Reviewerly/exHarmony huggingface dataset:
Research on Mechanical Arm Grinding Method Based on Improved CascadePSP Net

Aiming at the problems of manual processing and grinding workpieces, such as time-consuming and low accuracy, an intelligent method for grinding workpieces was proposed. The dataset is preprocessed using image graying and guided filtering. The mechanical arm is used for machining and grinding, Adding semantic segmentation technology to realize accurate identification and location of machining trajectory, An improved CascadePSP Net is proposed to realize faster recognition while ensuring accuracy. By comparing the improved CascadePSP Net with the original network, the segmentation accuracy and training speed are improved. Use the Sober operator to extract the contour of the workpiece to be machined to determine the final machining path. The trajectory planning of the three-degree-of-freedom Dobot Magician manipulator is carried out by the fifth-order polynomial interpolation method and the Cartesian coordinate system method. Build an experimental platform for an image recognition robotic arm, and the comparison of the trajectory recognition method and the test experiment of the mechanical arm grinding system were carried out respectively. It verifies the feasibility of the proposed grinding method. The experimental results show that the method reduces the network training time, realizes high-efficiency and high-precision segmentation processing, thus improves the workpiece grinding efficiency and realizes the intelligent processing of workpiece batches.
Modelling of Wireless OFDM System with Deep Learning-based Modulation Detection

Deep learning (DL) is a powerful classification technique that has excellent performance in processing audio, image, and video data. In this paper, we use DL in communication systems, especially for modulation classification. Convolutional neural network (CNN) is then utilized to complete the classification task. The deep learning methods are implemented in an OFDM system using Software Defined Radio (SDR). Experimental results indicate that the proposed CNN based modulation classification achieves comparable classification accuracy without the necessity of manual feature selection.
The Application of Neural Network in Dish Recognition

Food is a part of life, and it is something that everyone needs to consider. Everyone's time is precious in this busy modern city, and canteen efficiency has always been a criticism, with long lines taking up most of the time. The fully automatic dish recognition system can effectively solve this problem and reduce labor costs for the company. In this paper, the research work focuses on solving the problem of recognizing dishes in a fully automated dish recognition system and mainly employs a deep learning method of convolutional neural networks to identify different kinds of dishes. Firstly, the original images are pre-processed and divided into test and train data sets. Then the tensor flow is used to build a network model based on a convolutional neural network. The recognition accuracy reaches 81.21% after the algorithm's overall optimization and the parameters' adjustment in several trials.
Detecting Adversarial Examples Using Rich Residual Models to Improve Data Security in CNN Models

The convolution neural network (CNN) is vulnerable to the adversarial attack, because the attack can generate adversarial images to force the CNN to misclassify the original label of the clean image. To defend against the adversarial attack, we propose to detect the adversarial images first and then prevent feeding the adversarial image into the CNN model. In this paper, we employ a steganalysis based method based on rich residual models to detect adversarial images which are generated by the typical attacks including BIM and DEEPFOOL. The rich residual models not only reduce the influences from natural image contents, but also enhance the diversity of the feature. Two typical and complementary methods spatial rich model (SRM) and projected spatial rich model (PSRM) are used to extract the feature, where SRM finely capture the statistical changes on co-occurrence in a small neighborhood, and PSRM remedy the loss information caused by SRM. Experimental results on CIFAR-IO and ImageNet show that the proposed method obtained better performance than existing steganalysis methods for detecting adversarial images generated by BIM and DEEPFOOL attack. The research results are expected to improve the recognition ability of image adversarial samples in the convolutional neural network model, and support the data security of natural image content in image recognition.
A novel global method for edge extraction under Poisson noise: game theory

Edge extraction is a central problem in image processing and it is a necessary step for computer vision tasks. In this paper, a novel global method P-GSG for edge extraction of image under Poisson noise is given, which is based on sparse representation. Furthermore, a game model which combines P-GSG with total variation denoising is proposed to get better results. As two players, P-GSG model can apply with iteration latent clean image to robustly get the gradient under the Poisson noise, on the other hand, TV denoising can get an edge-preserving latent clean image, which overcomes the shortcoming of over-smoothing. By cooperation and competition between two tasks, we can attain a satisfactory solution for this game model-Nash equilibrium. The algorithms of P-GSG and TV denoising are given. Based on above algorithms, it is obvious that alternate iteration method is easily used to solve this game model. The effectiveness of these two models is shown by numerical experiments.
Extended Reality Visual Guidance for Industrial Environments: A Scoping Review

