Reviewerly/exHarmony huggingface dataset:
Efficient Storage Coding of Pruning MLP for Embedded System

Deep neural network is widely used in many intelligent computing scenarios, but the storage overhead caused by its huge number of parameters limits its application in embedded systems. The neural network pruning technology can remove a large number of redundant parameters in the network, theoretically, which reduces the amount of parameter storage and calculation. However, there are two problems in deploying sparse neural network algorithms on existing embedded hardware platforms: one is that using the existing coding to store sparse neural network parameters will increase additional memory access overhead; the other is the decoding process of sparse model is highly complex, which increases the amount of calculation. To solve the two problems, this paper proposes Dynamic ELL encoding format to represent the sparse weight matrix for pruning neural network. This new coding combines the advantages of the ELL encoding and relative indexing, which compromises the storage overhead and decoding overhead. The sparse MLP that uses dynamic ELL encoding storage is deployed on a SoC built on a RISC-V processor. Experiments show that compared to dense networks that use Dynamic ELL encoding storage, the sparse network storage space is reduced by 43%, and the system running time is reduced by 37%.
Situational Estimation of Sports Broadcasting using a Character Level Auto-Encoder for Live Tweets

Real-time videos enable people to experience real-world events in real time. However, it is challenging for people to keep watching an event when they are busy and have other activities to carry out. It is therefore crucial to develop a method for supporting the viewers' real-time video viewing effectively. Using sports broadcasting as a representative example of such videos, our goal is to develop a system that detects and automatically estimates the events in sports games that are likely to be of high value to the viewers and notifies them in real time during sports broadcasts. To this end, we propose a general-purpose method that efficiently models situations from small amounts of tweets without using domain-specific knowledge. The effectiveness of the method was verified via experiments by estimating the number of tweets posted from the modeled features of a situation with high accuracy.
Multi-fusion Network for Single Image Deraining

Single image deraining is regarded as an important research direction in image processing. To tackle the over-smoothing effect caused by the overlapping between rain streaks and the background, we propose a multi-fusion network for single image deraining. A novel local feature fusion block and a global feature fusion block are explored to fuse the high-level features with the low-level ones and correct the low-level representations. By stacking multiple fusion blocks, the proposed network can fully utilize the high-level information and extract powerful feature maps of rain streak layers. In addition, based on the prediction difficulty, a curriculum learning strategy is further explored to make the training process easier. Extensive experiments demonstrate that our network performs favorably against other deraining approaches.
Face Detection of Innovation Base Based on Faster RCNN

In order to improve the efficiency of teachers’ attendance and save teachers’ energy and time, this paper proposes a face detection algorithm based on improved Faster-RCNN, in order to overcome the problems of gradient disappearance and gradient explosion caused by too deep network depth. In this paper, ResNet50 is used to replace VGG16 backbone feature extraction network, and soft non-maximum suppression method is used to improve the recognition rate of overlapping faces based on the principle of Faster-RCNN algorithm, ResNet50 residual network model and RPN network structure principle are described in detail. Training and testing on Wider Face data set, carrying out a variety of network comparison experiments, provide a strong basis for the experimental results. At last, the experimental data show that ResNet50 feature extraction network is relatively ideal and the detection accuracy on the test set reaches 85.3%, which is 3.9% higher than that of VGG16 on average. The detection time of a single picture is 0.34 s, which meets the real-time monitoring requirements and provides a new idea for the face attendance system.
DANICE: Domain adaptation without forgetting in neural image compression

Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC.
Parsing Model and a Rational Theory of Memory

This paper explores how the rational theory of memory summarized in Anderson (1991) can inform the computational psycholinguistic models of human parsing. It is shown that transition-based parsing is particularly suitable to be combined with Anderson's theory of memory systems. The combination of the rational theory of memory with the transition-based parsers results in a model of sentence processing that is data-driven and can be embedded in the cognitive architecture Adaptive Control of Thought-Rational (ACT-R). The predictions of the parser are tested against qualitative data (garden-path sentences) and a self-paced reading corpus (the Natural Stories corpus).
Cycle-Derain: Enhanced CycleGAN for Single Image Deraining

