Reviewerly/exHarmony huggingface dataset:
Extensions of the open-source framework Aerostack 3.0 for the development of more interactive flights between UAVs

The basis for properly verified R&D works is to provide reliable prototyping tools at three most important stages: computer simulation, laboratory tests and real-world experiments. In the laboratory-limited conditions, particular importance is attributed to the first two stages, especially in the context of the safety development of autonomous flights of unmanned aerial vehicle (UAV) groups in various missions. The open-source framework Aerostack support those needs and its effectiveness has been proven in the International Micro Air Vehicle Indoor Competitions (IMAV 2013, 2016, 2017) and Mohammed Bin Zayed International Robotics Challenge (MBZIRC 2020). In the paper, the exemplary functionalities for the new version of Aerostack Version 3.0 Distribution Sirocco (Aerial robotics framework for the industry), extended additionally with a library of new behaviors, are presented. The mission of UAVs can be developed fast and effectively in order to conduct test flights with real drones in lab, before one will decide to fly autonomously outdoor. The representative results obtained for low-cost AR.Drone 2.0 UAV models in two missions, are presented. The first mission is autonomous patrolling the area by a pair of UAVs, the second - intercepting the intruder in guarded area by the guard UAV.
All Pairs Suffix-Prefix Matches using Enhanced Suffix Array

The problem of `all-pairs suffix-prefix overlap' has been studied earlier using the generalized suffix tree (GST) and generalized suffix array (GSA). An alternative form of generalized enhanced suffix array (GESA), name Alternative-GESA or AGESA was introduced and used to compute suffix-prefix-overlap. This study didn't consider multiple sentinel character in Alternative-GESA. It also exploited how to use AGESA without sentinel characters for the `all-pair suffix-prefix overlap' problem. In this approach, proposed algorithms utilized the advantage of locality-of-reference for matching suffixes, those occur enough apart in actual GSA or GESA due to the high ASCII value of sentinel characters. All-pairs suffix-prefix overlap was computed in O(n)+o(m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) time and space complexity, where m and n are the total number and total length of all input strings, respectively.
The Majority Rule: A General Protection on Recommender System

Recommender systems are widely used in a variety of scenarios, including online shopping, social network, and contents distribution. As users rely more on recommender systems for information retrieval, they also become attractive targets for cyber-attacks. The high-level idea of attacking a recommender system is straightforward. An adversary selects a strategy to inject manipulated data into the database of the recommender system to influence the recommendation results, which is also known as a profile injection attack. Most existing works treat attacking and protection in a static manner, i.e., they only consider the adversary's behavior when analyzing the influence without considering normal users' activities. However, most recommender systems have a large number of normal users who also add data to the database, the effects of which are largely ignored when considering the protection of a recommender system. We take normal users' contributions into consideration and analyze popular attacks against a recommender system. We also propose a general protection framework under this dynamic setting.
An Efficient OpenGL-Based Image Processing Method on Radar Echo Data

This paper is designed to explore a thinking of a software-based radar data processing to minimize the hardware dependence. Based on the analysis of present coordinate transformation method and by Graphics Processing Unit that can perform efficient parallel computation, a new method is proposed, which will first pre-process those original spoke data of radar into the two-dimensional undamaged azimuth and distance image cache and then convert the image cache into the polar coordinate image required by PPI display end through coordinate mapping matrix. OpenGL texture buffering rendering mechanism will be used for the efficient rendering of polar coordinate image while the multi-threading processing method will be used for radar target detection for undamaged images so as to meet the parallel processing requirement of efficient processing and real-time rendering for radar echo with large amount of data.
A Modified Syn2Real Network for Nighttime Rainy Image Restoration

The restoration or enhancement of rainy images at nighttime is of great significance to outdoor computer vision applications such as self-driving and traffic surveillance. While image deraining has drawn increasingly research attention currently and the majority of deraining methods are able to achieve satisfying performance for daytime image rain removal, there are few related studies for nighttime image deraining, as the conditions of nighttime rainy scenes are more complicated and challenging. To address the nighttime image deraining issues, we designed an improved model based on the Syn2Real network, called NIRR. In order to obtain good rain removal and visual effect under the nighttime rainy scene, we propose a new refined loss function for the supervised learning phase, which combines the perceptual loss and SSIM loss. The qualitative and quantitative experimental results show that our proposed method outperforms the state-of-the-arts whether it is on the synthetic nighttime rainy image or on the real-world nighttime rainy image.
Text Mining in Determining the Consistency of Lecturer Research on Science Discipline

