Reviewerly/exHarmony huggingface dataset:
A comic page for the first isomorphism theorem

Given a homomorphism between algebras, there exists an isomorphism between the quotient of the domain by its kernel and the subalgebra in the codomain given by its image. This theorem, commonly known as the first isomorphism theorem, is a fundamental algebraic result. Different problems have been identified in its instruction, mainly related to the abstraction inherent to its content and to the lack of conceptual models to improve its understanding. In response to this situation, in this paper, we present an illustration that explores the narrative and graphical resources of comics with the aim of describing the set-theoretic elements that are involved in the proof of this theorem.
Research on Automatic Location of Seed Points in Ultrasound Breast Tumor Images Based on Fuzzy Logic Algorithm

Breast cancer is the second leading cause of cancer death in women, which seriously endangers women's health. Breast tumor segmentation is one of the most critical and difficult tasks. In order to realize the automatic and rapid positioning of seed points and meet the needs of real-time online image segmentation, this paper presents an automatic positioning method of seed points in ultrasound breast tumor images based on fuzzy logic algorithm. The fuzzy logic is innovatively introduced into neural network, which is combined with the method of recalibrating the importance of characteristic elements in attention mechanism. By defining a reasonable fuzzy membership function, different diffusion coefficients are adopted for different pixel gradients. Automatic reference points are obtained by using the automatic reference point selection method based on texture features of image gray level co-occurrence matrix. On this basis, the seed points are iteratively obtained by Mean−Shift algorithm, which is the tumor area. Simulation results show that the proposed method is superior to most existing fuzzy closed-value segmentation methods.
Personalized Point-of-Interest Recommendation Based on Social and Geographical Influence

With the rapid development of location-based social networks (LBSNs), personalized Point-of-Interest (POI) recommendation has become an important personalized service to help users explore the surrounding environment. To better solve the data-sparse problem of POI recommendation, the main idea of existing research is to use neural networks to fuse context information such as social relationships and geographical influence. However, the existing models are still inadequate in integrating context information, and few studies consider privacy protection against users' activity trajectories. To solve these problems, this paper proposes a POI recommendation algorithm, SGGCN, which integrates social relationships and geographical influence. Based on desensitization of user activity trajectory, this method uses a graph convolutional neural network to explicitly learn the collaborative signal between users and users, POIs and POIs, and users and POIs to alleviate the data-sparse problem. Experiments on two real data sets show a 10% improvement over state-of-the-art POI recommendation methods.
Real-world sentence boundary detection using multitask learning: A case study on French

Abstract We propose a novel approach for sentence boundary detection in text datasets in which boundaries are not evident (e.g., sentence fragments). Although detecting sentence boundaries without punctuation marks has rarely been explored in written text, current real-world textual data suffer from widespread lack of proper start/stop signaling. Herein, we annotate a dataset with linguistic information, such as parts of speech and named entity labels, to boost the sentence boundary detection task. Via experiments, we obtained F1 scores up to 98.07% using the proposed multitask neural model, including a score of 89.41% for sentences completely lacking punctuation marks. We also present an ablation study and provide a detailed analysis to demonstrate the effectiveness of the proposed multitask learning method.
Efficient and Accelerated KYC Using Blockchain Technologies

Abstract Despite the rapid digital transformation of banks and financial institutions, state-of-the-art Know Your Customer (KYC) processes still require customers to provide multiple artifacts to the different banks they collaborate with. In an era where data sharing is facilitated from a technological and a regulatory point of view, there is huge potential for improving the efficiency of KYC processes. However, this requires a trustful environment for exchanging data across the various stakeholders, including customers, banks, and other financial organizations. This chapter illustrates how blockchain technology can be used to foster such a trusted environment. It also presents the implementation of a decentralized KYC solution over the Hyperledger Fabric permissioned blockchain infrastructure.
Solving Reward-Collecting Problems with UAVs: A Comparison of Online Optimization and Q-Learning

