Reviewerly/exHarmony huggingface dataset:
Dynamic NOMA-Based Computation Offloading in Vehicular Platoons

Both the Mobile edge computing (MEC)-based and fog computing (FC)-aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMA-based computation offloading scheme for vehicular platoons on highways, where the vehicles can offload their computing tasks to other platoon members. To cope with the rapidly fluctuating channel quality, we divide the timeline into successive time slots according to the channel's coherence time. Robust computing and offloading decisions are made for each time slot after taking the channel estimation errors into account. Considering a certain time slot, we first analytically characterize both the locally computed source data and the offloaded source data as well as the energy consumption of every vehicle in the platoons. We then formulate the problem of minimizing the long-term maximum task queue by optimizing the allocation of both the communication and computing resources. To solve the problem formulated, we design an online algorithm based on the classic Lyapunov optimization method and successive convex approximation (SCA) method. Finally, the numerical simulation results characterize the performance of our algorithm and demonstrate its advantages both over the local computing scheme and the orthogonal multiple access (OMA)-based offloading scheme.
The Art of Defense: Letting Networks Fool the Attacker

3D perception of objects is critical for many real-world applications, such as autonomous cars and robots. Among them, most state-of-the-art (SOTA) 3D perception systems are based on deep learning models. Recently, the research community found that 3D object classifiers on point cloud based on deep learning are easily fooled by adversarial point cloud craft by attackers. To overcome this, adversarial defenses are considered the most effective ways to improve the robustness of deep learning models, and most adversarial defenses on point cloud are focused on input transformation. However, all previous defense methods decrease the natural accuracy, and the nature of the point cloud classifiers itself has been overlooked. To this end, in this paper, we propose a novel adversarial defense for 3D point cloud classifiers that makes full use of the nature of the point cloud classifiers. Due to the disorder of point cloud, all point cloud classifiers have the property of permutation invariant to the input point cloud. Based on this nature, we design invariant transformations defense (IT-Defense). We show that, even after accounting for obfuscated gradients, our IT-Defense is a resilient defense against SOTA 3D attacks. Moreover, IT-Defense does not hurt clean accuracy compared to previous SOTA 3D defenses. Our code will be available at: https://github.com/cuge1995/IT-Defense.
Designing a Workplace in Virtual and Mixed Reality Using the Meta Quest VR Headset

The research examined the possibility of using the Meta Quest VR headset and the advanced Passthrough Sky 1:1 Mode in virtual reality VR and mixed reality MR to design the workspace. The development of Passthrough Sky technology enables the implementation of MR in the industrial environment to an extent greater than has been possible before. The article focuses on the research gap in this field. A room setup workspace was created in the GUI of the Meta Quest device, defining laboratory and references to objects in the real world. Later a Guardian Boundary was created, approximating the walls of the room. Subsequently, outline walls, desks, and a door were defined. The Passthrough Sky 1:1 Mode was selected with the alignment of the Room setup. Interactions were made possible between the virtual and real world in the mixed reality of MR. As part of the laboratory design, equipment models were designed and put in place. These objects were used to check the ergonomics and collisions and to verify the overall impression of the workplace designed based on the intuition of an experienced worker. The aim of the research is considered to be accomplished. The workplace design in this work focused on the description of the technology. The Meta Quest technology and the ShapesXR application are suggested for designing a specific workplace in the selected production plant within the proposed future work.
Objective Evaluation of Clutter Suppression for Micro-Doppler Spectrograms of Hand Gesture/Sign Language Based on Pseudo-Reference Image

Gesture and sign language recognition technology enables machines to understand the meaning of human hand movements. In human-computer interaction, it is expected that gesture/sign language recognition technologies will overcome equipment size and application environment constraints; in information communication, gesture/sign language recognition technology will assist healthy people in more easily entering the world of deaf people and better understanding and meeting their inner emotional needs; and in patient monitoring, gesture/sign language recognition technologies will detect abnormal behavior in the elderly or patients and reduce possible safety issues. As a result, it has significant implications for both research and broad application. Because the radar sensor can work normally in a wide range of illumination and weather conditions, as well as penetrate the shelter to receive the moving object echo signal and preserve individual privacy, it is becoming increasingly popular in a variety of recognition tasks. A primary step in using radar sensors for gesture/sign language recognition is to suppress clutter to highlight useful motion information. To evaluate the clutter suppression effect, however, an objective metric is required. We present an objective assessment metric based on the pseudo-reference image and an automatic threshold selection method based on Otsu for clutter suppression, as well as subjective and objective experiments demonstrating their effectiveness and universality in gesture/sign language recognition. Notably, our proposed metric can be used for any recognition task that uses micro-Doppler spectrograms as the dataset.
Electromagnetic Imaging Boosted Visual Object Recognition Under Difficult Visual Conditions

