Reviewerly/exHarmony huggingface dataset:
Causal structure learning of nonlinear additive noise model based on streaming feature

The current streaming feature structure learning needs to be improved in the processing of nonlinear continuous data and the dynamic acquisition of causal structures. In this paper, we propose a causal structure learning algorithm, CANSF, based on the streaming feature of additive noise models. We have made three contributions. First, by using the information carried by the noise of nonlinear continuous data, we propose a real correlation identification method based on logarithmic likelihood, which can identify the real correlation and redundant features of target features, and dynamically select parent and child nodes for each feature. Second, based on regression analysis, a method to determine the causal direction is proposed, which can be used for dynamic orientation. Third, a learning method of causal structure based on streaming features is proposed, which can obtain the Causal structure diagram directly and dynamically.
TSTrack: Tracking by Detector with Target Guidance and Self-Attention

Object tracking can be considered as an object detection task. However, in general, an object detector cannot differentiate among intraclass instances and cannot fully exploit the spatiotemporal clues between successive frames. In order to solve these problems, in this paper, we proposed a novel object tracking framework based on object detection. We name the new framework TSTrack. Firstly, transformer is used in the target guidance module to extract the object&#x2019;s features to guide the backbone network to complete the positioning task, which can solve the problem of distinguishing the instances within the classification. Secondly, self-attention mechanism is introduced into the model, which is mainly completed by spatial attention module (SAM) and channel attention module (CAM). By using the characteristics of self-attention mechanism, the attention matrix is dynamically self-learned in two dimensions of space and channel to obtain a more robust object representation. The proposed model is evaluated on the OTB-2013, OTB-50, OTB-2015 and VOT-2016 benchmarks, and the experimental results demonstrate the excellent tracking performance and robustness of our model.
Exceeding Information Targets in Fixed-Form Test Assembly

This work studies the automated assembly of ability estimation test forms (called tests, for short) drawn from an item bank. The goal of fixed-from test assembly is to generate a large number of different tests with information functions that meet a target information function. Thus, every test has the same ability estimation error. This work proposes a new way of automated test assembly, namely, drawing tests with information functions that exceed the target. This guarantees that every test has an ability estimation error that is less than the error set by the target. The work estimates the number of target exceeding tests as a function of the number of items in the test. It demonstrates that the number of target exceeding tests is far greater than the number of target meeting tests. A Monte Carlo importance sampling algorithm is proposed for target exceeding test assembly.
Image Encryption Using Lorenz's Attractor and Fractional Fourier Transform

In this study, a new image encryption and decoding algorithm is developed using fractional fourier transform, lorenz attractor and masking. In the proposed algorithm, firstly, the image is masked with a randomly generated mask and fractional fourier transform is applied to the masked image. These two processes were applied 3 times in a row, making the image more complex. The resulting image was then decomposed into 256 parts. 16 chaotic outputs were obtained from lorenz attractor. These outputs are calculated in base 256. The parts with the same index number as the output were selected and mixed among themselves. The scrambled image parts are combined and the encryption process is completed. The degree of the function applied in the algorithm is a rational number. The use of lorenz attractor provides chaotic outputs. These 2 features make the algorithm resistant to attacks. The use of a chaotic set of differential equations is reduced the probability of finding the scrambled method by an analytical approach and the linear predictability of the method for attacks on the scrambled image. The analysis of chaotically generated random numbers was made and produced successful results. The algorithm is confidential and usable against most common attacks such as brute force. The data contained in the key is encrypted using the RSA algorithm.
Machine Learning Based Adaptive Auto-scaling Policy for Resource Orchestration in Kubernetes Clusters

The wide availability of computing devices has cleared the path for the development of a new generation of containerized apps that can run in a distributed cloud environment. Furthermore, the dynamic nature of workload demands elastic application deployment that can adapt to any scenario. One of the most popular existing container orchestration systems, Kubernetes, has a threshold-based scaling strategy that can be application-dependent and difficult to modify. Furthermore, its vertical scaling approach is disruptive, limiting deployment availability. The scaling decisions, instead of being proactive, are of reactive nature. In this work, our goal is to dynamically collect resource utilization of pods and predict future utilization for a period of time, and use the maximum utilization of that time window for proactive scaling, improving the overall resource utilization. We also contrast Kubernetes' built-in threshold-based scaling policy with a model-based reinforcement learning policy and the suggested LSTM Recurrent Neural Network-based prediction model. We demonstrate the benefits of data-driven rules, which can be combined with the Kubernetes container orchestrator.
Designing a Corpus of Czech Monologues: Orator v2

