Miracle-dz/UniFAR-FTUs huggingface dataset:
HM-SAF: Cross-Layer Static Analysis Framework For HarmonyOS

HarmonyOS is a new operating system that is compatible with Android and widely used in various Internet of Things (IoT) applications. However, like the Android system, HarmonyOS is also susceptible to privacy risks due to numerous applications in the Android ecosystem. Given the unique architecture of HarmonyOS, the conventional Android analysis framework cannot be directly applied. Consequently, there is an urgent need to develop a security analysis tool specifically designed for reviewing HarmonyOS applications. This paper proposes a static analysis approach for analyzing HarmonyOS applications and addresses the challenge of cross-layer analysis in the existing framework. The approach introduces a bottom-up data flow analysis algorithm based on summarized memory to achieve more accurate taint analysis capabilities. Additionally, the paper introduces benchmark suites, HMICCBench, and HMNativeFlowBench, to evaluate the effectiveness and accuracy of taint analysis tools for HarmonyOS.The experimental results presented in this paper demonstrate that Static Analysis Framework for HarmonyOS (HM-SAF) can effectively detect data leakage within and across components, as well as in Native layers, with a high degree of analysis accuracy. On the above three benchmark sets, HM-SAF achieves 93.6% accuracy, which is 4.1% higher than the FlowDroid framework in the Android world, and 4.0% higher than the JN-SAF framework. At the same time, HM-SAF also detected data leakage in real applications, indicating that HM-SAF has practical value.
Classification of Documents with Variable Length Using Likelihood Extracted Discriminative Attributes

Document classification techniques are used to label textual unstructured data during organization. The length of textual document, which can either be short or long, is vital to the performance of a classifying algorithm. Long document classification has proved to be a more challenging task than classifying short documents. The performance of deep learning algorithms, transformer-based algorithms, and their variations are recently put to test for classifying documents of varying lengths. Bidirectional Encoder Representation from Transformers (BERT), a transformer-based technique, places a limitation on the number of tokens, which in some cases have resulted in impaired classification performance in addition to the complexity introduced during the training process, while classical algorithms developed using deep learning methods need optimization to capture the entirety of the feature space. We propose a novel Likelihood Extracted Discriminative Attributes (LEDA) algorithm, which is a simplified approach for short and long document classification without incurring complex training process of the classifier. LEDA creates a set of discriminative attributes using likelihood ratio with an extra layer of regression filtration and subsequently use the attributes in Keras concatenated sequential model for the classification of variable length documents. These vocabs can explain the variation within the data, thereby enabling an improved classifier performance. LEDA achieves a 100% classification accuracy on the IMDB movie review dataset, which is one of the public datasets used for benchmarking classifier performance for long documents. Furthermore, LEDA achieves a 94% classification accuracy on a sample of short documents from Twitter data stream focused on COVID-19.
Incorporating Time Perspectives into Detection of Suicidal Ideation

As a vital risk to the public heath, suicide has been a hot topic for related research. Time perspectives (TPs) have attracted increasing attention in recent years in that making use of TPs can help gain insights into the real motives behind suicide ideation. TPs take into consideration how people think of or appraise their past, present, or future life would shape their behavior. Conventional TP-oriented studies on suicide tendency detection tend to rely on questionnaire surveys to help identify suicide thoughts or attempts. Such efforts suffer from weaknesses including low data collection efficiency and self-report bias. We proposed a TP-enhanced deep multitask model, TP-GloVe-GRU, in which TP is regarded as the synergy of both time and emotions. The model performance was evaluated against the CEASE dataset using a range of metrics. Results show that incorporating TP into the detection of suicide ideation leads to a better performance in most cases with an increase of 2.27% in the accuracy of suicide risk assessment.
Progressive Digital Twinning of 6G and beyond: Vision, Challenges, and Research Directions

This article presents a vision of the progression of digital twinning in the context of future mobile networks to enable an era of ultimate intelligent networks. Digital twinning is the process where a physical twin gets attached to a digital replica of itself via a bi-directional link, allowing for a two-way influence between the physical and the digital worlds. We envision the process to come in stages where each stage unlocks a new level of twinning and empowers an array of features and services never touched upon before. The vision is thought to be enabled by the advances in the various technology sectors, including multi-model sensing, sensor fusion, evolved compute capabilities, data centers, machine learning, 6G and beyond, among others. The article details the twinning progression vision, presents the levels of twinning while highlighting areas of evolution, explains different approaches for constructing a digital twin, and touches upon the integration of the different twinning stages into the operation of today’s and future mobile networks. The article lists future research directions with emphasis on the fundamental areas that could support the advancement of the presented vision.
Interactive Scene-driven Multi-stream Graph Neural Network for ADHD Diagnosis

