Reviewerly/exHarmony huggingface dataset:
Image super-resolution reconstruction based on dual-branch and residual network

In recent years, computer vision is more and more widely used in the field of intelligent control, and super-resolution reconstruction technology solves the problem of making the image clear. In order to solve the problem of insufficient image feature extraction and low information dissemination efficiency in the FSRCNN algorithm, this paper puts forward a dual-branch and residual network for image super-resolution reconstruction. The model designs a dual-branch feature extraction channel, expands the feature extraction channels, and improves the high-frequency information extraction ability of the input image; adopts an improved residual block to reduce the loss of information transmission. As shown in the experimental results, the peak signal-to-noise ratio (PSNR) of the Set5 dataset is 0.14dB and 0.52dB higher than that of the FSRCNN algorithm under the 2 and 3 scale factors, and the Set14 dataset is 0.13dB and 0.41dB higher respectively.
Design of people count system based on object detection algorithm

At present, the Covid-19 epidemic is still spreading globally. Although the domestic epidemic has been well controlled, the prevention and control of the epidemic must not be taken lightly. Being able to count the number of people in public places in real time has played a vital role in the prevention and control of the epidemic. Deep learning networks usually cannot be directly deployed on embedded devices with low computing power due to the huge amount of parameters of convolutional neural networks. This article is based on the YOLOv5 object detection algorithm and Jetson Nano embedded platform with TensorRT and C++ accelerating, it can realize the function of counting the number of people in the classroom, on the elevator entrance, and other scenes.
A survey of hybrid knowledge reasoning oriented to KG

Knowledge reasoning is a key technology for constructing knowledge graphs and realizing knowledge graph completion. A single category of reasoning has its own advantages and disadvantages, so hybrid reasoning, which integrates the advantages of multiple single reasoning methods, has become a hot research direction. This paper will briefly introduce the background and definition of hybrid reasoning, introduce the classification of knowledge reasoning oriented to knowledge graph in detail, and then focus on the research progress, future research directions and possible challenges of hybrid reasoning in recent years.
Research on student group tracking algorithm based on teaching scene

Expectations by computer vision technology to change traditional teaching model has become the research focus of many scholars, but due to lack of specific training data, and the classroom students intensive distribution, variety, easy to keep out each other, the existing multiple object detection and object tracking technique is applied to the poor performance of the teaching scene. For the above problems, in the stage of multi object detection, considering that the head can not only represent the multi-pose student object, but also is not easy to be completely blocked, this paper established a small dataset of teaching scenes with the head as the detection object to train and improve the accuracy of the Faster R-CNN detector. In the multi object tracking stage, the Kalman filter and Hungarian algorithm are used for tracking, and then the unmatched trajectory is remapped to the depth feature map based on the strong feature extraction ability of Faster R-CNN backbone neural network. Then, we correct the unmatched trajectory by fusing the depth feature information and historical trajectory position information of the object, and calculate the trajectory similarity, which improves the problems of identity switching and trajectory interruption caused by the change of object attitude and occlusion. The research of this paper is used in real teaching scenes, which can assist teachers to track students' status and the formed student object trajectory sequence, and can provide data input for subsequent classroom behavior analysis.
Research on adversarial attacks and defense

Deep learning is widely used in our daily life to solve some complex and tedious problems. In practical applications, if it was exploited by attackers would affect the reliability and security of the deep learning model. This article mainly introduces some attacks methods of generating adversarial examples from Generative Adversarial Networks(GAN) in recent years and related algorithms that use adversarial training to improve the robustness of deep learning models. At the end of the article, drawing on the reviewed literature, we present a broader outlook of adversarial attacks research direction.
Object tracking based on siamese network with multiple graph attentions

The object tracking algorithm based on siamese network converts the object tracking problem into the problem of the similarity matching between the feature maps of the template branch and the search branch. Currently, most trackers use the entire template feature as a kernel to match the similarity of features with the search region, while this global matching approach does not cope well with rapid changes in the target. To solve this problem, we construct a graph for template and search features and embed the template feature information into the search features through multiple graph attentions. The proposed method is evaluated on advanced test benchmarks such as OTB100 and GOT10k and compared with some of the latest algorithms. Experimental results demonstrate the excellent performance of our algorithm, with significant advantages on multiple datasets.
Set-Valued Medical Image Classification with Evidential CNN: A First Test with Covid-19 Dataset