Uncrewed autonomous vehicles (UAVs) have made significant contributions to reconnaissance and surveillance missions in past US military campaigns. As the prevalence of UAVs increases, there has also been improvements in counter-UAV technology that makes it difficult for them to successfully obtain valuable intelligence within an area of interest. Hence, it has become important that modern UAVs can accomplish their missions while maximizing their chances of survival. In this work, we specifically study the problem of identifying a short path from a designated start to a goal, while collecting all rewards and avoiding adversaries that move randomly on the grid. We also provide a possible application of the framework in a military setting, that of autonomous casualty evacuation. We present a comparison of three methods to solve this problem: namely we implement a Deep Q-Learning model, an $$\varepsilon$$ -greedy tabular Q-Learning model, and an online optimization framework. Our computational experiments, designed using simple grid-world environments with random adversaries showcase how these approaches work and compare them in terms of performance, accuracy, and computational time.
The Application and Implementation of Clustering Algorithm in New Small-and-medium Sized Enterprise Management System

In order to reduce the operation cost of modern small-and-medium-sized enterprises, it is necessary to introduce a convenient and efficient management system to assist enterprises in making decisions. A K-means clustering algorithm based on adaptive attribute weight optimization is proposed for small-and-medium-sized enterprises to meet the needs of the scientific classification management of suppliers. The system, based on the technology architecture designed in this paper, integrates the algorithm proposed in this paper, and implements the core management module of the system according to the design requirements, which meets the needs of small-and-medium-sized enterprises in terms of function and performance.
The Steganographic Method of Introducing Additional Information, Resistant to Transcoding and Conversion

In many current videos is necessary to include hidden information. It can be done with the use of steganography. Steganography is based on the limited capabilities of human senses, which is why people are not able to distinguish minor variations of colors. Based on the research of limit to human vision, here we offer a new method and test equipment for research and measure the characteristic of hiding information in the digital image. The presented steganographic method is based on Discrete Wavelet Transform (DWT) for the transition to the lower frequency domain. The main goal is to embed information in a digital image so that both the message and the fact of its presence are hidden. The presented method reflects the internal mechanisms of the visual system and is convenient for describing the process of image perception. The experimental results were compared with other methods and proved to be satisfactory
Research and Practice on Security Protection and Disaster Recovery Strategy of Oracle Database in Colleges and Universities

Database security protection, database backup and disaster recovery are important tasks for all colleges and universities to ensure the safe and stable operation of information systems.Based on the operating environment of the Oracle production database in China University of Geosciences (Beijing), combined with the practical operation and maintenance experience, this paper provides a design and implementation case of Oracle database security protection system and disaster recovery architecture.The network security protection architecture of the three-layer firewall and fortress machine, the detection and repair of security vulnerabilities, the management of system accounts and permissions, data encryption and database audit constitute the security protection system of the database.Oracle RAC (Real Application Clusters), Oracle DataGuard, redundant backup management and backup recovery constitute the disaster recovery architecture of the database.The case has practical significance for database operation and maintenance management in other colleges and universities.
Legal Technology

This chapter discusses practical applications of emerging technologies in the context of legal services. The newly established legal technology (legal tech) industry is based on the application of IT services in the legal sector, radically changing the current nature of the lawyer's work. Importantly, the COVID-19 pandemic has significantly accelerated the digitization of individual processes and fundamental aspects of lawyers' work, contributing to the development of legal tech. The first part contains definitions and descriptions of the term ‘legal technology' and some related ones, pointing out to the context of the relationship between dynamic technological development, the use of artificial intelligence, and the practice of the profession. The following sections describe the research on the current legal tech scene in Poland, based on integrative literature review. Then follows an analysis of the collected material, looking for the industry's map and an analysis of its specification.
AQtrans: Q&amp;A Sentence Matching Method for Single-Hop Knowledge Based Question Answering

KBQA is the key technology of natural language processing. The single-hop KBQA task is usually divided into entity recognition and answer link. Among them, improving the effect of answer links is the key to improving performance. The current mainstream answer linking methods often face the challenge that the identified predicate cannot be linked to the knowledge base. Therefore, this paper proposes to match question and answer sentences by transforming candidate answers into natural language answer sentences and forming context with the question sentences. Since question and answer sentences are essentially different expressions of the same triple, the unsupervised triple data in the knowledge base can be used to further improve the effect of the model by training the answer-question conversion model. It solves the problem that the current mainstream methods cannot use unsupervised triple data at low cost. And achieved state-of-the-art results in the WC2014 dataset and the NLPCC 2016 KBQA dataset.
Meta-Modeling for Autoencoder-Based End-to-End Communications Systems

