Reviewerly/exHarmony huggingface dataset:
SAU-net: Monocular Depth Estimation Combining Multi-Scale Features and Attention Mechanisms

Monocular depth estimation technology is widely utilized in autonomous driving for sensing and obstacle avoidance. Recent advancements in deep-learning techniques have resulted in significant progress in monocular depth estimation. However, monocular depth estimation is mainly optimized for the luminosity error of pixels, mostly disregarding the related problems of result ambiguity and boundary artifacts in the image. To address these issues, we developed an improved network model called SAU-Net. The superposition of excessive convolutional layers in conventional convolution networks impairs the network’s timeliness and results in the loss of primary information. Therefore, we propose a convolution-free stratified transformer as an image feature extractor at the network’s coding end, which limits self-attention to innumerable windows and leverages sliding windows for characterization to reduce the network delay. This study also addresses the issue of critical information loss. We connect each feature map directly to another from a different scale. In addition, an attention module is introduced to focus on the effective features, which increases the amount of target information in the depth map. We employ the gradient loss function during the training stage to improve the segmentation accuracy of the network and the smoothness of the output image. Training and testing were conducted using the KITTI dataset. To ensure the robustness of the algorithm in practical applications, we also validated the algorithm using a campus dataset that we collected. The experimental results indicated that the accuracy of the algorithm was 89.1%, 96.4%, and 98.5% under three proportional thresholds. The estimated depth map was clear in details and edges, with fewer artifacts.
Online Payment Fraud Detection Model Using Machine Learning Techniques

In a world where wireless communications are critical for transferring massive quantities of data while protecting against interference, the growing possibility of financial fraud has become a significant concern. The ResNeXt-embedded Gated Recurrent Unit (GRU) model (RXT) is a unique approach precisely created for real-time financial transaction data processing. Motivated by the need to address the rising threat of financial fraud, which poses major risks to financial institutions and customers, our technique takes a systematic approach. We initiate the process with data input and preprocessing, addressing data imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). Feature extraction uses an ensemble approach that combines autoencoders and ResNet (EARN) to reveal critical data patterns, while feature engineering further enhances the model’s discriminative capabilities. The core of our classification task lies in the RXT model, fine-tuned with hyperparameters using the Jaya optimization algorithm (RXT-J). Our model undergoes comprehensive evaluation on three authentic financial transaction datasets, consistently outperforming existing algorithms by a substantial margin of 10% to 18% across various evaluation metrics while maintaining impressive computational efficiency. This pioneering research represents a significant advancement in the ongoing battle against financial fraud, promising heightened security and optimized efficiency in financial transactions. In defense against wireless communication interference, our work aims to strengthen security, data availability, reliability, and stability against cyber warfare attacks within the financial industry.
Asymptotic Performance Limitations in Cyberattack Detection

In this paper, we consider the difficulty of cyberattack detection with d sensors and n observations, and derive performance bounds that are valid independent of the attack detection algorithm used. In other words, regardless of whether it is an artificial intelligence (AI) or sensor fusion based approach or it is derived using a completely new innovative idea, a cyberattack detector using multiple observations does have certain fundamental performance bounds that are independent of the algorithm used. Cyberattacks introduce different forms of anomalies that may be small or large, and given enough measured data, even tiny anomalies will become more noticeable and cyberattack detection problem will be easier provided that a carefully designed attack detection algorithm is used. For example, False Data Injection (FDI) attacks with small injected error may be harder to detect, but such attacks can cause major failures if continued over a long time period. A natural question to ask is to what degree the cyberattack detection problem becomes easier if more and more measurements acquired over a long time period are used for threat assessment, and the risk level reduction achieved for each new observation. For a cyberattack detector, the false alarm rate is the probability of triggering an alarm when there is no cyberattack, and the probability of miss is the probability of not detecting a cyberattack. The risk level of a cyberattack detector is defined as the sum of the probability of false alarm and the probability of miss. By using the notion of Hellinger distance and total variation norm between probability distributions, we derive upper and lower bounds for the minimum possible (achievable) risk level under multiple measurements, and study asymptotic properties of such bounds. These performance bounds are valid regardless of the cyberattack detection algorithm selection; they imply certain fundamental performance limits in cyberattack detection applications with given number of observations; and also help us to understand the number of observations needed for a given cyberattack detection performance level.
Scientist Musicians

