such as retrieving lost data, providing assists for data analysis, testing data encryption security in cryptography based on chaotic synchronization of lasers. We propose and demonstrate a new method of using time delayed photonic reservoir computing (RC) to forecast the continuous dynamical evolution of chaotic laser from previous measurements. Specifically, the time delayed photonic RC based on semiconductor laser with optical injection and feedback structure is established as a prediction system. Chaotic laser, as input signal, is generated by semiconductor laser with external disturbance.&lt;/sec&gt;&lt;sec&gt;The time delayed photonic RC used in this stage is a novel implementation, which consists of three parts: the input layer, the reservoir and the output layer. In the input layer, the chaos laser from the semiconductor with an optical feedback needs to preprocess and multiply by a mask signal. The reservoir is the master-slave configuration consisting of a response laser with the optical feedback and light injection. In the feedback loop, there are &lt;i&gt;N&lt;/i&gt; virtual nodes at each interval &lt;i&gt;θ&lt;/i&gt; with a delay time of &lt;i&gt;τ&lt;/i&gt; (&lt;i&gt;N&lt;/i&gt; = &lt;i&gt;τ&lt;/i&gt;/&lt;i&gt;θ&lt;/i&gt;). The reservoir performs the mapping of the input signal onto a high-dimensional state space. In the output layer, the output of the reservoir is a linear combination of the reservoir state and the output weight. The output weight is optimized by minimizing the mean-square error between target value and output value through using the ridge regression algorithm.&lt;/sec&gt;&lt;sec&gt;The results demonstrate that time delayed photonic RC based on semiconductor laser can forecast the trajectory of chaotic laser in about 2 ns. Moreover, we also investigate the influence of critical parameters on prediction result, including the type of the mask, the quantity of the virtual nodes, the length of the training data, the input gain, the feedback strength, the injection strength, the ridge parameter and the leakage rate.&lt;/sec&gt;&lt;sec&gt;The method used here in this work has many attractive advantages, such as simple configuration, low training cost and eminently suitable for hardware implementation. Although the prediction length is limited, the significant innovation using time delayed photonic RC based on semiconductor lasers as the prediction system of chaotic laser presents a new opportunity for further developing a technique for predicting chaotic laser. &lt;/sec&gt;
LP Based Integration of Computer Network and Security in University College

Abstract There is a need on incorporating computing with network and security teaching in University College. In this paper, a framework is proposed for integrating computer network and security teaching using Logic Programming. Two 5-sessions incorporation modules on computer network and security for 1st, 2nd, and 3rd year students were developed and implemented. Pre- and post- tests, class observations and interviews prove the framework viability in terms of 1) development and execution of the modules, and 2) the students’ learning outcomes on computer network and security content and Computational Thought, and the acceptance of the incorporation.
Devising a Distinguishable Feature Set for Sinhala and English Script Separation on Social Media Images

In the last decade, social media has become the most popular and ubiquitous facilitator for creating, and sharing content, accessing information, and connecting people around the world. Nowadays, social media content generation is rapidly growing in the Sri Lankan community. Some content generation was a threat to peace, which led to unpleasant situations in the country within the last two years. Content moderation is the best solution for this problem, but manually processing millions of posts is a difficult task. For automation, separation and identification of Sinhala and English character in social media images is an essential project in content identification. In this work, Sinhala and English characters are separately identified from image posts and video thumbnails which are on public Facebook pages. This paper introduces a novel algorithm to separate Sinhala and English characters. It has utilized topological features, contour-based technique, and water reservoir-based technique. The proposed algorithm achieved 93% accuracy. Separated characters are recognized using the Convolutional Neural Network in TensorFlow framework.
Data Security System for IoT Applications

The Internet of Things (IoT); can be defined as a think connected to the internet anywhere and anytime. Data security is one of the vital concepts in IoT security. So, the data to be uploaded should be made secure. There are many differenced cryptography algorithms such as Advanced Encryption Standard (AES), Nth degree truncates polynomial ring (NTRU), RSA, DES, and others to make security for data in IoT. In this paper, we suggest an algorithm that combines the feature of symmetric and asymmetric cryptography algorithms. Where the AES algorithm and NTRU public key used to create the special key at the receive side, then send the key to the sender side to make data security for IoT. The proposed algorithms provide strong security and low computation. The model has been simulating by MATLAB. The execution time for text (526 characters) for key generation is 0.092414 seconds, 0.020521 seconds for encryption and 0.060921 seconds for decryption and the execution time for image with size (512 * 512) for key generation is 0.101900 seconds, 1.699665 seconds for encryption and 12.82071 seconds for decryption.
Single-scale motion blur kernel estimation method based on continuous double frames

