Reviewerly/exHarmony huggingface dataset:
AI-based Chatbot for Physically Challenged People

Blindness is a state where a person loses his/her ability to see completely or partially. It is also said to be one of the most widely found disabilities across the world. Most of the time, it is impossible to find a complete cure for this condition. Thus, people with blindness adapt themselves to living with this blindness as a part of them. To help visually impaired people, this study aims in developing a chatbot which uses artificial intelligence to predict the most relevant answers to the users’ questions. This bot is designed in such a way that it allows the user to send voice notes instead of texts like the conventional chatbots. The chatbot is developed based on a particular architecture. The user-sent text is first converted to text by this architecture. Deep neural networks and APIs that are used for speech-to-text and text-to-speech conversion make up the chatbot's main architecture. The optimal response is then determined for the user from this text, and it is sent to the user. The inner parts of the architecture consist of an AI algorithm named the deep neural networks, speech-to-text, and text-to-speech APIs, libraries like GTTS, etc. This chatbot can be trained to add more functions, such as the ability to automatically order essentials from the websites indicated, transmit emergency signals, and remind users to take their medications, eat their meals, and drink their water in the future.
Earthquake Early Warning Services Based on Very Low-Cost Internet of Things Devices

The performance of an Earthquake Early Warning system is often tied to the density of its stations. The use of Internet of Things and Cloud technologies could further enhance the capabilities of these systems, providing tools to analyze, store and process data, and also to efficiently monitor devices. Moreover, the technological developments in Micro-electromechanical systems have allowed the creation of low-cost and small size seismic monitoring devices that can be used to create denser seismic networks. In this work we propose and evaluate the performance of an IoT-based Early Warning device based on MEMS technology. The results of the performance evaluation show that the device is able to detect events of magnitude greater than 4.1 and located less than 30km away. Furthermore, the integration of the device into a Cloud IoT Architecture gives the proposed solution event detection, device monitoring and alarm dissemination capabilities.
Practice on the Data Service of University Scientific Research Management Based on Cloud Computing

With the continuous development of computer technology, the coverage of informatization solutions covers all walks of life and all fields of society. For colleges and universities, teaching and scientific research are the basic tasks of the school. The scientific research ability of the school will affect the level of teachers and the training of students. The establishment of a good scientific research environment has become a more important link in the development of universities. SR(Scientific research) data is a prerequisite for SR activities. High-quality SR management data services are conducive to ensuring the quality and safety of SRdata, and further assisting the smooth development of SR projects. Therefore, this article mainly conducts research and practice on cloud computing-based scientific research management data services in colleges and universities. First, analyze the current situation of SR data management in colleges and universities, and the results show that the popularity of SR data management in domestic universities is much lower than that of universities in Europe and the United States, and the data storage awareness of domestic researchers is relatively weak. Only 46% of schools have developed SR data management services, which is much lower than that of European and American schools. Second, analyze the effect of CC(cloud computing )on the management of SR data in colleges and universities. The results show that 47% of SR believe that CC is beneficial to the management of SR data in colleges and universities to reduce scientific research costs and improve efficiency, the rest believe that CC can speed up data storage and improve security by acting on SR data management in colleges and universities.
Research on encryption of accounting data using DES algorithm

In recent years, with the rapid development and wide application of modern information technology, the management environment and management concept of enterprises have been changed. The environment of enterprise accounting has changed greatly, and the accounting data system came into being. The application of accounting data system not only brings convenient and fast information services to enterprises, but also brings many security risks due to its own openness and the vulnerability of data storage media. With the expansion of the scope of use of computers, it is common to use computers for corruption, fraud and criminal activities. For example, the data stored on computer disks is easy to be tampered with; The data of the database is highly centralized, and unauthorized personnel may invade the enterprise's database through the computer network, browse all data files, copy, forge and destroy important data of the enterprise. Because computer crime has great concealment and harmfulness. Therefore, it is particularly important to strengthen the security of accounting data. This paper uses DES algorithm to encrypt accounting data.
Deep Reinforcement Learning Based Mobile Robot Navigation in Unknown Environment with Continuous Action Space