With the rapid development of neural network in various applications, it has also attracted great attention in communication. However, neural network is a black box, it is not clear why neural networks can effectively improve the performance of traditional communication technologies. In this paper, we use the meta-model to approximate the autoencoder, and obtain the symbolic explanation of autoencoder, which consists of incomprehensible neural network. Most importantly, we use the symbolic explanation to explain the principle of autoencoder-based transceiver, and give some reasons why the performance of autoencoder is better than that of traditional coding technology. So that users can understand and trust the complex neural network better. In addition, this paper also gives the symbolic explanation of autoencoder trained under different SNR, and analyzes the influence of different SNR on autoencoder.
DEV-ETA: An Interpretable Detection Framework for Encrypted Malicious Traffic

Abstract Traffic encrypted technology enables Internet users to protect their data secrecy, but it also brings a challenge to malicious package detection. To tackle this issue, researchers have investigated into encrypted traffic analysis (ETA) in recent years. Existing works, however, only focus on the accuracy of malicious flow identification. Using ETA as a technical black box, they pay little attention to the internal details and explanation of models. In this paper, we, for the first time, introduce interpretable machine learning into ETA. We aim to provide a reasonable explanation for detection results, so as to enable one to understand and further trust network security analysts. We develop a complete analysis framework, named DEV-ETA (detection, explanation and verification of ETA). DEV-ETA applies post hoc interpretation methods to explain the detection results and verify the explanation using the joint distribution of support features on the dataset. We run thorough experiments to explain the detection result using three popular explanation approaches, namely SHAP, LIME and MSS, and we verify the explanation via the feature distribution plot. The experimental results show that our design can interpret the detection result of ETA model instead of just simply treating the model as a black box.
Dynamic Interval-based Watermarking for Tracking down Network Attacks

Transaction systems, such as e-commerce, banking systems, and blockchain-based transaction systems, take the advantages of great developments of network technologies. At the same time, such systems face a variety of threats and attacks. Passive traffic analysis (TA) usually consumes a lot of resources, and responds with tardiness, and no longer meets the current requirements. Network flow watermarking is a typical active TA technique, and IPD, IBW and ICBW are the typical ones, which can track attacks in a covert way. However, these approaches are also disturbed by the complex network conditions, e.g., packet splitting and merging during the transmissions. To improve the robustness of network flow watermarking, this paper proposes an improved scheme, dynamic interval-based watermark (DIBW). The experiments compare DIBW performance with that of IPD, IBW, and ICBW under packet splitting and merging conditions; and change the parameters of DIBW to observe the impacts on the performance of DIBW. The results show that DIBW can improve the robustness of the system. This paper utilizes LDPC code that consumes less time than the traditional ones. In addition, this paper proposes a hybrid watermark scheme of DIBW and IPD to adapt to the file sizes and the situations of network flow, and analyzes the feasibility of the adaptability by experiments.
Object Segmentation for Autonomous Driving Using iseAuto Data

Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real-world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR–camera fusion and semi-supervised learning implemented in a fully convolutional neural network. Our method was tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all weather scenarios, featuring night and rainy weather. In this work, it is shown that with LiDAR–camera fusion, with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm was measured in terms of intersection over union, precision, recall, and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios.
Classification of URDU headline news using Bidirectional Encoder Representation from Transformer and Traditional Machine learning Algorithm

the web is full of unstructured data and getting the right information from that is a crucial problem. Recent advancements in the field of data mining on English Corpus provided the opportunity to automatically classify the text from different sources into broad categories. However, due to the lack of Urdu language resources, less attention has been paid to classifying text written in Urdu. Urdu is one of the most commonly spoken languages in Asian countries and also a national language of Pakistan. Urdu is a morphologically rich language, contains special diacritics, and free word order which makes it a difficult task for automatic text classification. In this work, we introduce the fine-tuning BERT architecture for the classification of Urdu headline news. Also, perform Logistics regression (LR) and multilayer perceptron (MLP) with Bert features. The proposed model classifies the Urdu headline news based on their predefined labels. We performed extensive experiments on Urdu news dataset iNLTK. Our proposed BERT-fine-tuned model outperforms baseline MLP and LG models and produces average accuracy for BERT, MLP, and LG with 95%, 94%, and 93% respectively.
A Real 3D Embodied Dataset for Robotic Active Visual Learning

