Reviewerly/exHarmony huggingface dataset:
Autonomous Navigation with Active SLAM for Disinfecting Robot

This paper introduces autonomous navigation using the active SLAM for a disinfecting robot. For a safe service and navigation of the disinfecting robot, sensors such as distance sensors and four cameras were set up. To prevent getting stuck between columns and obstacles, in this paper, we generate fake distance data from a LiDAR sensor. We also propose a planner that uses the active SLAM to enable SLAM and path planning simultaneously to generate coordinates that allow the robot to navigate to the goal position. For safe sterilization using UV-C, furthermore, human detection is very crucial because UV-C radiation can be fatal to humans. Therefore, a MobileNetSSD was used to detect humans accurately with 15 FPS for the disinfection robot. Using these approaches, we present autonomous navigation with the A* and DWA algorithms for disinfection. Finally, through experiments, we verified our system of autonomous navigation for the disinfecting robot in a simulation and a real environment. In particular, we confirmed that the disinfecting robot can disinfect effectively through the possibility of sterilization by UVC dosimeters.
Supporting Confidential Workloads in SPIRE

The migration of traditional deployment to clouds has driven the need for a more robust security model, the Zero-Trust model. The application of zero-trust principles addresses known security issues such as lateral movement attacks but adds extra identity management complexity. In addition, to cover a broader range of attacks, one must think of strategies to protect data, code, and credentials in such applications. Confidential computing aims to fulfill this goal. Nevertheless, confidential computing is even more complex to implement than Zero-Trust. In this work, we combine the Zero-Trust model with confidential computing by leveraging the SPIFFE standard through its reference implementation (SPIRE), and Intel SGX through the SCONE framework, to seamlessly supply software identities to confidential microservices. Furthermore, we also protected the whole identity-provisioning stack with Intel SGX and assessed the performance overhead. We believe this combination not only improves the security of SPIFFE deployments but also leverages SPIFFE to facilitate the integration between confidential computing components and native applications.
Selective Data Augmentation for Improving the Performance of Offline Reinforcement Learning

This study proposes a new data augmentation technique for offline reinforcement learning (RL). Rather than randomly choosing data points to carry out the data augmentation, our methodology selectively chooses data from sparse subspaces of the dataset to effectively augment the data region that is insufficient in the original dataset. For the augmentation, the subspaces of the dataset would be represented in the latent space created by the variational autoencoder (VAE). Data is then sampled from the latent space and converted back to the original space by using the decoder of the VAE so that the augmented data can be added to the original dataset. By using the VAE, virtual data that does not severely deviate from the original data could be generated because the VAE creates new data points by using the latent space that captures the original data distribution. We evaluate the performance of our methodology using several offline RL datasets generated from OpenAI Gym benchmark control simulations which mainly use state-based inputs.
Performance Analysis of HElib on a Privacy Preserving Search for Genome Information with Fully Homomorphic Encryption

Recently, numerous studies have been conducted on the practical implementation of fully homomorphic encryption (FHE) for large data analysis. Many systems using FHE are cloud-based and use many DRAMs because the progression of encryption or bootstrapping increases the time and space complexities. However, DRAMs in the cloud cannot be effectively used because they are shared by several systems and cost us inefficiently. Here, we combine Solid State Drive (SSDs) with DRAMs for an application using FHE. They have several different architectural advantages from DRAMs: they cost less per one bit and have high parallelism. Our primary objective is to utilize SSDs instead of DRAMs and reduce the cost on the cloud when using an application of FHE. This study uses the HElib library and an application for a privacy-preserving search for genome information. First, we identified the bottleneck of this application for using SSDs effectively. We analyzed the features of the application using Hyper-Threading technology and found that it consumes considerable CPU power. It is highly parallel, and the processing of load/store instructions from DRAMs to CPUs in sequential is the bottleneck. Second, we revealed the bottleneck in detail by the function in HElib library. The ”rotate” function is found to be the bottleneck in it. It must be implemented many times because of the FHE algorithm which cannot compare a ciphertext to another one. Finally, the compatibility of this application using the HElib library with SSDs is determined.
Improving Naive Bayes by Reducing the Importance of Low-Frequency Words Based on Entropy of Words for Spam Email Classification