This work aims to propose the use of deep reinforcement learning for mobile robot navigation and obstacle avoidance in previously unknown areas or without pre-made maps with continuous action control to increase the capabilities of mobile robots beyond conventional map-based navigation. Deep reinforcement learning is used to enable the robot to learn how to make decisions and interact with the environment observed from sensor data to safely navigate itself to its destination. The robot has a two-dimensional laser scanner, ultrasonic sensors and odometry sensor. Deep Deterministic Policy Gradient, which can function in continuous action space, was chosen as the deep reinforcement learning model. The robot is trained and tested in a Gazebo simulator with Robot Operating System. After the training process, the robot is put to the challenge to complete a waypoint navigation mission in four unknown areas as part of an assessment. The results indicate that the mobile robot is adaptable and has the capability of traveling to the specified waypoints and completing the job without the need for a pre-drawn route or an obstacle map in unidentified environments with the minimum success rate of 69.7 percent.
A Novel Representation of Graphical Patterns for Graph Convolution Networks

In the context of machine learning on graph data, graph deep learning has captured the attention of many researcher. Due to the promising results of deep learning models in the most diverse fields of application, great efforts have been made to replicate these successes when dealing with graph data. In this work, we propose a novel approach for processing graphs, with the intention of exploiting the already established capabilities of Convolutional Neural Networks (CNNs) in image processing. To this end we propose a new representation for graphs, called GrapHisto, in the form of unique tensors encapsulating the features of any given graph to then process the new data using the CNN paradigm.
Graph Augmentation for Neural Networks Using Matching-Graphs

Both data access and data collection have become increasingly easy over the past decade, leading to rapid developments in many areas of intelligent information processing. In some cases, however, the amount of data is still not sufficiently large (e.g. in some machine learning applications). Data augmentation is a widely used mechanism to increase the available data in such cases. Current augmentation methods are mostly developed for statistical data and only a small part of these methods is directly applicable to graphs. In a recent research project, a novel encoding of pairwise graph matchings is introduced. The basic idea of this encoding, termed matching-graph, is to formalize the stable cores of pairs of patterns by means of graphs. In the present paper, we propose to use these matching-graphs to augment training sets of graphs in order to stabilize the training process of state-of-the-art graph neural networks. In an experimental evaluation on five graph data sets, we show that this novel augmentation technique is able to significantly improve the classification accuracy of three different neural network models.
Improved Seagull Optimization Algorithm Incorporating Golden Sine and Tent Chaotic Perturbations

Aiming at the defects of slow convergence and easy to fall into local optimum of the seagull optimization algorithm, this paper proposes an improved seagull optimization algorithm incorporating golden sine and chaotic perturbation of tent mapping. This algorithm enhances the global search ability through tent chaotic disturbance and Levy flight, accelerates the convergence through golden sine to improve the local search ability. In this paper, the original fixed convergence factor is transformed into a nonlinear decreasing convergence factor to improve the optimization efficiency. The performance is tested on the benchmark functions, and it is used to solving the multiprocessor task scheduling problem. Compared with other algorithms, experiments show that TGSOA has significant improvement over other algorithms in terms of convergence speed and robustness.
Cache attack on recursive structure of MISTY1

Side-channel attacks exploit information from physical implementations of cryptographic systems. Cache attacks have improved at recovering information by combining observations of the victim's cache access and knowledge of the cipher's structure. Cache attacks have been implemented for most Feistel- and SPN-structured block cipher algorithms, but the security of algorithms for special structures has seen little attention. We perform a Flush+Reload attack on MISTY1, a class of block cipher with a recursive structure. The FL function is performed before the plaintext input S- box and after the ciphertext output S-box, making it difficult to attack the first and last rounds. However, the key scheduling part of MISTY1 leaks many bits of the key, which, together with the leakage of partial bits of the round key during encryption, is sufficient to recover it. We design an algorithm that can recover the MISTY1 128- bit key after observing one encryption and then use the leakage during encryption to reduce its complexity. We experiment on 32- and 64-byte cache line environments. An adversary needs to observe as little as 5 encryptions to recover the 128-bit key in 0.035 s in the first case and 10 encryptions to recover the key in 2.1 h in the second case.
Container Load Prediction Algorithm Based on ARIMA Model and BP Neural Network

Docker container technology has brought great changes to the field of cloud computing, which has greatly improved the cloud platform in terms of resource scheduling delay, resource granularity, and resource utilization. Container cloud represented by Docker and Kubernetes has been widely concerned and applied in the cloud computing industry. This paper proposes a container load prediction algorithm based on ARIMA model and BP neural network, which provides a reference for Kubernetes resource scheduling, and verifies the reliability of the algorithm through experiments.
Research on distributed agricultural environment monitoring system based on LoRa technology

