Reviewerly/exHarmony huggingface dataset:
Research on 3D Object Detection Method Based on Vlidar

Aiming at the problem of insufficient accuracy of vision-based 3D object detection algorithms, this paper proposes a Vlidar-based 3D object detection network framework. In order to eliminate the difference between the two detection modes of lidar point cloud and image, it is proposed to convert the depth information of stereo vision into Vlidar representation. First, use the stereo disparity estimation algorithm to obtain the disparity map, convert the disparity map into a depth map after preprocessing, and then convert the depth map into Vlidar point cloud information. The experimental results show that compared with this advanced monocular 3D object detection algorithmic program smoke, the planned algorithmic program features 22 percent increase within the accuracy of 3D object detection among 30m, that is 27 percent completely different from the object detection algorithmic program supported lidar point cloud.
Few-shot Classification with First-order Task Agnostic Meta-learning

Meta-learning approaches are typically used to solve few-shot learning tasks by training on a variety of data in the hopes of developing generalization abilities that can be applied to new tasks. Model-Agnostic Meta-Learning (MAML) is one of the popular ways for few-shot learning currently. However, the generalizability is impacted when the meta-learner is over-trained in the meta-training, leading to a bias toward existing tasks. Besides, MAML involves a second-order gradient, which costs a lot. In this paper, we propose a First-order Task-agnostic Meta-learning algorithm (TA-Reptile). Our entropy-based method utilizes a first-order gradient update to learn an unbiased model with a fine set of initialization parameters, aiming to address the difficulty of overperforming in classification tasks. Experiments on benchmark datasets illustrate that TA-Reptile has competitive performance in few-shot classification tasks.
Multi-style Migration of Chinese Characters based on Self-attention Mechanism and StarGAN v2

Due to the complex structure and large number of Chinese characters, it is a challenging task to generate Chinese characters and convert font styles. At present, most of the models on Chinese character transformation cannot efficiently deal with the multi-style migration of Chinese characters. This paper introduces the self-attention mechanism on the basis of StarGAN v2, and proposes the SA-StarGAN v2 model. Through the model in this paper, it only needs one training to complete the conversion between different styles of Chinese characters, instead of one training for every two styles of Chinese characters. Experiments are carried out on the six Chinese character datasets. Compared with StarGAN v2, the proposed model SA-StarGAN v2 generated Chinese characters with more regular structure, clearer strokes and higher overall quality.
Multi-Scale Fusion RA-CNN for Fine-Grained Classification

Fine-grained image recognition has gained increasingly popular as a sub-domain of computer vision, where numerous works have been proposed. These works mainly focus on researching discriminative region localization and fine-grained feature learning independently instead of simultaneously. However, we believe that the relationship between them absolutely deserves more attention. In this paper, we aim to propose an improved recurrent attention convolution neural network (RA-CNN) as we consider discriminative region localization and fine-grained feature learning can reinforce with each other. RA-CNN includes three scales, each of which consists of a classification sub-network and an attention proposal subnetwork (APN). The procedure of RA-CNN can be described by three steps. Firstly, discrimination features can be extracted from the input image in the first scale. Secondly, the extracted discriminative feature is trained by APN to get the information of attention region. Third, we crop the attention region and zoom in it to be the input of the next scale. Experiments on CUB-200-2001 dataset show our method is capable of handling find-grained image recognition issues with 76.86% and 94.06% precision@1 and percision@5, respectively. Our method outperforms the original RA-CNN with a large margin. The strength of our method lies in the fact that it can gradually generate the most discriminative regions from coarse to fine. Consequently, our method has achieved higher precision than others.
Analysis between different types of smart contract fuzzing

The security problem within cryptocurrencies has high priority since it is published. The news of cryptocurrencies attack is keeping rising, and types of attacks are recorded as the classical security issues such as the most famous one decentralized autonomous organization (DAO) attack and which caused 3.6 million cryptocurrencies stolen. Thus, with the ripe fuzzing technique, using such testing skills on smart contracts to discover more bugs and leaks hidden in cryptocurrencies in a more automatic and intelligent approach could bring huge benefits. This paper is aimed to analyze the variation from the very first smart contract tools to the newest one, to discuss their pros and cons of them. This experiment mainly chose three fuzzing tools, several basic and classic smart contracts as tested material, and recorded the performance of fuzzing contracts of each tool. And the results are clear to show that even the newest fuzzer has a better performance than the other, but it is better to use it depending on the specific requirements while the rest of the two fuzzing tools has some advantages in some aspect. Therefore, this paper could make guidance for the later researcher to use exactly the type of fuzzer they need.
General Saliency Detection Model for 2D and Panoramic Images

