Reviewerly/exHarmony huggingface dataset:
SAR Image Aircraft Target Recognition Based on Improved YOLOv5

Synthetic aperture radar (SAR) is an active ground-surveillance radar system, which can observe targets regardless of time and weather. Passenger aircrafts are important targets for SAR, as it is of great importance for accurately recognizing the type of aircraft. SAR can provide dynamic monitoring of aircraft flights in civil aviation, which is helpful for the efficient management of airports. Due to the unique imaging characteristics of SAR, traditional target-detection algorithms have poor generalization ability, low detection accuracy, and a cumbersome recognition process. Target detection in high-resolution SAR images based on deep-learning methods is currently a major research hotspot. You Only Look Once v5 (YOLOv5) has the problems of missed detection and false alarms. In this study, we propose an improved version of YOLOv5. A multiscale feature adaptive fusion module is proposed to adaptively assign different weights to each scale of the feature layers, which can extract richer semantic and textural information. The SIOU loss function is proposed to replace the original CIOU loss function to speed up the convergence of the algorithm. The improved Ghost structure is proposed to optimize the YOLOv5 network to decrease the parameters of the model and the amount of computation. A coordinate attention (CA) module is incorporated into the backbone section to help extract useful information. The experimental results demonstrate that the improved YOLOv5 performs better in terms of detection without affecting calculation speed. The mean average precision (mAP) value of the improved YOLOv5 increased by 5.8% compared with the original YOLOv5.
Unlocking the black box of CNNs: Visualising the decision-making process with PRISM

Technology has grown rapidly in recent years, and new solutions that rely on Machine Learning (ML) and Artificial Intelligence (AI) are introduced every day. With such fast-paced advancement, inspecting and fully comprehending how given models make decisions is becoming problematic. The complex decision-making process of these models has become a black box, making it challenging to unravel how they work; therefore, eXplainable Artificial Intelligence (XAI) methods are crucial for further development. This paper discusses how state-of-the-art techniques determine classifications and why they need to be revised to understand the prediction-generating process fully. It compares those existing solutions with the new method called Principal Image Sections Mapping - PRISM, which relies on Principal Component Analysis and allows visualising the most significant features recognised by a given Convolutional Neural Network. PRISM is implemented in a piece of software called TorchPRISM that can generate and present the clustering based on the method's output. The result can indicate ambiguous classes discrimination; thus, the possibility of automating the output analysis process is also discussed. The paper's main objective is to examine how PRISM enhances the current understanding of the decision-making process and introduce a tool that can facilitate analysing the output. PRISM implementation (TorchPRISM) can be found in the public GitHub repository: https://github.com/szandala/TorchPRISM
UsbVisdaNet: User Behavior Visual Distillation and Attention Network for Multimodal Sentiment Classification

In sentiment analysis, biased user reviews can have a detrimental impact on a company's evaluation. Therefore, identifying such users can be highly beneficial as their reviews are not based on reality but on their characteristics rooted in their psychology. Furthermore, biased users may be seen as instigators of other prejudiced information on social media. Thus, proposing a method to help detect polarized opinions in product reviews would offer significant advantages. This paper proposes a new method for sentiment classification of multimodal data, which is called UsbVisdaNet (User Behavior Visual Distillation and Attention Network). The method aims to identify biased user reviews by analyzing their psychological behaviors. It can identify both positive and negative users and improves sentiment classification results that may be skewed due to subjective biases in user opinions by leveraging user behavior information. Through ablation and comparison experiments, the effectiveness of UsbVisdaNet is demonstrated, achieving superior sentiment classification performance on the Yelp multimodal dataset. Our research pioneers the integration of user behavior features, text features, and image features at multiple hierarchical levels within this domain.
Android-Based Audio Video Navigation System Forensics: A Case Study