Deep learning based models have been achieving ever high accuracy for precise image classification. However, in the medical sector where decisions should to be made more cautiously and where inaccuracy is less of a concern than precision and recall, it might be more appropriate to resort to imprecise classifiers, e.g. set-valued classifiers. In this work, an evidential convolutional neural network (ECNN) method is applied for set-valued medical image classification with Covid-19 X-ray dataset. Experimental result shows that the ECNN classifier is able to assign confusing image patterns to multi-class sets, while maintaining high accuracy compared to traditional probabilistic CNN classifiers. This result reveals that the ECNN classifier holds good promise of being applied for imprecise medical image classification.
The Method of Industrial Internet Image Super-resolution Based on Transformer

As a fundamental task in the field of computer vision, super-resolution reconstruction has a wide range of applications on the industrial Internet. However, most of the existing super-resolution reconstruction methods are based on convolutional neural networks for feature extraction, which have the problems of low model representation efficiency and insufficient explanation of the extraction process, which limit the performance of the model in industrial Internet scenarios. In recent years, although transformers have been proposed to solve the above problems well and achieve good results in image classification tasks, there is still room for improvement in the adaptability of super-resolution reconstruction tasks. Therefore, in this paper, an improved transformer-based image super-resolution reconstruction model is designed to effectively improve the performance of the image super-resolution reconstruction model. Experiments show that the adaptation of the Transformer mechanism on the super-resolution reconstruction task in this paper can practically improve the performance of the model on public datasets and industrial Internet scenario datasets.
DBHDR: Dual branch Network Guided Multi-Exposure HDR Image Reconstruction

This paper addresses the problems of artifacts, blur, and color patches that can result from multi-exposure fusion when there are more pronounced moving objects in multi-frame LDR images. We propose a new dual-branch high dynamic range image reconstruction network, namely the multi-frame images fusion branch and the reference image structure enhancement branch. In the fusion branch, we perform deformable convolution between the reference feature and adjacent features to perform feature alignment, and increase the weight of detailed features such as texture in the high-light and low-light regions. The aligned features are concatenated and fed into the dilated residual dense blocks (DRDBs) to obtain dense local features. In the enhancement branch, we use six cascading residual local feature blocks (RLFBs) modules to extract structural features, and then add the shallow features of the input reference image to the extracted features to ensure that the network can guarantee the structural information of the reference image. Through the comparison of experimental results, it is verified that the proposed algorithm can remove artifacts and color blocks while maintaining image saturation and detail recovery in the shadow area.
Designing a Multiple-User Wearable Edge AI system towards Human Activity Recognition

Human Activity Recognition (HAR) using artificial intelligence has a broad range of applications. These applications reach a set of disciplines and areas as home activity monitoring, sports, traffic, and healthcare. Using Edge Computing as a tool to enhance is a recent but promising research front. In this work, we propose an architecture for an Edge AI system based on wearable devices. We validate aspects such as the algorithm and functioning based on an edge computing system. Our research displays that the developed system is capable of recognizing 18 different activities with 94% global average precision. Furthermore, it is suitable for usage in both mobile edge computing and cloudlets perspectives.
Distilling the Knowledge in Object Detection with Adaptive Balance

Deep neural network has a significant improvement in accuracy for object detection. A great challenge for deep learning is deployment on low-latency inference systems. Knowledge distillation is widely used for the reduced computational complexity and the compatibility with embedded hardware devices. In this paper, we proposal two adaptive balance distillation methods for object detection, Positive-Feature Balance Distillation and Hard-Feature Balance Distillation. Positive-Feature Balance Distillation helps to alleviate the imbalance of the positive and negative feature and Hard-Feature Balance Distillation is used for forcing the student to focus on the tiny amounts of hard feature in object detection task. These methods are used to improve the accuracy of student networks by transferring knowledge from a complicated teacher’s network to a simplified student’s network. We conduct comprehensive empirical evaluation with different knowledge distillation configurations over KITTI that is one of the most important automatic driving datasets. The proposed methods outperform consistent improvement in accuracy-speed trade-offs for modern object detection method.
Lightweight Object Detection Based on Feature Soft Fusion and Adaptive Enhancement

Object detection plays an important role in modern life. Aiming at the problems of detection algorithm at the feature level, a one-stage decoupled detection algorithm based on feature soft fusion and adaptive feature enhancement is proposed. Taking the fusion strategy of shallow and deep features as the starting point, Multi-layer Feature Fusion Module based on channel attention mechanism is proposed, which further considers the factors of priority, weight and number of fusion layers; In the multi-scale detection, for the problem that detail feature information is possibly insufficient or missing in prediction layer, a simple branch network is designed to extract new features different from backbone network on the basis of the multilayer feature fusion of of the backbone network, which achieve the purpose of feature enhancement by adaptively supplementing the corresponding prediction layer features. It is called Adaptive Enhanced Feature Network. To optimize the model detection performance, the classification and regression tasks are decoupled at the detection head of the network and we designed a Decoupled SubNetwork based on the synthetic convolution operator. The experimental results show that the proposed algorithm achieves 40.5% AP and 58.8% AP (IoU=0.5) on the challenging COCO dataset, and all the components effectively improve the detection performance. This shows a great advantage among one-stage detection algorithms with parameters of the same order of magnitude.
How Much Data is Sufficient for Neural Transliteration?