Panoramic images have geometric distortions different from 2D images, so the current significance detection model cannot be used in two forms of images. Although viewers need to use head-worn devices to watch panoramic images, human visual attention mechanism will not change with the change of observation mode. Therefore, we propose a significance detection model that shares weights between two images, so that it can be applied to 2D images and panoramic images at the same time. Our model uses different convolution sampling modes for 2D images and panoramic images respectively, so that the saliency detection model based on the same set of parameters can be correctly applied to the images of two modes at the same time. Firstly, we separate the sampling and calculation steps in spherical mapping convolution, and then design two different sampling methods to make them suitable for 2D and panoramic images respectively. Finally, we use 2D convolution to convolute the sampled results. In addition, in order to eliminate the influence of 2D and panoramic data set annotation methods on the calculation results, we annotate the two kinds of data sets in a unified way. The experimental results show that our model can be applied to 2D and panoramic images, and the detection accuracy is better than the existing models under the condition of less training times.
Sequential Recommendation Model That Combines Attention Mechanism with User Scoring Information

Most commonly used sequential recommendation models are based on behavior sequence data. In these models, they seldom consider the user rating factors and the different extraction abilities of a single attention extraction module for different sequence lengths. To overcome this problem, we propose a method to calculate the propagation ability of items and to correct ratings by SI (Sensitive Infected) module. In addition, we also propose a new attention mechanism that combines CNN and Transformer. In this new model (SRec-AU), we first apply the information dissemination model to calculate the dissemination capability of goods, and apply the dissemination capability to correct user ratings. Then, we use the attention module to extract the hidden information. Our proposed model embeds the modified user rating sequence and the original user behavior sequence, and then uses the attention layer to extract the feature information, and finally predicts the next item in the sequence by matrix factorization. The experimental results show that our method outperforms the state-of-the-art sequential models on both sparse and dense datasets and different evaluation metric. Experimental results show that the proposed SRec-AU outperforms the state-of-the-arts approaches (+8.51% in Reca11@10, +6.2% in NDCG@10, compared to the maximum value in baseline models). We also validate the effectiveness of dissemination model in improving the recommendation performance.
An Efficient Transformer with Shifted Windows for Low-light Image Enhancement

In low-light environments, the images have different degrees of color and texture degradation and contain a lot of noise, which seriously affect human visual perception. The state-of-the-art methods for low-light image enhancement are usually based on convolutional neural networks (CNN). Due to the lack of ability of CNN to capture global contextual information, the enhanced images of these methods lack details and cannot effectively suppress the noise. The Transformer can effectively capture global contextual information, we try to combine the Transformer to make up for the defects of CNN and improve the luminance, texture and color of images. In this paper, we propose an efficient Transformer with Shifted Windows for low-light image enhancement (SwinLIE). SwinLIE includes two important modules: Enhanced Swin Transformer block (ESTB) and adaptive feature fusion block (AFFB). ESTB is proposed to be embedded in each stage of the model to fully capture the global contextual information, while AFFB is proposed to fuse features from different stages. We conduct extensive comparisons with the state-of-the-art methods on the LOL dataset, and experiments show that the proposed method has the best PSNR, SSIM and NIQE. At the same time, the proposed method has low requirements for equipment and quick running time. The images reconstructed by our method have rich colors and textures, and the noise is effectively suppressed.
Research on single-character image classification of Tibetan ancient books based on deep learning

With the development of deep learning, classification neural networks have been widely used in the field of computer vision, and have achieved remarkable results in the direction of image classification. In order to realize the classification of Tibetan ancient books and characters, this paper uses LeNet, VGGNet, RestNet, and Wide-ResNet models to classify images of Tibetan ancient books and characters, and compare and analyze the experimental results. Experiments show that the above neural network models have achieved good classification results, and the Wide-ResNet model has the best classification effect on Tibetan single-character images, with an accuracy rate of about 94%. The experimental data in this paper provides a reference value for the classification research of Tibetan ancient books and images.
Target tracking based on staring multispectral imager

In the actual dim target tracking problems, there are often interference targets similar to the detected targets. At this time, it is often difficult to ensure the accuracy of recognition and tracking if only rely on the spectral feature of a single infrared band or the motion feature of the detected targets. Therefore, based on the staring multispectral imaging system, a target tracking method based on multispectral feature fusion is proposed, which solves the problem of indistinguishable small and weak targets in single-band infrared images and improves the accuracy of recognition and tracking.
Sitting Posture Detection System Based on Keras Framework