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent childhood mental disorder that is mainly characterized by inattention, hyperactivity and impulsiveness. ADHD is not a self-limited disease, which means the symptoms may not resolve on their own as the child grows. Therefore, it is crucial to study the diagnosis methods and provide patients with early intervention treatment to control ADHD symptoms. At present, interactive diagnosis task is considered as a feasible quantitative evaluation method. In this paper, we present a novel approach for ADHD diagnosis utilizing a multi-stream graph-based model, which is based on the Schulte Grid task. Specifically, we propose an interactive scene-driven graph construction method, which builds scene graphs based on the spatio-temporal constraint relations to effectively model unstructured information in the interactive scene. To address the challenge of limited ADHD motion data, we employ both coarse-grained time-frequency domain statistical features and fine-grained local time series variation features for feature extraction. Moreover, we leverage lightweight multi-stream graph neural networks to integrate interactive scene information and motion sensor data to model the entire testing process, and then fuse them for ADHD subtype classification. We conducted comparison and ablation experiments on a real dataset to evaluate the effectiveness of our proposed model. Our results show that the proposed approach outperforms other baseline models and demonstrate the potential of our method to improve ADHD diagnosis.
Reinforcement Learning for Testing Android Applications: A Review

This paper offers a review of current research studies that use reinforcement learning (RL) to test Android applications. The primary purpose of this study is to simplify future research by collecting and investigating the current state of Android app testing approaches using the RL technique. We provide a well-defined criterion comprising of seven key points. The key points are: addressed problems, reasons for using the RL technique, RL algorithms, supported events, testing techniques, validation, and evaluation methods. In the literature, we have analyzed various techniques to evaluate their efficiency. This study showed that model-based testing is the most commonly used testing technique. Q-learning is the best algorithm in terms of predictive accuracy. We identified that code coverage is the most widely used evaluation metric and comparison with other tools and techniques is the preferred validation approach.
Face Detection Approach to Classify Emotions Based on Facial Expression in Depressive Disorder

The Multi-Task Cascaded Convolution Neural Network (MTCNN) is presented in this paper to classify the emotion and generate the facial dots as a representative of the patient. In depressive disorder diagnosis, the facial expressions can be used to observe the behavior of the patient. From the results, the system can be classified the emotion into 5-class i.e., happy, angry, disgusted, neutral, and surprised. For emotions of happy, angry, neutral, and surprised, the accuracy is more than 98 %, and for disgusted emotion is 96 %. Furthermore, the system can generate the real-time facial dots for emotion classification. Therefore, it can be a candidate to apply to collect and analyze the emotions of the patient under the privacy policy in a depressive disorder.
Digital Twins in Industry 4.0 Cyber Security

The increased adoption of sophisticated Cyber Physical Systems (CPS) in critical infrastructure and various aspects of Industry 4.0 has exposed vulnerabilities stemming from legacy CPS and Industrial Internet of Things (IIoT) devices. The interconnectedness nature of these systems have been severely affected in the event of a successful attack by Advanced Persistent Threats (APT), as evidenced by the Colonial Pipeline and the Stuxnet attacks. This paper explores the application of Digital Twins for cyber security, a promising technology that addresses the multifaceted issues in Industry 4.0. Therefore, this paper first presents a summary of the state-of-the-art on several related topics, including CPS security, APT and mitigation, and Digital Twins for cyber security. Then, it discusses how Digital Twins have been used to address the current research limitations on APT detection, including attack detection, attack coverage, dataset collection and usage. Finally, a brief discussion of the future research directions will be provided.
CM-Unet: A Convolutional Neural Network for Retinal Vessel Segmentation

Accurate segmentation of retinal vessels from retinal images is critical for the detection and diagnosis of many ocular diseases and associated cardiovascular diseases. However, the complex structure of retinal vessels, the low contrast between the background and the vessels, and the various noises generated by medical imaging systems make retinal vessel segmentation a great challenge. To address these challenges, we propose an architecture called CM-Unet for accurate segmentation of retinal blood vessels by improving the U-Net medical image segmentation model in this paper. Specifically, we achieve cross-layer cascading and reuse of features by constructing dense connections in the encoder part. In addition, we introduce global context blocks to filter noise in low-level semantic information and suppress irrelevant features to make the network more focused on the target feature region. Meanwhile, we use depthwise separable convolution instead of standard convolution to reduce the number of parameters. Experimental results of vascular segmentation on DRIVE and ARIA datasets show that the proposed method has significantly improved the segmentation accuracy demonstrating its effectiveness in vascular segmentation tasks.
Probing Mechanical Properties: Tools for Investigating Solid Polymer Electrolytes