Object imaging and recognition under difficult visual conditions is extremely challenging due to the captured low-quality images, and traditional optical-based recognition methods always fail in this task. In this paper, we propose to utilize the visual-microwave image pairs captured by both visual cameras and microwave sensors for imaging and recognition. To address the heavy noises in the low-quality optical images, we retrieve the physically quantitative images from associated scattered field data, and enhance visual features by both optical and retrieval images. We develop a cross-modal Enhanced Attentive Visual-Microwave Fusion (EAVMF) object recognition model to jointly learn the cross-modal generator and multimodal recognizer. In addition, an attention module for the visual subnetwork is utilized to highlight the regions of interest. Two multimodal datasets with synthetic visual-microwave image pairs are built to simulate the difficult visual condition. The numerical results on these datasets demonstrate that: 1) both the multimodal fusion, cross-modal enhancement, and visual attention module can enhance the performance; and 2) compared with existing methods, the proposed EAVMF not only performs better in terms of accuracy but also has good scalability and one-shot learning ability.
PLCPrint: Fingerprinting Memory Attacks in Programmable Logic Controllers

Programmable Logic Controllers (PLCs) constitute the functioning basis of Industrial Control Systems (ICS) and hence are often a focal point for attackers to exploit.Previous attacks have seen PLC memory maliciously altered in order to disrupt the underlying physical process.Different types of memory attack can cause a similar impact on the PLC's operation and result in indistinguishable physical manifestations.Consequently, delays in triaging attacks through digital forensic practices can induce significant financial loss, physical damage to the infrastructure, and degradation of safety.In this work, we propose PLCPrint, a novel vendor-independent fingerprinting approach that utilises PLC memory artefacts to perform detection and classification of memory attacks.PLCPrint uses PLC memory register mapping, a novel method exploiting the relationship between PLC registers and memory artefacts including the PLC application code.Through this, registers are assigned a Mapping Condition (MC) to indicate how they exist within the PLC memory artefacts.We evaluate the performance of PLCPrint over realistic emulations conducted at a real testbed emulating water filtration and distribution.Through PLCPrint we depict how MC deviations are utilised within supervised learning schemes such as to adequately classify PLC memory attacks with high accuracy performance.In general, we demonstrate that PLCPrint fills the gap in the context of attack technique triaging since this has been a missing element within current ICS forensics schemes.
A Lightweight Brain Tumor Segmentation Network Based on 3D Inverted Residual Modules

Semantic segmentation technology based on deep learning has played an important role for doctors in identifying brain tumor regions and formulating treatment plans. Popular automated segmentation methods for brain tumors include 2D and 3D convolution networks. The 3D networks give better results but lead to a significant increase in parameters and computational cost. In this paper, we propose a lightweight brain tumor segmentation network composed of 3D inverted residual modules, which can significantly reduce the computational complexity of 3D models. Based on a lightweight depthwise separable convolution, our 3D inverted residual module extracts high-dimensional brain tumor features through an intermediate expansion layer, thus improving performance. On the brain tumor dataset BraTS 2018, our network achieves dice scores of 80.8%, 90.7%, and 84.3% (for ET, WT, and TC, respectively) with only 0.68M parameters and 51.46G FLOPs. The results show that our method can significantly reduce the complexity of the 3D model and achieve very competitive performance.
Improved Fusion of Visual and Semantic Representations by Gated Co-Attention for Scene Text Recognition

Recognizing variations of text occurrences in scene photos is still difficult in the present day. In recent years, the performance of text recognition models based on the attention mechanism has vastly increased. However, these models typically focus on recognizing image regions or visual attention that are significant. In this paper, we present a unique paradigm for scene text recognition named gated co-attention. Using our suggested model, visual and semantic attention may be jointly reasoned. Given the visual features extracted by a convolutional network and the semantic features extracted by a language model, the first step involves combining the two sets of features. Second, the gated co-attention stage eliminates irrelevant visual characteristics and incorrect semantic data before fusing the knowledge of the two modalities. In addition, we analyze the performance of our model on several datasets, and the experimental results demonstrate that our method has outstanding performance on all seven datasets, with the best results reached on four datasets.
Object Detection Algorithm Based on Coordinate Attention and Context Feature Enhancement

