Reviewerly/exHarmony huggingface dataset:
Towards Environment-Dependent Model Switching for Performance and Accuracy Conscious Object Detection in Day- and Nighttime Urban Traffic Environments

In the area of highly automated driving, high inference speed and accuracy of object detection algorithms are safety-critical requirements. By utilizing an approach that dynamically switches between multiple, specialized object detection models during runtime, it is possible to meet both requirements in different lighting conditions, which is still a challenge for many camera-based methods. Although model switching in itself is not an entirely new concept, it has not yet been combined with modern object detection models based on neural networks. We use MobileNetV3 as a lightweight model to classify images as either day- or night-time images to forward them to the corresponding specialized object detector based on YOLOv5. All models are trained and tested using the Berkeley DeepDrive dataset which was reduced to the four most common class instances. Using our proposed model-switching approach, we are able to achieve mAP-50 of 61.5%, outperforming a singular, generalized YOLO model by 1.6% and an ensemble approach by 2%, using the same specialized models as the model switcher. The model switcher achieves a per-image inference time of 13.7ms and is therefore around 51% slower than the singular model, while still being 43% faster than the ensemble.
HRMP3+TECS: Component Framework for Multiprocessor Real-time Operating System with Memory Protection

The scale and demand for protection functionalities in embedded systems continue to grow as Internet of things technology develops. Simultaneously, multiprocessor real-time operating systems (RTOSs) with memory protection functionalities are broadening in use. On the other hand, the large development effort and poor reusability of multiprocessor RTOS-based development remain hindrances to their use. To solve this problem, this paper proposes a component framework for multiprocessor RTOSs that includes memory protection. In the proposed framework, OS functionalities are reframed as components. The allocation of objects to processors and protection settings, which are supported by the OS, can then be configured based on the component description. Plugins are implemented here to generate files for object generation from component descriptions of OS functionalities. In addition, access to the protection domain can be defined based on the component description. Finally, test programs are used in a performance evaluation. The proposed framework enables extensions to the component-based OS while maintaining most of the functionality and performance of the target OS.
Research on Semantic segmentation co-evolutionary method for Automated Driving Haze Images

In this paper, we address the problem of semantic segmentation of advanced automatic driving image in hazy weather. We mainly adopt the co-evolution method of image defogging model and semantic segmentation model, the defogging model adopts generative adversarial GAN network, the semantic segmentation model adopts BiSeNet,and the training data is a public self-driving data set Cityscapes. The co-evolution of these two models is used to improve the accuracy of semantic segmentation of fog maps. The experimental results show that the semantic segmentation of fog map by co-evolution has a more obvious improvement effect, and to a certain extent, it can meet the target extraction and recognition of autonomous driving requirements.
Session-Based Recommendation with Multi-layer Parallel Graph Neural Network

For the task of anonymous session-based(SR) product recommendation in e-commerce scenarios, in order to improve the accuracy of product prediction and the effectiveness of recommended rankings. This paper proposes a scalable multi-layer parallel graph neural network based architecture(SR-PGNN). This structure enables parallel composition of as many explicit and fully-crossed features in anonymous sessions as possible. Besides, it adopts parameter sharing and cross-fusion mechanism between parallel graph neural networks. Finally, in the case when both the parameters and the memory usage of the multi-layer graph neural network are similar to those of the single-layer graph neural network, the Precision rate and the accuracy of MRR are improved. Experimental results on two classic public datasets show that the model is effective and scalable for anonymous session-based recommendation tasks in e-commerce scenarios.
Study on Assessment Methods of Developmental Coordination Disorder in Children

Poor fine motor performance is an important feature of children's developmental coordination disorder. To improve the diagnosis efficiency of developmental coordination disorder, computer vision-based evaluation methods have become a research hot topic. Most of the current methods are based on the evaluation of artificial features, while this paper proposes an automatic evaluation method of children's fine movements based on deep learning. By extracting human key points from videos, the method extracts the time series features of key points and uses deep learning neural networks to classify and predict children's fine movements. The experimental results show that the highest accuracy of this method is 81%, which provides an effective tool for the auxiliary diagnosis of children's developmental coordination disorder.
Stable Control Policy and Transferable Reward Function via Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) can solve the problem of complex reward function shaping by learning from expert data. However, it is challenging to train when the expert data is insufficient, and its stability is difficult to guarantee. Moreover, the reward function of mainstream IRL can only adapt to subtle environmental changes. It cannot be directly transferred to a similar task scenario, so the generalization ability still needs to be improved. To address these issues, we propose an IRL algorithm to obtain a stable control policy and transferable reward function (ST-IRL). Firstly, by introducing the Wasserstein metric and adversarial training, we solve the problem that IRL is challenging to train in a new environment with little expert data. Secondly, we add state marginal matching (SMM), hyperparameter comparison and optimizer evaluation to address the model's generalisability problem. As a result, the control policy obtained by ST-IRL achieves outstanding control results in all four Mujoco benchmarks. Furthermore, in both the custom Ant and PointMaze environments, the reward function obtained by our algorithm exhibits promising transferability.
Multivariate Time Series Anomaly Detection in a Regularization Perspective