Abstract ORATOR v2 is a new 1.5M word corpus of Czech monologues, delivered to a live audience in semi-formal to formal settings. It was designed to chart the space of naturally occurring monologues which can be obtained for corpus processing. As such, it aims for diversity but does not attempt any balancing of subcategories, recognizing that some types of data are inherently easier to obtain in high volume than others. The transcription guidelines and annotation tools employed are the same as other recent spoken corpora published by the CNC, which facilitates interesting comparisons between various types of spoken Czech. The present paper sketches out three case studies, comparing ORATOR to the informal conversations of ORTOFON v2 in terms of the frequencies of demonstratives and hesitations, as well as lexical richness.
A Moving Object Detection Method for Mobile Robots Using Feature Point Sticky Tree

Moving object detection is a common task for mobile robots when working in dynamic environments. Due to the nonstatic nature of the mobile robots themselves, we need to conduct the motion detection work under the moving camera assumption. Feature-point-based object detection is an important research direction to solve this problem. The distances and motion conditions of the feature points in both 2D image space and 3D physical space are critical criteria to cluster the points to objects. However, previous work never fully considered all the criteria and explored their complex relationship with object segmentation outputs. As a result, accurate segmentation can not be achieved when the differentiation condition only presents in part of the criteria space. Therefore, we proposed a novel feature point stickiness method to include all the above considerations. A fuzzy neural network is designed to assess the stickiness between any feature points, e.g., the possibility of being on the same object. The network is trained with small data set to capture the complex nonlinear relationship between the point distance and motions and the stickiness. In addition, a sticky tree growth algorithm is used to connect the feature points according to their stickiness distributions. The formation of the trees autonomously completes the object segmentation task. The method is testified in various experimental situations, including some challenging scenarios. It improved the object recall rate and precision rate to a large extent compared to previous works and showed high potential in providing precise trajectories of moving objects in the robot’s workspace.
A Hierarchical Vehicle Motion Planning Method For Cruise In Parking Area

Unstructured roads, as a scene that autonomous vehicles must pass before they are truly applied, have received more and more attention in recent years. The technologies related to automatic valet parking and memory parking have also been extensively studied by the industry flied and academia flied. Motion planning is an important part of ensuring that autonomous vehicles can obtain reasonable driving trajectories, achieve driving goals, and avoid collision. Therefore, this paper summarizes the previous research on vehicle motion planning on unstructured environment such as parking area, and proposes a hierarchical vehicle motion planning method for the cursing in parking area. Three levels of planning, including reference trajectory generation based on APF method, initial value extraction with dynamic programming and trajectory optimization with TEB algorithm are combined to obtain a reasonable trajectory for autonomous vehicle in real time. In order to verify the above algorithm, the vehicle experiment is carried out in a parking area in Suzhou, China. Scenes such as cruise and straight-way avoidance are tested to illustrate the effectiveness of the algorithm. The results show that the proposed algorithm can effectively solve these scenes. In the future, more scenes will be tested to verify the effectiveness of the above algorithms.
An Interaction Design Dome to Guide Software Robot Development in the Banking Sector

Robotics, robotic processes, artificial intelligence and machine learning are concepts that create uncertainty among people. Technological expansion influences the preconceived, adverse feelings of dislike that users are harboring. This uncertainty influences the acceptance of software robots that are implemented in systems used by employees. This may also be the case regarding the interaction of banking employees with automated information systems used to perform their daily work activities. It is against this background that a framework for interaction design is required. Such a framework could guide designers to consider employed users and establish a positive, accepting interaction. Due to the lack of existing interaction design frameworks aimed at addressing the implementation of software robots, the study aims to answer the question: Which core components will contribute to a framework that will guide the interaction design of a software robotic system in the banking sector? An initial systematic review approach was performed to identify the influence of robotics on user experience. This literature review has identified principles in other industries that were pulled through to financial institutions. This study contributes to filling the gap in academic knowledge by exploring related literature to identify and evaluate applicable principles and integrating these into a new synthesized framework. The result of the study is a new framework called an interaction design dome. The dome can be used to guide software robot design and development from a human-computer interaction viewpoint, with specific reference to the financial sector.
Semantic Similarity and Paraphrase Identification for Malayalam Using Deep Autoencoders