Vehicle digital forensics includes the process of collecting and analysing digital data stored in vehicles to find evidence related to traffic accidents or crime scenes. Through this process, we can reconstruct digital events by recognizing various information such as driver behaviour, driving patterns, vehicle destinations, and smartphones connected to a vehicle. Recently, many vehicle digital forensic studies have been conducted, but few of them have dealt with Android-based infotainment (or audio video navigation, AVN) systems. While many AVN systems adopt Android as the operating system, digital forensics of Android-based AVN systems is not easy. This is because Android-based AVN systems support various storage devices and data formats and have various data sources, making consistent data collection or analysis difficult. In this article, we perform digital forensics on four AVN systems: two Android 4.2.2 Jellybean-based and two Android 4.4.2 KitKat-based, aiming to develop a data acquisition and analysis method appropriate for each Android version. As a data collection method for the AVN system forensics, logical data acquisition is performed on the Jellybean-based systems and physical data acquisition on the KitKat-based systems. For the collected data, we identify and analyse Bluetooth data, navigation data, and system logs individually. Next, we investigate the differences in the storage structure and file location of major digital artefacts depending on the Android versions and show the limitations of the individual data analysis. Finally, we construct a timeline for the driver’s activities by integrating and analysing the diverse artefacts which consist of Bluetooth data, navigation data, and system logs.
A Software Vulnerability Management Framework for the Minimization of System Attack Surface and Risk

Current Internet of Things (IoT) systems comprise multiple software systems that are deployed to provide users with the required functionalities. System architects create system blueprints and draw specifications for the software artefacts that are needed; subsequently, either custom-made software is developed according to these specifications and/or ready-made COTS/open source software may be identified and customized to realize the overall system goals. All deployed software however may entail vulnerabilities, either due to insecure coding practices or owing to misconfigurations and unexpected interactions. Moreover, software artefacts may implement a much broader set of functionalities than may be strictly necessary for the system at hand, in order to serve a wider range of needs, and failure to appropriately configure the deployed software to include only the required modules results in the further increase of the system attack surface and the associated risk. In this paper, we present a software vulnerability management framework which facilitates (a) the configuration of software to include only the necessary features, (b) the execution of security-related tests and the compilation of platform-wide software vulnerability lists, and (c) the prioritization of vulnerability addressing, considering the impact of each vulnerability, the associated technical debt for its remediation, and the available security budget. The proposed framework can be used as an aid in IoT platform implementation by software architects, developers, and security experts.
Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence

Recently, breast types were categorized into four types based on the Breast Imaging Reporting and Data System (BI-RADS) atlas, and evaluating them is vital in clinical practice. A Japanese guideline, called breast composition, was developed for the breast types based on BI-RADS. The guideline is characterized using a continuous value called the mammary gland content ratio calculated to determine the breast composition, therefore allowing a more objective and visual evaluation. Although a discriminative deep convolutional neural network (DCNN) has been developed conventionally to classify the breast composition, it could encounter two-step errors or more. Hence, we propose an alternative regression DCNN based on mammary gland content ratio. We used 1476 images, evaluated by an expert physician. Our regression DCNN contained four convolution layers and three fully connected layers. Consequently, we obtained a high correlation of 0.93 (p < 0.01). Furthermore, to scrutinize the effectiveness of the regression DCNN, we categorized breast composition using the estimated ratio obtained by the regression DCNN. The agreement rates are high at 84.8%, suggesting that the breast composition can be calculated using regression DCNN with high accuracy. Moreover, the occurrence of two-step errors or more is unlikely, and the proposed method can intuitively understand the estimated results.
Case study identification: A trivial indicator outperforms human classifiers

The definition and term "case study" are not being applied consistently by software engineering researchers. We previously developed a trivial "smell indicator" to help detect the misclassification of primary studies as case studies. To evaluate the performance of the indicator. We compare the performance of the indicator against human classifiers for three datasets, two datasets comprising classifications by both authors of systematic literature studies and primary studies, and one dataset comprising only primary-study author classifications. The indicator outperforms the human classifiers for all datasets. The indicator is successful because human classifiers "fail" to properly classify their own, and others', primary studies. Consequently, reviewers of primary studies and authors of systematic literature studies could use the classifier as a "sanity" check for primary studies. Moreover, authors might use the indicator to double-check how they classified a study, as part of their analysis, and prior to submitting their manuscript for publication. We challenge the research community to both beat the indicator, and to improve its ability to identify true case studies.
An Empirical Comparison of Interpretable Models to Post-Hoc Explanations

Recently, some effort went into explaining intransparent and black-box models, such as deep neural networks or random forests. So-called model-agnostic methods typically approximate the prediction of the intransparent black-box model with an interpretable model, without considering any specifics of the black-box model itself. It is a valid question whether direct learning of interpretable white-box models should not be preferred over post-hoc approximations of intransparent and black-box models. In this paper, we report the results of an empirical study, which compares post-hoc explanations and interpretable models on several datasets for rule-based and feature-based interpretable models. The results seem to underline that often directly learned interpretable models approximate the black-box models at least as well as their post-hoc surrogates, even though the former do not have direct access to the black-box model.
Sub-Character Tokenization for Chinese Pretrained Language Models

