Reviewerly/exHarmony huggingface dataset:
Multi-scale vehicle detection method for expressway based on YOLOv7

A YOLOv7-based multi-scale vehicle detection method for expressways is proposed for the problem of low accuracy of multi-scale vehicle detection due to insufficient extraction and representation of vehicle features in multi-scale vehicle detection in expressway scenarios. Firstly, Efficient Attention-Receptive Field Block Cross Stage Partial Network has been designed to facilitate the extraction and fusion of feature information from vehicles of various scales. This module effectively achieves different sizes of receptive fields, generating higher-resolution feature maps to capture a wider range of information. This module is based on Receptive Field Block with the addition of the backbone structure of Cross Stage Partial Network module and Efficient Attention Module. Secondly, the introduction of the Efficient Attention Module in the designed Efficient Attention-Receptive Field Block Cross Stage Partial Network can enhance the sensitivity of the model to channel features and spatial features, which in turn can effectively improve the feature extraction and expression capability of the detection model. The vehicle data set GG-Vehicle is constructed from the Inner Mongolia expressway surveillance video and experiments are conducted, the results show that the mean average precision of the method reaches 91.5% , which improve 1.6%, and the F1-Score reaches 88.9, which improve 1.5, compared with the original YOLOv7 model, indicating that the method proposed in this paper can enhance the multi-scale vehicle feature extraction and expression capability and improve the accuracy of multi-scale vehicle detection.
Mal-lightDet: A light method to detect malicious encrypted traffic based on machine learning

Encryption not only protects the network security and privacy, but also encrypts attackers’ malicious traffic to evade detection. Thus, how to detect malicious encrypted traffic is critical for network security. From the perspective of privacy, methods based on ciphertext seem to be more attractive than decryption-based methods. However, due to performance limitation or privacy violations, most existing methods are far from satisfactory. Machine learning models provide a solution to the malicious encrypted traffic detection, but they place great demand on data features. To balance the detection performance and feature effectiveness, this paper proposes a framework called Mal-lightDet for light detection of malicious encrypted traffic based on machine learning. Mal-lightDet includes a feature-group selection model that mines the relevance between features to represent the traffic flow, which is less considered in the existing methods, and a voting-based detection model to eliminate the bias of different machine learning models. The selected feature subset not only preserves the detection effectiveness but also reduces the computation brought by massive training data. The experimental results prove the efficiency and stability of Mal-lightDet to distinguish malicious flow data by comparing it with other state-of-art methods. Besides, Mal-lightDet performs faster, more accurately, and more stably than some deep learning methods.
Multi-scale ultrasound image denoising algorithm based on deep learning model for super-resolution reconstruction

Effective suppression of speckle noise in ultrasound images is of great significance for improving image quality and clinical diagnostic analysis. The traditional edge enhancement nonlinear coherent diffusion (EENCD) and multi-scale filtering based on pyramid transformation are not obvious enough for the subtle features after the image is denoised. Since the super-resolution deep learning model SRGAN can restore more realistic texture details of the image, this attempt is made to improve the traditional Laplace pyramid, and the 2x magnification factor reconstruction image of the SRGAN model is used to replace the upsampling of the Laplace pyramid reconstruction stage, and the EENCD method is still used on the Laplace pyramid to suppress the image speckle. By comparing this method with the traditional method in the simulation image and the real ultrasound image, the experimental results show that the proposed method is superior to the original algorithm in terms of structure, edge protection and noise suppression.
A Survey on Review - Aware Recommendation Systems

Current advances in Deep Neural Networks have motivated recent studies to return their interest in Review-Aware Recommender Systems (RSs). In this sense, we employ a systematic mapping approach by selecting 56 papers published on the main vehicles of the area, such as RecSys, VLDB, SIGIR, and others. Reading these works and synthesising their achievements, we provide an updated picture of this field by highlighting relevant outcomes, contributions, and limitations. Especially, we have identified two huge limitations in the area. First, many of these works do not provide their code sources. Second, there is a prevalent tendency to prioritize accuracy above other quality dimensions, despite the consensus within the community to assess the practical effectiveness of RSs through many metrics. Both observations create constraints that restrict reproducibility and hinder straightforward comparisons in the field. Addressing this gap, this work also provides an open-source library that compiles the principal strategies proposed in the literature. Furthermore, we conducted a comprehensive evaluation through several datasets and metrics. Our findings indicate that while no single algorithm demonstrates absolute superiority, RSs based on neural networks have particularly exhibited the most competitive performance.
CluSent – Combining Semantic Expansion and De-Noising for Dataset-Oriented Sentiment Analysis of Short Texts