This research is focused on evaluating the consistency of research conducted by lecturers on their scientific knowledge. With so many studies and diverse schemes, of course, it also produces varied research. Nevertheless, the quality of research is also the main focus of LPPM. To maintain this, one way to maintain the quality of research is to maintain the consistency of lecturers in researching following their scientific fields. Research that focuses on scientific fields will produce quality research. The products produced can also be accepted by the public and the market. in determining the consistency value of research conducted by lecturers on their scientific disciplines can be achieved with a text mining approach. Using mining and text analysis to gain insight into the topics of lecturer research, it can help writers detect the linear value of research with their field of science. TF-IDF algorithm can recognize the similarity of data in a query that has been stored as a data set. Big data sets greatly affect the success of the system in recognizing the consistency of lecturers’ research titles on their knowledge.
Simple scheme for creation of persistent optical event horizons

We demonstrate numerically and experimentally a simple scheme for self-generation of a highly efficient and long-lasting fiber optical event horizon which requires only standard equipment in an optical laboratory.
A novel tree-topology based routing algorithm for partially-trusted QKD networks

For the security requirements of high-speed and large-capacity transmission, quantum key distribution technology plays an important role in optical networks. In the process of deploying QKD networks in the future, the partially- trusted QKD network will be a more realistic scenario. To solve the QKD with an untrusted relay, MDI-QKD protocol is designed, In MDI-QKD, two QKD nodes send the quantum states to the untrusted relay and the measurement device in the untrusted relay measures the received quantum states. Based on the overall feasibility of QKD-integrated optical networks and the common tree topologies in backbone optical networks, this paper proposes a quantum key distribution method and a routing algorithm in a partially-trusted QKD tree-topology network by combining the trusted relay technology and MDI- QKD protocol and considering the quantum key pool. Simulation results show that the proposed TT-AR algorithm is effective for tree topologies.
The Customization and Implementation of Computer Teaching Website Based on Moodle

Moodle is based on the theory of constructivism, it is used for construction of internet-based courses and the package of web site’s open source code. Moodle supports diversified ways of teaching and learning. It can create a good network learning environment for learners’ autonomous learning, collaborative learning and personalized learning. It also can bring convenience for teachers and school teaching. In view of the advantages of Moodle system, we select it as a platform for the construction, development and application of the college of computer science and technology subject. At first, this paper used the literature research to understand the development idea and the function characteristics of Moodle, and analyzes the architecture of Moodle and related technologies, especially the required environment, conventions and ways of the second exploitation of Moodle theme. It shows the difficulty of the development of each way. Then redefining professional subdirectory according to different requirements of activities module and function module. We designed a typical of college computer science and technology of Moodle theme taking into account from the overall style, color system, font and layout, fresh graduates courses and graduate thesis submit. Finally summarizes the main work of development theme and the problems still needing to improve, equally looks forward to the future work.
AAYU APP - Mobile Application for Healthcare Management System

In the healthcare domain, the mortality rates are increasing unprecedentedly by shifting a critical patient from one hospital to another hospital due to the lack of necessary medical equipment. This arises the need to propose a novel mobile application, which can show the information about different hospitals with necessary equipment in a particular location. This paper has proposed the novel application to mange the information about different hospitals in the nearby localities of the user and also it is developed to provide a basic information about medicines. Further, in the case of emergency situations, the proposed application plays an indispensable role by assisting the user in taking the appropriate first aid measures.
Laying the path to consumer-level immersive simulation environments

Virtual reality is slowly transitioning from a specialized laboratory-only technology, to a consumer electronics appliance. In this transition, two interesting research questions amount to how 2D-based content and applications may benefit (or be hurt) by the adoption of 3D-based immersive environments and to how to proficiently support such integration. Acknowledging the relevance of the former, we here consider the latter question, focusing our attention on the diversified family of PC-based simulation tools and platforms. VR-based visualization is, in fact, widely understood and appreciated in the simulation arena, but mainly confined to high performance computing laboratories. Our contribution here aims at characterizing the simulation tools which could benefit from immersive interfaces, along with a general framework and a preliminary implementation which may be put to good use to support their transition from uniquely 2D to blended 2D/3D environments.
Construction and Leverage Scientific Knowledge Graphs by Means of Semantic Technologies

Scientific production is a key issue in the assessment of universities’ research. Due to the information overload in the publishing domain, it is increasingly difficult for experts, and decision-makers to find relevant information to carry out their research or management tasks. To address this problem, Knowledge Graphs are used to explore and connect publishing sources by increasing their value. In this paper, the authors propose a process to construct a scientific knowledge graph based-on publications’ metadata. A case study implemented in a given university is explained. Finally, some visual representations are created from results returned by SPARQL queries. Thus, people will find relationships and patterns underlying scholarly production.
A Hybrid Multiple Classifier System Applied in Life Insurance Underwriting