Real-world scenarios such as Internet, industrial equipment and finance field generate a large number of multivariate time series all the time which are important for describing the operational state of a system. Therefore, anomaly detection on the multivariate time series has become a hot topic today. How to utilize regularization to eliminate overfitting is an important issue since it inhibits the representative power of existing models. In this paper, a reconstruction model called Autoregressive Graph Adversarial Network (ARGAN) is proposed. First, we develop a latent space reconstruction strategy to guarantee ARGAN’s representative ability for the key features. Then, the autoregressive regularization using temporal dependency is proposed to inhibit overfitting. Finally, a regularized annealing strategy is designed to balance reconstruction and regularization. The proposed model can achieve better performance on four real-world datasets compared with other six algorithms.
Multiple-Model Filtering For Nonlinear Estimation

A new Multiple-Model filter has been developed to solve the problem of state estimation for complex systems with large initial state error or strong nonlinearities. The main idea of the algorithm is to construct multiple Gaussian distributions by decomposing the Gaussian distribution in order to increase the level of detail of probability density and obtain a closed form solution. In proposed algorithm, the first two moments of priori distribution is characterized by designing multiple Gaussian distribution models. For each Gaussian distribution model, a nonlinear Kalman filter algorithm is used to perform time update and measurement update. Finally, the estimation of the state posterior distribution is calculated by a probability weighting method. The proposed algorithm has been tested on two simulation scenarios including a complex single-dimensional tracking and a classic multi-dimensional target tracking problem, which exhibited a good performance on state estimation.
PatchSR: Achieving HD Video Streaming with Pre-downloaded Universal SR Models

The high-definition (HD) video streaming has gained tremendous popularity with the proliferation of smartphones and mobile networks. However, it is quite challenging to deliver HD online video streams directly to devices with very low bandwidth in current systems. In this paper, we propose a neural-enhanced HD video streaming system named PatchSR to provide HD video streaming for bandwidth-constrained devices. PatchSR delivers universal super-resolution (SR) models with high performance to devices in advance. Only low-resolution video streams are sent to bandwidth-constrained devices, and the video quality at the device side can be enhanced with SR techniques. The main challenge is training multiple universal SR models with high performance and selecting the dedicated SR model for each video content. To overcome this new challenge, we propose an image classification algorithm of texture features according to the Discrete Fourier Transform (DFT) feature map of the training patch. We also design a dynamic selection algorithm of SR models for clients to improve video quality. Finally, we achieve and evaluate our proposed PatchSR system with real network traces and the experimental results show that PatchSR achieves higher video quality and up to 28.65% QoE improvement compared to baselines.
FL-PTD: A Privacy Preserving Defense Strategy Against Poisoning Attacks in Federated Learning

Federated learning allows participants to share models (gradients) rather than raw data to collaboratively train a global model, enhancing participants’ privacy protection but making the global model more vulnerable to poisoning attacks. Poisoning attacks not only lead to the degradation of model performance but also cause a security risk. A mainstream defense strategy against poisoning attacks is that the server identifies malicious models by analyzing the models uploaded by participants. However, the attackers can use the models uploaded by the participants to recover their privacy data, leading to a privacy disclosure. Therefore, participants need to encrypt or perturb the models before uploading them so that all individuals, including the server, cannot access the model, which poses a great challenge for the defense against poisoning attacks. Moreover, many existing defense strategies against poisoning attacks only work well when the data distribution is non-independently and identically distributed. To address these issues, we propose a novel defense strategy for poisoning attacks of federated learning called FL-PTD, which can judge whether the global model is subjected to poisoning attacks without accessing the participant’s local model. Besides, Our method incorporates a trust evaluation mechanism, which computes reputation scores based on the historical behavior of participants to identify malicious participants. Finally, we verify our proposed method on several real world datasets. The experimental results show that our method can effectively defend against poisoning attacks and accurately identify attackers without compromising the model’s performance.
An Empirical Perspective on Learning-to-rank

