Reviewerly/exHarmony huggingface dataset:
Dynamic CNN Accelerator Supporting Efficient Filter Generator with Kernel Enhancement and Online Channel Pruning

Deep neural network achieves exciting performance in several tasks with heavy storing and computing costs. Previous works adopt pruning-based methods to slim deep network. For traditional pruning, either the convolution kernel or the network inference is static, which cannot fully compress the model parameter and restrains their performance. In this paper, we propose an online pruning algorithm to support dynamic kernel generation and dynamic network inference at the same time. Two novel techniques including the filter generator and the importance-level based channel pruning are proposed. Moreover, we validate the success of the proposed method by the implementation on Ultra96-v2 FPGA. Compared with state-of-art static or dynamic pruning methods, our method can reduce the top-5 accuracy drop by nearly 50% for ResNet model on ImageNet at similar compressing level. It can also achieve better accuracy while up to 50% fewer weights are reduced to be saved on chip.
Digital Watermarking on Package Printing for Handheld Devices

Bar codes on package printing are limited by poor anti-destructive capability, poor anti-counterfeiting performance, large space occupied and long code-searching time. In this paper, a novel digital watermark, which is difficult to detect by human eyes but can be extracted by cameras, is introduced into print products. The proposed digital watermark can be embedded in package printing image, which is anti-destructive, difficult to counterfeit and easy to identify. Firstly, digital information such as commodity information, production date and anti-counterfeiting label is encoded to generate watermark blocks. And the watermark information is embedded by slightly tuning the corresponding pixel values in the device-independent CIE L*a*b color space. Then the geometric correction of extracted watermark is achieved using positioning blocks, and the product information is decoded and recovered. The experimental results indicate that the proposed embedded watermark has strong imperceptibility, strong robustness against conventional image processing and geometric conversion attacks such as JPEG compression, rotation and scaling. The embedded watermark is applicable for both information storage and copyright protection of package printing images.
openEuler: Advancing a Hardware and Software Application Ecosystem

openEuler is an open source operating system for digital infrastructure that can be widely deployed in servers, cloud computing, edge computing, and embedded and other devices in various forms. Its application scenarios cover IT, communications technology (CT), and operational technology. It aims to achieve a unified operating system to support multiple devices and one application development to cover many scenarios.
Exploratory State Representation Learning

Not having access to compact and meaningful representations is known to significantly increase the complexity of reinforcement learning (RL). For this reason, it can be useful to perform state representation learning (SRL) before tackling RL tasks. However, obtaining a good state representation can only be done if a large diversity of transitions is observed, which can require a difficult exploration, especially if the environment is initially reward-free. To solve the problems of exploration and SRL in parallel, we propose a new approach called XSRL (eXploratory State Representation Learning). On one hand, it jointly learns compact state representations and a state transition estimator which is used to remove unexploitable information from the representations. On the other hand, it continuously trains an inverse model, and adds to the prediction error of this model a k-step learning progress bonus to form the maximization objective of a discovery policy. This results in a policy that seeks complex transitions from which the trained models can effectively learn. Our experimental results show that the approach leads to efficient exploration in challenging environments with image observations, and to state representations that significantly accelerate learning in RL tasks.
Angles-Only Initial Orbit Determination via Multivariate Gaussian Process Regression

Vital for Space Situational Awareness, Initial Orbit Determination (IOD) may be used to initialize object tracking and associate observations with a tracked satellite. Classical IOD algorithms provide only a point solution and are sensitive to noisy measurements and to certain target-observer geometry. This work examines the ability of a Multivariate GPR (MV-GPR) to accurately perform IOD and quantify the associated uncertainty. Given perfect test inputs, MV-GPR performs comparably to a simpler multitask learning GPR algorithm and the classical Gauss–Gibbs IOD in terms of prediction accuracy. It significantly outperforms the multitask learning GPR algorithm in uncertainty quantification due to the direct handling of output dimension correlations. A moment-matching algorithm provides an analytic solution to the input noise problem under certain assumptions. The algorithm is adapted to the MV-GPR formulation and shown to be an effective tool to accurately quantify the added input uncertainty. This work shows that the MV-GPR can provide a viable solution with quantified uncertainty which is robust to observation noise and traditionally challenging orbit-observer geometries.
AA3DNet: Attention Augmented Real Time 3D Object Detection