The lack of sufficient information, mainly in short texts, is a major challenge to building effective sentiment models. Short texts can be enriched with more complex semantic relationships that better capture affective information, with a potential undesired side effect of noise introduced into the data. This work proposes a new strategy for customized dataset-oriented sentiment analysis – CluSent – that exploits a powerful, recently proposed concept for representing semantically related words – CluWords. CluSent tackles the issues mentioned above of information shortage and noise by: (i) exploiting the semantic neighborhood of a given pre-trained word embedding to enrich document representation and (ii) introducing dataset-oriented filtering and weighting mechanisms to cope with noise, which takes advantage of the polarity and intensity information from lexicons. In our experimental evaluation, considering 19 datasets, five state-of-the-art baselines (including modern transformer architectures), and two metrics, CluSent was the best method in 30 out of 38 possibilities, with significant gains over the strongest baselines (over 14%).
A New Dynamic Window Approach Algorithm with Informed Rapidly Exploring Random Tree* Algorithm Implementation on a Robot Operating System

To enhance the performance of the Dynamic Window Approach (DWA) algorithm in complex obstacle environments and address its inability to handle negative-weighted edge graphs, this study proposes a path-planning algorithm that combines DWA with informed rapidly exploring random tree* (informed-RRT*). When the ROS robot is caught in a local optimum, informed-RRT* randomly setting temporarily goal as final goal to escape from the local optimum. The feasibility of the proposed algorithm is verified through practical testing experiment. The results show that the proposed algorithm can efficiently implement robot path searching in complex obstacle environments.
EDREW - Enhanced Data Representation for Extraction in Web

Extracting data from Web sites is still a challenge since pages have a complex and changeable structure, and the reason is simple: Web pages are designed to be visually user-friendly to users and not for the task of extracting data. In addition, each of them has its own and varied structures based on the HTML DOM structure. Since Web page designers can have their own standards for designing the pages, web page structures are widely divergent. So, identifying and extracting information still represents a significant barrier. To overcome this challenge, we propose a new approach called EDREW, which uses the information from the HTML DOM structure and the information generated through the HTML elements to represent the context of the elements on the page without the need for rendering. We use the ELMo model to extract information and classify them as noise or useful content. The experiments were performed on the public dataset Structured Web Data Extraction (SWDE) and on a new dataset created for this work, based on the most current versions of the pages in the dataset SWDE. Using EDREW, it was possible to overcome the baselines using the original SWDE dataset and extract twice as much page content using a new version of SWDE built by us with updated pages.
Chinese Implicit Binary complex Sentence Relation Recognition Based on RDAGCN Hybrid Model

Chinese implicit complex sentence relation recognition aims to analyze the semantic logical relationships between two sub-sentences. The main challenge lies in capturing accurate semantic interactions due to the absence of explicit conjunctions. Existing methods often rely on a single attention mechanism to encode structures and capture semantic representations, which overlooks the diversity in modeling semantic interactions, resulting in feature singularity and suboptimal recognition performance. To address this issue, we propose a novel recurrent graph convolutional network model based on the dual attention mechanism for identifying implicit relations in Chinese complex sentences. In the proposed model (RDAGCN), we design a dual attention network (DAN) that employs both self-attention and interactive attention mechanisms to capture diverse semantic interaction representations not only within words but also across sub-sentences, promoting feature diversity learning. Additionally, to capture fine-grained and important interaction representations, we introduce a recurrent graph convolutional module (RC-Encoder) that iteratively learns significant semantic interaction information through local convolution filtering. Finally, by constructing a graph structure with graph convolutional networks (GCN), the model effectively exploits the information interaction features among sentence components for relation recognition. Experimental results on the benchmark dataset, Chinese Complex Sentence Corpus (CCCS), demonstrate comparable performance to existing methods. The proposed model achieves an accuracy of 78.6% and a macro-averaged F1 score of 78.4%.
Towards Immersive Inclusivity for C2C: How Immersive Multimodal Interactions Can Make Online Customer-to-Customer Shopping More Inclusive

