Reviewerly/exHarmony huggingface dataset:
Sequencing of multi-robot behaviors using reinforcement learning

Abstract Given a collection of parameterized multi-robot controllers associated with individual behaviors designed for particular tasks, this paper considers the problem of how to sequence and instantiate the behaviors for the purpose of completing a more complex, overarching mission. In addition, uncertainties about the environment or even the mission specifications may require the robots to learn, in a cooperative manner, how best to sequence the behaviors. In this paper, we approach this problem by using reinforcement learning to approximate the solution to the computationally intractable sequencing problem, combined with an online gradient descent approach to selecting the individual behavior parameters, while the transitions among behaviors are triggered automatically when the behaviors have reached a desired performance level relative to a task performance cost. To illustrate the effectiveness of the proposed method, it is implemented on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation.
Comparison and Analysis of Convex Optimization Methods for Potential Field-based Path Planning

This paper addresses the problem of the optimization of potential field-based path planning which is a well-known path planning method using attraction fields to goal and repulsive fields from obstacles. The convex optimization methods can be applied to potential field-based path planning to obtain the more efficient path. This paper presents how the convex optimization methods can converge a mobile robot to the goal and analyzes the convergence time according to different environments and methods. The simulation results show that the convex optimization methods were conducted successfully. and the Newton&#x0027;s method was conducted faster than the Gradient descent method.
Lawyering Social: Navigating Legal Issues on Social Media Posts with a Low-cost Data Algorithm

Along with the increasing use of online communication during the current Covid-19 pandemic, people have become more active in the social media platforms like Facebook and Twitter, whether it be through by posting their content online or by reading the posts of others. Undoubtedly, it has a negative impact in the form of cybercrimes, especially when public understanding of cyber law is still low. To address this issue, we propose an approach to identifying cybercrime on social media posts, with case studies on Indonesian cybercrime law. The main challenge in solving the problem is the use of informal language and non-standard writing. Therefore, we exploit the informative value of terms in that post by paying attention to standard patterns in Indonesian grammar and writing structure with a classifier based on the voting ensemble learning model. The experimental results show that the proposed approach significantly outperformed baselines, with an accuracy of more than 90&#x0025;. Thus, it proves the effectiveness of the approach in generalizing the content of social media posts according to the category of cybercrime law.
Research on Recognition and Classification Technology Based on Deep Convolutional Neural Network

Deep convolutional neural network is one of the most popular research topics in the field of computer vision. It has the function of extracting image feature information, has strong nonlinear classification ability, fast learning speed, and can be used for image recognition and classification. This paper makes use of its image recognition and classification function to carry on the research of its recognition and classification technology in oil painting schools. Through the ResNet network structure of a deep convolutional neural network, a data set is constructed by load data function, and then embedded into a SEBlock model, the accuracy and generalization ability of image recognition and classification of the deep convolutional neural network can be greatly improved. Among them, the SE model has strong effectiveness and generalization ability. For example, the accuracy of the SE-ResNet-34 is 1.73% higher than that of the ResNet-34, and the accuracy of the SE-ResNet-50 has reached that of the ResNet-101. The SE model is applied to the deep convolutional neural network to improve classification accuracy and reduce errors.
Algorithmic Optimization Effects on Energy Consumption for Real time Object Tracking

Due to the massive quantity of information and the excessive use of filters, Image processing becomes more and more costly at the runtime level. It remains a very complex operation at the programming level mostly in specific real time application. However, several methods have been developed to improve the system performance. In this work, we investigate the performance of object tracking algorithm implemented in raspberry pi3. We use an hybrid method based on combination of color and edge method. Subsequently, we prove that this embedded system presents high performance with an optimal conservation of energy. We present The Simulation results into the support of theoretical analysis.
Security Analyze with Malicious Nodes in Sharding Blockchain Based Fog Computing Networks

Blockchain technology is used to improve the security of users data in the network. However, the traditional blockchain structure is not suitable for the fog network due to the low throughput and scalability limitations. To solve the above issues, in this paper, we propose a sharding blockchain to improve the security of the fog network, while increasing the throughput. Sharding blockchain will divide the network into several shards, and each of them could process the transactions parallelly. In addition, the normalized entropy of the fog network could be calculated by the main opinion and secondary opinion in the consensus result. Then, the main-chain layer could calculate the probability of malicious fog nodes (FN) in the network and the maximum number of shards. Finally, the S-type fog nodes assignment algorithm (S-NA) is proposed to optimize the association between shards and FNs, which combines the greedy algorithm and the max-min fair algorithm. Simulation results verify the efficiency of the proposed S-NA algorithm.
Design of College Teaching Quality Evaluation Based on Apriori Algorithm