Before Kepler, the keeper of the Pythagorean tradition was Severinus Boethius (480–524) author of the treatise De instituzione musica, in which he divided musical expression into three areas: Musica Humana, Mundana, and Instrumentalis. His model was Platonic: sound was produced by an impulse and percussion, and these could not occur without motion, so sound was defined as a continuous percussion of the air right up to the point of hearing [1]. For Boethius, theoretical treatises were superior to actual musical practice in the sense that “it is far more urgent and important to know what one does, than to implement what one knows; for material artifice serves as a slave, while reason commands almost as a lady [...] How superior is the science of music in theoretical knowledge, compared to practical implementation!” [2].
Vertical Association Modeling: Latent Interaction Modeling

The vigorous development of e-commerce breeds cybercrime. Online payment fraud detection, a challenge faced by online service, plays an important role in rapidly evolving e-commerce. Behavior-based methods are recognized as a promising method for online payment fraud detection. However, it is a big challenge to build high-resolution behavioral models by using low-quality behavioral data. In this work, we mainly address this problem from data enhancement for behavioral modeling. We extract fine-grained co-occurrence relationships of transactional attributes by using a knowledge graph. Furthermore, we adopt the heterogeneous network embedding to learn and improve representing comprehensive relationships. Particularly, we explore customized network embedding schemes for different types of behavioral models, such as the population-level models, individual-level models, and generalized-agent-based models. The performance gain of our method is validated by the experiments over the real dataset from a commercial bank. It can help representative behavioral models improve significantly the performance of online banking payment fraud detection. To the best of our knowledge, this is the first work to realize data enhancement for diversified behavior models by implementing network embedding algorithms on attribute-level co-occurrence relationships.
Secure Key-Based Substitution-Boxes Design Using Systematic Search for High Nonlinearity

The significance of data security has become more critical due to the ever-changing goals and capabilities of attackers. As a result, many cryptosystems employing diverse approaches are being developed to safeguard the sensitive data. A Substitution box (S-box) plays a vigorous role in modern cryptosystems because of its credence for inducing confusion during the encryption process and ultimately protecting the data. Currently, chaotic maps are being developed and widely employed to yield S-Boxes as the use of chaotic maps aids in the randomness and resistance to mitigate many cryptanalytic attacks. In this paper, a novel chaotic map and an inventive systematic search method are proposed for the generation of key-dependent dynamic and highly nonlinear S-boxes. A variety of standard cryptographic tests such as fixed-point analysis, nonlinearity (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), linear probability (LP), differential probability (DP), etc.) are applied to assess and analyze the cryptographic strengths of S-boxes generated using the proposed method. The findings from experimental and comparative analyses show that the proposed S-box provides stronger and better cryptographic features (no fixed point, no opposite fixed point, average NL = 111.75, SAC offset = 0.0000, BIC-NL = 103.9, LP = 0.125, and DP = 0.039) than many of the existing S-boxes studies presented in recent years. Hence, the proposed S-box construction technique has a lot of potential and genuine prospects for its utilization in cryptographic applications to protect sensitive data from attackers.
Development of Text to Speech Technology for Medical Robot Interaction

Health sector is getting better and improving is one of the contributions that can help national development. This is supported by the number of hospitals that have developed and provide medical devices to detect disease. During the Covid-19 pandemic, this disease spread quickly through air and saliva, so social distancing was necessary. Thus, many medical devices, such as measuring body temperature in public places, use sensors that can measure within 5–10 centimeters. Therefore, it is necessary to develop a robot that can detect body temperature and PPG in real-time. The data obtained will be read by text to speech using the GTTS and python text to speech library then the robot will sound in Indonesian. The results obtained show that the data has been successfully read with percentage success rate is 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and data reading speed of 2–6 seconds until the robot speaks.
Estimated Service Duration in Information Technology Unit Using Support Vector Machine

During the current COVID-19 pandemic, the government must change the conventional learning system to online. That way, the use of LMS and Igracias will greatly support this distance learning system or can be called online learning. Of course, with the implementation of the media as a medium of information for students and lecturers, problems will occur on the website. For this reason, Telkom University has created an IT Unit where students can ticket to PUTI for problems experienced by LMS or Igracias. Therefore, in this research, a test will be carried out to find out how the quality of service from the IT Unit in providing maximum service to Telkom University students using the SVM method will be carried out.
Face Liveness Detection Using CNN for Face Verification on Medical Robot

The development of science in the field of technology has accelerated due to the global need for society 5.0 which emphasizes creativity, critical thinking, communication, and collaboration to solve existing problems. One of its applications is to collaborate to create a smart robot using computer vision that can recognize a person's face and verify its authenticity. face recognition uses the identification and classification of faces by comparing matrix values between models and new inputs. Face liveness detection on the robot uses the mediapipe and the CNN algorithm. Facial recognition results get an accuracy value of 92% with an optimal distance of 100 cm.
Hardware Implementation of Optimized LeNet-5