Visual Guidance (VG) techniques in combination with Extended Reality (XR) technology promise to innovate workforce augmentation, especially in industrial environments. However, evidence-based insights from the literature remain fragmented. To provide an overview of this emerging topic, and particularly the reported outcomes of applying XR Visual Guidance (XRVG) to industrial contexts, we conducted a scoping review to identify and highlight evidence from experimental results. This review sheds light on the potential of VG as part of workforce augmentation. We propose a standardised terminology and a VG framework to categorise different techniques of XRVG, aiming to spark a discussion on how XRVG may support workers in industrial environments and identify gaps and future research opportunities to advance the topic of XRVG.
An Adaptive Neuro-Fuzzy Approach for Activity Recognition in Situation-aware Wearable Systems

Wearable computing systems are capable of unobtrusively and continuously monitoring humans by collecting reliable data. Situation-aware human activity recognition, based on wearable systems, is the task of identifying the activities performed by one or more users with respect to the situation in which such activity happens. In this paper, starting from a reference architecture of a situation-aware wearable computing system, an approach for situation-aware human activity recognition is proposed. The approach uses an adaptive neuro-fuzzy inference system to identify the activities by processing wearable sensor data. Such activities are used as the input of a fuzzy inference system, together with other contextual information, to identify the situations involving the users. The approach is evaluated using the MHEALTH dataset, comparing its performance with an artificial neural network technique, showing promising results.
Identification Technology of RKE System Using Multi-dimension RF Fingerprints

Recently, remote keyless entry system (RKE system) has been gradually replacing traditional way to unlock car doors for convenience. However, it has been shown that RKE system is vulnerable to cyber attacks including relay attack, amplification attack and cryptographic attack. In order to solve this dilemma, RF fingerprints method was applied to identify car key fobs in this paper. Power spectrum of preamble signal envelope was proposed to extract features while carrier frequency offset and least mean square-based adaptive filter were also used as auxiliary ones. Multi-dimension RF fingerprints were presented in this paper based on three features mentioned above to increase identification accuracy. Support vector machine(SVM) was chosen with 10-fold cross-validation to train classifier model. Corresponding to current research on keyless entry car theft, the classification results in this paper show that signals from various key fobs can be classified with 99.3% accuracy when using Rf fingerprints extracted from multiple features, with false acceptance rate (FAR) of 0.7% and false rejection rate (FRR) of 0.7% under Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML) classifier.
Content-based Multiclass Classification on Indonesian SMS Messages

Short Message Service (SMS) is a text-based communication service without internet connection, provided in most cellular phones worldwide, including Indonesia. SMS is used for multiple purposes, starting from ads, notifications, and daily conversations. The convenience given by SMS also comes with risks, where fraud messages are commonly sent to phones. Some types of SMS texts are sent in large amount, which make it difficult for phone users to access certain type of SMS. In this research, SMS texts are classified into 4 types of contents: ads, information, fraud, and regular. Shallow learning and deep learning methods are both used to classify text messages, including logistic regression, decision tree, CharCNN (Zhang et al., 2015), McM (Shakeel et al., 2019), and pretrained model IndoBERT (Wilie et al., 2020). Based on observation from experiment, IndoBERTbase-p2 outperformed the others with macro-F1 score 94.05%. In addition to prediction evaluation, storage size and inference time of the best model also analyzed on mobile devices. Model deployment on Android phones shows that storage space for IndoBERT model is 241.34 MB, and average inference time 0.2279 second on Samsung Galaxy A52s5G and 0.789 second on Vivo Y65.
Arbitrary Direction Inkjet Character Recognition Based on Spatial Transformation

Aiming at the problem of the low recognition accuracy caused by the arbitrary characters. In this paper, we propose an arbitrary direction character recognition network. Firstly, a lightweight spatial transformation network (STNet) is designed based on the MobileNetV2, which is used to extract the spatial features of the arbitrary characters and perform spatial transformation. Simultaneously, we introduced the SE attention block into the feature extraction backbone network, which makes the network focuses on the key regions of characters. Then, we build a text recognizer based on recurrent neural network and introduce the Connectionist Temporal Classification (CTC) loss to achieve the flexible alignment between the visual features and the prediction outputs. Extensive experiments are carried out on the IIIT5K and a self-made inkjet characters dataset. The recognition accuracy of our proposed method reaches 95.7% and 86.3% respectively. Compared with the benchmarks, the maximum accuracy of the proposed method is improved by 17.5%. Experimental results show the effectiveness of our proposed method.
Densely Connected and Multiple Temporal Graph Convolution Networks for Skeleton-based Action Recognition