In recent years, object detection has been widely used in various fields such as face detection, remote sensing image detection and pedestrian detection. Due to the complex environment in the actual scene, we need to fully obtain the feature information in the image to improve the accuracy of object detection. This paper proposes an object detection algorithm based on coordinate attention and contextual feature enhancement. We design a multi-scale attention feature pyramid network, which first uses multi-branch atrous convolution to capture multi-scale context information, and then fuses the coordinate attention mechanism to embed location information into channel attention, and finally uses a bidirectional feature pyramid structure to effectively fuse high-level features and low-level features. We also adopt the GIoU loss function to further improve the accuracy of object detection. The experimental results show that the proposed method has certain advantages compared with other detection algorithms in the PASCAL VOC datasets.
Yoga Pose Assessment for Self-Learning

Amid the coronavirus pandemic, more number of people started practicing yoga by themselves by watching online videos because going to classes is not feasible these days. This paper provides a review on yoga pose self-assessment system, which assesses a yoga practitioner’s pose correctness with the help of deep learning algorithms, so that injuries caused out of incorrect postures can be prevented. The system detects a yoga pose and then calculates the difference between the pose of the user and that of the instructor using dynamic time warping (DTW) for video input and using angle difference calculation for static image input. Our primary contributions include a novel solution that employs a lightweight pose estimation model and DTW for time series analysis of the videos.
IOT Based Event Logger for Railway Signaling

In railway systems, signaling plays a vital role in railway monitoring system which operates the trains in a safe manner and to find the signal failure as early as possible to avoid mean time between failure. Also signaling is helpful to find the fault location, cause of failure and to know all the train movements in the yard. At present, Signaling used in railways suffers from drawbacks like use of number of relays, circuit design cost, space required, cable cost, requires lot of time to identify and locate fault. An IoT Based event logger is proposed in this paper for railway signaling to monitor and log the data related to train movements in the yard and also to communicate the failure information to the signal maintainer immediately. Code is written for Signal blank condition in C language and data logger is used for investigation purpose in case of train accident and derailment of train. The performance of IoT based event logger is compared with real time signaling in railway systems.
A Semi Supervised Approach to Fake Product Review Detection

As the E-commerce system continues to evolve progressively, maintaining a very good reputation is essential. Hence with regards to these online surveys show a majority role in maintaining and building a strong E - commerce platform. Online reviews play the role of decision making for the consumers at the receiving end of the E - Commerce network. Experiences, suggestions, and opinions on the variety of products available in the market is given through online reviews. A small variation in the way the review is expressed can bring about a positive or negative impact on sales, brand value, reputation of the business etc. There are high chances where falsification of reviews can also take place with the intended motive to bring down the reputation of a targeted brand, organization, or e-commerce platform. Hence detection and classification of reviews based on genuinely is very much essential. This paper uses a semi- supervised machine learning methodology in which traditional ML algorithms like naive bayes and random forest classifiers are used to solve the above stated problem faced by the E- commerce industry. An existing Food review dataset by Amazon has been used to analyse, extract, and interpret diverse review behaviors.
A Hybrid Model Based on Pre-trained Language Model and Graph Neural Networks for Scientific Paper Recommendation

Due to the overload of published scientific articles, paper recommendation has long been a critical research problem for automatically recommending high quality scientific papers for researchers. For scientific paper recommendation, the existing methods are mainly based on the user-paper interaction and the textual content of the paper. Although many scientific paper recommendation systems have been proposed, there are still two shortcomings: 1) the interaction information-based approaches cannot capture the higher-order user paper interaction information. 2) the content-based approaches cannot exploit the deep semantic information of the text, which impairs the interpretability of the models. To improve recommendation quality and model interpretability, we propose Citation Graph Collaborative Filtering (CGCF), which unifies document representation and Graph Neural Network (GNN). Our method consists of three steps:1) Build the user-paper bipartite graph based on citation relationship. 2) Use a pre-trained language model to initialize the paper’s embedding with its title and abstract. 3) Tune node embeddings using high-order connectivity information captured by several stacked message-passing layers on the graph built in the first step and finally make predictions predicted by these learned embeddings. Experiment shows that our model performs better while ensuring training efficiency.
Classification of Imbalanced Data in E-Commerce