Abstract Tokenization is fundamental to pretrained language models (PLMs). Existing tokenization methods for Chinese PLMs typically treat each character as an indivisible token. However, they ignore the unique feature of the Chinese writing system where additional linguistic information exists below the character level, i.e., at the sub-character level. To utilize such information, we propose sub-character (SubChar for short) tokenization. Specifically, we first encode the input text by converting each Chinese character into a short sequence based on its glyph or pronunciation, and then construct the vocabulary based on the encoded text with sub-word segmentation. Experimental results show that SubChar tokenizers have two main advantages over existing tokenizers: 1) They can tokenize inputs into much shorter sequences, thus improving the computational efficiency. 2) Pronunciation-based SubChar tokenizers can encode Chinese homophones into the same transliteration sequences and produce the same tokenization output, hence being robust to homophone typos. At the same time, models trained with SubChar tokenizers perform competitively on downstream tasks. We release our code and models at https://github.com/thunlp/SubCharTokenization to facilitate future work.
V-A3tS: A rapid text steganalysis method based on position information and variable parameter multi-head self-attention controlled by length

Text length varies in social networks, such as IMDB long text, Twitter short text, and long–short mixed text. For these complex situations, the time series, convolution, or fine-tuned BERT models are used in existing text steganalysis methods almost. However, these methods do not simultaneously consider higher detection accuracy and lower training time at the same time. To alleviate this dilemma, this paper proposes a novel text steganalysis method. First, the proposed method maps words into a semantic space containing position information. Second, a variable parameter attention layer scaled appropriately according to text length is designed, it achieves the purpose that the entire parameter amount of the model is not redundant and can ensure effective detection. Finally, the steganalysis features are enhanced by the residual linear layer. For long, short, and mixed text datasets, comparing experiments show that the proposed method has higher detection accuracy, fewer parameters, and shorter training time than existing methods. Among them, the advantage of this method is more obvious for long and mixed texts.
Towards Supercomputing Categorizing the Maliciousness upon Cybersecurity Blacklists with Concept Drift

In this article, we have carried out a case study to optimize the classification of the maliciousness of cybersecurity events by IP addresses using machine learning techniques. The optimization is studied focusing on time complexity. Firstly, we have used the extreme gradient boosting model, and secondly, we have parallelized the machine learning algorithm to study the effect of using a different number of cores for the problem. We have classified the cybersecurity events’ maliciousness in a biclass and a multiclass scenario. All the experiments have been carried out with a well-known optimal set of features: the geolocation information of the IP address. However, the geolocation features of an IP address can change over time. Also, the relation between the IP address and its label of maliciousness can be modified if we test the address several times. Then, the models’ performance could degrade because the information acquired from training on past samples may not generalize well to new samples. This situation is known as concept drift. For this reason, it is necessary to study if the optimization proposed works in a concept drift scenario. The results show that the concept drift does not degrade the models. Also, boosting algorithms achieving competitive or better performance compared to similar research works for the biclass scenario and an effective categorization for the multiclass case. The best efficient setting is reached using five nodes regarding high-performance computation resources.
Safety helmet detection method based on semantic guidance and feature selection fusion

Safety helmet detection is a hot topic of research in the field of industrial safety for object detection technology. Existing object detection methods still face great challenges for the detection of small-scale safety helmet object. In this paper, we propose a safety helmet detection method based on the fusion of semantic guidance and feature selection. The method is able to consider the balance between detection performance and efficiency. First, a multi-scale non-local module is proposed to establish internal correlations between different scales of deep image features as well as to aggregate semantic context information to guide the information recovery of decoder network features. Then the feature selection fusion structure is proposed to adaptively select deep features and underlying key features for fusion to make up for the missing semantic and spatial detail information of the decoding network and improve the spatial location expression capability of the decoding network. Experimental analysis shows that the method in this paper has good detection performance on the expanded safety helmet wearing dataset with 5.12% improvement in mAP compared to the baseline method CenterNet, and 6.11% improvement in AP for the safety helmet object.
Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

