Reviewerly/exHarmony huggingface dataset:
IoTTFID: An Incremental IoT Device Identification Model Based on Traffic Fingerprint

Driven by 5G communication technology, IoT devices are widely deployed in various scenarios to provide automated services. However, a large number of IoT devices cannot install strong encryption suites and become the preferred target of cyber attackers. Specific vulnerabilities target specific types of IoT devices. Screening and repairing corresponding vulnerabilities based on device information can improve device protection capabilities. Traditional device identification models are static and have limitations in the identification range. The model needs to be trained from scratch to identity new types of devices, which consumes a lot of computing resources and training time. To overcome these limitations, we propose IoTTFID, an incremental IoT device identification model based on traffic fingerprint. Extract the traffic fingerprint of the new device, convert it into an input vector after preprocessing, and input it to the original model to update some network parameters, so that the model has the ability to identify new devices. The results of evaluation on two open datasets show that the accuracy of IoTTFID is 98.09% on UNSW dataset and 98.29% on Yourthings dataset, which outperforms the existing methods. IoTTFID has an accuracy rate of 80.4% after five incremental learning stages, and an F1 of over 96% for encrypted IoT devices. IoTTFID can dynamically adjust with the actual environment to increase the range of identifiable device types, providing strong support for the security management of IoT devices.
GridOnto: Knowledge Representation and Extraction for Fault Events in Power Grid

An accurate and comprehensive fault knowledge representation is indispensable for an automated and intelligent processing of power grid failures. Current knowledge graphs are incapable of capturing the complex relations among power grid failures. This paper extends the current knowledge graph representation mechanism through temporal, spatial, and causal representations to facilitate the knowledge representation of power grid faults, allowing for ontology modeling of power event elements and event relationships. During the modeling process, this paper proposes an extraction method which includes Bi-directional Encoder Representation from Transformers (BERT), Bi-directional Long Short-Term Memory (BiLSTM), and Conditional Random Field (CRF) for entities and relationships in power grid faults. The innovation of the method lies in the clever combination of the three, BERT learning semantic representation, BiLSTM further learning semantic features, and CRF joint modeling of labels to improve accuracy, and the results verify the effectiveness and practicality of the method presented in this paper.
Neural Network Intelligent Control Based on MPSO

With the increasing complexity of mechanical equipment, the control effectiveness of traditional intelligent control systems can no longer meet the needs of modern industrial production. In order to reduce errors in intelligent control systems while ensuring system performance, this study proposes a new Particle Swarm Optimization (PSO) optimization scheme. The study simplified the PSO algorithm from three aspects: algorithm parameters, speed, and position formula, and corrected the formulas for individual optimal values and global optimal values. Research will name the optimized algorithm Modified PSO (MPSO). On the basis of the MPSO algorithm, neural network intelligent control has been innovatively improved. In the experimental results, the MPSO optimized controller controlled the error within 0.01 within 0.02 seconds. At this time, the Whale Optimization Algorithm (WOA) optimized error was 0.072, and the PSO optimized error was 0.478. Compared to PSO and WOA, the control error of MPSO has decreased by 98.95% and 93.06%, respectively. In addition, the proposed method not only has the best control effect, but also has the shortest system response time, with an average time of 1.294 seconds. Compared to PSO and WOA optimization, it reduces by 61.48% and 43.07%, respectively.The results verified that the proposed method in this study can effectively improve the accuracy of intelligent control and control the error within the target range within 0.02 seconds. The research not only simplifies the calculation of the PSO algorithm, but also effectively reduces the error of the algorithm, providing a reference for research in the field of intelligent control.
Unsupervised Semantic Segmentation with Feature Fusion

Despite the significant progress in semantic segmentation, its further development is still limited by the requirement for large amounts of annotated data. As unsupervised/self-supervised methods have been successfully applied to image classification tasks, some research has turned to unsupervised semantic segmentation. Unsupervised semantic segmentation methods generally use pre-trained classification networks to extract features as supervision signals, but the richness of information in these features is not well considered. In this paper, we demonstrate the richness issue in features and propose a hierarchical feature fusion and multi-scale fusion method to address it specifically for unsupervised semantic segmentation, which ultimately improves the network’s performance on the general semantic segmentation datasets Cityscapes and Coco-Stuff.
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing Instances