In this chapter, we deal with the sentence-level paraphrase identification for the Malayalam language. We use recursive autoencoder architecture for the unsupervised learning of phrase representations to extract features for paraphrase identification. Sentence’s features of varying lengths are converted to fixed-size representation using the convolution method of dynamic pooling. Initially, the Malayalam paraphrase identification system was designed to identify paraphrases and non-paraphrases alone and later extended to identify semi-equivalent paraphrases. Along with semantic features, conventional statistical features are further taken into account, resulting in improved system performance. The proposed system was implemented using word2vec embedding and obtained 77.67% accuracy for the two-class system and 66.07% for the three-class system. This chapter also discusses different experiments done for choosing the best parameters and embedding models.
Application of New 4-D Chaotic Map for Secure IP-Communications

Chaos systems have been studied for decades due to their applications in several domains such as: economy, communications, cryptography, etc. In recent years, designing and proposing new and higher dimensional chaotic systems become an increased tendency in particular for chaotic systems applied in network security domain. In this paper, we propose a new 4-dimension chaotic map with four (04) control parameters and five (05) non-linear terms. Then, we investigate the chaotic behaviors of the proposed system by considering the bifurcation and the Lyapunov exponents (LE) theories. The proposed map is applied for generating cipher keys to perform data encryption and secure an Internet Protocol (IP) communication.
Analyzing simplified Geister using DREAM

Geister is a board imperfect information game created in Germany and presenting an interesting challenge for the field of artificial intelligence. In this study we apply DREAM (Deep Regret Minimization with Advantage Baselines and Model-free Learning), a neural-network variation of Counterfactual Regret Minimization developed by Steinberger et al., to multiple variants of Geister. This paper shows a methodological approach of evaluating game strategies on different variants of Geister and illustrates the possible generalizability of the DREAM algorithm on other board games.
Implicit Certificate Based Signcryption for a Secure Data Sharing in Clouds

Signcryption is a sophisticated cryptographic tool that combines the benefits of digital signature and data encryption in a single step, resulting in reduced computation and storage cost. However, the existing signcryption techniques do not account for a scenario in which a company must escrow an employee's private encryption key so that the corporation does not lose the capacity to decrypt a ciphertext when the employee or user is no longer available. To circumvent the issue of non-repudiation, the private signing key does not need to be escrowed. As a result, this paper presents an implicit certificate-based signcryption technique with private encryption key escrow, which can assist an organization in preventing the loss of private encryption. A certificate, or more broadly, a digital signature, protects users' public encryption and signature keys from man-in-the-middle attacks under our proposed approach.
3D Brawler Game Using a Hybrid Planning Approach

We present a hybrid planning architecture that combines both Goal-Oriented Action Planning (GOAP) and Hierarchical Task Network (HTN) planning to avoid the enemies' predictable behaviors and dynamically adapt their actions according to the game-play context. The proposed approach is assessed and illustrated through a 3D custom brawler game where the player character fights invaders by controlling fluid material. Although the planning is computationally expensive, the first results show that our planner is efficient/fast and the game session has enjoyable playability.
On the estimation of the Bernoulli regression function using Bernstein polynomials for group observations

Abstract The estimate for the Bernoulli regression function is constructed using the Bernstein polynomial for group observations. The question of its consistency and asymptotic normality is studied. A testing hypothesis is constructed on the form of the Bernoulli regression function. The consistency of the constructed tests is investigated.
Cooperative Learning With and About Internet Forums: A Case Study on a Unit on the Consumption and Chemistry of Mineral Water vs. Tap Water

In today’s society, digital media play an increasing role in gathering and exchanging information. A growing part of communication takes place in the Internet and many people are increasingly influenced by information provided via digital and social media. Development of critical media literacy is needed, if the general public is expected to effectively deal with this flood of information and to become able to distinguish between correct and false information sources. Thus, critical media education becomes an important aim of education in general, and of chemistry education in particular when considering questions directly related to chemistry and its associated consumer products or technologies. The article describes a curriculum development case study investigating the integration of media education with chemistry learning along the case of learning with and about Internet forums on the topic of water chemistry. A unit integrating theoretical and practical chemistry learning based on student communication is described, which is built around a digital forum operated by Moodle. The unit design and findings from the implementation are presented.
An Unknown Protocol Clustering Analysis Method Based on Spectral Clustering