In an insurance company, manual underwriting is costly, time consuming, and complex. Simulating underwriters with AI is an absolutely time saving and cost fitting solution. As a result, a Hybrid Multiple Classifier System, combining three classifiers with rejection options: XGBoost, Random Forest, and SVM, was designed and applied on production. An optimal rejection criterion on classification, so-called Linear Discriminant Analysis Measurement (LDAM), is applied the first time in industry. This system is the first AI driven underwriting system in Canadian life insurance, and it helps Manulife expand digital capabilities, reorient customer experience focus and grow its business.
TriSpaSurf: A Triple-View Outline Guided 3D Surface Reconstruction of Vehicles from Sparse Point Cloud

Vehicle is one of the important subjects studied in the domain of computer vision, autonomous driving and intelligent transportation system. 3D models of vehicles are used widely in the literature of vehicle categorization, pose estimation, detection, and tracking. However, previous work uses only a small set of 3D vehicle models either from CAD design or multi-view reconstruction, limiting their representation ability and performance. A feasible approach to acquire extensive 3D vehicle models is desired. In this paper, we are interested in 3D surface reconstruction of on-road vehicles from sparse point cloud captured by laser scanners equipped ubiquitously on autonomous driving platforms. We propose an innovative reconstruction pipeline and method, called TriSpaSurf, which could reconstruct unbroken and smooth surface robustly from just a single frame of noisy sparse point cloud. In the TriSpaSurf, triple-view 2D outlines are first fitted on the 2D points from the projection of 3D point cloud under each view, and then 2.5D surface reconstruction is carried out under the guidance from triple-view outlines. By projecting 3D point cloud onto 2D views, 2D outlines could be estimated robustly due to the reduced complexity and higher signal-to-noise ratio in 2D views, and could provide fairly stable and tight multi-view constraints for 3D surface reconstruction. The effectiveness of our method is verified on the KITTI and Sydney dataset.
Background Cleaning and Direction Weight in Salient Object Detection

Background prior is widely-used knowledge in salient object detection, and geodesic distance is based on background prior knowledge. To measure the difference between a pixel and image boundary, geodesic distance depends on a path which has the lowest cost to access the image boundary, and the cost is the value of distance. Areas with low geodesic distances are considered to be backgrounds, and the high are foregrounds. However, when some background areas are surrounded by areas that differ greatly in their colors, this best path to the boundary also needs a large cost and contributes to the high saliency of these areas. To address this problem, we propose a background cleaning (BC) method. For more ideal results in datasets, this BC method should be adaptively used. RBD (robust background detection) is a successful method in salient object detection. It is based on the global contrast information, but global contrast is easily affected by noise. In previous work, MDC (minimum directional contrast) was proposed. And we find that the directional contrast information can improve RBD method, so we use the minimum contrast information of four directions. Experimental results on two public datasets show that our method performs better than RBD, MDC and some widely-used methods.
Socio-economic aspects of cash withdrawal from use

The notion of a cashless society is slowly becoming an inevitability of the modern way of doing business. Withdrawal of cash from use is the result of wide application of information and communication technologies. Increasing digitalization has contributed to the fact that most transactions are performed via smart devices (phones, tablets, desktops), without the use of cash and without going to the bank. The development of technological innovations, as well as innovations in finance, has undoubtedly contributed to increasing efficiency in business, but the question is whether the increasing digitalization of life and business, which is reflected in the creation of a cashless society, is still so desirable for humanity. The paper focuses on the socio-economic aspects of withdrawing cash from use. On the one hand, states are given the opportunity to influence economic activities even more directly through their central banks, while on the other hand, the issue is raised concerning human freedoms and rights in the digital world, in which it will be possible to electronically control the entire business.
Anomaly monitoring in high-density data centers based on gaussian distribution anomaly detection algorithm

Anomaly monitoring has always been an important issue for data center operations and maintenance. Traditional data centers use human and physical sensors to conduct inspections and risk analysis, which consumes a lot of manpower and material resources. Therefore, based on the Anomaly Detection algorithm based on Gaussian distribution, this paper skillfully proposes an anomaly detection algorithm Hddcad suitable for high-density data centers, which greatly improves the efficiency of anomaly monitoring in the data center and reduces the data center. Management costs in high-density designs.
E-Chain: Blockchain-Based Energy Market for Smart Cities