Based on Apriori algorithm, this paper designs a university TQES. This system selects the university TQES based on Apriori algorithm as the research object, and provides a large number of teaching behavior data for the network teaching mode. The evaluators can comprehensively and objectively evaluate each learning node, and summarize the corresponding teaching data and evaluation results in order to complete the analysis of network teaching mode. Firstly, this paper discusses the theoretical research of the system and introduces the Apriori algorithm, then designs each module of the system, and finally tests the function of the system. The experimental results show that it can effectively play the role of class education department, promote the reform of teaching methods, and promote the improvement of teachers’ teaching skills and students’ learning ability.
Semantic interaction learning for fine‐grained vehicle recognition

Abstract Fine‐grained vehicle recognition is a challenging problem due to high inter‐class confusion among vehicle models under the influence of pose and viewpoint. To effectively describe the discriminative characteristics, many approaches try to learn detailed information from an individual image. Inspired by Siamese network that addresses the case where two inputs are relatively similar, the semantic interaction learning network (SIL‐Net) is designed to discover semantic differences between two fine‐grained categories via pairwise comparison. Specifically, SIL‐Net first collecting contrastive information by learning the mutual feature of input image pair, and then compare it with individual features to generate corresponding semantic features. These features learn semantic differences from contextual comparison, this gives SIL‐Net the ability to distinguish between two confusing images via pairwise interaction. After training, SIL‐Net can adaptively learn feature priorities under the supervision of the margin ranking loss and converge quickly. SIL‐Net performs well on two public vehicle benchmarks (Stanford Cars and CompCars), showing the suitability of SIL‐Net to fine‐grained vehicle recognition.
Strong Euler well-composedness

Abstract In this paper, we define a new flavour of well-composedness, called strong Euler well-composedness. In the general setting of regular cell complexes, a regular cell complex of dimension n is strongly Euler well-composed if the Euler characteristic of the link of each boundary cell is 1, which is the Euler characteristic of an $$(n-1)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -dimensional ball. Working in the particular setting of cubical complexes canonically associated with $$n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> D pictures, we formally prove in this paper that strong Euler well-composedness implies digital well-composedness in any dimension $$n\ge 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> and that the converse is not true when $$n\ge 4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> .
Analysis of Artificial Intellectual Agents for E-Learning Systems

The key terms and basic concepts of the agent are analyzed. The structured general classification of agents according to the representation of the model of the external environment, by the type of processing information and by the functions performed is given. The classification of artificial agents (intellectual, reflex, impulsive, trophic) also is s analyzed. The necessary conditions for the implementation of a certain behavior by the agent are given, as well as the scheme of functioning of the intelligent agent. The levels of knowledge that play a key role in the architecture of the agent are indicated. The functional diagram of a learning agent that works relatively independently, demonstrating flexible behavior. It is discussed that the functional scheme of the reactive agent determines the dependence on the environment. The properties of the intelligent agent are described in detail and the block diagram is indicated. Various variants of agent architectures, in particular neural network agent architectures, are considered. The organization of level interaction in the multilevel agent architecture is proposed. Considerable attention is paid to the Will-architecture and InteRRaP- architecture of agents. A multilevel architecture for an autonomous agent of a Turing machine is considered.
Attacks on Voice Assistant Systems

Voice assistant systems (e.g., Siri, Alexa) have attracted wide research attention. However, such systems could receive voice information from malicious sources. Recent work has demonstrated that the voice authentication system is vulnerable to different types of attacks. The attacks are categorized into two main types: spoofing attacks and hidden voice commands. In this chapter, how to launch and defend such attacks is explored. For the spoofing attack, there are four main types, such as replay attacks, impersonation attacks, speech synthesis attacks, and voice conversion attacks. Although such attacks could be accurate on the speech recognition system, they could be easily identified by humans. Thus, the hidden voice commands have attracted a lot of research interest in recent years.
An Experiential Approach to Support Learning of Cyber Physical Systems Concepts involving Mixed Reality Platforms