The Naive Bayes algorithm (NB algorithm) is a popular one for spam email classification due to fast training, using simple techniques and high accuracy. One of many research improving NB algorithms are the AWF-NB algorithm. In this paper, we call the research an AWF-algorithm for convenient mention. The AWF-NB algorithm focuses on solving the equally important word in each class because it is not always the case. Another problem of the NB algorithm to solve this problem, the AWF-NB extremely reduces the importance of words in the class that has lower importance. However, this action will lead to reducing the accuracy in cases that slightly differ among the importance of words in each class. Therefore, the goal of the research is to improve the AWF-NB algorithm by reducing the importance of words based on entropy of words. We compute the entropy of a word to decide if it should be reduced in importance. The experimental results on ten spam email datasets from Kaggle website indicated that the RIWE-NB algorithm can remarkably increase the classification accuracy of the NB algorithm and the AWF-NB algorithm in majority datasets while the execution time is still conserved.
Towards an approach Integrating Mixed Reality and IoT for Smart Agriculture

In this paper we propose a mixed reality application for smart agriculture. More precisely, we develop an application that can monitor plant needs in terms of humidity and weather conditions. For this end, we connect an ESP32 card with humidity and soil moisture sensors. Sensor data are collected and sent to the cloud using Firebase platform. In the front-end side, sensor data are collected and visualized on an Oculus Quest 2 headset configured in Passthrough API mode. Based on the sensor data, a water pump is activated. To the best of our knowledge, this is the first mixed reality application dedicated to smart irrigation. The benefits of this application are twofold. First, it can be used by the farmer in order to control and monitor the plant wellbeing in realtime and provide only the necessary quantity of water needed by the plant. Second, the proposed application can be used for training purposes where a simulated environment (e.g. serious game) could be added to our User Interface.
Stereo matching with local cost volume refinement network

Deep learning methods have been widely used to complete the task of stereo matching in recent years, which is the key step in machine vision measurement. State-of-the-art methods are three-dimensional (3D) end-to-end networks that forms a cost volume by concatenating extracted features and processes it with 3D modules. Despite the strong performance in terms of accuracy, 3D networks mostly have high computational cost, heavy memory storge and long run-time. In this paper proposed Local Cost Volume Refinement Network (LCRN), which is a two-dimensional (2D) end-to-end network composed of feature extraction, disparity initialization, disparity refinement and disparity mergence module. LCRN initializes disparity maps by using correlation layer and residual blocks, and refines them by using local cost volumes, residual blocks and disparity regression. Local cost volumes are constructed by warping right features and giving a small disparity shift. To verify the effectiveness of LCRN, the network was pre-trained on SceneFlow dataset and fine-tuned on ROBI dataset. The network is evaluated on the test set of ROBI for robotic bin-picking. Experimental results show that LCRN maintains a competitive accuracy while having fast run-time and requiring less memory storage.
Maximum Correntropy-Based Extended Particle Filter for Nonlinear System

To solve the particle degradation and sample impoverishment, a maximum correntropy-based extended particle filter method is proposed by optimizing the important density function. In the proposed method, the importance sampling is guided by the measurement information, the entropy criterion and high-order moments information of the signal are fully utilized, and the non-Gaussian heavy-tailed distribution noise and the noise with outliers are effectively suppressed. The simulation results demonstrate the effectiveness and feasibility of the filtering method.
Research on Influencing Factors of Technological Innovation in Industrial Clusters Based on Data Mining and Artificial Intelligence Technology

At present, realizing the upgrading and sustainable development of industrial clusters has become an urgent problem. Based on this, based on data mining and artificial intelligence technology, this paper combines the actual needs of industrial clusters and technological innovation to construct an analysis model of the influencing factors of technological innovation in industrial clusters. Based on the complex network foundation, this paper regards the activity of the innovation network as the result of the joint influence of the presence index, closeness index, betweenness index, agglomeration index and path length index. Moreover, this paper combines the actual situation of the cluster to construct an evaluation system for the activity of the innovation network. In addition, this paper uses the PROMETHEE method based on the cloud model to evaluate the activity of the innovative network to be studied in this paper. Finally, this paper designs experiments to verify the performance of the algorithm model constructed in this paper. The research results show that the system model constructed in this paper has a certain effect.
A method for realizing real-time abnormal behavior recognition based on compressed video

A robust algorithm is proposed for real-time abnormal behavior recognition in dynamic challenge including illumination change and pose variations. To cope with these factors, we present a new method through segmented sampling processing evenly divides the original video into several fragments and according to a certain sampling density, the description of one I frame and the description information of several P frames are obtained from each video clip. Through the Res2Net18 network, a behavior classifier based on the cumulative motion vector of P frames and a behavior classifier based on the cumulative residuals of P frames are built. The various frame information extracted from each video clip is entered into the corresponding network, and each network outputs a classification score. According to the type of frame information entered, the classification score based on each type of input in all video clips is summed and averaged, and the classification score based on each type of input is obtained at the original video level. The ensemble is carried out by means of weighted summation, and the total classification score is obtained as the output of the abnormal behavior recognition network. Experimental results on benchmark datasets demonstrate that the proposed method performs robustly and favorably.
An Improved Unscented Kalman Filter for Interrupted and Drift Sensor Faults of Aircrafts