The considerable surge in energy consumption within data centers can be attributed to the exponential rise in demand for complex computing workflows and storage resources. Video streaming applications are both compute and storage-intensive and account for the majority of today's internet services. In this work, we designed a video encoding application consisting of codec, bitrate, and resolution set for encoding a video segment. Then, we propose VE-Match, a matching-based method to schedule video encoding applications on both Cloud and Edge resources to optimize costs and energy consumption. Evaluation results on a real computing testbed federated between Amazon Web Services (AWS) EC2 Cloud instances and the Alpen-Adria University (AAU) Edge server reveal that VE-Match achieves lower costs by 17%-78% in the cost-optimized scenarios compared to the energy-optimized and tradeoff between cost and energy. Moreover, VE-Match improves the video encoding energy consumption by 38%-45% and gCO2 emission by up to 80% in the energy-optimized scenarios compared to the cost-optimized and tradeoff between cost and energy.
Image Super-Resolution Using Dilated Window Transformer

Transformer-based networks using attention mechanisms have shown promising results in low-level vision tasks, such as image super-resolution (SR).Specifically, recent studies that utilize windowbased self-attention mechanisms have exhibited notable advancements in image SR.However, windowbased self-attention, results in a slower expansion of the receptive field, thereby restricting the modeling of long-range dependencies.To address this issue, we introduce a novel dilated window transformer, namely DWT, which utilizes a dilation strategy.We employ a simple yet efficient dilation strategy that enlarges the window by inserting intervals between the tokens of each window to enable rapid and effective expansion of the receptive field.In particular, we adjust the interval between the tokens to become wider as the layers go deeper.This strategy enables the extraction of local features by allowing interaction between neighboring tokens in the shallow layers while also facilitating efficient extraction of global features by enabling interaction between not only adjacent tokens but also distant tokens in the deep layers.We conduct extensive experiments on five benchmark datasets to demonstrate the superior performance of our proposed method.Our DWT surpasses the state-of-the-art network of similar sizes by a PSNR margin of 0.11dB to 0.27dB on the Urban100 dataset.Moreover, even when compared to state-of-the-art network with about 1.4 times more parameters, DWT achieves competitive results for both quantitative and visual comparisons.
Agent talks about itself: an implementation using Jason, CArtAgO and Speech Acts

Thinking to oneself is a prerogative of man when he needs to think about or repeat what he is doing or experiencing. It is a way of processing information and setting in motion a decision-making process. When this is done aloud, there is also a chance that someone else will understand the meaning or reasons for the action. Equipping an agent with the ability to reveal the reasons for its decisions is both a way to improve human interaction and a way to improve the triggering of a decision process. In this work, we propose to use the speech act to enable a coalition of agents to exhibit inner speech capabilities to explain their behavior, but also to guide and reinforce the creation of an inner model. The BDI agent paradigm, Jason, and CArtAgO are used to give agents the ability to act in a human-like manner. The BDI reasoning cycle has been extended to include inner speech. The proposed solution continues the research path that started with the definition of a cognitive model and architecture for human-robot teaming interaction and aims to integrate the believable interaction paradigm in it.
On the number of error correcting codes

Abstract We show that for a fixed $q$ , the number of $q$ -ary $t$ -error correcting codes of length $n$ is at most $2^{(1 + o(1)) H_q(n,t)}$ for all $t \leq (1 - q^{-1})n - 2\sqrt{n \log n}$ , where $H_q(n, t) = q^n/ V_q(n,t)$ is the Hamming bound and $V_q(n,t)$ is the cardinality of the radius $t$ Hamming ball. This proves a conjecture of Balogh, Treglown, and Wagner, who showed the result for $t = o(n^{1/3} (\log n)^{-2/3})$ .
Tsallis relative α entropy of coherence dynamics in Grover′s search algorithm

Abstract Quantum coherence plays a central role in Grover’s search algorithm. We study the Tsallis relative <?CDATA $\alpha $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>α</mml:mi> </mml:math> entropy of coherence dynamics of the evolved state in Grover’s search algorithm. We prove that the Tsallis relative <?CDATA $\alpha $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>α</mml:mi> </mml:math> entropy of coherence decreases with the increase of the success probability, and derive the complementarity relations between the coherence and the success probability. We show that the operator coherence of the first <?CDATA ${H}^{\otimes n}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>⨂</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:msup> </mml:math> relies on the size of the database <?CDATA $N,$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>N</mml:mi> <mml:mo>,</mml:mo> </mml:math> the success probability and the target states. Moreover, we illustrate the relationships between coherence and entanglement of the superposition state of targets, as well as the production and deletion of coherence in Grover iterations.
Maximum Likelihood-Based Estimation of Finite Multivariate Libby-Novick Beta Mixture Models in Medical Applications