The potential of adopting cyber learning approaches to improve student learning and engagement is an important area of educational research. The advent of Virtual Reality (VR) based learning environments or Virtual Learning Environments (VLEs) has ushered in the cyber-based educational era which involves smart technologies supporting experiential learning and other approaches to enrich the learning experiences in Science, Technology, Engineering, and Mathematics + Computing (STEM+C) topics. In this paper, the discussion of such experiential activities to teach advanced concepts in cyber-physical systems using process contexts from NASA's Moon Mission is discussed. There are two thrusts: (1) studying the potential of experiential activities where engineering and computer science students are taught to design and build such cyber-physical environments using Mixed Reality (MR) involving astronaut training scenarios (2) assessing the impact of immersive VLEs using low-cost platforms such as the Vive in learning science and engineering concepts. The results of such learning experiences in terms of engagement and learning on students are discussed. The overall conclusion is that such experiential activities involving VR/MR technologies have the potential to impact student learning in a positive manner.
INKAD COVID-19 IntelliSearch: a multilingual search engine for answering questions about COVID-19 in real-time from the scientific literature

Thousands of research papers on COVID-19 have been published since the start of the pandemic. To find relevant information in this vast literature, researchers and healthcare information professionals, spend increasingly more time per search query. In this paper, we present INKAD COVID-19 IntelliSearch, a multilingual search engine that we built to help researchers and healthcare information professionals in finding precise and relevant information from the COVID-19 literature in real-time, while considerably reducing time spent per search query. We used the COVID-19 Open Research Dataset as the main source of papers. The search engine has a BM25 based document retrieval component, and a neural question-answering component returning the exact answer span. The overall system is evaluated against a COVID-19 question-answering test set with different information retrieval and question-answering models. We have made INKAD COVID-19 IntelliSearch accessible online for broader use by researchers and medical information professionals.
Dependable Decentralized Reputation Management System for Vehicular Ad Hoc Networks

Reputation management systems are essential for establishing trust among network users. They are tools for reinforcing cooperation and sanctioning malicious behavior. Their importance becomes a requirement in highly mobile and volatile environments, such as vehicular ad-hoc networks (VANET). In the present work, we propose a dynamic and decentralized reputation system that fits VANET characteristics like dynamism and volatility without conceding on security. The novel system uses blockchain for reputation information aggregation and storage and secure multiparty computation (SMC) to achieve privacy. Unlike previous VANET reputation systems, our system does not rely on a central authority to evaluate trustworthiness. Instead, it limits its role to maintaining the blockchain and system integrity leaving reputation evaluation to peers. It preserves feedback privacy in the presence of up to n − 2 dishonest parties and shows good performance. Remarkably, our system takes advantage of the infrastructure of VANET when vehicles are nearby while remaining fully functional in extra-urban areas.
Investigation of the algorithm for face substitution in a video sequence using deep learning

In this article, a number of scientific articles on the topic under study were studied, analysis and structuring of the studied information were carried out. The existing implementations of this technology were studied and a comparative analysis was carried out. As a result of the work, this review article was written, which combines structured information from various sources, both Russian and foreign. Also, a software module was developed that shows its effectiveness in the process of work. This module will be used further in the further stages of the program development. The effectiveness of this work lies in the study and generalization of a large amount of foreign material, since in Russian science, this topic has been little studied and there is only a small amount of scientific material corresponding to the topic.
See something, say something? Coordinating the disclosure of security vulnerabilities in Canada’s infrastructure

Ill-intentioned actors are rapidly developing the means to exploit vulnerabilities in the software and infrastructure of governments around the world. Numerous jurisdictions now facilitate coordinated vulnerability disclosure for such public systems, providing good faith security researchers a predictable and cooperative process to disclose security vulnerabilities for patching before they are exploited. This study identifies that Canada may be falling behind its global peers by failing to implement such reporting procedures. It indicates the need for a straightforward vulnerability disclosure and remediation path involving federal systems, linked to improved legal frameworks and government policies for security vulnerability discovery and disclosure in Canada and beyond.
A High-Performance and Flexible FPGA Inference Accelerator for Decision Forests Based on Prior Feature Space Partitioning