Aiming at a low-power embedded real-time sitting posture detection system, a real-time sitting posture detection system based on deep learning is designed. The system obtains the pressure of the sitting posture of the human body through a thin-film pressure sensor and the human body pressure in different sitting postures is collected and analyzed, and an analysis model is established under the Keras framework. Burn the model into STM32 through cubemax to realize real-time collection, analysis and detection of human sitting posture. Finally, the communication between the STM32 and the Android application is realized through the MQTT protocol, which realizes the real-time detection and discrimination of the sitting posture and gives the relevant sitting posture correction prompts.
Deep Q-Network Based Dynamic Spectrum Access for Cognitive Networks with Limited Spectrum Sensing Capability SUs

In dynamic spectrum access (DSA) technology, it is particularly important for the secondary users (SUs) to accurately determine the vacant spectrum to ensure that the activity of the primary users (PUs) is not interrupted. However, in reality, SUs can’t sense all channels or accurately sense the channel state. In this paper, we consider DSA problem where PUs and SUs share the spectrum in an overlay manner under the condition that the priori information of the system is unknown and SUs have limited sensing capability. An algorithm that combines dropout with multi-layer perceptron (MLP) and deep Q-network (DQN) is proposed. SUs obtain channel occupation pattern of PUs through learning. Simulation results show that the algorithm has better performance in protecting PUs from interference, reducing conflicts between SUs, and increasing the average channel capacity compared with three other algorithms: myopic, random access and DQN-RC (reservoir computing).
A Distance-Based Attention Mechanism for Object Detection

Attention mechanisms have been widely studied and applied in various computer vision tasks because of their ability to establish inter-dependencies between channels and spatial positions. Recent works show it necessary to provide light-weight and effective implementations for higher computation efficiency. To this end, this paper proposes to restrict the attention to a local region for object detection and presents a distance-based attention mechanism to incorporate contextual information from regional areas, instead of the full image, in an efficient and effective way. Furthermore, we implement the proposed approach in an object detection model that combines tricks from several state-of-the-art models. Specifically, the popular one-stage RetinaNet is used as the backbone and the two-step classification and regression, feature enhancement module, max-out operation and multi-scale testing are adopted to obtain a high detection accuracy. Experimental results on WIDER FACE show that the proposed detection model outperforms many state-of-the-art models and the proposed attention mechanism can greatly increase the detection accuracy without introducing extra computational overheads.
Research on the Implementation of Real-Time Intelligent Detection for Illegal Messages Based on Artificial Intelligence Technology

In recent years, the detection of illegal and harmful messages which plays an significant role in Internet service is highly valued by the government and society. Although artificial intelligence technology is increasingly applied to actual operating systems, it is still a big challenge to be applied to systems that require high real-time performance. This paper provides a real-time detection system solution based on artificial intelligence technology. We first introduce the background of real-time detection of illegal and harmful messages. Second, we propose a complete set of intelligent detection system schemes for real-time detection, and conduct technical exploration and innovation in the media classification process including detection model optimization, traffic monitoring and automatic configuration algorithm. Finally, we carry out corresponding performance verification.
VMA: Domain Variance- and Modality-Aware Model Transfer for Fine-Grained Occupant Activity Recognition

The growth of the Internet of Things (IoT) sensing systems leads to a large number of multimodal datasets over different deployments. Labeling costs for these datasets, especially fine-grained labels, are often tremendous. On the other hand, different data distributions (domain variance) of these datasets prevent models built with labels of one dataset (source domain) from being directly used in another (target domain). This domain variance may be caused by one or more physical factors change in the deployments, such as buildings and/ or people. Existing model transfer studies mainly focus on adapting the model to the domain variance caused by only one physical factor change. When multiple factors change between the source and target domains, the model transfer often yields low accuracy due to significant domain variance. We present VMA, a model transfer framework for multimodal IoT sensing data that handles multi-factor domain variance. VMA first decouples the multi-factor domain variance between two datasets to multiple single-factor domain variance dataset pairs with other available datasets. Then, VMA leverages sensing modalities robust to each single-factor domain variance for accurate prediction by weighing them more in the fusion. We apply VMA to the fine-grained occupant activity recognition application with a multi-modal sensing system of structural vibration and wearable IMU. We collect real-world datasets to evaluate the proposed framework. VMA achieves a model transfer accuracy up to 76.1% on the target domain with multi-factor domain variance, demonstrating a 1.6x and 1.9x error reduction compared to direct prediction baselines with and without modality-aware learning design.
Accelerating Neural Network Training with Processing-in-Memory GPU