Online shopping is becoming ubiquitous in everyday life, with shopping experiences primarily delivered through either websites or specialized applications. This format may create inclusivity problems for users with different needs, as not all websites or applications are optimized for accessibility or have multiple alternative forms of interaction with the content. This can be especially challenging in customer-to-customer (C2C) platforms. While in online shopping a single platform can make merchants provide a unified display for description, standardized packaging, and ensure the item quality, in C2C platforms this responsibility falls mostly on users, thus creating a need for a more versatile way of processing, display, and interaction with C2C content. We are interested in how this issue can be alleviated through addition of online assistants that support immersive and multimodal forms of interactions. To gain a better understanding of the problem and needs of users, we have built a prototype application that uses a voice-activated assistant for item search and purchase and evaluated it in a pilot study. The findings suggest that participants value efficiency in C2C shopping and find current platforms problematic, while showing interest in voice search, immersive and multimodal interfaces to improve the overall shopping experience, despite some issues with voice recognition and response time.
On the Use of Early Fusion Operators on Heterogeneous Graph Neural Networks for One-Class Learning

Multimodal data fusion generates robust and unified representations considering supplementary and complementary information from different modalities, such as audio, image, and text. Different strategies for data fusion have been explored for decades, from simple concatenation-based strategies of the modalities’ features to the use of vector fusion operators (sum, average, subtraction, multiplication, etc.) between feature vectors in latent spaces of each modality. However, existing studies do not investigate multimodal fusion operators for heterogeneous graphs, which are powerful representations for modeling real-world data through a powerful structure that considers the different relations between different node types. Those representations are suited for important multimedia-related tasks, such as classification, recommendation, summarization, web sensing, and content-based retrieval. This paper presents a Graph Neural Network (GNN) method for heterogeneous graphs that explores different types of early fusion operators to deal with multiple modalities. Moreover, we evaluated the proposal’s performance with different early fusion operators considering one-class learning, a popular learning approach for real-world applications. A statistical analysis of the experimental results shows that early fusion operators improve the f1-Score when considering GNNs from heterogeneous graphs. We highlight the subtraction, multiplication, and minimum operators outperforming the other operators. Thus, we argue that our early-fusion operators’ proposal in heterogeneous graph neural networks leads to improved performance and is also a competitive alternative to the well-often-used concatenation technique or costly hand-based approaches of combining different modalities.
Liver cutting algorithm based on virtual plane

At present, the algorithm for virtual liver cutting requires a lot of computation, has poor real-time performance, and is easy to produce degenerated triangle and deformed triangle, and the physical characteristics are lost during the cutting process. In this paper, a virtual plane cutting method based on collision detection is proposed. The position of virtual tool and virtual liver is obtained through collision detection. At the same time, the triangular surface of the model is optimized according to delauney characteristics, and the degenerated triangle and deformed triangle are eliminated, so that the physical characteristics of virtual liver are closer to the real situation. The construction and optimization of the cutting surface were studied. The cutting surface could be generated in real time during the cutting process, and the refresh rate in the experiment was up to 297Hz on average. The comparison between this method and the ordinary method shows that the method proposed in this paper has good real-time performance and stability, can effectively eliminate the degenerate triangle, and effectively improve the authenticity and physical characteristics of the cutting simulation.
IHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data

Abstract Vertical Federated Learning (VFL) has many applications in the field of smart healthcare with excellent performance. However, current VFL systems usually primarily focus on the privacy protection during model training, while the preparation of training data receives little attention. In real-world applications, like smart healthcare, the process of the training data preparation may involve some participant’s intention which could be privacy information for this participant. To protect the privacy of the model training intention, we describe the idea of Intention-Hiding Vertical Federated Learning (IHVFL) and illustrate a framework to achieve this privacy-preserving goal. First, we construct two secure screening protocols to enhance the privacy protection in feature engineering. Second, we implement the work of sample alignment bases on a novel private set intersection protocol. Finally, we use the logistic regression algorithm to demonstrate the process of IHVFL. Experiments show that our model can perform better efficiency (less than 5min) and accuracy (97%) on Breast Cancer medical dataset while maintaining the intention-hiding goal.
CIMNet: A Contextual Information Mining Network for Real-time Instance Segmentation