Data size is vital for neural transliteration. But getting lots of transliteration pairs is difficult, especially for low-resource languages. So how much data is sufficient for an often-used neural transliteration model with good performance? Firstly, the Tensor2Tensor (T2T) neural Transformer transliteration model is selected for its good performance in transliteration. Then select all language pair datasets from English to other languages with more than 40k data and freely available from the website (six datasets in total). Afterwards, conduct neural transliteration experiments for the six data sets. According to experimental results, when the training data size is 20k, the accuracy of six tasks is more than 90% of the best accuracy based on all training data, and the accuracy can exceed 0.45. When the training data size is 15k, the accuracy of six tasks is more than 85% of the best accuracy based on all training data. Therefore, 20k training data is likely sufficient for the Tensor2Tensor neural Transformer transliteration model.
A Four-Phase Self-Timed Ring Based True Random Number Generator on FPGA

The random number generators (TRNGs) provide unpredictable data for various encryption operations such as information security. In this paper, a novel structure of TRNG is proposed based on the random jitter of a four-phase self-timed ring (FPSTR) as the entropy source on FPGA. The proposed TRNG is implemented on Xilinx KCU116 FPGAs with high throughput. Compared with existing designs, our proposal occupies 324 LUTs and 62 DFFs, and its throughput is up to 200 Mbps. The random bitstream generated by TRNG is statistically tested and passes the NIST SP800-22 and the NIST SP800-90B test.
Developing Context-aware Applications for Cyber Physical Systems based on Discrete Timed Petri Nets

With real-time data from the IoT infrastructure, Cyber-Physical Systems (CPS) are able to respond to the internal or external events in enterprises based on the up-to-date information. The ability to efficiently provide information needed by the workers or other types of resources for them to perform the upcoming operations is very important. Such information must be dependent on the context of the workers or other types of resources involved. The frequently changing nature of workflows in enterprises poses a challenge in provision of such context-aware information. The context-aware information must be generated dynamically according to the current workflows. Therefore, the software used to generate context-aware information for workers or other types of resources must be dynamically adapted to the current workflow in use. In this paper, a context-aware information generator will be proposed to generate contextual information for workers or other types of resources to guide their operations. The idea is realized by transforming models. The models used in this paper to represent the Cyber-World models are Discrete Timed Petri Nets. The objective of this study is to propose a method to generate context-aware information for workers/resources with the Cyber-World models described by Discrete Timed Petri Nets. In this paper, we will present the software development methodology and illustrate the idea by an example.
Correlations for estimating optimal slope of a photovoltaic system in Algeria

Technologies of photovoltaics is currently underrated in Algeria. Energy potential maps can help the development of the use of photovoltaic energy. Using accurate models and database of solar radiation for calculating the electricity produced by a photovoltaic system, energy production was estimated in several sites in Algeria and maps of solar and photovoltaic potential was drawn. In addition, correlations were developed to calculate the potential, solar and photovoltaic, and the corresponding optimum tilt angle maximizing it.
A Controllable Luminance Neural Camera

The image signal processing (ISP) pipeline reconstructs compelling RGB images from raw images. Recently, deep neural networks have been proposed to replace conventional divide-and-conquer ISP pipeline solutions. But previous methods cannot control the luminance level necessary for the camera system and suffer from severe color distortion in output images. In this paper, we propose a controllable luminance neural camera based on a convolutional neural network (CNN). Experiments show that our method can achieve flexibility in adjusting luminance levels and higher fidelity in restoring color.
Performance Evaluation Between Tiny Yolov3 and MobileNet SSDv1 for Object Detection