In this work, we address the problem of 3D object detection from point cloud data in real time. For autonomous vehicles to work, it is very important for the perception component to detect the real world objects with both high accuracy and fast inference. We propose a novel neural network architecture along with the training and optimization details for detecting 3D objects using point cloud data. We present anchor design along with custom loss functions used in this work. A combination of spatial and channel wise attention module is used in this work. We use the Kitti 3D Bird’s Eye View dataset for benchmarking and validating our results. Our method surpasses previous state of the art in this domain both in terms of average precision and speed running at >30 FPS. Finally, we present the ablation study to demonstrate that the performance of our network is generalizable. This makes it a feasible option to be deployed in real time applications like self driving cars.
Influence of Contrastive Learning on Source Code Plagiarism Detection through Recursive Neural Networks

Plagiarism during programming assignments is a problem in academia. It hinders the ability of academic instructors to truly judge students’ performance and thus, prevents students from receiving adequate help from their instructors. In cases where the number of code submissions for a particular assignment is relatively small, the instructor can inspect each code submission to determine whether they are similar. But as the number of code submissions grows, it becomes difficult to detect similarities between them. Therefore, this induces the need for an automatic source code plagiarism detector. Previous studies showed that we could use the abstract syntax tree (AST) of a source code to get an accurate representation of the source code for neural network computations. Although a study even presented a recursive artificial neural network named Abstract Syntax Tree-based Neural Network (ASTNN) that could represent source codes into vector embeddings using their ASTs, it does not use contrastive learning paradigms, shown to increase the performance of Siamese networks in similarity detection tasks. Therefore, this paper presents an improved version of the ASTNN for code clone detection, where we modify the original model for contrastive learning. Experiments demonstrated that we outperform the original ASTNN model in code clone detection tasks, with a+5% improvement in the F1-score of our model. This study aims at improving the way we perform similarity detection tasks involving programming languages.
The tensor rank of semifields of order 16 and 81

We determine the tensor rank of all semifields of order 16 over F2 and of all semifields of order 81 over F3. Our results imply that some semifields of order 81 have lower multiplicative complexity than the finite field F81 over F3. We prove new results on the correspondence between linear codes and tensor rank, including a generalisation of a theorem of Brockett and Dobkin to arbitrary tensors, which makes the problem computationally feasible.
Multi-resolution transmission image registration based on “Terrace Compression Method” and normalized mutual information

Multispectral transmission images provide the possibility for the early diagnosis of breast cancer, and promote the study of family self-screening of breast tumors. However, the scattering characteristic of biological tissue leads to transmission images with fuzzy boundaries and poor contrast. And there are different offsets in the captured image sequences because of the instability of the human body such as breathing and slight jitter, which will affect the data precision. In view of the above problems, a multi-resolution image registration method combining “Terrace Compression Method” and normalized mutual information (NMI) is proposed, and the effectiveness of the method is verified by taking the transmission image of wavelength at 435 ​nm as an example. First of all, the gray-scale values of the selected boundary region of heterogeneity are sorted. According to the “Terrace Gradient” of gray-scale values, multiple gray-scale intervals are divided to enhance the gradient information of the image. Then, the image of the terraced shape of each gray-scale interval is extracted respectively, and the edge detection result is obtained by the Sobel operator. Finally, the Gaussian pyramid model is used for image down-sampling to achieve multi-resolution image registration by combining with NMI similarity measure. Compared with the other registration methods, the method proposed in this paper can detect the transformation relationship between images more accurately and effectively, and the similarity of the registered image with the reference image is also higher. The combination of the “Terrace Compression Method” and NMI effectively improves the registration accuracy of transmission images, and provides favorable conditions for achieving heterogeneity detection in multispectral transmission images.
Improving Personalized Search with Dual-Feedback Network

Personalized search improves the quality of search results by modeling historical user behavior. In recent years, many methods based on deep learning have greatly improved the performance of personalized search. However, most of the existing methods only focus on modeling positive user behavior signals, which leads to incomplete user interest modeling. At the same time, the user's search behavior hides much explicit or implicit feedback information. For example, clicking and staying for a certain period represents implicit positive feedback, and skipping behavior represents implicit negative feedback. Intuitively, this information can be utilized to construct a more complete and accurate user profile. In this paper, we propose a dual-feedback modeling framework, which integrates multi-granular user feedback information to model the user's current search intention. Specifically, we propose a feedback extraction network to refine the dual-feedback representation in multiple stages. For enhancing the user's real-time search quality, we design an additional dual-feedback feature gating module to capture the user's real-time feedback in the current session. We conducted a large number of experiments on two real-world datasets, and the experimental results show that our method can effectively improve the performance of personalized search.
Blockchain Technology and Waste Management: A Systematic Literature Review