As the basis of image processing, Single Image Deraining (SID) has always been a significant and challenging theme. On the one hand, due to lack of enough real rainy images and corresponding clean images, most derain networks train in the synthetic datasets, which makes the outputs unsatisfactory in real environment. On the other hand, heavy rainfall is accompanied by fog. Traditional networks for deraining is used to remove the rain streaks in the rain image. The processed image may still have the problem of blurring. In this paper, we comprehensively consider the problems existing in SID, propose a Cycle-Derain network based on unsupervised attention mechanism. Specifically, this network makes full use of generative adversarial networks with two mappings and cycle consistency loss to train the unpaired rainy images and clean images. Besides, it introduces unsupervised attention mechanism and uses the loop-search positioning algorithm to make the network better deal with the details of rain and fog in images. Many experiments based on public datasets show that Cycle-Derain network is very competitive with other rain-removing networks, especially in the restoration of real rainy images.
Online Optimal Investment Portfolio Model Based on Deep Reinforcement Learning

Combining deep reinforcement learning with portfolio management is one of the research hotspots in the field of quantitative investment. In recent years, quantitative investment has become one of the research hotspots in the field of deep reinforcement learning. Combining portfolio management with deep reinforcement learning has become a hot research direction. The model in this paper combines the perception ability of deep learning and the online decision-making ability of reinforcement learning to construct an intelligent model of portfolio management based on deep reinforcement learning DDPG algorithm. This model addresses issues such as the limited ability of reinforcement learning models to perceive complex environments and the strong correlation between the internal features of state data, introducing the convolutional neural network into the DDPG model. The use of convolutional neural networks to process multi-dimensional state data improves the perception ability of the reinforcement learning model and solves the problem of data correlation. Therefore, the model in this paper can obtain the dynamic optimal investment portfolio within the specified trading time range. At the same time, this paper introduces the action vector into the state value representing the stock market environment, and solves the problem of static model. Experiments show that our model improves the income acquisition ability of the portfolio management model.
Mask R-CNN Based Laptop Parts Detector for Disassembly

The efficient recycling of waste electronic 3C products depends on the development of automatic disassembly process, including the robust vision detection system. Here we choose laptop parts as the target object, and build a dataset that contains 620 images. Our work focused on parts detection at disassembly scenario and implemented a universal and extensible laptop parts detector that can be applied to disassembly pipeline which based on a light and improved Mask R-CNN. After generating the mask images of parts, a rotary rectangle fitting method for parts was performed to predict the parts’ rotation angle, and apply specific rules based on quantity and space constraints to improve the accuracy.
Plaster: an Embedded FPGA-based Cluster Orchestrator for Accelerated Distributed Algorithms

The increasing use of real-time data-intensive applications and the growing interest in Heterogeneous Architectures have led to the need for increasingly complex embedded computing systems. An example of this is the research carried out by both the scientific community and companies toward embedded multi-FPGA systems for the implementation of the inference phase of Convolutional Neural Networks. In this paper, we focus on optimizing the management system of these embedded FPGA-based distributed systems. We extend the state-of-the-art FARD framework to data-intensive applications in an embedded scenario. Our orchestration and management infrastructure benefits from compiled language and is accessible to end-users by the means of Python APIs, which provides a simple way to interact with the cluster and design apps to run on the embedded nodes. The proposed prototype system consists of a PYNQ-based cluster of multiple FPGAs and has been evaluated by running an FPGA-based You Only Look Once (YOLO) image classification algorithm.
Determining the composition of an object based on its hyperspectral image

The article presents results of the development and research of a hyperspectral imaging spectrometer for analyzing borehole fluids in real operating conditions in the spectral range from 0.35 microns to 2.1 microns. A mathematical model and an algorithm for identifying the borehole fluid by composition and percentage content based on the results of hyperspectral image analysis are developed.
Characterising Players of a Cube Puzzle Game with a Two-level Bag of Words

This work explores an unsupervised approach for modelling players of a 2D cube puzzle game with the ultimate goal of customising the game for particular players based solely on their interaction data. To that end, user interactions when solving puzzles are coded as images. Then, a feature embedding is learned for each puzzle with a convolutional network trained to regress the players’ completion effort in terms of time and number of clicks. Next, the known bag-of-words technique is used at two levels. First, sets of puzzles are represented using the puzzle feature embeddings as the input space. Second, the resulting first-level histograms are used as input space for characterising players. As a result, new players can be characterised in terms of the resulting second-level histograms. Preliminary results indicate that the approach is effective for characterising players in terms of performance. It is also tentatively observed that other personal perceptions and preferences, beyond performance, are somehow implicitly captured from behavioural data.
On Skew Convolutional and Trellis Codes