Convolutional Neural Network (CNN), a feedforward neural network, is widely used in large-scale machine learning tasks such as image recognition, image classification, and object detection. Although CNN has strong performance, its application in daily embedded systems is limited due to the complex network hierarchy and a large amount of data. In this paper, the LeNet-5 network structure is optimized. The recognition accuracy of the optimized network on the MNIST dataset is 98.32%, but the number of weights is reduced by more than ten times compared with the LeNet-5 network, and data quantization is used to convert the weights into 8-bit fixed-point numbers, which is more suitable for deployment in the hardware with limited resources and power consumption. At the same time, different hardware structures and optimization strategies are designed for each layer of the network, and the convolution, pooling and fully connected layers are computed in parallel and optimized using methods such as loop unrolling and pipelining. Finally, the feasibility of the hardware design is verified by simulating each layer using the MNIST dataset and quantized weights.
A Method of Abnormal Measurement Screening for Special Transformer Users Based on Correlation Measurement Algorithm

In order to improve the accuracy of measurement anomaly identification for users of special transformers, this paper proposes a method of measurement anomaly screening for users of special transformers based on correlation measurement algorithm. Based on the monitoring results of user measurement data of special transformer in the abnormal monitoring sub module program, the ontology model of user measurement data of special transformer is constructed. On this basis, the user measurement data of special transformer substation is collected, and the data distribution model of user measurement similarity of special transformer substation is constructed based on the correlation measurement algorithm. Filter and screen the global information through the filter to realize the abnormal data of measurement data of special transformer users and complete the abnormal screening of measurement data of special transformer users. The experimental results show that the proposed method has a good performance in the screening of user measurement anomalies in special transformers.
Improve the Traditional Autoencoder Student Classroom Behavior Recognition Algorithm

Most of the existing behavior recognition methods are aimed at the dynamic behavior in action. When applied to the static behavior recognition environment in the classroom, it is difficult to achieve the ideal effect and waste computing resources. Therefore, aiming at the specific environment of classroom, the paper firstly describes the model theories of traditional Auto Encoder (AE) and Variational Auto Encoder (VAE). On this basis, the optimized variational autoencoder (AVAE) model structure proposed in this paper for the classroom behavior recognition of students is introduced. By loading the encoder with pre-training weight, the feature extraction results are obtained, and the loss function is optimized to reduce the supervision of the decoder during training, so as to identify the student behavior more efficiently. The AVAE model is compared with the current common deep learning feature extraction classification methods, and the performance advantages of the proposed method are verified by experiments.
Application of Cloud Computing in Information Management System in the Era of Big Data

Cloud computing can provide long-term maintenance technology for enterprise management planning, and improve the operational efficiency of enterprises without increasing energy consumption. Therefore, this paper studies how to improve the information management efficiency of small and medium-sized enterprises by introducing cloud computing technology to build software application system. Cloud computing enterprise management system of big data adopts Saas mode of cloud computing, cloud server is created by Alibaba Cloud, and virtual cluster is created by hadoop to realize distributed storage and calculation of enterprise information data. The application design adopts J2EE technology, java language and SSH framework to establish the system. In this paper, a storage resource pool is built in the virtual cluster, and then different servers are used as storage shared resource pools of different resources. Cloud platform can control and manage all virtual machines.
An Analysis about Federated Learning in Low-Powerful Devices

Federated Learning allows us to train Machine Learning models in a distributed way. This improves users' security and privacy and allows the computational load to be distributed. One of the advantages is the application of these models on low-powerful devices, bringing the processing closer to the devices that generate them, as is pursued with Edge Computing for the continuum. However, there is a need to conduct more extensive studies on the limitations of these devices such as the lack of processing power, limited memory available, or training time. Current works highlight these constraints and emphasize real experiments that evaluate the performance and limitations of these models. Therefore, this paper presents an analysis that evaluates the training and execution requirements, limitations, and resource consumption by running a Federated Learning algorithm proposed by Google and using the dataset Fashion-MNIST on different devices. This allows us to determine the requirements when selecting devices to be included in the federations.
Toward Generative Data Augmentation for Traffic Classification