Electricity is the most critical input product for most businesses in the world nowadays. The utilization of electricity has assisted in the discovery of breakthrough disclosures and advances and has in time, become the most crucial constraint in the development of economies. With the introduction of distributed energy resources in microgrids, energy users are gradually turning into prosumers i.e., users who simultaneously produce and consume energy. This has brought about peer-to-peer (P2P) energy markets where prosumers can trade their surplus energy. However, there are privacy and security issues associated with such markets. Prosumers find it insecure to trade their power in an untrusted and nontransparent environment. In a P2P energy market, an intermediary oversees the transaction process between parties on the market and this poses a threat to the privacy of the prosumers. Consequently, a unified and robust energy trading market is required for prosumers to trade their energy. To address these security and privacy challenges, we leverage the properties of the blockchain innovation to propose a secure energy trading market where prosumers can trade their energy without compromising their privacy.
AI-Driven Contextual Virtual Teaching Assistant Using RASA

With traditional classes moving to online platforms and the need for online content growing exponentially, there is a deficit of customized content that helps students understand concepts well. It is the need of the hour to have round the clock support and resources to ensure smooth transition and continuity of academics. Our solution proposes the use of RASA, an open-source conversational Artificial Intelligence (AI) framework, to support students with contextual help and provide them with resources like specific slides of a presentation or real-world problems discussed in class in the form of a Virtual Teaching Assistant. It can also ask leading questions to provide tailored answers. Its responses are based on the material the Instructor teaches in class, making it relatable to the students, unlike generic responses. The Contextual Virtual Teaching Assistant will also assist the Instructor in identifying students that need additional help with academics, answering queries related to upcoming tests or assignments, and can even alert students about deadlines
Database for storing and organizing data of neurophysiological experiments: description and basic functionality

In present paper, we introduce a database containing multichannel experimental data of brain electrical activity (M/EEG signals) supplemented by necessary experimental protocols and additional characteristics of the subject (ECG, EOG, EMG, etc.), as well as the results of various processing methods application to the initial data. Data management and access is implemented with web-interface containing the toolkit for viewing and editing existing data and adding new items. Developed database provides increased level of data storage reliability and instant access to the neurophysiological data for analysis, processing and evaluation of accumulated experimental information.
Review of Intelligent Data Analysis and Data Visualization

In recent years, machine learning, deep learning, neural network and other artificial intelligence technologies have made significant development and breakthroughs. Thus more mature and reliable technologies start to be applied in the field of data analysis. Data analysis is the process of studying and summarizing data in detail in order to extract useful information, that is, collecting, sorting, processing and analyzing data. Moreover, in order to make the analysis results easier to understand, data visualization technology is widely employed. This paper tries to review the state-of-the-art data analysis methods based on artificial intelligence. Besides, the mainstream data visualization technology and cases are elaborated. Finally, some open problems and challenges are also put forward for the future research.
An Off-line Handwritten Numeral Recognition Method

Off-line handwritten numeral recognition is a pattern recognition problem of the images of ten numbers. To improve the recognition efficiency, the number’s image’s character dimension should be decreased. In order to improve the recognition veracity, the character mode instability resulting from different writing styles and habits should be considered. The article proposed a numbers recognition method which combined with the statistical characteristics and structural features of numbers. Firstly, the principal component analysis (PCA) method was adopted to extract the numeral image’s statistical characteristics. The numeral recognition will be realized through analysis of the reconstruction error of the model, which reconstructed by the principal components. To further determine the type of numeral, the structural features of width and height rate should be added. Finally, through experiments on the numeral image identification, the reliability and accuracy of this method of digital recognition were verified. The deficiency of this method in real-time recognition was analyzed.
k satisfiability programming by using estimation of distribution algorithm in Hopfield neural network

Hopfield Neural Network (HNN) is a sort of neural network that is strongly dependent to energy minimization of solution. Although HNN managed to solve various optimization problem, the output of HNN suffered from a lack of interpretability and variation. This has severely limited the practical usability of HNN in doing logic programming. Inspired by random neuron perturbation, Estimation of Distribution Algorithm (EDA) has been proposed to explore various optimal neuron state. EDAs employs a probabilistic model to sample the neuron state in order to move toward the various optimal location of global minimum energy. In this paper, a new Mutation Hopfield Neural Network (MHNN) will be proposed to do k Satisfiability programming. Based on the experimental result, the proposed MHNN has outperformed conventional HNN in various performance metric.
XMPP Compliance Suites 2021

This document defines XMPP application categories for different use cases (Core, Web, IM, and Mobile), and specifies the required XEPs that client and server software needs to implement for compliance with the use cases.