Two new classes of skew codes over a finite field F are proposed, called skew convolutional codes and skew trellis codes. These two classes are defined by, respectively, left or right sub-modules over the skew fields of fractions of skew polynomials over $\mathbb{F}$. The skew convolutional codes can be represented as periodic time-varying ordinary convolutional codes. The skew trellis codes are in general nonlinear over $\mathbb{F}$. Every code from both classes has a code trellis and can be decoded by Viterbi or BCJR algorithms.
False Positive Reduction in Mammographic Mass Detection Using Image Representations for Textural Analysis

Breast cancer is a prominent disease affecting women and is associated with low survival rate. Mammogram is a widely accepted and adopted modality for diagnosing breast cancer. The challenges faced in the early detection of breast cancer include poor contrast of mammograms, complex nature of abnormalities and difficulty in interpreting dense tissues. Computer-Aided Diagnosis (CAD) schemes help radiologists improve the sensitivity by rendering an objective diagnosis, in addition to reducing the time and cost involved. Conventional methods for automated diagnosis involve extracting handcrafted features from Region of Interest (ROI) followed by classification using Machine Learning (ML) techniques. The main challenge faced in CAD is higher false positive rate which adds to patient anxiety. This paper proposes a new CAD scheme for reducing the number of false positives in mammographic mass detection using a Deep Learning (DL) method. Convolutional Neural Network (CNN) can be considered as a prospective candidate for efficiently eliminating false positives in mammographic mass detection. More specifically, image representations that include Hilbert's image representation and forest fire model which contain rich textural information are given as input to CNN for mammogram classification. The proposed system outperforms ML approach based on handcrafted features extracted from the image representations considered. In particular, forest fire- CNN combination achieves accuracy as high as 96%.
A Deeplab-Based Segmentation Network for Screw Images

Aiming at the problem that the screwdriver cannot be precisely embedded in the screw groove area during automatic screw removal, we propose an image semantic segmentation model fused with a lightweight convolutional neural network. Based on the classic DeeplabV3 model, a lightweight MobileNetV2 structure is used to replace original feature extractor, and its unique spatial pyramid structure is used for multi-scale fusion of the convolution feature of screw head image, and adding a dual attention module to extract the high-dimensional feature to reduce the loss of detail in segmentation. Finally, deconvolution is used to restore the resolution through the improved decoding network. By comparing our method with the state-of-the-art semantic segmentation network, It turns out that our method has better segmentation performance, with the mIoU up to 94.6%, and the testing time of a picture is 0.12ms, which can meet the demand of real-time task.
Designing Digital Certificate Issuance Information System

Abstract Nowadays, some training and webinars are conducted frequently. Especially during the Covid-19 pandemic, online training and webinars became rational choices in the education sector. Evidence of participation from training and webinars is usually a digital certificate. Manual digital certificate creation will usually take a lot of time. It will be easier if the process is made systematically. This research aims to design a website-based application that can publish digital certificates that event participants can download. The method used in designing and making this website is descriptive. It is expected to present a complete picture of the subjective or objective of the related phenomena. The result of this research is a website-based application design to make it easier to issue digital certificates. Automation of the system can help simplify work and broad accessibility due to the website platform’s use. From the results obtained, it can be concluded that by using this application, making certificates utilizing the system is more efficient than the manual method and can reduce the amount of paper usage. With this application, it is hoped that it will make it easier for the event committee to process the issuance of digital certificates and make it easier for participants to get their certifications.
Handwritten Digit Recognition Scheme Based on Noise Reduction and Trestle Self-Coding

Abstract In this paper, in order to improve the accuracy of handwritten number recognition, a convolutional neural network based on noise reduction and stack auto coding is formed by combining the denoising auto coding algorithm with stack auto coding algorithm. In this paper, the MNIST dataset is preprocessed first, then the noise reduction and stack auto coding network layer are constructed, and finally the stack auto coding algorithm based on the improved denoising auto coding is used to train and test the dataset. Experimental data show that the proposed method achieves low loss value and effectively improves the recognition accuracy in the MNIST test set.
Stories Incarnate