Data Augmentation (DA)--augmenting training data with synthetic samples--is wildly adopted in Computer Vision (CV) to improve models performance. Conversely, DA has not been yet popularized in networking use cases, including Traffic Classification (TC). In this work, we present a preliminary study of 14 hand-crafted DAs applied on the MIRAGE19 dataset. Our results (i) show that DA can reap benefits previously unexplored in TC and (ii) foster a research agenda on the use of generative models to automate DA design.
Investigating Search Engines Terms Leakage

This paper delves into the growing concerns about user privacy on the Internet, emphasizing the lack of transparency in web tracking. We study the phenomenon of search engines' keyword leakage - instances where search terms inputted by users are unintentionally and unknowingly transmitted to third parties. We conduct automated experiments on the top 15 popular search engines and repeat them in three distinct locations: Japan, Chile, and the Czech Republic. Preliminary findings reveal that six of the 15 examined search engines exhibited leakage, showing slight differences across regions. These results emphasize the need for further analysis to characterize this privacy leakage.
Benchmarks for Job Scheduling in Ultra-Distributed Systems

As the number of edge devices rapidly multiplies into the millions in the post-5G era, there is a simultaneous surge in demand for job execution driven by the data generated by these devices. Ultra-distributed computing systems have emerged to support this notable proliferation of interconnected devices and in addressing the unprecedented data generated over low-latency networks. To ensure the high availability and reliability of these systems, an efficient job scheduling method is required to schedule jobs across available resources. However, there are currently no large-scale benchmarks available to evaluate job scheduling methods. This paper introduces large-scale benchmarks of up to one million devices for job shop scheduling problems. We evaluate the makespan reduction of widely used combinatorial optimizations on these benchmarks, including simulated annealing, ant colony, tree Parzen search, particle swarm, artificial bee colony, cuckoo search, whale, grey wolf, firefly, and bat optimizations. We investigate the impact of execution time on finding the optimal makespan of job shop scheduling for each method. The experimental results can be used to guide the selection of a job scheduling method for particular applications.
Human performance measures for cyber security operations centers

AbstractHuman performance metrics and measures are important to Security Operations Centers (SOCs). Metrics are used by SOC managers to assess analysts’ performance. However, existing metrics for analysts tend to focus solely on task performance. Researchers and practitioners may value ideas on other dimensions by which the performance of an analyst can be measured. As studies on performance metrics for analysts continue to evolve, this paper provides timely insights and recommendations for researchers and practitioners on different dimensions by which the performance of an analyst can be measured. This paper identifies and presents various dimensions by which analysts’ performance could be measured. Beyond the identification of the different dimensions, this paper provides examples of measurable constructs under each suggested dimension in the context of a SOC. Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsEnoch AgyepongEnoch Agyepong is a Cyber Security Architect at Airbus, UK. He has a Bachelor’s Degree in Computing from the University of Greenwich, England and a Master’s Degree in Advanced Security and Digital Forensics from Edinburgh Napier University, Scotland. He obtained his PhD in Cyber Security from Cardiff University, UK. His research interest is in Cyber Security and Security Operation Centers.
Infinitary logic with infinite sequents: syntactic investigations

Abstract The present paper deals with a purely syntactic analysis of infinitary logic with infinite sequents. In particular, we discuss sequent calculi for classical and intuitionistic infinitary logic with good structural properties based on sequents possibly containing infinitely many formulas. A cut admissibility proof is proposed which employs a new strategy and a new inductive parameter. We conclude the paper by discussing related issues and possible themes for future research.
Improved secure PCA and LDA algorithms for intelligent computing in IoT‐to‐cloud setting

Abstract The rapid development of new technologies such as artificial intelligence and big data analysis requires the simultaneous development of cloud computing technology. The application of IoT‐to‐cloud setting has been fully applied in various industry sectors, such as sensor‐cloud system which is composed of wireless sensor network and cloud computing technology. With the increasing amount and types of collected data, companies need to reduce the dimension of massive data in cloud servers for obtaining data analysis reports rapidly. Due to frequent cloud server data leaks, companies must adequately protect the privacy of some confidential data. To this end, we designed a dimension reduction method for ciphertext data in the sensor‐cloud system based on the CKKS encryption scheme, principal component analysis (PCA) and linear discriminant analysis (LDA) dimension reduction algorithm. As data cannot be directly calculated using traditional PCA and LDA algorithm after encryption, we add some interactive operations and iterative calculations to replace some steps in traditional algorithms. Finally, we select the classification dataset IRIS which is commonly used in machine learning, and screen out the best encryption and calculation parameters, and efficiently realize the dimension reduction method of ciphertext data through a large number of experiments.