Population growth and increased trash generation are expected to worsen living conditions in LCDs. However, waste management organizations have been hindered by a lack of stakeholder participation and coordination as well as obsolete disposal methods. By automating the collection and transportation of rubbish, current technology only aids to reducing human participation. People could be more engaged in waste management activities if they were remunerated for it. The enabling system should automatically record significant activities and respond appropriately. The blockchain technology could be the solution as it may help waste management by increasing public knowledge, transparency, and stakeholder confidence. This study looks at how the blockchain technology could improve waste management by increasing public awareness, transparency, and trust among stakeholders. By ensuring immutability using a cryptographically secure distributed ledger system, blockchain links people, processes, and technical developments. Through a systematic literature review on blockchain technology deployment in waste management, this research seeks to add to the content of previous studies and to enlighten the path for future studies. Furthermore, the study's findings will help addressing research gaps on using new technology to conserve the environment. They would also help authorities and politicians to raise awareness and involve stakeholders in social issues.
Machine Learning model for detection of Breast Cancer

Machine Learning(ML) as a sub-domain of Artificial Intelligence has been proved not less than a boon for the mankind. Machine Learning algorithms and techniques, when chosen wisely, enhances the overall output of the process by a significant margin. It has proven its worth in various sectors and finds application in almost every walk of life. Medical field is just another ground where ML plays a very vital role, especially in early detection of various disease with high accuracy. The goal of this paper is to demonstrate the implementation of Machine Learning algorithms to detect breast cancer in early stages with high accuracy to save the lives. Breast cancer is one of the most common type of cancer amongst women. Its spread among the community is a serious concern across the globe. Timely detection of the cases and due treatment are very important in this case to save the patients' life. More than 2.3 million women were diagnosed with breast cancer, out of which around 0.7 million died last year. Manual diagnosis of the disease is not very effective and usually early detection is almost impossible that leads to the death of the patients. The work presented here aims to classify the tumor diagnosed as benign or malignant, with the help of the 30 specific attributes taken from the dataset of a group of normal as well as patients that have breast cancer, by implementing multiple Machine Learning algorithms and selecting the classification model on the basis of highest attained accuracy.
Estimating Image Depth in the Comics Domain

Estimating the depth of comics images is challenging as such images a) are monocular; b) lack ground-truth depth annotations; c) differ across different artistic styles; d) are sparse and noisy. We thus, use an off-the-shelf unsupervised image to image translation method to translate the comics images to natural ones and then use an attention-guided monocular depth estimator to predict their depth. This lets us leverage the depth annotations of existing natural images to train the depth estimator. Furthermore, our model learns to distinguish between text and images in the comics panels to reduce text-based artefacts in the depth estimates. Our method consistently outperforms the existing state-of-the-art approaches across all metrics on both the DCM and eBDtheque images. Finally, we introduce a dataset to evaluate depth prediction on comics.
Stakeholder Identification Overview and Challenges in Requirements Engineering Prospective

Stakeholder identification (SI) illustrates a critical part of the requirements elicitation activity. It helps software analysts gather accurate system requirements to ensure high quality and avoid system failure. Stakeholder identification considered one of the earliest stages of project development. The main problem related to project success and failure is identifying the real stakeholders and selecting the right one. The lack of stakeholder identification methodologies and the shortage of previous research in identifying the stakeholders for different systems, encourage the research to propose a guideline framework, assist analysts in building the convenient methodology for identifying real stakeholders. Stakeholders are defined based on different projects and the various services they provide. This difference leads to the fact that a particular and systematic technique must be used to identify stakeholders based on the project&#x0027;s types. The exploration of stakeholder identification matters and the guideline framework is a baseline for future research in requirements engineering.
A Peep into the Future: Adversarial Future Encoding in Recommendation