More and more researchers are devoting themselves to skeleton-based action recognition owing to its high research value. Due to the property of the background suppression and the natural topological graph structure, most of the current researches based on the skeleton graphs construct spatial-temporal graph convolutions. However, due to the forward propagation of the network, the semantic features from joints and bones in the shallow layers may be dispersed in the long diffusion process. To make better utilization of the semantic feature information, we proposed a densely connected and multiple temporal graph convolution network (SMT-DGCN), which fully utilizes the features of each layer by introducing the dense connectivity mechanism into the ST-GCN network, and uses multiple temporal convolution to extract discriminative temporal motion features. Compared to traditional GCNs, our network architecture has the following two innovative advantages: 1) By densely connecting each layer to the semantic features, we are able to reuse features and improve feature utilization compared to the base network. 2) In the temporal modeling stage, the multiple temporal convolution module is employed, which can enrich and refine the temporal features. Experiments on the NTU-RGBD dataset demonstrate that our proposed model outperforms most existing studies.
Visual Task Recognition for Human-Robot Teams

Human teammates in human-robot teams operate in uncertain, dynamic environments to accomplish a wide range of tasks. These tasks often involve multiple activity components: gross motor, fine-grained motor, tactile, cognitive, visual, speech and auditory. Most existing task recognition algorithms focus primarily on detecting tasks involving gross and fine-grained motor components; however, some tasks (e.g., assessing a victim’s triage level) may involve little to no motor components. Robots need a holistic understanding of a task’s various activity components in order to be aware of the human’s current task state. The presented algorithm detects the tasks’ visual activity component for a human-robot team operating in a non-sedentary supervisory environment. Metrics acquired from a wearable eye tracker and head motion tracker are used to train the machine learning-based visual task recognition algorithm.
F-LIC: FPGA-based Learned Image Compression with a Fine-grained Pipeline

Recently, learned image compression (LIC) has shown a superior ability in the compression ratio as well as the quality of the reconstructed image. By adopting the framework of variational autoencoder, LIC [1] can outperform the intra prediction of the latest traditional coding standard VVC. To accelerate the coding speed, most LIC frameworks are operated on GPU with the floating-point arithmetic. However, the mismatch of floating-point calculation results on various hardware platforms will cause the decoding error if encoding and decoding are performed on different platforms. Therefore, LIC with a fixed-point arithmetic [2–3] is highly required. This paper gives an FPGA design for a LIC with 8-bit fixed-point quantization. Different from existing FPGA accelerators [4–6], we propose a fine-grained pipeline architecture to realize high DSP efficiency. Cascading DSP and the deconvolution with zero skipping are also developed to enhance the hardware performance.
Algorithm and RTL Architecture Design of Q-Learning Algorithm for Path Planning

In this paper, we propose two simulations designed for the implementation of Q-learning on path planning. The first simulation of a modeling design using MatLab to find the best route by agent and calculate the updated reward from the environment. The second simulation of a modeling design RTL using Xilinx Vivado and written in Verilog. Q-learning is an off-policy reinforcement learning technique and is used for decisive action by value in path planning’s navigation. The scheme of the environment is a grid world map with a volume-sized 5x5 number state position and has four possible actions. The result illustrates the shortest step route decided by an agent with convergence accumulated reward from the q-table and the goal position achievement after the learning process. Implementation design architecture RTL is implemented to prove the architecture can be applied. From both results, the final condition to achieve the goal’s position by the agent in the model simulation was 64
Fake Reviews Detection with Hybrid Features Using Time-Sequential Deep Learning Model

This study aims to train a time-sequential deep learning model to classify reviews as genuine or fake. The real-life dataset from Yelp contains review text and user behavior, which are taken together as features within the LSTM model. The results suggest that a time-sequential model such as LSTM is promising to predict fake reviews with an 89% accuracy score. Besides, the textual features represented in word embedding vectors perform better than user behavior features. The results provided by deep learning models are expected to extend further to explainable AI (XAI) in future work.
Ingredients for Hybrid Intelligence: Towards an Integrated Theory and Application

Human-autonomy teaming aims at integrating humans with capable machine agents. In this work, we discuss a framework that is aimed at providing a theoretical basis for the integration of heterogeneous agents into a hybrid agent, i.e., a sociotechnical organism that is comprised of interacting human and artificial agents. The approach is tuned to identifying the challenges that any intelligent agent would face and translating these into designs for hybrid agent mechanisms. Specifically, an agent architecture that integrates different classes of intelligence is discussed in the context of hybrid intelligence.
Visualizations for human-machine teams in complex environments: design concepts and review of current approaches