Active interaction with environments is one of the striking characteristics of robotic active vision, which allows robots to move to facilitate visual tasks. Recently, several embodied AI platforms have been proposed as the synthetic environments to study robotic active vision, without needing to interact in real world. However, by using synthetic data, model trained on these platforms will suffer performance degradation when applied in reality. In this letter, a real 3D embodied dataset is proposed for robotic active visual learning. The proposed dataset consists of real point cloud data densely collected in 7 real-world indoor scenes. In our embodied dataset, researchers are able to simulate the movements and interactions of robots in indoor environments and obtain real visual data, which will not lead to performance degradation in reality. Furthermore, we proposed a 3D divergency policy that can guide robots to move and collect data to improve visual performance in novel environments. The proposed policy is designed following a simple fact: a good 3D detector should produce consistent 3D detection results for the same object from different viewpoints. Therefore, our policy encourages the robot to explore the area where the detector generates different 3D bounding boxes for the same object and helps the robot improve its visual performance in novel scenes.
Exhibition of Noise-Aided Stochastic Resonance by Discontinuity Detectors in Smartphone Images

The use of smartphone cameras for capturing photographs has seen an exponential growth in the last decade. The noise present in these photographs significantly deviates from the popular Independently Identically Distributed (i.i.d.) Additive White Gaussian Noise (AWGN) noise, and thus the conclusions drawn from simulated-AWGN cannot be directly applied to the smartphone’s true noise. This paper is the first reporting of the exhibition of Stochastic Resonance (SR) or noise-induced threshold-crossing in three genres of discontinuity detectors used in image processing — corner detector, line detector, and edge detector for real-world smartphone images. For the images under investigation, the performance of these detectors is quantified w.r.t. parameters representing intrinsic noise. Observations suggest that all these detectors inherently exhibit the phenomenon of SR due to the fundamental assistance offered by controlled amount of noise in crossing detector thresholds. The manifestations of SR — constant parameter value with varying noise and varying parameter value with constant noise — are demonstrated to exhibit SR in each of the three detectors.
Novelty driven evolutionary neural architecture search

Evolutionary algorithms (EA) based neural architecture search (NAS) involves evaluating each architecture by training it from scratch, which is extremely time-consuming. This can be reduced by using a supernet for estimating the fitness of an architecture due to weight sharing among all architectures in the search space. However, the estimated fitness is very noisy due to the co-adaptation of the operations in the supernet which results in NAS methods getting trapped in local optimum. In this paper, we propose a method called NEvoNAS wherein the NAS problem is posed as a multi-objective problem with 2 objectives: (i) maximize architecture novelty, (ii) maximize architecture fitness/accuracy. The novelty search is used for maintaining a diverse set of solutions at each generation which helps avoiding local optimum traps while the architecture fitness is calculated using supernet. NSGA-II is used for finding the pareto optimal front for the NAS problem and the best architecture in the pareto front is returned as the searched architecture. Exerimentally, NEvoNAS gives better results on 2 different search spaces while using significantly less computational resources as compared to previous EA-based methods. The code for our paper can be found here.
Improved Capsule Network Optimization Hierarchical Convolution Algorithm for Mental Health Recognition

To address the shortcomings of standard convolutional neural networks (CNNs), the model structure is complex, the training period is lengthy, and the data processing technique is single. A modified capsule network is presented to optimize hierarchical convolution-the algorithm for identifying mental health conditions. To begin, two types of data processing are performed on the original vibration data: wavelet noise reduction and wavelet packet noise reduction; this retains more valuable information for mental health identification in the original signal; secondly, the CNN employs the concept of hierarchical convolution, and three distinct scaled convolution kernels are utilized to extract features from numerous angles; ultimately, the convolution kernel's extracted features are fed into the pruning strategy's capsule network for mental health diagnosis. The enhanced capsule network has the potential to significantly speed up mental health identification while maintaining accuracy. It is time to address the issue of the CNN structure being too complex and the recognition impact being inadequate. The experimental findings indicate that the suggested algorithm achieves a high level of recognition accuracy while consuming a small amount of time.