Lithium-ion batteries (LIBs) have become an essential energy storage technology for a variety of applications, such as energy storage systems, electric vehicles, and flexible electronics. However, due to safety concerns regarding the flammability of the liquid electrolytes used in conventional LIBs, their use has been restricted; thus, there is considerable interest in the use of solid polymer electrolytes (SPEs) and other solid electrolytes. These SPEs inhibit the growth of lithium dendrites and prevent thermal runaway, which is responsible for the instability of liquid electrolytes. Despite this, SPEs have inferior mechanical properties, resulting in a reduced battery cycle life. Thus, SPEs have been modified by incorporating particles, particularly nanoparticles, into their polymer matrix in order to improve their mechanical properties. Due to their inherent properties, including a large surface area and mechanical rigidity, these nanoparticles improve the interfacial contact and electrochemical stability of these modified SPEs. Consequently, it is essential to investigate the mechanical properties of these SPEs using appropriate techniques. Even though numerous reports have investigated the mechanical properties of some of these SPEs, there is insufficient literature on the tools/techniques for analyzing these mechanical properties. In this review, the experimental techniques used to explore the mechanical properties of SPEs are described.
How to improve model performance on affNIST?

The affNIST dataset is a common choice for evaluating the model robustness to affine transformations. However, there is a lack of comprehensive study on how to improve classification accuracy on affNIST. In an effort to close this gap, we design an experiment on MNIST to investigate the effects of various transformations that are also used to construct affNIST. The experiment reveals that the robustness of models to translation serves a dominant influence on their performance on affNIST. Based on the findings, a simple yet effective method is developed, which contributes greatly to the improvement on the robustness of models to translation and helps achieve record-high accuracy on affNIST. However, our experiment revealed that the enhanced models did not perform well when generalized to rotated images of MNIST. This suggests that affNIST may not be a suitable benchmark dataset for assessing model robustness to all types of affine transformations.
Characteristics of BVOC emissions from a Swedish boreal forest : Using chambers to capture biogenic volatile organic compounds (BVOCs) from trees and forest floor

Plant-emitted biogenic volatile organic compounds (BVOCs) are a large group of hydrocarbons released by plant leaves, bark, flowers and fruits into the atmosphere and from plant roots into the soil. BVOCs have important physiological and ecological functions, such as mediating within-plant and plant-plant communication, defense against herbivores attack, protection against heat or other oxidative stress, attracting pollinators etc., and BVOCs are also important for microorganisms’ communication and nutrient cycling in the soil. These emitted reactive BVOCs also have impact on atmospheric chemistry, such as affecting the oxidation rate and atmospheric concentration of other trace gases, and the oxidized BVOCs contribute to particles formation and growth. The most abundant BVOCs are terpenes, such as isoprene (C5H8), monoterpene (MT, C10H16) and sesquiterpene (SQT, C15H24), and woody plants tend to emit various blends of terpenes. Norway spruce and Scots pine are two dominant boreal species, which have been recognized as MT emitters, and Norway spruce is also known as a low isoprene emitter. The contribution of forest floor to the ecosystem BVOC emissions has not been thoroughly studied and evaluated.A dynamic branch chamber system was used in this thesis to quantify the BVOC emission rates and emission spectra (composition of compounds) from 20-m and lower canopy levels of a Norway spruce and from 20-m canopy of a Scots pine from June to September of 2013 and 2014. The observed BVOC emissions from 20-m canopy of Norway spruce peaked in August 2013 and July 2014, and the minimum was found in September in both two years. The total BVOC emission rates of 2013 were significantly higher than those of 2014, and these high emissions in 2013 were likely induced by insect attack. High induced MT emissions from Scots pine were also observed in September 2014. Besides the long-term observation including seasonal variations are needed for accurately estimating or scaling up BVOC emissions, the stress-induced BVOC emissions are necessary to be incorporated into the emission models given the observed high amount of induced emissions. No clear vertical distribution pattern of BVOC emission rates was found within-canopy of the Norway spruce, and the compounds detected on different canopy levels were quite similar. A dynamic soil chamber was used to quantify the BVOC emissions from the forest floor from June to October of 2015. The peak emission was observed in October. Litterfall might be an important source for MT emissions, especially in autumn. Air temperature inside chamber and PAR (photosynthetically active radiation) were the most influential environmental variables affecting MT and SQT emissions. The understorey vegetation coverage and composition and soil moisture also have impact on the BVOC emissions from the forest floor. (Less)
A New Algorithm for Small Target Detection From the Perspective of Unmanned Aerial Vehicles