Abstract Feature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a novel meta‐heuristic algorithm, has gained popularity in feature selection due to its fast convergence speed and easy implementation. In this paper, an improved binary GWO algorithm incorporating a novel Population Adaptation strategy called PA‐BGWO is proposed. The PA‐BGWO takes into account the characteristics of the feature selection problem and designs three strategies. The proposed strategy includes an adaptive individual update procedure to enhance the exploitation ability and accelerate convergence speed, a head wolf fine‐tuned mechanism to exert the impact on each independent feature of the objective function, and a filter‐based method ReliefF for calculating feature weights with dynamically adjusted mutation probabilities based on the ranking features to effectively escape from local optima. Experimental comparisons with several state‐of‐the‐art feature selection methods on 15 classification problems demonstrate that the proposed approach can select a small feature subset with higher classification accuracy in most cases.
Time Series Self-Attention Approach for Human Motion Forecasting: A Baseline 2D Pose Forecasting

Human motion forecasting is a necessary variable to analyze human motion concerning the safety system of the autonomous system that could be used in many applications, such as in auto-driving vehicles, auto-pilot logistics delivery, and gait analysis in the medical field. At the same time, many types of research have been conducted on 3D human motion prediction for short-term and long-term goals. This paper proposes human motion forecasting in the 2D plane as a reliable alternative in motion capture of the RGB camera attached to the devices. We proposed a method, the time series self-attention approach to generate the next future human motion in the short-term of 400 milliseconds and long-term of 1,000 milliseconds, resulting that the model could predict human motion with a slight error of 23.51 pixels for short-term prediction and 10.3 pixels for long-term prediction on average compared to the ground truth in the quantitative and qualitative evaluation. Our method outperformed the LSTM and GRU models on the Human3.6M dataset based on the MPJPE and MPJVE metrics. The average loss of correct key points varied based on the tolerance value. Our method performed better within the 50 pixels tolerance. In addition, our method is tested by images without key point annotations using OpenPose as the pose estimation method. Resulting, our method could predict well the position of the human but could not predict well for the human body pose. This research is a new baseline for the 2D human motion prediction using the Human3.6M dataset.
An Analysis of Fusion Functions for Hybrid Retrieval

We study hybrid search in text retrieval where lexical and semantic search are fused together with the intuition that the two are complementary in how they model relevance. In particular, we examine fusion by a convex combination (CC) of lexical and semantic scores, as well as the Reciprocal Rank Fusion (RRF) method, and identify their advantages and potential pitfalls. Contrary to existing studies, we find RRF to be sensitive to its parameters; that the learning of a CC fusion is generally agnostic to the choice of score normalization; that CC outperforms RRF in in-domain and out-of-domain settings; and finally, that CC is sample efficient, requiring only a small set of training examples to tune its only parameter to a target domain.
Prioritizing Factors of Panic Buying Behaviour: A Neutrosophic Analytical Approach

In a crisis, panic buying (PB) has negative ramifications for manufacturers, suppliers, retailers, consumers and the overall economy. There is a plethora of research in extant literature identifying the factors causing PB, but none has prioritized them so that firms can devise focused strategies to tackle the situation of PB. In this research, through extant literature and experts’ opinion, 18 PB factors (PBFs) are identified. The neutrosophic analytical hierarchy process is used for prioritizing the factors based on single-valued triangular neutrosophic numbers. From the 18 PBFs, on the basis of ranking received from results, top 50% most influencing factors are social media, fear of missing out, peer influence, social learning, product importance, cultural values, rumours, sense of security and government regulations. Thereafter, in close conjunction with these factors, most significant interventions/measures to curb PB are presented in the form of a PB prevention and control framework. The knowledge of the top influencing factors of PB is vital for policymakers, administrators, marketers, retailers and consumers so that they can devise appropriate interventions when required.
Quantum Neural Networks for a Supply Chain Logistics Application

Abstract Problem instances of a size suitable for practical applications are not likely to be addressed during the noisy intermediate‐scale quantum (NISQ) period with (almost) pure quantum algorithms. Hybrid classical‐quantum algorithms have potential, however, to achieve good performance on much larger problem instances. One such hybrid algorithm on a problem of substantial importance: vehicle routing for supply chain logistics with multiple trucks and complex demand structure is investigated. Reinforcement learning with neural networks with embedded quantum circuits is used. In such neural networks, projecting high‐dimensional feature vectors down to smaller vectors is necessary to accommodate restrictions on the number of qubits of NISQ hardware. However, a multi‐head attention mechanism is used where, even in classical machine learning, such projections are natural and desirable. Data from the truck routing logistics of a company in the automotive sector is considered, and the methodology is applied by decomposing into small teams of trucks and results are found comparable to human truck assignment.