The present study offers a Libby-Novick Beta mixture model, which is based on a generalisation of Beta distribution. As a result of having an additional shape parameter, Libby-Novick Beta distribution provides more flexibility than conventional Beta distribution or other common distributions such as Gaussian distribution. To estimate the parameters of this novel mixture model, we applied maximum likelihood technique. To demonstrate the robustness of our proposed model, we compared it to other alternatives. We tested this novel unsupervised model on three real and publicly available medical datasets. The results indicate that this model has better performance compared to Gaussian mixture model and Beta mixture model.
The Key Technology of Quality Safety Risk Identification and Early Warning of Transmission Tower Construction based on Multi-Source Information Intelligent Learning

The erection of transmission tower group is mostly a high-altitude operation, and the risk level of construction operation is high. The quality of construction personnel is uneven, they are not familiar with the construction environment, habitual violation, and the management of construction management personnel is not in place, which is easy to lead to accidents. At present, the wrong type selection of tower bolts, construction not tightened or even not installed bolts often occur, which very easy to causes the transmission tower to topple, casualties and property losses, resulting in serious consequences. This question uses image recognition technology for intelligent identification and measurement of multiple groups of key objects in tower operation, adopts automatic measurement technology and edge distributed processing method for information block processing, and combines wireless communication technology to realize mutual high-speed and stable communication between equipment in tower operation area. Risk assessment and alarm of tower based on installation quality of tower bolts and nuts and distribution of failure points.
Garbage Classification and Detection Based on Improved YOLOv7 Network

With the improvement of people’s living standards, garbage classification is gradually forced. However, due to people’s awareness and knowledge, the classification accuracy and disposal of garbage are difficult to keep pace with guideline changes. With the consideration of the problems of low efficiency, heavy task and poor environment of garbage manual classification, an improved YOLOv7 target detection method is proposed to realize the effective classification of garbage. In this study, the recursive gated convolutional gnconv was used to establish the HorNet network architecture, and the model was trained by making specific data sets. The C3HB module is added to the YOLO model, and the pooling layer is optimized to replace SPPFCSPC to improve the detection accuracy of the target. The experimental results show that the garbage detection and classification method proposed in this study has excellent accuracy. Experiments show that the map value, accuracy and recall rate of the proposed model on garbage datasets are 99.25%, 99.33% and 98.03%, respectively, which are 1.50%, 3.99% and 1.41% higher than those of YOLOv7. The overall results are better than the original model.
Web Scraping for Data Analytics: A BeautifulSoup Implementation

Web scraping is an essential tool for automating the data-gathering process for big data applications. There are many implementations for web scraping, but barely any of them is based on Python's BeautifulSoup library. Therefore, this paper aims at creating a web scraper that gathers data from any website and then analyzes the data accordingly. For results and analysis, the web scraper has been implemented on the Amazon website which collects a product's name, price, number of reviews, rate, and link. We further highlighted the web scraper's capabilities by assimilating the results into an interface that integrates data visualization techniques to analyze the results gathered. The web scraper proved to be efficient upon execution, in which it scraped five pages and analyzed them, and visualized the information in approximately ten seconds. The limitations as a result of this implementation mainly revolved around applying it to specific product names rather than generic ones and extracting specific information that we wanted from the resulting products. Moreover, BeautifulSoup cannot extract all the data available to not compromise on speed. Further studies can be done by researchers who wish to reuse this implementation and modify it according to the data they want to extract, the analysis they wish to perform, and the website they wish to scrape. The implementation can be helpful, thus, to developers who are novices in the web scraping field or to researchers that wish to reuse the code for small data analytics projects.
Binary-like Real Coding Genetic Algorithm

A new real coding genetic algorithm is proposed, which discretizes the continuous feasible region and then makes it continuous and complete by mutation operator and local search operator, thus achieving the uniformity of the discretization and continuity of the genetic algorithm. By comparison with binary genetic algorithm, differential evolution algorithm (DE), particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA), and artificial bee colony algorithm (ABC), the results show that the proposed algorithm outperforms the others in all test functions. The algorithm is applied to the case of optimizing the weights of neural networks and excellent results are obtained, which validates the effectiveness of the algorithm.
Navigation control of mobile robot based on fuzzy neural network

Aiming at the current problem of mobile robot navigation control accuracy, this paper proposes a control method based on fuzzy neural networks and PID. The image processing algorithm extracts the navigation line and calculates the robot position by using RTK-GPS/UWB after obtaining the visual information. A navigation controller based on a fuzzy neural network is designed to generate the target heading angle, calculated by the lower computer combined with the motion model. A PID-based controller is designed to control walking and steering. The deviation is calculated to adjust the position of the robot to further improve the navigation accuracy. This method is verified in a simulated farmland environment. The experiments show that the position calculation results are consistent with the measured results. The proposed method can improve the control accuracy of the robot. The whole navigation system has high real-time performance and can meet actual needs.
Feature Ratio Method: A Payload Feature Extraction and Detection Approach for SQL Injection Attacks