Since the UAV is far away from the detected target when performing target detection at high altitudes, there is a significant difference in the size of the detected object, and there are problems such as the detected target being blocked by the object. This paper proposed an improved algorithm BDH-YOLO for UAV high-altitude small target detection based on YOLOv8s. The backbone network of YOLOv8s is used, and the weighted bidirectional feature pyramid network (BiFPN) is combined with the backbone network to enhance the integration of multi-level features and obtain more multi-scale semantic information. Deal with multiple downsampling of the feature map will lead to the reduction of the resolution of the feature map, which will make the pixels on the feature map overlap, thus losing a large amount of spatial information, a dynamic detection head DyHead (Dynamic Head) combined with self-attention is introduced, which significantly improves the performance of the object detector by combining the perception capabilities of the three dimensions of scale perception, spatial perception, and task perception. Experimental results show that on the VisDrone2019-DET dataset, Compared with the YOLOv8s model, the BDH-YOLO model has an average accuracy (mAP@0.5) improvement of 3 targets than other mainstream models.
Learning Instance Geometric Graph for Monocular 3D Object Detection

Obtaining and handling geometric information is vital to monocular 3D object detection. The performance of current monocular 3D object detection models is largely limited by inaccurate depth estimation of object centers. Most existing methods improve object depth estimation accuracy either by introducing additional geometric information, or by novel network design. However the geometric correlation among objects is often omitted. In this work, we analyze the reason of inaccurate object depth estimation, and propose an instance geometric graph (IGG) based on self-attention mechanism. Specifically, we extract deep geometric features of instances, and construct a fully connected graph based on instance features. The edge of the graph encodes geometric correlations among instances and the instance features are updated by propagating geometric information for improved performance. Our IGG is independent of any geometric constrains and can be implemented within any monocular 3D object detection models. Experiment results on benchmark dataset show that our IGG outperforms state-of-the-art methods.
Deep Learning based Intrusion Detection for IoT Networks

The Internet of Things (IoT) holds vast potential across a diverse range of applications, spanning from industrial automation to healthcare and defense networks. The security of an IoT network is crucial, as it directly impacts the overall security of the underlying computing and communication infrastructure. However, owing to resource constraints and limited computational capabilities, IoT networks usually using Message Queuing Telemetry Transport (MQTT) protocol are susceptible to various types of attacks and security threats. In this paper, we propose an Intrusion Detection System (IDS) for IoT networks that is based on Deep Learning concepts. In particular, we propose a Long Short Term Memory (LSTM) model for IoT intrusion detection and attack mitigation focusing on MQTT protocol. We have trained and tested our model on a typical IoT tailored dataset using optimal feature set. Extensive experiments have shown that, with less features, the proposed LSTM remains its ability with lower complexity.
Depth-guided Robust Face Morphing Attack Detection

Recently, morphing attack detection (MAD) solutions have achieved remarkable success with the aid of deep learning techniques. Despite the good performance achieved by binary label or binary pixel-wise supervised MAD models, the robustness of such models drops when facing variations in morphing attacks. In this work, we propose a novel process that leverages facial depth information to build a robust and generalized MAD. The depth map, representing the 3D shape of the face in a 2D image, is more informative compared to binary and binary pixel-wise map labels. To validate the idea we synthetically generated 3D depth map ground truth. Furthermore, we introduce a novel MAD architecture designed to capture subtle information from the 3D depth data. In addition, we analyze the training loss formulation to further enhance the MAD performance. Driven by the need for developing MAD solutions while preserving the privacy of individuals for legal and ethical reasons, we conduct our experiments on privacy-friendly synthetic training data and authentic evaluation data. The experimental results on existing public datasets in SYN-MAD 22 competition demonstrate the effectiveness of our proposed solution in terms of both robustness and generalization.
MG-HetGNN: Metapath-Guided Heterogeneous Graph Neural Network with Semantic Aggregation for Medical Diagnosis

Deep learning-based medical diagnosis has become an essential research area with significant practical value and wide application prospects. However, modeling heterogeneous medical data, which contains diverse data types with different semantics, remains a challenge. Existing approaches mostly focus on single-modal data or fuse the data of each modality without considering the relationships between them. However, better results could be achieved when modeling heterogeneous data while considering the relationships between heterogeneous data. To address this issue, we propose a medical diagnosis method that formulates medical diagnosis as a link prediction task in heterogeneous graphs and develops a heterogeneous graph neural network MG-HetGNN. MG-HetGNN, which includes a metapath-based ego graph encoder and semantic-level feature fusion module, is a metapath- and attention-based heterogeneous graph neural network. Specifically, MG-HetGNN encodes the structural relationships under each semantics and aggregates semantic-level information to generate node embeddings. Experiments on public open datasets demonstrate that the MG-HetGNN has remarkable performance compared to the baselines.