Providing embodied, interactive experiences for live audiences in traditional performance venues remains an ongoing challenge in the live entertainment industry. This paper presents a proof-of-concept performance piece which engages a seated audience in an embodied, interactive storytelling experience during a live circus show. We used a research-through-design methodology, with the goal of creating a piece in which the audience's actions and the way they collaborate determine the outcome of the performance. We describe our design process and informal evaluation of the piece. We conclude with considerations for designing embodied, interactive storytelling experiences for live audiences that provide a sense of agency and communion, as well as reflections on research through design for live performance.
Machine Learning Differences in Machine Translation of Urban Publicity Texts

Through the parallel evaluation of domestic commonly used machine translation platforms, this paper studies the shortcomings of the existing machine translation platforms in the translation from Chinese to English. Through the four machine translation platforms Sogou, Youdao, Google and Baidu, the selected case texts are translated into Chinese into English, and compared with the manual translation, the typical machine translation problems that are easy to appear in the Chinese English translation of domestic machine translation platforms are analyzed. Through the parallel comparison between machine translation and manual translation of a Chinese English speech translation example on α hina Daily website, it is found that there are some common problems in the translation of Chinese into English in the four machine translation platforms tested, such as translation errors at the word and sentence level, omission of translation, and case and case format errors. These translation problems need to be solved by the improvement of machine translation platform and the translator’s post editing.
A Data-Driven Intelligent Medical Management System via Neural Networks

For human health, medical diagnosis plays an irreplaceable role, conventional medical diagnosis methods cannot ensure the accuracy of diagnosis due to the interference of various external factors. Therefore, this paper proposes a data-driven intelligent medical management system via neural networks(MMS-ID). The essence of this method is to predict the survival time of cancer patients with the aid of gradient boosting decision tree (GBDT) and hybrid neural network model. Firstly, GBDT screens the feature factors of matching conditions according to the set value domain, and inputs them into the neural network. Subsequently, a hybrid neural network that combines the convolutional neural network (CNN) and the long short-term memory (LSTM) model is employed to predict survival length of cancer patients. Finally, the stability of MMS-ID is analyzed and compared with a series of baseline methods. A series of experiments prove that MMS-ID has excellent performance.
A CycleGAN Adversarial Attack Method Based on Output Diversification Initialization

Abstract The powerful image generation capabilities of generative adversarial networks (GAN) bring great threats to applications related to images. Style transfer networks realize the style transform between image domains through which we can easily modify images like portraits and calligraphy. To eliminate the negative impact caused by the forged images, there emerged technical methods to detect forged images, which might trigger remedial actions afterwards but cannot prevent maliciously tampered content from spreading over network media. Therefore, some scholars put forward the idea of protecting images from hostile generative networks with adversarial attack. However, the initial random noise of adversarial perturbation cannot be effectively mapped to the output space. In order to improve the visual effect of adversarial attacks, this paper proposes an adversarial attack algorithm based on output diversification initialization (ODI) for CycleGAN. We firstly utilize output diversification initialization to find an effective starting point for the adversarial attack, and then we use Project Gradient Descent (PGD) to iteratively attack the style transfer network by modifying the adversarial loss function. Experimental results demonstrate that the introduction of ODI can effectively enlarge the distance between the adversarial output and the original output. It achieves better results in identifying the forged images generated by the targeted model, and does not significantly increase the disturbance, which can guarantee the normal use of original images.
Fixed Feature With Attention Based on Recurrent Criss-Cross Salient Object Detection Network

Salient object detection (SOD) is a basic research direction in the computer vision task. Its main work is to simulate the human visual system by establishing a visual-aware model. However, most of existing models have suffered from two challenges. First, edge-aware models only use edge feature to improve segmentation features but lack of utilizing the complementarity of them. Second, the problem of losing part of information in the process of extracting features in Convolutional Neural Networks (CNN) leads to the incompleteness of final result. In this paper, we proposed a criss-cross refined salient object detection method based on hybrid attention to solve these problems. Specifically, we applied a Criss-Cross Attention module (CCA) to capture useful contextual information of surrounding pixels from long-range dependencies effectively. Fully considering the complementary between salient detection and edge information, our model simultaneously refines multilevel features of SOD and edge detection by overlapping a Cross Refinement Unit (CRU). And finally we used a U-Net to output the refined result. Extensive experiments conducted on five benchmark datasets demonstrate that our method can significantly improve the performance of low-contrast scenes, optimize the problem of partial missing and error detection of salient objects. Besides, our method achieved state-of-the-art performance in three different indicators.