Processing-in-memory (PIM) architecture is promising for accelerating deep neural network (DNN) training due to its low-latency and energy-efficient data movement between computation units and the memory. This paper explores a novel GPU-PIM architecture for DNN training, where streaming multiprocessors of GPU are integrated into the logic layer of 3D memory stack, and multiple such stacks are connected to form a PIM-network. Two corresponding optimization strategies are proposed. The first is to increase the computational parallelism of the data-parallel training mode with the large memory, high bandwidth/high network transmission speed of GPU-PIM. The second is further utilizing the optimized model-parallel training to significantly reduce the communication overhead: We propose a mapping scheme to decide the proper parallelization for different DNN layers on the proposed architecture. Experiments show that the proposed architecture outperforms the baseline GPU by 35.5% and 59.9% and reduces energy consumption by 28.2% and 27.8% for the two benchmarks we evaluated.
Demo Abstract: Understanding Internal Structure Of Hollow Objects Using Acoustics

In this paper, we present the idea of using acoustic sensing over smartphones to understand the internal structure of hollow objects. In the core, we use an elegant, yet lightweight, signal processing pipeline that intelligently uses acoustic chirps to understand the internal structure of the hollow objects. Preliminary experiments on regularly used hollow objects show the potential of the idea.
Poster Abstract: Adapting Pretrained Features for Efficient Unsupervised Acoustic Anomaly Detection

Faults in industrial equipment lead to significant costs due to down-time and unplanned maintenance interventions. Acoustic Internet of Things (IoT) sensors combined with machine learning offers the possibility of early fault detection to mitigate these costs. How-ever, prior approaches to Acoustic Anomaly Detection (AAD) are poorly suited to operating within the resource constraints of IoT systems as they require the acquisition of large volumes of data to train models from scratch for different machine types or oper-ating environments. To overcome the limitations, we introduce a system utilizing pretrained low-dimensional features and Gaussian mixture models. Preliminary results on real-world datasets show that our proposed approach outperforms state-of-the-art solutions based on the area under the curve (AUC) score with an average of 4 different machine types. Furthermore, our approach requires far less training data, making it more suitable to operate within the power and network constraints of IoT devices.
Poster Abstract: Trade-off Analysis of Inference Accuracy and Resource Usage for Energy-Positive Activity Recognition

Energy-positive activity recognition classifies human activities, including walking, running, and sitting, while harvesting kinetic energy from such activities. In this setting, the device's lifetime de-pends on the user's activity profile and the resources needed to run inference to classify activities. Thus, the selection of machine learning classification models for energy-positive activity recognition must consider both model's classification accuracy and energy con-sumption compared to the harvested energy from human activities. In this paper, we study the trade-off between accuracy and resource usage of a neural network model when different feature extraction techniques are used. Our results indicate that an on-board sched-uling algorithm can be used to dynamically switch between the optimal feature input tuned for accuracy and energy consumption.
Demo Abstract: A Real Time Control System for Replaying Motion Data

This paper demonstrates a laboratory setup to replay previously collected experimental human motion data in real-time, allowing data re-purposing for activity recognition. The setup proposed is to examine the suitability of simulating motion by replaying a previ-ously collected data using an Electrodynamic Shaker - a table that can generate motion based on pre-recorded accelerometer traces. Building upon the information gathered from the sensor input, ac-curate continuous motion data from human dynamic movements can be recreated. By reusing data from past projects with novel or alternative sensor attachments, new datasets are created gen-erating activity data for movement recognition without requiring volunteers or time consuming data collection. This demo illustrates the potential for accurate low-cost in-house motion data collection without requiring field experiments.
How to increase and balance current DBT datasets via an Evolutionary GAN: preliminary results

Deep learning techniques have led to a vast improve-ment in various fields of computer vision, mainly using large-scale labelled datasets. Obtaining a large dataset of medical images for diagnostics is still an open challenge; the most significant obstacles are data imbalances and privacy issues related to sensitive patient information. Furthermore, the limited size of datasets for the training of a neural network can affect the performance of supervised learning and cause model overfitting problems. For this reason, data augmentation techniques are used to expand existing datasets. Generative Adversarial Networks (GANs) represent an innovative solution for acquiring additional information from a dataset since they can generate synthetic samples indistinguishable from real sample images. This work explores the use of GAN networks on Digital Breast Tomosynthe-sis (DBT) images, which is, in our knowledge, a completely new approach in this domain. In particular, we apply an optimization approach to the learning process of GAN networks based on evolutionary techniques.