Reliable sensor signals are key factors in the flight control system. The development and application of various sensors not only improve flight performance and maneuvering capability but also bring challenges to the reliability and safety of the system. When sensor faults occur in nonlinear aircraft models, it is crucial to quickly recover system states. In this article, a new state estimation approach is presented using an interrupted-estimation unscented Kalman filter (IEUKF), which is integrated with maximum-likelihood estimations and Gaussian probability distribution. In general, aerial sensors involve complex fault conditions and a large number of strongly nonlinear systems. In spite of that, the proposed method can compute estimations robustly and achieve high precision under hard/soft and interrupted/permanent sensor faults. Compared with cutting-edge methods, the IEUKF can be applied to featureless interrupted faults without redundant sensors, foregone-fault hypotheses, or slow-varying fault assumptions. With a nonlinear F16 Simulink model as the example in simulations, the results illustrate the advantages of the proposed method.
Argumentation: Reasoning Universalis

Abstract Can argumentation form the basis for any form of reasoning, informal or formal logical reasoning? We examine this question from the particular perspective of the recent developments in logic-based Artificial Intelligence (AI). We propose that argumentation provides the wider framework encompassing uniformly all reasoning, with strict or formal logical reasoning being a special boundary case. We also attempt to link this unifying role of argumentation with Aristotle’s original investigation of methods and forrmalisms for the systematic study of human reasoning.
Multi-field relation mining for malicious HTTP traffic detection based on attention and cross network

This paper introduces AutoHTTP, a novel end-to-end trainable framework for detecting malicious HTTP traffic. It can automatically analyze plain-text network traffic data without any manual labor and present an interpretable detection report for better human understanding. The purpose of the framework is to detect malicious HTTP traffic by mining multi-field inexplicit semantic characteristics and correlation. To conquer the problems in reality, we first divide the multi-field plain-texts (e.g. user-agent, URL, method) into two types: R-field and S-field. Then, an elementary feature extraction module is proposed to turn these fields into a compact field representation. Finally, the field interactions and significant parts of different fields are simultaneously extracted by feeding the compact feature vector into a newly proposed attention and cross network, which couples two important components, the attention portion and the cross part. We show that the network offers strong interpretability and reliable results for further analysis. Extensive experiments on CTU-13, CICAndMal, and ISCX-URL datasets demonstrate that our approach outperforms existing methods based on manually-designed features and other auto-designed features.
Constructing an architecture-based cybersecurity solution for a system

Cybersecurity can be effectively managed with an architecture-based approach, composed with three viewpoints, namely system, security and process. Using models for describing a system and its security objectives enables a systemic and exhaustive risk management process. The architecture approach produces an integral set of security policies and controls that can be fully maintained during the entire system life-cycle. Furthermore, architecture models support automation and high scalability, thus providing an innovative way for constructing and maintaining the cybersecurity for very large systems or even for system of systems. This work describes details, technical aspects, and examples for the risk management process of the architecture, including the establishment of the system representation, the security goals, going through risk identification and analysis, up to the policies and control definition. Some highlighting points of the methodology follow. •System representation is simple because it focuses only on aspects relevant to security purposes.•Security objectives behave as an end-to-end guidance of the security, for the whole system and also during its life-cycle.•Risk management can be done with existing methods and standards, but additionally supported with the comprehensive capability provided by the system representation and the security objectives.
Carbon footprint and service coverage tradeoffs in geo-diverse sites

Data centers are growing hulks on energy consumption, thus carbon emissions. It is critical to maintain the services, i.e., data intensive computing, while preserving the greenness in the data center management. However, the more data centers required data replicated in, the faster exponential increase in the services' carbon footprint. What is worse, the carbon coefficient of the data center is time-dependent as the power source varies from local renewable farms to power grids, making the problem dynamic. To address the challenges above, in this paper, we consider the temporal and spatial characteristics of cross-region data replication and propose a data replication framework (Carbon Aware Geo-diverse DAta Replication) CAGDAR to reduce carbon footprint and maximize service coverage simultaneously. CAGDAR balances the carbon footprint of data centers, i.e., sites, while respecting storage capacity, taking into account the coverage of data copies and the locations. We evaluate CAGDAR with real carbon traces and show that CAGDAR can rationally allocate data replicas in the temporal-spatio domain, reducing the services' carbon footprint by 26% and improving the service coverage by 33.5%.
Early Lung Cancer Detection by Using Artificial Intelligence System