Due to the complexity of the deployment environment and the differences in the syntax of the backend database engine, SQL injection attacks have the characteristics of high complexity and poor timeliness. In recent years, the ability to detect and predict SQL injection attacks based on machine learning has been widely recognized in preventing unknown network security risks. However, most of these studies use the method in text classification to extract features of SQL injection attack payloads. The SQL injection attack payload is different from ordinary natural language, so there are problems with inaccurate segmentation and large vocabulary in the conventional feature extraction methods. This paper proposes a feature ratio method for feature extraction and detection of SQL injection attack payloads and compares it with various machine learning algorithms. The experimental results show that the average F1 Score of this method reaches 96.29%, which is better than the methods of the previous studies in terms of accuracy and precision. It shows that our method can better help machine learning algorithms to classify and detect SQL injection attack payloads.
Research on automatic lawn mower system based on differential GPS and IMU fusion

Aiming at the problem that lawn mowing in cities is time-consuming and laborious by manual mowing, and the GPS signal is lost due to the low positioning accuracy and easy to be blocked by buildings and trees in the environment by using intelligent mowers, an automatic mower based on differential GPS and IMU fusion is designed. The system hardware power supply module, sensing module, control module, driver module, and interaction module are designed by analyzing the requirements; the software is based on the system operation flow, and Keil software is selected to realize the automatic control program and complete the control system design; the Kalman filter for GPS/IMU pine combination navigation is designed, and the traceless Kalman filter is used for data fusion because of the nonlinearity problem of the state equation of the system, and the experiment shows that the use of GPS and IMU fusion has significantly improved the positioning accuracy.
MAMBO–V: Dynamic Side-Channel Leakage Analysis on RISC–V

RISC–V is an emerging technology, with applications ranging from embedded devices to high-performance servers. Therefore, more and more security-critical workloads will be conducted with code that is compiled for RISC–V. Well-known microarchitectural side-channel attacks against established platforms like x86 apply to RISC–V CPUs as well. As RISC–V does not mandate any hardware-based side-channel countermeasures, a piece of code compiled for a generic RISC–V CPU in a cloud server cannot make safe assumptions about the microarchitecture on which it is running. Existing tools for aiding software-level precautions by checking side-channel vulnerabilities on source code or x86 binaries are not compatible with RISC–V machine code. In this work, we study the requirements and goals of architecture-specific leakage analysis for RISC–V and illustrate how to achieve these goals with the help of fast and precise dynamic binary analysis. We implement all necessary building blocks for finding side-channel leakages on RISC–V, while relying on existing mature solutions when possible. Our leakage analysis builds upon the modular side-channel analysis framework Microwalk, that examines execution traces for leakage through secret-dependent memory accesses or branches. To provide suitable traces, we port the ARM dynamic binary instrumentation tool MAMBO to RISC–V. Our port named MAMBO–V can instrument arbitrary binaries which use the 64-bit general purpose instruction set. We evaluate our toolchain on several cryptographic libraries with RISC–V support and identify multiple leakages.
Honey, I Chunked the Passwords: Generating Semantic Honeywords Resistant to Targeted Attacks Using Pre-trained Language Models

Honeywords are fictitious passwords inserted into databases in order to identify password breaches. The major challenge is producing honeywords that are difficult to distinguish from real passwords. Although the generation of honeywords has been widely investigated in the past, the majority of existing research assumes attackers have no knowledge of the users. These honeyword generating techniques (HGTs) may utterly fail if attackers exploit users’ personal identifiable information (PII) and the real passwords include users’ PII. The literature has demonstrated that password guessing is more effective when focusing on each of the chunks that compose a password (e.g., “P@ssword123” contains two chunks: “P@ssword” and “123”) and it has been suggested that, when available, PII should be used to generate honeywords. We thus leverage these findings to base our HGT method on any possible PII contained within passwords, and introduce a new, and more robust than its literature counterparts, method to generate honeywords, which consists of generating honeywords with GPT-3 using the semantic chunks of their corresponding real passwords. Furthermore, we propose a new metric, HWSimilarity, to evaluate the capability of HGTs. HWSimilarity is a pre-trained language model-based similarity metric that considers the semantic meaning of passwords when measuring the indistinguishability of honeywords and their counterparts. Comparing our chunk-level GPT-3 HGT to two state-of-the-art HGTs and using GPT-3 alone, we show that our HGT can generate honeywords that are more indistinguishable than its counterparts.