In order to solve the problems of complex networking and short transmission distance in ZigBee network agricultural monitoring system, this paper proposes a distributed agricultural environment monitoring system based on LoRa technology. The LoRa network is used to realize the system perception layer design. Based on the simple star networking architecture, the low-power wide-area Internet of Things intelligent agriculture system is constructed. The network architecture, LoRa node and LoRa base station software and hardware design and communication protocol are described. Through the analysis of the experimental test results, the scheme has the advantages of simple networking, wide coverage, high reliability of data transmission, low power consumption and long working cycle, which is very suitable for the promotion and application of complex agricultural production site.
Single-station meteor detection filtering using machine learning on MOROI data

ABSTRACT Nowadays, extensive data are collected in an automated regime. Combining this, with the increase in accessible computational power, led to large-scale implementations of machine learning (ML). This is also the case of meteor science, where object detection often requires tracking of a moving light source between frames, and the number of false positives can be up to an order of magnitude higher than true meteoric phenomena. While spatiotemporal coincidence of events recorded by close, multiple cameras can eliminate most of the false positives, single-station detections in some camera networks are currently discarded. In this paper, we explore a set of ML models aiming to find an optimal method for re-analysis of this single-station observations, in order to identify and extract real meteors. A set of 15 ML models were trained on features extracted from the meteor movement. Upon testing, we found a top accuracy score of 98,2 per cent, and a recall (i.e. percentage of meteors correctly classified) score of 96 per cent for the best performing models. When combined with the spatiotemporal coincidence of the detection, the recall increases to 99.92 per cent. These 15 ML techniques were selected according to their ability classify tabular data, hence the bundle can be applied to other studies. The same goes for the computed features, which are independent on the camera configuration, thus, the process can be scaled and applied to other networks. These methods are to be implemented to re-analyze the events recorded by the larger, FRIPON network.
Vaccines and more: The response of Dark Web marketplaces to the ongoing COVID-19 pandemic

Early analyses revealed that dark web marketplaces (DWMs) started offering COVID-19 related products (e.g., masks and COVID-19 tests) as soon as the COVID-19 pandemic started, when these goods were in shortage in the traditional economy. Here, we broaden the scope and depth of previous investigations by considering how DWMs responded to an ongoing pandemic after the initial shock. Our dataset contains listings from 194 DWMs collected until July 2021. We start by focusing on vaccines. We find 248 listings offering approved vaccines, like Pfizer/BioNTech and AstraZeneca, as well as vendors offering fabricated proofs of vaccination and COVID-19 passports. Then, we consider COVID-19 related products. We show that, as the regular economy has become able to satisfy the demand of these goods, DWMs have decreased their offer. Next, we analyse the profile of vendors of COVID-19 related products and vaccines. We find that most of them are specialized in a single type of listings and are willing to ship worldwide. Finally, we consider a broader set of listings mentioning COVID-19, in order to assess the general impact of the pandemic on the broader activity of DWMs. Among 10,330 such listings, we show that recreational drugs are the most affected among traditional DWMs product, with COVID-19 mentions steadily increasing since March 2020. We anticipate that our results will be of interest to researchers, practitioners, and law enforcement agencies focused on the study and safeguard of public health.
Variants of Bellman equation on reinforcement learning problems

Reinforcement learning is a popular tool nowadays applied in a wide range of fields. Markov Decision Process is the basis of reinforcement learning and the Bellman equation derived from the value function of the Markov decision process has become a fundamental tool for finding optimal policies. However, the application of the basic Bellman formula has significant limitations and is computationally complex, resulting in high time costs and low efficiency. Therefore, there are different variants of the Bellman formula and various solutions to different problems. These solutions include MonteCarlo method, Temporal-Difference learning, Sarsa algorithm, Q-learning algorithm, Deep Q-Networks, Hamilton-Jacobi-Bellman equation and others. This paper reviews these algorithms and compares the complexity of each algorithm.
Deep neural networks for Chinese traditional landscape painting creation