In the scene of recognizing the driving posture or behavior of the driver, the problem of motion blur in the image frame usually affects the recognition result. Because of the long calculation time of the existing multi-scale fuzzy kernel estimation method, it is not completely suitable for the driver's cab recognition scene. Therefore, based on the characteristics of the motion blur image in the cab scene, this paper proposes a single-scale motion blur kernel estimation method based on continuous double frames. First of all, in order to accurately extract the effective edge information of the blurred image, this paper improves the edge extraction effect by extracting the edge gradient information without crossing. Secondly, according to the characteristics of the motion blur kernel in the cab scene, this paper proposes to construct a new fuzzy kernel calculation model based on the information of the continuous two-frame blurred image. The method in this paper aims to use as much known information as possible to ensure the accuracy of the fuzzy kernel and reduce the calculation time of the fuzzy kernel estimation. Experiments have proved that the method in this paper can not only reduce the calculation time, but also achieve comparable or even better deblurring effects.
Design and Development of Mobile Application in Indonesian Language Learning For Foreign Speakers Level A1

Abstract This paper describes about the design and development of Android mobile platform application in the learning of Indonesian Language for Foreign Speakers as the term used Bahasa Indonesia bagi Penutur Asing or as abbreviated BIPA. The Android Studio is used to create and set up a set of application programming interfaces (APIs) and Kernel Linux Platform. The interface program was designed to give effectiveness and flexibilities required in the process of learning. The program code was written and installed in the users Smart Phone. The official language programming used for Android development is Java to make the program running easily for the users to use the application because the mobile application does not use many resources. Once the application was ready, then installed into various types of Smart Phone with various Android platforms, and tested. The program was tested to BIPA foreign students who were learning BIPA Level A1 to see if the program ran well. This aimed to test its effectiveness and impact. The data of pre-test and interview were collected from foreign students who have already learned Indonesian language before and who were never in order to see the baseline of respondents competencies. Pre-test, Post-test and interview test scores were compared to see if there was an increase in respondents competencies. The research findings shows that there was a significant increase on the foreign students’ skills. The increase on the written test through pre-test and post-test showed by 30% and 18 % on the interview. In contrast, the most significant increase was occurred in the group of students who never learned before. This increase showed by 56%, whereas the increase in the group of foreign students who have already learned Indonesian language showed by 12%. Furthermore, this increase significantly also occurred in interview at 32% to the group of foreign students who never learned Indonesian language before and 8% to the group of foreign students who already learned Indonesian language. This findings shows that Mobile Application provides effectiveness, efficiency, flexibilities, and accelerated learning to the group of foreign speakers who never learned BIPA before.
MATHEMATICAL ANALYSIS AS THE BASIS OF STUDENT TRAINING SECONDARY VOCATIONAL EDUCATION

The definition of the content of the standard in mathematics for most humanitarian specialties (we are talking about the current standards of the third generation) allows you to vary the content and develop educational programs in mathematics, taking into account the professional needs of individual humanitarian specialties. First of all, variation can concern the choice of topics that have a professionally-mathematical nature.
Clockwork: A Delay-Based Global Scheduling Framework for More Consistent Landing Times in the Data Warehouse

Recurring batch data pipelines are a staple of the modern enterprise-scale data warehouse. As a data warehouse scales to support more products and services, a growing number of interdependent pipelines running at various cadences can give rise to periodic resource bottlenecks for the cluster. This resource contention results in pipelines starting at unpredictable times each day and consequently variable landing times for the data artifacts they produce. The variability gets compounded by the dependency structure of the workload, and the resulting unpredictability can disrupt the project workstreams which consume this data. We present Clockwork, a delay-based global scheduling framework for data pipelines which improves landing time stability by spreading out tasks throughout the day. Whereas most scheduling algorithms optimize for makespan or average job completion times, Clockwork's execution plan optimizes for stability in task completion times while also targeting predefined pipeline SLOs. We present this new problem formulation and design a list scheduling algorithm based on its analytic properties. We also discuss how we estimate the resource requirements for our recurring pipelines, and the architecture for integrating Clockwork with Dataswarm, Facebook's existing data workflow management service. Online experiments comparing this novel scheduling algorithm and a previously proposed greedy procrastinating heuristic show tasks complete almost an hour earlier on average, while exhibiting lower landing time variance and producing significantly less competition for resources in the cluster.
Optimization Method for the Parallel Algorithm for Finding Faces in Graphic Images