The Privileges for the machine learning algorithms are actuality to lead the online exchange world with their does and power to match the fully customizable plot involvement. From expectable actions to social media to perceptive ways to shop online, big data quickly suppers within and is used frequently. Unfortunately, rare data conditions, called algebraic data, are still essentially unnoticed due to the uncommon of analytics method designed to manage the static data sets and external "quickening". As a result, very important rare functioning condition are being mistreated and leads to high cost loss Over the past applies, a number of ways to address the issue raised ranging from data processing to changes of the present algorithms having appeared with changing success. Though, most of them having major downsides when there are used indifferent claims domains due to the different nature of the active data presence. In the big E-Commerce domain, we suggest an unabled at a management methods, which consists of diverse subdivision solution that mix multi-methods data systems and algorithms to create the right balance between forecasts, accuracy and precision. Our solution helps in improving data service ability, class accuracy and results in cost analysis of large data sets used to mark client cognizance in E-Commerce.
Research on Local Path Planning Algorithm Based on Frenet Coordinate System

Aiming at the problem of real-time trajectory planning of unmanned vehicles in highway environment, this paper proposes a local trajectory planning algorithm based on rule sampling. Taking the globally planned path as the reference line, the local trajectories are decoupled horizontally and vertically based on the Frenet coordinate system. According to the designed end sampling rules, polynomial fitting is used to generate horizontal and vertical trajectory clusters. A cost function based on horizontal and vertical evaluation is designed to rank the trajectory clusters respectively. A collision detection method based on the three-circle method is proposed. According to the sorting results, the trajectories are merged and collision detection is performed, and the optimal collision-free trajectory is output. Finally, for the obstacle avoidance scene in driving, Python is used to simulate the simplified algorithm. The experimental results show that the proposed local path planning algorithm meets the requirements of security and real-time performance.
A Lightweight Binarized Convolutional Neural Network Model for Small Memory and Low-Cost Mobile Devices

In recent years, the high cost of implementing deep neural networks due to their large model size and parameter complexity has made it a challenging problem to design lightweight models that reduce application costs. The existing binarized neural networks suffer from both the large memory occupancy and the big number of trainable params they use. We propose a lightweight binarized convolutional neural network (CBCNN) model to address the multiclass classification/identification problem. We use both binary weights and activation. We show experimentally that a model using only 0.59 M trainable params is sufficient to reach about 92.94% accuracy on the GTSRB dataset, and it has similar performances compared to other methods on MNIST and Fashion-MNIST datasets. Accordingly, most arithmetic operations with bitwise operations are simplified, thus both used memory size and memory accesses are reduced by 32 times. Moreover, the color information was removed, which also reduced drastically the training time. All these together allow our architecture to run effectively and in real time on simple CPUs (rather than GPUs). Through the results we obtained, we show that despite simplifications and color information removal, our network achieves similar performances compared to classical CNNs with lower costs in both in training and embedded deployment.
Effect of Transparency Levels and Real-World Backgrounds on the User Interface in Augmented Reality Environments

With the introduction of the metaverse, augmented reality (AR) is expected to become the next interface between people and computers. Research is conducted to improve the usability of AR applications and devices. Color transparency and background effect are the two critical factors to consider while developing AR applications. Ten user interface conditions (5 color transparency levels × 2 background conditions) were evaluated in an AR environment. The task completion time and mean error were noted along with the subjective scores of button visibility, background visibility, and motion sickness. The results revealed a significant difference in task completion time, button visibility, and background visibility between transparency conditions, but not in the number of errors and total motion sickness. The transparency level of T0.5 and T0.75 is recommended for the interface of AR applications. This study could be helpful when creating augmented reality applications for the metaverse and other platforms.
A LIGHTWEIGHT <scp>WHATS</scp>APP END-TO-END ENCRYPTION

AbstractAbstractWhatsApp is one of the world’s most widely used messaging applications, with more than 2.44 billion monthly active users worldwide as of 2022. To ensure the security and privacy of its users’ communications, WhatsApp uses end-to-end encryption (E2EE) to protect messages and calls from unauthorized access, including WhatsApp itself. However, the E2EE can be computationally intensive and pose challenges for low-end devices or slow network connections. This paper proposes a lightweight E2EE protocol optimized for mobile devices that reduces energy consumption and computational overhead while maintaining the same security level. DISCLOSURE STATEMENTNo potential conflict of interest was reported by the author(s).