Personalized recommendation often relies on user historical behaviors to provide items for users. It is intuitive that future information also contains essential messages as supplements to user historical behaviors. However, we cannot directly encode future information into models, since we are unable to get future information in online serving. In this work, we propose a novel adversarial future encoding (AFE) framework to make full use of informative future features in different types of recommendation models. Specifically, AFE contains a future-aware discriminator and a generator. The future-aware discriminator takes both common features and future features as inputs, working as a recommendation prophet to judge user-item pairs. In contrast, the generator is considered as a challenger, which generates items with only common features, aiming to confuse the future-aware prophet. The future-aware discriminator can inspire the generator (to be deployed online) to produce better results. We further conduct a multi-factor optimization to enable a fast and stable model convergence via the direct learning and knowledge distillation losses. Moreover, we have adopted AFE on both a list-wise RL-based ranking model and a point-wise ranking model to verify its universality. In experiments, we conduct sufficient evaluations on two large-scale datasets, achieving significant improvements on both offline and online evaluations. Currently, we have deployed AFE on a real-world system, affecting millions of users. The source code is in https://github.com/modriczhang/AFE.
Recaptured image forensics based on local ternary count of high order prediction error

With the advancement in image display technology and the widespread use of various image acquisition devices, recapturing high-quality images from high-fidelity LCD (liquid crystal display) screens becomes very convenient and easy. These recaptured images pose serious threats on current image forensic technology and intelligent recognition systems. In order to solve these security problems, we propose a recaptured image forensics method based on local ternary count (LTC) of high order prediction error. Specifically, given an image, the second-order difference maps on different directions are calculated in HSV color space for the original image and its average pooled version. Then, we calculate the prediction error maps from these second-order difference maps through a linear predictor. Finally, the normalized LTC histograms of all the downsampled versions of the obtained prediction error maps are calculated as features for training and testing. Single database experiments demonstrate that our proposed method performs very similar with the state-of-the-art traditional method on the most difficult-to-detect ICL-COMMSP database with obvious less execution-time and lower feature dimension. Meanwhile, it performs very close to the existing well-performed deep learning based method on all the databases, which verifies the effectiveness of our proposed features. Besides, mixed database experiments verify the superiority on generalization ability of our proposed method. Moreover, it is robust to JPEG compression.
Dimensional Learning Strategy-Based Grey Wolf Optimizer for Solving the Global Optimization Problem

Grey wolf optimizer (GWO) is an up-to-date nature-inspired optimization algorithm which has been used for solving many of the real-world applications since it was proposed. In the standard GWO, individuals are guided by the three dominant wolves alpha, beta, and delta in the leading hierarchy of the swarm. These three wolves provide their information about the potential locations of the global optimum in the search space. This learning mechanism is easy to implement. However, when the three wolves are in conflicting directions, an individual may not obtain better knowledge to update its position. To improve the utilization of the population knowledge, in this paper, we proposed a grey wolf optimizer based on the dimensional learning strategy (DLGWO). In the DLGWO, the three dominant wolves construct an exemplar wolf through the dimensional learning strategy (DLS) to guide the grey wolves in the swarm. Thereafter, to reinforce the exploration ability of the algorithm, the Levy flight is also utilized in the proposed method. 23 classic benchmark functions and engineering problems are used to test the effectiveness of the proposed method against the standard GWO, variants of the GWO, and other metaheuristic algorithms. The experimental results show that the proposed DLGWO has good performance in solving the global optimization problems.
Research on Trash Classification based on Artificial Intelligence and Sensor

This topic combines artificial intelligence technology and sensor technology to design intelligent trash cans in application to the environmental protection classification system. In this system, the CNN network is used to recognize faces, YOLOv3 is used to recognize objects, and sensor technology is used to sense the human body temperature and to monitor the capacity of the trash can for switch control of its cover. In this case, there would be complete zero contact during the whole trash dumping process and it is instrumental in avoiding the spread of the epidemic. This topic provides a new method and genuine case for intelligent environmental protection.
Transductive Weakly-Supervised Player Detection using Soccer Broadcast Videos

Player detection lays the foundation for many applications in the field of sports analytics including player recognition, player tracking, and activity detection. In this work, we study player detection in continuous long shot broadcast videos. Broadcast match videos are easy to obtain, and detection on these videos is much more challenging. We propose a transductive approach for player detection that treats it as a domain adaptation problem. We show that instance-level domain labels are significant for sufficient adaptation in the case of soccer broadcast videos. An efficient multi-model greedy labelling scheme based on visual features is proposed to annotate domain labels on bounding box predictions made by our inductive model. We use reliable instances from the inductive model inferences to train a transductive copy of the model. We create and release a fully annotated player detection dataset comprising soccer broadcast videos from the FIFA 2018 World Cup matches to evaluate our method. Our method shows significant improvements in player detection to the baseline and existing state-of-the-art methods on our dataset. We show, on average, a 16 point improvement in mAP for soccer broadcast videos by annotating domain labels for around a 100 samples per video.