In computer-human interaction, many applications use automatic face detection that is a complex problem in image processing. The purpose of the research is to design the parallel version of the Viola-Jones object detection framework (VJOD) and to optimize it with the help of high-performance Symmetric Multiprocessing (SMP) systems to achieve a minimum operation time. The article discusses the VJOD in more detail. And also methods for optimizing the parallelization of this algorithm. Although the VJOD can be implemented to detect various objects, it is mainly used to detect faces in an image or in a video stream. Result: The face detection system based on VJOD is built and tested. Efficiency of the approach is illustrated with examples.
Row Echelon based Spectral Clustering Framework for Key Frame Extraction

Video key frames are the abstraction of content rich frames of a shot or a video that reflects the nature of the whole video without redundancy. Key frame extraction plays a vital role in video summarization, content based video retrieval, video indexing, etc. Key frame extraction techniques utilize the features such as color, texture, edge, motion, object, events, etc. Among the existing key frame extraction techniques, the performance of the cluster based key frames extraction techniques is better due to its computational simplicity. However, the missing temporal order of the frames results in poor accuracy. To enhance the accuracy, this paper proposes a Row Echelon based Spectral Clustering framework (RES C) which includes the temporal grouping constraints by means of permutation matrix. The proposed technique is tested on the benchmark datasets viz. VSUMM, UCLA, VIRAT, TvSum, and WEB datasets and the accuracy is improved to 90%.
Evolutionary Algorithms for Face Recognition with Mask

Due to the Covid-19 pandemic, wearing masks in public places has become a necessity. But it also comes with its challenges, existing face recognition systems are trained to recognize faces with all the features and therefore are failing to work efficiently due to masks. To provide a potential solution to this problem and to recognize faces with masks two evolutionary algorithms, Crow Search Algorithm (CSA) and Cuttle Fish Algorithm (CFA), are used for feature selection which select an optimal subset of features from the existing dataset with vast number of features. In the last step four machine learning classifiers (Support Vector Machine, Random Forest classifier, K-Nearest Neighbor, and Decision tree classifier) are practiced on each subset of features received by both the feature selection algorithms. Experimental results show that CSA removed most of the irrelevant features by selecting only 41% of the original featured and CFA selected 60% of the features. Highest accuracy of classification was received by CSA of 86.5% with Random Forest classifier. Therefore, it shows that CSA and CFA can be used in various other real time applications due to their reduced computational cost and high accuracy.
Matching visual induction effects on screens of different size

In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images.
Research on Effectiveness Assessment of Ecological Conservation in Xinjiang Based on the Cloud Model

Abstract Aiming for effective ecological conservation in Xinjiang, an effectiveness assessment system is built on the cloud model to examine the controllability of ecological conservation based on the practice in Xinjiang. It has been found that the cloud model for effectiveness assessment fully takes into account ambiguity and randomness where the expert review falls short and can effectively improve the influence of subjective factors in the assessment of ecological conservation through parameter setting and calculation. Therefore, it leads to accurate and practical assessment results. These findings can play an important referential role in improving the practicality and effectiveness of ecological conservation in Xinjiang.
L'uso dell'articolo determinativo davanti ai singenionimi affettivi preceduti da un possessivo: un quadro statistico

Among the most complex rules of Italian grammar are those relating to the use of the definite article with possessive adjectives followed by kinship names: in this case, the article is not employed, but its omission is subject to various structural, syntactical, and semantic restrictions concerning the noun / phrase in question. Grammar books usually highlight the use of the article with plural nouns or with the possessive form loro, but they are much less careful about other features of the kinship noun / phrase in which possessives are involved (compound nouns, nouns followed by prepositional complements, etc.). Furthermore, they disagree with regard to the syntax of affective names, in which case the use of the article is often considered optional. With this study, then, I intend to analyze the use of the definite article with possessive adjectives followed by the affective kinship names mamma and papà (therefore, nouns without suffixes) in the largest corpus of written Italian, Italian Web 2016 of the Sketch Engine system. The results of this research are interesting because, statistically, the use of the article depends on the possessive adjective used in the text ( mio, tuo, suo, etc.) and, therefore, on the degree of the speaker's emotional involvement.
A Novel Feature Selection with Many-Objective Optimization and Learning Mechanism