Deep learning techniques have been popularly applied for artistic tasks such as turning photographs into paintings or creating paintings in the style of modern art. However, East Asian arts are largely ignored. In this paper, we aim to apply deep learning models to create Chinese traditional landscape paintings. We achieve the goal through two deep learning techniques: image style transfer and image synthesis. We apply the Visual Geometry Group (VGG) Network to do the style transfer, which is trained on a pair of a content image and a style image, and the goal is to output an image that renders the target content with the desired style. For the image synthesis, we apply the Deep Convolutional Generative Adversarial Network (DCGAN), which requires a large set of painting images to produce as realistic as possible non-exist paintings that mimic the training dataset.
Real-time detection for mask wearing based on YOLOv5 R6.0 algorithm

With the advent of large-scale data sets and the widespread usage of convolutional neural networks, deep learning technologies have advanced quickly in recent years, particularly target identification methods in the field of computer vision have become particularly important. This work offers a real-time detection system for mask wearing utilizing the upgraded YOLOv5 R6.0 algorithm in the context of the standardized prevention and control of the new crown epidemic, for the phenomenon that individuals still do not wear masks in public places as necessary. First, we collect the mask wearing dataset from the network, then input it into the YOLOv5 R6.0 model, and finally, the training and testing results are visualized by tensorboard. On the test set, the accuracy (precision), recall (recall), and average precision (mAP) of this algorithm can reach 91%, 97%, and 93.7 percent, respectively, according to the experimental data.
Explaining predictive factors in patient pathways using autoencoders

This paper introduces an end-to-end methodology to predict a pathway-related outcome and identifying predictive factors using autoencoders. A formal description of autoencoders for explainable binary predictions is presented, along with two objective functions that allows for filtering and inverting negative examples during training. A methodology to model and transform complex medical event logs is also proposed, which keeps the pathway information in terms of events and time, as well as the hierarchy information carried in medical codes. A case study is presented, in which the short-term mortality after the implementation of an Implantable Cardioverter-Defibrillator is predicted. Proposed methodologies have been tested and compared to other predictive methods, both explainable and not explainable. Results show the competitiveness of the method in terms of performances, particularly the use of a Variational Auto Encoder with an inverse objective function. Finally, the explainability of the method has been demonstrated, allowing for the identification of interesting predictive factors validated using relative risks.
Learning Path Optimization Based on Multi-Attribute Matching and Variable Length Continuous Representation

Personalized learning path considers matching symmetrical attributes from both learner and learning material. The evolutionary algorithm approach usually forms the learning path generation problem into a problem that optimizes the matching degree of the learner and the generated learning path. The proposed work considers the matching of the following symmetrical attributes of learner/material: ability level/difficulty level, learning objective/covered concept, learning style/supported learning styles, and expected learning time/required learning time. The prerequisites of material are considered constraints. A variable-length representation of the learning path is adopted based on floating numbers, which significantly reduces the encoding length and simplifies the learning path generating process. An improved differential evolution algorithm is applied to optimize the matching degree of learning path and learner. The quantitative experiments on different problem scales show that the proposed system outperforms the binary-based representation approaches in scaling ability and outperforms the comparative algorithms in efficiency.
Wavelet Scattering Transform Depth Benefit, An Application for Speaker Identification

This paper assesses the interest of the multiscale Wavelet Scattering Transform (WST) for Speaker identification (SI) applied in several depths and invariance scales. Our primary purpose is to present an approach to optimally design the WST to enhance the identification accuracy for short utterances. We describe the invariant features offered by the depth of this transform by performing simple experiments based on text-independent and text-dependent SI. To compete the state-of-the-art (SOTA), we propose a fusion method between WST and x-vectors architecture, we show that this structure outperforms HWSTCNN by $$7.57\%$$ on TIMIT dataset sampled at 8 kHz and makes the same performance in the SOTA at 16 kHz.
White Blood Cell Classification of Porcine Blood Smear Images

Differentiating white blood cells has been a fundamental part of medical diagnosis as it allows the assessment of the state of health of various organ systems in an animal. However, the examination of blood smears is time-consuming and is dependent on the level of the health professional's expertise. With this, automated computer-based systems have been developed to reduce the time taken for examination and to reduce human error. In this work, an image processing technique was explored to investigate the classification of white blood cells. Through this technique, color and shape features were gathered from segmented nuclei and cytoplasms. Various deep learning algorithms where transfer learning methods were also employed for comparison. Experimental results showed that handcrafted features via image processing are better than features extracted from pre-trained CNNs, achieving an accuracy of 91% when using a non-linear SVM classifier. However overall, deep neural networks were superior in WBC classification as the fine-tuned DenseNet-169 model was found to have the highest accuracy of 93% against all used methods.