Learning-to-rank has been widely studied and applied in document retrieval. Typically, existing learning-to-rank methods treat ranking as an independent matching process among different queries. Hence, their ranking functions remain unchanged once the learning process is accomplished. However, these methods ignore the fact that previous ranking performance could help fine-tune the ranking function. In a contrast, this paper proposes an empirical perspective-based learning-to-rank model (EPRank) by formulating ranking among different queries as a sequential ranking process where previous ranking feedback could help refine the ranking function for future ranking. Specifically, EPRank constructs a clip feedback mapping where the ranking feedback is mapped to distinct feature embeddings to roughly represent ranking performance for efficient learning. Then, EPRank explicitly incorporates previous queries’ clip feedback embeddings upon earlier ranking performance into a recurrent neural network to iteratively refine the ranking function. Experiments demonstrate the effectiveness of EPRank compared to previous learning-to-rank methods in terms of Normalized Discounted Cumulative Gain (NDCG) and Expected Reciprocal Rank (ERR).
PLNNet: Parameter-learning Neural Network for Global Optimization of Light Field Depth Estimation

Accurate depth estimation is a significant method for the development of digital wisdom medical, target detection, and 3D reconstruction. However, current methods may produce noise points and blurry edges due to the complicated texture images, which makes it essential to improve their accuracy based on the abundant information of light field (LF) images. In this paper, we propose an end-to-end parameter-learning neural network (PLNNet) for the global optimization of depth estimation. With the central LF sub-aperture image as the guide image, the differentiable convolutional layers and combined loss function are applied to establish the edge-preserving smoothing optimization algorithm. Compared to the initial depth maps, the experimental results computed by the PLNNet can achieve 9.13 MSE(Mean Squared Error) gain and 10.46 BP(Bad Pixel) gain on average, demonstrating its effectiveness on the edge and occlusion areas. The implementation of our PLNNet is open-sourced at https://github.com/lymwxq/PLNNet.
Examining the Relationship Between Stress Levels and Cybersecurity Practices Among Hospital Employees in Three Countries: Ghana, Norway, and Indonesia

This study aims to investigate the relationship between stress levels among hospital staff and their risky cybersecurity practices. A web-based survey was conducted with a sample of 353 hospital staff from Ghana, Norway, and Indonesia. The results indicate a statistically significant positive correlation between the stress levels of hospital staff and their engagement in unsafe cybersecurity practices (r = 0.201, p < 0.01). Specifically, the study finds that staff members’ inclination to click on links from unknown sources is the cybersecurity practice most strongly influenced by stress levels. The study did not observe any significant differences in cybersecurity practices based on gender, age, job, position level, or work experience. However, it does highlight notable differences in cybersecurity practices across countries, with Norwegian hospital staff exhibiting better cybersecurity practices than their counterparts from Ghana and Indonesia.
Human Posture Detection on Lightweight DCNN and SVM in a Digitalized Healthcare System

The domains of Human Posture (HP) and artificial intelligence (AI) have placed significant emphasis on Human Posture Classification (HPC). By recognizing the standing, sitting, and walking positions of aged people, HPC could effectively monitor their health condition and ensure their well-being. Detection of various postures is difficult owing to the need for enough datasets and a classification framework. MobileN et is a lightweight Deep Convolution Neural Network (DCNN) with a better recognition rate and fewer parameters. To further increase the generalization ability while reducing the number of models hyperparameters, techniques such as regularization, Transfer Learning (TL) and neural architecture search can be employed. To address the issue of a shortage of annotated data and improve approach effectiveness, TL and Data Augmentation (DA) are utilized for MobileNet and Xception in this study. The Support Vector Machine (SVM) model boosts performance instead of the final Fully Connected Layer (FCL). To facilitate the effectiveness of our approach, TL and DA are both used for SVM. The investigation associated the efficacy of the suggested approach with that of other cutting-edge image classification techniques, including ResNet50V2, Inception V3,and DenseN et201, and found that the presented methodology is better. Our recommended method captures temporal and depth characteristics from the image separately and incorporates them into classification computations. The suggested approach founded on MobileN et hybridized with SVM attains the top performance with 92.12% test accuracy (Acc), 95 % area under the curve (AUC), 92% recall (Rec), 93% precision (Prec), 92 % F1 score and a computational period of 3974secs.
All of Low-Rank and Sparse: A Recast Total Variation Approach to Hyperspectral Denoising

Hyperspectral image (HSI) processing tasks frequently rely on Spatial-Spectral Total Variation (SSTV) to quantify the local smoothness of image structures.However, conventional SSTV only considers a sparse structure of gradient maps computed along the spatial and spectral dimensions, while neglecting other correlations.To address this limitation, we introduce low-rank guided SSTV (LRSTV), which characterizes the sparsity and low-rank priors of the gradient map simultaneously.Firstly, we verify through numerical tests and theoretical analysis that the gradient tensors are not only sparse but also low-rank.Subsequently, to model the low-rankness of the gradient map, we use the tensor average rank to represent the low Tucker rank of gradient tensors.The convex envelope of the tensor average rank is then employed to penalize the rank on the gradient map after Fourier transform along the spectral dimension.By naturally encoding the sparsity and low-rank priors of the gradient map, LRSTV results in a more accurate representation of the original image.Finally, we demonstrate the effectiveness of LRSTV by integrating it into the HSI processing model, replacing conventional SSTV, and testing it on two public datasets with nine cases of mixed noise and two datasets with realistic noise.The results show that LRSTV outperforms conventional SSTV in terms of accuracy and robustness.
GVGNet: Gaze-Directed Visual Grounding for Learning Under-Specified Object Referring Intention