Object detection plays a crucial role in the field of computer vision. It is viewed as a challenging task as it identifies instances of objects from a particular class in digital images or videos. However, since the invention of deep learning methods, the performance of object detection has significantly improved. They are now able to learn semantic, high-level, and deeper features to address existing issues found in traditional architectures. In this paper, an evaluation framework has been proposed to assess the performance of Tiny Yolov3 and MobileNet SSD v1 for detecting people. In addition, both Tiny Yolov3 and MobileNet SSD v1 consist of a lightweight architecture that eliminates the expensive computation to run the models in real time detection using a NON-GPU platform. A fair comparison was made between the pre-trained models by using the two available datasets which are COCO and PASCAL VOC. The model’s performance was evaluated in a classroom scenario, where people were detected and counted. A mobile application was built to view the detection results and its performance was assessed when used with deep learning models. To have a more expansive evaluation, different parameters such as platform, cameras, and conditions were considered. From those parameters, different test cases were formulated and tested to determine which models excel the most and where. Following the evaluation, this paper proposes an evaluation framework for MobileNet SSD v1 and Tiny Yolov3 and provides a domain recommendation for future applications.
A Multimodal Machine Learning Approach to Diagnosis, Prognosis, and Treatment Prediction for Neurodegenerative Diseases and Cancer

Despite early diagnosis and estimating the future course of neurodegenerative and cancerous diseases being integral for survival of patients, clinical and algorithmic methods fail to effectively utilize the multimodal data available, are time-inefficient, and expensive, making it difficult to access accurate screenings for these diseases. Therefore, a novel end-to-end quantum machine learning approach using multiple data modalities for the identification of diagnoses, prognoses, and effective treatments is proposed. In a procedural flow, data is sourced from one or more of the following: CT scan images, webcam, patient-physician audio, Whole Slide Images, and clinical data. For image data, a Convolutional Neural Network, is employed to detect high level features. With text-based clinical data (including audio-derived data), a Bidirectional Encoder Representation model is used to extract text embeddings. For video data, pupil progression and average fixation duration features are manually crafted. All feature vectors are concatenated, normalized, passed through a Deep Neural Network, and then mapped to one of 38 neurodegenerative and cancerous diseases. For prognosis, features are pooled, concatenated with the diagnosis feature vector, and passed through another neural network with an output of survival times. Treatment prediction involves an information-retrieval task matching feature vectors to treatments/drug lists from the FDA. The proposed approach was tested on 5,000 patient profiles sourced from the public TCGA and JPND databases, outperforming all other state-of-the-art approaches. The model predicted diagnoses with an accuracy of 98.53%, achieved a Concordance Index of 0.94 in predicting prognoses, and in treatment prediction achieved a 99.32% accuracy.
Wavelet leader-based multifractal analysis for characterizing subsurface heterogeneities using velocity logs

In petroleum engineering, well logs are the key to characterize a hydrocarbon reservoir, and to extract meaningful features related to the lithology and the type of fluids present in the reservoir. Several approaches have been suggested to exploit well log data. In this view, this paper presents a wavelet leader-based multifractal analysis (WL), applied to velocity log data measured at two scientific deep boreholes: the pilot well (KTB-VB) and the ultra-deep main well (KTB-HB), to study the local behavior and the invariant scale properties of the investigated data. The suggested approach allows distinguishing different lithology types based on the wavelet leaders. First, local regularity profiles have been computed using the Peltier and Lévy-Véhél (PLV) algorithm from the different velocity logs, and a lithological segmentation has been carried out. Then, a multifractal analysis has been carried out on velocity logs using the WL algorithm. A clear correlation is shown between lithology and multifractal properties extracted from the investigated logs, specifically significant values singularity spectrum width $( \Delta h)$ correspond to the local filled fractures. To conclude, the singularity spectrum may then serve as a tool for characterizing subsurface heterogeneity and identifying a zone of macro- and micro-fractures.
A Low Power Speech Recognition Processor with Precision Recoverable CRNN

In this paper, Convolutional Recurrent Neural Network (CRNN) is deployed in the speech recognition system for recognition of 5 keywords. We propose an 8-bits quantization scheme to quantize the weights and activations. Cartesian Genetic Programming (CGP) to generate approximate multipliers is proposed to drastically reduce hardware power consumption with only 1.56% loss of accuracy. The proposed partial retraining method compensates for the loss of network accuracy caused by approximate computing, and can basically restore the accuracy to the initial level. Using process library for hardware verification based on synthesis, the proposed speech recognition system can reduce power consumption by 11.6%~24.7%, and reduce area by 10.2%~25.6% with accuracy loss is 0.29%~ 0.55%.
Image Enhancement for Improved OCR and Deck Segmentation in Shipbuilding Document Images

In this paper, we develop a CycleGAN model to enhance shipbuilding document images for downstream tasks including optical character recognition (OCR) and ship deck segmentation. We train the CycleGAN model with public image denoising datasets and utilize the trained model as an image denoising model to enhance the Navy’s Military Sealift Command (MSC) document images. We then apply our previous optical character recognition (OCR) pipeline and deck segmentation model to the enhanced MSC documents images. Experiment results show that the proposed model achieved promising improvements in signal-to-noise ratio (SNR), OCR accuracy, and deck segmentation.