Real-time instance segmentation seeks to partition and localize instances with desired efficiency, which has been applied to autonomous driving, robot navigation, and medical image analysis, and so forth. By learning hierarchical features, convolutional neural networks (CNNs) have dominated the instance segmentation task and achieved remarkable results. However, the inherent limitation of receptive field makes the CNNs capture deep semantic features at the costs of spatial details, leading to the lack of contextual information which are essential for learning discriminative representations of semantic instances. To address these problems, we propose a contextual information mining network, named CIMNet, to exploit extensive contexts for real-time instance segmentation. Firstly, a coarse-to-fine feature mining module (CFFM) combines channel attention with multi-scale pooling to explore multi-scale contextual information in the deepest feature. We then devise a multi-path feature enhancement module (MFEM) to expand effective receptive field, which captures spatial contextual information to enhance instances localization. Finally, aiming at improving the accuracy of small object, we design a dual-path FPN framework with a negligible extra computational cost to learn the scale invariance of each instance, achieving competitive speed while maintaining high segmentation accuracy. Extensive experiments have been conducted on the challenging COCO2017, our method can achieve 35.3 AP at 59.0 FPS, which shows that the proposed network achieves better accuracy/speed trade-off in real-time instance segmentation.
Embodied Voice Assistant Markup Language

Due to advances in robotics, the complexity of robots has increased greatly, allowing these devices to be used in increasingly challenging tasks. As a result, there is an increase in the complexity of developing programs for these robots, especially when using general purpose languages (GPLs). Even in simple programs this difficulty persists, as the algorithms need to communicate with sensors, obtaining and processing their values and, after processing, send some command to the robot’s actuators. In order to facilitate the programming of interactive sessions for social robotics platforms, this work proposes Embodied Voice Assistant Markup Language, a domain specific language based on XML, which can be applied to different social robots. The proposed language has elements for creating and manipulating variables, generating random numbers and conditional controls. In addition, it also proposes command abstractions for controlling multimodal interaction elements that are important in the human-robot interaction process. The Goal Question Metric paradigm (GQM) was used to structure the language assessment with 12 software developers, and then analyzed its clarity, effectiveness and perceived ease of use. This exploratory work presented very promising results, providing evidence that the proposed language is easy to use and understand.
EyePursuitLinks - an Eye-pursuit Based Interface for Web Browsing Using Smart Targets

Web accessibility and digital inclusion are still one of the great research challenges in computer science. For people with severe motor disabilities, eye-gaze interaction enables hands-free operation of graphical interfaces. Nonetheless, gaze tracking devices typically require frequent user calibration that weakens the overall user experience and performance. In this paper we propose EyePursuitLinks, an eye-pursuit based technique that allows people with severe motor disabilities to browse the web by following multiple moving targets corresponding to the links they want to follow. The main contribution of this paper is the use of Smart Targets (ST) to select potentially large number of hyperlinks within a relatively small area. We have performed a user experiment with 10 volunteers to evaluate the performance of the Smart Targets selection against conventional pursuit selection mechanism using 4, 8, and 16 simultaneous targets. Our results show that the use of ST is significantly more robust than the conventional method for larger number of targets.
Performance Modelling of Attack Graphs

This paper represents an initial study into using Performance Evaluation Process Algebra (PEPA) to model and analyse attack graphs. Such an approach adds timing information into the model and therefore extends the range of available analysis techniques. Two methods are proposed to generate a PEPA model based on a pre-existing attack graph with known vulnerabilities. The first method builds a PEPA model consisting of a single sequential component representing both a system and an attacker. The second method generates two sequential components and the system equation. The created PEPA models allow us to perform path analysis, sensitivity analysis and to estimate the time it takes for an attacker to compromise a system. We present two case studies of building and evaluating PEPA models of an attack graph. The PEPA Eclipse plug-in is used to support the evaluation of the PEPA model. We perform passage-time analysis on the models for each attack path in the attack graph, from the first vulnerability in a path until the system was compromised by the attacker. The results illustrate the most and least threatening attack paths and the time it takes the attacker to compromise the system for each path in the attack graph. They also show the impact of the attacker skills and the probability of exploit code availability on an attacker’s time to compromise the system.
Progressive Hartley image secret sharing for high-quality image recovery

This paper introduces a highly efficient approach for progressive image secret sharing through the Discrete Hartley Transform (DHT), offering distinct advantages over existing methods. Leveraging DHT in the color domain, our approach achieves progressive sharing of color images while maintaining robustness and minimal visual impact. By optimizing quantization table selection, our method focuses on maximizing image recovery fidelity. Our extensive experiments showcase the efficacy of our proposed method, highlighting its potential for secure image sharing and superior image quality enabled by the real-number DHT transformation. Notably, our approach demonstrates strong security robustness and high-quality image reconstruction through histogram analysis. Objective assessment values of 88.106 (PSNR), 0.884 (NCC), 0.095 (NAE), and 0.895 (SSIM) underscore the superiority of our method. These results, coupled with its unique contributions and key factors, position our work as a promising solution in the field of image security and distribution, motivating further exploration and inspiring related research endeavors.