To study unknown protocols in networks, the primary task should be to unravel the unsupervised agglomeration of unknown protocols; To the current finish, associate degree algorithmic rule for unknown protocols combined with spectral agglomeration is projected, borrowing the idea of the longest common subsequence; For unknown protocols with totally different payload characteristics like camouflage or customization, the longest common subsequence is employed to live the similarity of samples, combined with spectral agglomeration, the protocol agglomeration drawback is regenerate into a graph partition drawback. The experimental results show that the algorithmic rule encompasses a smart agglomeration effect; and also the optimum agglomeration sequence threshold for the corresponding dataset is tested throughout the experiment, showing that totally different protocols square measure well-distinguished inside the edge.
Selective part‐based correlation filter tracking algorithm with reinforcement learning

In visual object tracking methods, improving both the run time and the accuracy in the face of complex situations has always been an important issue. Many complex tracking algorithms, such as part-based algorithms, have better accuracy when facing occlusions, but they have much greater computational complexity. In response to the above problems, this paper proposes a selective part-based correlation filter (SPCF) tracking algorithm with a reinforcement learning to achieve more stable and efficient tracking of targets. First, according to the conditions of the response map of the correlation filter (CF), the entire tracking process is divided into three states: simple environments, complex environments, and harsh environments. Second, this paper uses reinforcement learning to determine the states of frames in different situations to improve the tracking effect of the algorithm. Third, the process of the online selection of states is transformed into a Markov decision process (MDP), where the policy learning of the MDP is achieved by reinforcement learning. Additionally, different strategies are used to track a target in different states: the overall filter is used to increase the speed in simple environments; part-based filters are used to improve the accuracy in complex environments; and in harsh environments where the target completely disappears, a redetection algorithm is used to find the target when it reappears. Finally, the performance of the tracking algorithm is verified on the VOT2018, OTB-2015, and LaSOT datasets.
A Flight Simulation Model Architecture for Reinforcement Learning

For the reinforcement learning of agents in virtual environment, a flight simulation model architecture is designed by combining the integration method, flight algorithm, external force calculation, earth model and other factors in flight simulation. This architecture realizes the separation of data and algorithm in the process of flight computation at the software level, and adopts inheritance and pointer registration mechanism to achieve unified and hierarchical management of data related to flight computation. The state transfer model of aircraft is designed to realize the smooth connection of flight states between different stages, which can realize efficient large-scale parallel computation in reinforcement learning. The architecture proposed in this paper provides a flexible and efficient interaction mechanism for enhancing reinforcement learning of aircraft agents.
A Software Environment to Develop Radar Resource Management Algorithms

Multifunction radars can search for targets, confirm new tracks, and revisit tracks to update the state. To perform these functions, a multifunction radar is often controlled by a resource manager that creates radar tasks for search, confirmation, and tracking. Multi-target tracking often requires sensor resources to be allocated efficiently to update the different targets while continuing to search for new targets to track. In this work, we present a software environment to develop radar resource management algorithms. Resource management and tracking are closely coupled, especially when tracking maneuvering targets that require the radar to revisit the targets more frequently than tracking non-maneuvering targets. We demonstrate how using an Interacting Multiple Model (IMM) filter, which estimates when the target is maneuvering, helps to manage the radar revisit time and therefore minimizes the resource needed for tracking. We show how existing software tools can help develop and test resource management systems.
ONSET: Opinion and Aspect Extraction System from Unlabelled Data

Online businesses are highly interested in finding practical solutions to opinion mining, but it is challenging to extract aspects and sentiments from the text. One way to solve this problem is to fine-tune good quality extractions from reviews using state-of-the-art pre-trained language models. However, such fine-tuned language models can produce good results if trained with a large amount of relevant data. In this paper, a technique that can fine-tune language models for opinion extractions using unlabelled training data. This paper proposes a novel opinion mining system called ONSET. This system is developed through a fine-tuned language model using an unsupervised learning approach to label aspects using topic modeling and then using semi-supervised learning with data augmentation. With extensive experiments performed during this research, the proposed model can achieve similar results as some state-of-the-art models produce with a high quantity of labelled training data. F1-scores of 87.30% and 88.35% are achieved on SemEval Aspect-Based Sentiment Analysis and Twitter datasets, respectively.