Lung cancer is by far the primary cause of cancer deaths globally. Computer-aided diagnosis (CAD) system is used for the prediction of lung cancer which helps to attain a high detection rate and reduces the time consumed for analyzing the sample. In this paper, CAD system based on sputum color images is proposed which consists of four main processing steps. It starts with the preprocessing step using a heuristic rule-based and a Bayesian classification method using the histogram analysis. In this step, the region of interest (ROI) representing the sputum cell is detected and extracted. In order to segment the nuclei from the cytoplasm, mean shift segmentation is used. The next step is feature analysis. Finally, the diagnosis is done using a rule-based algorithm alongside the artificial neural network (ANN) and support vector machine (SVM) for identifying cancerous and non-cancerous cells. The performance evaluation was done based on the sensitivity, specificity, and accuracy. Our methods are validating by using a set of experiments conducted with a data set of 100 images. The final results showed that the techniques used outperformed conventional methods. The proposed CAD system achieved a reasonable accuracy above 95% with high true positive rates that can basically meet the requirement of clinical diagnosis.
The New Evolutionary Trajectory of University Libraries: The Editor-in-Chief’s View

The paper provides a brief overview of the events/issues that the authors of the 7(2022) issue of the UniLibNSD journal cover in their articles. The authors' many issues are highlighted through evidence of the nature of these challenges, as well as theoretical and concrete examples of how to address them. At a time of tectonic shifts in global geopolitics, climate change, digital development, the fight against the COVID 2019 pandemic, social transformations, librarians have witnessed russia's full-scale aggression against Ukraine and against world democracy, including the terrible destruction of Ukrainian libraries and archives by russian terrorists who continue their fierce offensive against the world's documentary heritage. That is why most of the authors challenge traditional concepts of librarianship, argue that libraries are not neutral, and call on the world's librarians to take active measures to prevent genocide, anti-racist and anti-oppressive practices for the benefit of both users and the profession itself.
Design of Annotated Bibliography of Traditional Medicine in West Java, Indonesia

Objective. The existence of traditional medicine, especially in the province of West Java, Indonesia, has been recognized and has been used by the general public, one of which concerns the traditional medicine. West Java is one of the largest provinces in Indonesia which has many medicinal plants. The purpose of this study is to create an information package that stores various sources of information related to traditional medicine in West Java by designing an annotated bibliography of traditional medicine in West Java. Methods. The research method used is action research method. The stages of action research used are planning, action, evaluation, reflection, and the process of publishing book products. Results. The results obtained are the stages of action in accordance with the stages of the plan for the annotated bibliographic manuscript to be printed as a book that can be tested by experts with a questionnaire containing suggestions. After being tested and corrected, the contents of the book were then tested publicly with a background in health sciences, information science, and libraries, as well as people who use traditional medicine in West Java by means of a questionnaire containing an assessment. Conclusions. The results of the public test are above 89%, which means the book product is feasible and can be published as an annotated bibliographic book related to traditional medicine.
Secure Banking Transaction and Digital Banking Using Blockchain Technology

Internet Banking offers clients the availability of dealing with one's assets whenever, anyplace. In any case, any web-based exchanges will be inclined to security dangers. Existing framework utilizes two way validation factors(OTP) that are handily broken by digital assailants prompting clients having their record subtleties compromised while not having the arrangement of aggressor's recognizable proof. Thus in this paper we propose a versatile SIM sequential based check framework to protect portable exchanges on cell phones with Blockchain based waiter side secure framework. The supporter character module(sim) chronic number is enlisted with the client's record and in the event that a gatecrasher attempts to start an exchange from some other sim, there emerges a befuddle of the login certifications then framework sends area data of the interloper to the bank. The bank then cautions the enrolled client via mailing them the interloper's subtleties. Since the versatile sim chronic number is remarkable to the sim and isn't physically rather separated, there is no chance of gatecrasher starting the exchange from another gadget, subsequently defeating the worries connected with OTP. The Ethereum Blockchain innovation gives server side information base security by checking the advanced marks during the exchange and agreement calculation for exchange affirmation. Blockchain based security is numerically demonstrated for secure financial exchange.