Recent advances in artificial intelligence expand the possibilities for partnering humans and machines in complex environments. However, the design of visualizations on interfaces for supporting effective communication between humans and machines remains a significant challenge. In this paper, we explore the problem of designing visualizations to support effective human-machine interaction especially in complex environments with high levels of instability and ambiguity. We review current approaches to visualization design and articulate the limitations of these approaches for our problem space. We propose an approach that focuses the design of visualizations on system constraints, or event-independent boundaries of successful operation, rather than tasks and user characteristics. This approach goes beyond specific events and individuals to cover a larger space of possibilities for action by human-machine teams in complex environments. These concepts can be used to design visualizations that provide value to human-machine teams even in the face of uncertainty. The results of this work provide a starting point for future research addressing shortfalls in human-machine interactions which can have real-world consequences.
A Comparative Study of Machine Learning Approaches for Predicting Close-Price Cryptocurrency

This study aimed to evaluate the effectiveness of several algorithms for predicting the close-price of various cryptocurrencies. Three algorithms employed in this comparative study were Support Vector Regression (SVR), Random Forest (RF), and Long Short-Term Memory (LSTM), while the three cryptocurrency datasets examined were Bitcoin, Ethereum, and Litecoin. Furthermore, in the stage of the data preparation, we compared two popular data normalization methods: min-max and z-score. After examining the close-price prediction results of each approach using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), it was revealed that the predictive model generated by the LSTM algorithm together with z-score normalization yielded the most effective results for each cryptocurrency dataset.
ADA: Mobile application for music dictation

ADA is a mobile application for automatic generation of music dictations guided by the teacher’s settings. The application is designed to meet the training needs of undergraduate students in music performance. It responds to the need for a tool developed in open source software that allows the teacher to guide the training of his students by minimizing the effort dedicated to the production of the dictations and maximizing the training possibilities of the students. The first version of the ADA application developed in open source JavaScript for the generation of melodic and rhythmic dictations is presented.
PointerChecker: Tag-Based and Hardware-Assisted Memory Safety against Memory Corruption

The exploitation of memory corruption continues to be a major threat to computer software and systems. Enforcing memory safety can stop all memory corruption exploits. Unfortu-nately, existing approaches for memory safety either suffer from high-performance overhead or have poor source compatibility. A compromise is only to detect spatial violations to get a trade-off between performance and security. However, temporal violations are still preferably targeted with the improvement of boundary checks. In this paper, we propose a lightweight solution, called PointerChecker, a complete memory safety architecture based on tagged memory. We design a 2-bit tag to represent four different memory states to protect both spatial and temporal safety. Besides, we design tag-based pointer metadata representations to reduce the storage of pointer metadata and the overhead of runtime checking. Combining them, we can successfully identify the memory safety violations at runtime. Based on LLVM infrastructure and LowRISC architecture, we implement the prototype with lightweight hardware extensions. The evaluation results show that the average performance overhead is 8.42%, and PointerChecker can provide source compatibility and strong security.
UA V Swarm Scheduling Based on Weighted Multi-Objective Particle Swarm Algorithm

In order to solve the problem of low node allocation efficiency of UAV swarms in forest fire disaster relief scenarios, a weighted multi-objective particle swarm UAV swarm scheduling algorithm was proposed, and the visualization of simulation scheduling was realized. Through the improvement of the target point allocation model, the standardization of the target point weight, and the definition of the comprehensive evaluation index of the UAV, after selecting the UAV with the optimal performance and the largest distribution probability, the remaining UAVs are distributed according to the average probability. Scheduling to improve the real-time performance of UAV swarm scheduling. Finally, through the simulation experiment and performance analysis of the simulation system, the results show that the improved algorithm of UAV swarm scheduling average convergence time is reduced by about 30s compared with the original algorithm, has better convergence, and the UAV swarm scheduling efficiency is improved.
Robust Secret Image Sharing Based on Robust Chinese Reminder Theorem

In a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(k,\ n)$</tex> threshold secret image sharing (SIS) scheme, the original secret image can be reconstructed losslessly by collecting no less than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> shadow images without errors. However, during the transmission or storage of the shadow image, if the transmission channel is noisy or the storage medium is unreliable, the shadow images will be erroneous. In this case, most of the existing SIS schemes cannot reconstruct the secret image. In an effort to cope with the issue, we propose a robust SIS (RSIS) scheme based on the Robust Chinese Remainder Theorem (RCRT), namely RSIS-RCRT, which can realize the reconstruction of the secret image with high quality under a certain error in the shadow image. In addition, a simple and effective Image Correction Method (ICM) is proposed, which improves the quality of the reconstructed image significantly in RSIS-RCRT scheme. Experiments show that the RSIS-RCRT scheme and ICM are effective.