Feature selection is extremely important in machine learning and data mining. Typical two-objective feature selection methods aim to minimize the number of features and maximize classification performance. However, they overlook the fact that there may be multiple subsets with similar information content for a given cardinality. The paper presents a many-objective feature selection approach to address this problem. Firstly, we establish a five-objective optimization model, which consists of classification accuracy, the number of features, feature relevance, feature redundancy, and feature complementarity. Therefore, the proposed model can enlarge the search space with more Pareto solutions. Secondly, we propose a wrapper structure for many-objective feature selection, which integrates a learning algorithm. Thirdly, in order to reduce the computional overhead, we propose a filter structure, which separates the learning algorithm. For implementation, we adopt NSGA-III multi-objective evolutionary algorithm and extreme learning machine. The experiments on mainstream datasets confirm the superiority of the proposed method.
Research on Fast Encryption Method for Smart Energy Management System in Smart Gird

Smart energy management system in smart grid carries a large number of sensitive data, which needs encryption algorithm to ensure the security of system communication. At present, most of the terminal devices of smart grid are embedded devices with limited computing resources, and their communication encryption mostly relies on AES encryption algorithm. It is difficult in key management and key distribution. Therefore, this paper proposes an improved ECC-AES hybrid encryption algorithm. Firstly, ECC algorithm is improved to improve the speed of encryption and decryption, and then the improved ECC algorithm is used as a supplement to AES algorithm. ECC is used to encrypt the AES key, which improves the security of the algorithm. At the same time, the experimental simulation also proves that the improved ECC algorithm has obvious performance improvement in computing time, CPU occupancy and memory usage.
Temporal sampling annealing schemes for receding horizon multi-agent planning

This paper deals with multi-agent scenarios where individual agents must coordinate their plans in order to efficiently complete a set of tasks. Our strategy formulates the task planning problem as a potential game and uses decentralized stochastic sampling policies to reach a consensus on which sequences of actions agents should take. We execute this over a receding finite time horizon and take special care to discourage agents from breaking promises in the near future, which may cause other agents to unsuccessfully attempt a joint action. At the same time, we allow agents to change plans in the distant future, as this gives time for other agents to adapt their plans, allowing the team to escape locally optimal solutions. To do this we introduce two sampling schemes for new actions: a geometric-based scheme, where the probability of sampling a new action increases geometrically in time, and an inference-based sampling scheme, where a convolutional neural network provides recommendations for joint actions. We test the proposed schemes in a cooperative orienteering environment to illustrate their performance and validate the intuition behind their design.
Blockchain-Enabled IoT Security in Automotive Supply Chain

The current automotive supply chains spread across countries and continents. With the increase in the Internet of Things (IoT), many operations involved in the automotive supply chains are automated. The current applications of IoT are centralized cloud-based systems. Such IoT-enabled automotive supply chains are prone to several security issues and cyber-attacks. The current centralized systems are not capable of handling identity management and authentication of billions of IoT devices and sensors. These systems are also susceptible to security problems such as a single point of failure for identity management and firmware updates malware. Blockchain due to its inherent decentralized and secure nature is considered to be a boon for the digital automotive supply chains. In this paper, we have discussed the IoT security threats which are observed recently in the digital supply chains. We have explored the challenges posed by the centralized nature of IoT deployments in the automotive supply chain and how Blockchain can address such challenges effectively. Further, we have discussed how Blockchain can serve as the best solution for the identified IoT security problems. Subsequently, we have proposed a decentralized architecture using Blockchain technology for the identity management and firmware update.
Soft computing model for students’ evaluation in educational institute

Abstract Now a day’s higher education is become more competitive. Students are the main pole of today’s education. All educational institutions are focusing on the quality improvement and change in the traditional evaluation methods. Due to the high competition among Private Universities and existing National Universities the scenario of evaluation methods has important role so that the students shall be kept on track as an active learner through their modified methods of evaluation. Consequently, the evaluation of students through traditional methods has limitations as it is based on the crisp boundaries. The students having a very small difference of marks can be placed into different grades. Also the students who have missed the chance of appearing for one of the subject head may be fail due to absolute method of grading. A soft computing model for students’ evaluation of student in educational institute using subject wise and other activities performance is developed in this paper. To consider uncertainties occur during the semester fuzzy logic technique is applied.