Recent studies have demonstrated the potential of FPGAs for accelerating the inference computation of decision forests (DFs). However, designing a high-performance architecture that is flexible enough to be adopted in various scenarios of FPGA resource requirements remains a challenge. To address this, we propose a DF inference method that makes a transformation from traversing trees into traversing feature spaces. Specifically, as a preprocessing step, we partition each feature space into multiple regions based on thresholds. The inference task for an input data point is then conducted by (1) determining which region in each feature space the data point belongs to and (2) combining the inference information in these regions. The regularity of the computation allows us to design a DF inference architecture, called FT-DFP (Feature-space Traversing Decision Forest Processor), that can be flexibly configured for different performance and FPGA resource usage requirements. We prototype FT-DFP on a low-end FPGA (Artix-7) board and evaluate it using four real-world datasets. The evaluation results show that (1) the flexibility of FT-DFP allows us to fit a wide variety of DF models into low-end FPGA devices with limited resources; (2) FT-DFP's performance is comparable to the best of existing accelerators implemented on high-end FPGA devices and 3.04 × higher than Hummingbird, a state-of-the-art GPU-optimized implementation, running on a high-end GPU; and (3) FT-DFP is 130.96 × more energy-efficient than Hummingbird.
Robust Cyber-threat and Vulnerability Information Analyzer for Dynamic Risk Assessment

Cyberspace has been expanding drastically integrating new generation hardware, software, and other devices. The scope of adversary risks expands exponentially as the depth and variety of this integration rises. Attackers can use sophisticated tools and strategies to continually investigate these systems for crippling flaws. To conduct cyber risk analysis, cyber defenders typically rely on cyber-threat information to uncover vulnerabilities, exploitation methodologies, possible impact, and patch availability. The accuracy of the analysis is contingent on the availability and accuracy of the obtained cyber-threat data which is quite challenging to achieve given that multiple sources report them without enough completeness, therefore, it is critical to design a reliable and comprehensive knowledge-base. In this paper, we present Cyber-threats and Vulnerability Information Analyzer (CyVIA), a dynamic, scalable framework for conducting continuous risk assessments of cyber infrastructures. CyVIA leverages concrete ways of analyzing anomalies and is designed to 1) effectively combine vulnerability information from multiple sources into a unified multi-formatted knowledge-base, 2) identify vulnerabilities within a target network, 3) classify vulnerabilities based on their relationships with other vulnerabilities and computing products. We evaluate CyVIA on an industrial network to discover vulnerabilities and discuss their relationships with other vulnerabilities through our results. Furthermore, we identify several inconsistencies observed in various vulnerability information sources so that they can be corrected or removed during risk analysis.
OSTGazeNet: One-stage Trainable 2D Gaze Estimation Network

Gaze estimation refers to estimating the user's gaze information, such as the gaze direction. Recently, various deep learning-based methods for gaze estimation which are robust to lighting conditions or occlusions have been introduced. Previously proposed methods for gaze estimation were mainly composed of 2 different steps, one is for localizing the eye landmarks and another is for regressing the gaze direction. In this paper, we propose a novel one-stage trainable 2D gaze estimation network, namely One-stage Trainable 2D Gaze Estimation Network(OSTGazeNet), in which the localization of eye landmarks and the regression of the 2D gaze direction vector are integrated into the one-stage trainable deep learning network. OSTGazeNet used Stacked Hourglass Network as a backbone network, and the pixel coordinates of eye landmarks in 2D image space and a normalized gaze direction vector in the spherical coordinate system are estimated simultaneously in OSTGazeNet. About the learning of the network, we used synthetic eye images dataset named UnityEyes for training and also used an unconstrained eye images dataset named MPIIGaze for the evaluation. We performed experiments to determine the hyperparameters of learning and used mean square error as a performance metric. The best performance was a mean square error of 0.038838, and the inference time was 42 FPS.
Prototyping of Deep Learning for Selling Tourism Merchandise and Donating Traditional Dance Performance

This paper examines the utilization of deep learning which is designed for selling-buying transaction in tourism industry as well as giving donation to traditional dance video. The designated application is named ART LIVE. It uses a method of Deep Learning which is specified into Convolutional Neural Network (CNN). It is a method that can detect and classify objects through video as well as categorize them into the names of specific items. The video that has been shown and watched via the application is broadcasted live. The customers can watch the video and conduct purchasing transaction of the tourism merchandise or giving donation to the dance performance which is performed live as well. The sellers of tourism merchandise can easily post the video which show their products without bothering to type the stuffs that they sale. The dance performers can also easily promote and broadcast their traditional dance while getting income via donation.