Referring Expression Comprehension (REC) and Referring Expression Segmentation (RES) enable robots to infer human's object referring intention through natural languages. In this letter, Gaze-directed Visual Grounding Network (GVGNet) is proposed to disambiguate human's under-specified object referring intention in the joint task of REC and RES. To reduce the uncertainty of the referred object, the human gaze is first introduced to explicitly indicate the target location. A multi-modal feature fusion module is further applied to model the context between the language, image, and gaze modalities for the subsequent localization and segmentation modules. To train our network, a tabletop object dataset with human gaze, namely OCID-underRef, is extended from the existing OCID-Ref dataset through synthetic gaze modeling. The domain gap between the synthetic and the real-world gaze distribution is further reduced through a gaze rectification method using dynamic grid assignment and head pose constraint. Experimental results validate the effectiveness of our method in both OCID-underRef and real-world scenarios for referred object grasping tasks with under-specified object referring expressions.
Neural Networks

We introduce the most common type of neural networks: feedforward neural networks. We include a detailed derivation of the backpropagation algorithm for regression and multi-class classification. We discuss a number of common activation functions that contribute nonlinearity in an otherwise linear network. We cover vanishing and exploding gradients, weight initialization—to attenuate the vanishing gradient problem—stochastic gradient descent using mini-batches, batch normalization, and dropout and weight-decay regularization. Our case study classifies the value of a poker hand based on the suit and rank of the five cards in a poker hand. One interesting nuance is the strong imbalance of the ten outcome classes.
Assessing the Cybercrime Legislation in Pakistan: a Comparative Study of European Union and Pakistani Cybercrime Laws

Background: With the recent development in the field of information technology, people are able to communicate in a more efficient way in cyberspace. As this efficient way of communication is used by almost everyone in this era so the interactions and the reasons for these interactions are also increased. In a normal society, disputes arise due to which laws are framed similarly in cyberspace as well these disputes are in large numbers across the world and these need well-framed laws. From this perspective, it is obvious that with the passage of time, the need for laws and proper enforcement mechanisms becomes necessary. For this purpose, the EU has made a number of cybercrime legal frameworks. Method: The research work is aimed at focusing on a comparative study of the cyber Crimes legislation in the European Union and Pakistan. The doctrinal method of research is used to critically analyze the cybercrime laws of the EU and Pakistan. This method is used in legal research to examine laws. The research design for this work is of comparative nature. This research work analyzes the EU legislation which is related to cybersecurity. It mentions all the relevant EU regulations. The second part of this study discusses the kinds of crimes which are committed in cyberspace. The third part of this research work focuses on cybercrimes in Pakistan. It focuses on the relevant cybercrime laws in Pakistan. It also comparatively analyzes the EU cybercrime laws and Pakistani cybercrime laws. It mentions a number of cases in this regard from both regions.Conclusion: It can be observed that there is a presence of lacunas in the smooth delivery of Cyber security in Pakistan as compared to the EU and there are some suggestions for pragmatic solutions to the matter regarding cybersecurity issues faced by Pakistan. This research work has helped in observing the loopholes in the Pakistani legal system. It has observed the ways in which changes in Pakistani legal framework related to cyber laws can be made so that it is in accordance with modern needs of cybersecurity. Systematic review and analysis of this data aims to highlight that updated legislation plays an important role in countering cybercrimes and that legislation should be strengthened, enhanced, and made up to mark the rapid advancement of technology in order to address the rising number of cybercrimes. It concludes that there is a need for more refined and precise cybercrime laws in Pakistan along with the need of staying updated with the changing needs of the time
Improvement of the square-root Vélu's formulas for isogeny-based cryptography

Isogeny-based cryptography is a promising candidate for post-quantum cryptography. Speeding up the isogeny computation of large prime degrees up to several thousands is a main research topic in isogeny-based cryptography. The currently most efficient method for computing isogenies is the square-root Vélu's formulas. In this paper, we propose an efficient variant of the square-root Vélu's formulas by introducing an optimal index system using a redundant integer representation. Our index system improves the isogeny computation in 44% of 367 primes between 97 and 2689 and is approximately 6.6% faster for primes 1279 and 2687.