Reviewerly/exHarmony huggingface dataset:
E-Voting System Using Blockchain and Homomorphic Encryption

Only about half of the elections are deemed to be free and fair, according to a study. Electoral fraud not just mutilates the nature of portrayal, but also has an impact on political, social, and monetary outcomes. Homomorphic encryption and blockchain technologies can be utilized to secure free and fair elections. In this paper, we aim to allow only the eligible citizens to vote by automatically checking their eligibility status from a federally approved application and then securing the voter’s data using homomorphic encryption rather than encrypting the vote cast by the voter. By doing this, statistical analysis can be performed on the data which results in a unique set of insights that may otherwise remain unknown. We have developed a system using the ganache as the local blockchain and the voter data and vote cast is stored in the blockchain.
Predicting change propagation between code clone instances by graph-based deep learning

Code clones widely exist in open-source and industrial software projects and are still recognized as a threat to software maintenance due to the additional effort required for the simultaneous maintenance of multiple clone instances and potential defects caused by inconsistent changes in clone instances. To alleviate the threat, it is essential to accurately and efficiently make the decisions of change propagation between clone instances. Based on an exploratory study on clone change propagation with five famous open-source projects, we find that a clone class can have both propagation-required changes and propagation-free changes and thus fine-grained change propagation decision is required. Based on the findings, we propose a graph-based deep learning approach to predict the change propagation requirements of clone instances. We develop a graph representation, named Fused Clone Program Dependency Graph (FC-PDG), to capture the textual and structural code contexts of a pair of clone instances along with the changes on one of them. Based on the representation, we design a deep learning model that uses a Relational Graph Convolutional Network (R-GCN) to predict the change propagation requirement. We evaluate the approach with a dataset constructed based on 51 open-source Java projects, which includes 24,672 pairs of matched changes and 38,041 non-matched changes. The results show that the approach achieves high precision (83.1%), recall (81.2%), and F1-score (82.1%). Our further evaluation with three other open-source projects confirms the generality of the trained clone change propagation prediction model.
AttVAE: A Novel Anomaly Detection Framework for Multivariate Time Series

Anomaly detection plays a significant role in building a secure and reliable system. Multivariate time series contain important system information, such as system load and time delay. Temporal-dependent methods like RNNs are usually used for anomaly detection on time series. However, inner correlations of time series have shown great potentials in anomaly detection than temporal-dependent methods. In this paper, we propose a novel anomaly detection framework, namely AttVAE, which utilizes attention mechanisms on multivariate time series. This attention mechanism exploits the inner correlations of different time-series dimensions to discover the robust latent variables. Extensive experiments are conducted on two real-world datasets, and results show that AttVAE achieves the best F1-score at 0.79 and 0.97 compared with existing traditional and sophisticated methods. In addition, the missing and false alarm rate by AttVAE is reduced by $$20\%$$ on average compared with the state-of-the-art models.
Label Guidance based Object Locating in Virtual Reality

Object locating in virtual reality (VR) has been widely used in many VR applications, such as virtual assembly, virtual repair, virtual remote coaching. However, when there are a large number of objects in the virtual environment(VE), the user cannot locate the target object efficiently and comfortably. In this paper, we propose a label guidance based object locating method for locating the target object efficiently in VR. Firstly, we introduce the label guidance based object locating pipeline to improve the efficiency of the object locating. It arranges the labels of all objects on the same screen, lets the user select the target labels first, and then uses the flying labels to guide the user to the target object. Then we summarize five principles for constructing the label layout for object locating and propose a two-level hierarchical sorted and orientated label layout based on the five principles for the user to select the candidate labels efficiently and comfortably. After that, we propose the view and gaze based label guidance method for guiding the user to locate the target object based on the selected candidate labels. It generates specific flying trajectories for candidate labels, updates the flying speed of candidate labels, keeps valid candidate labels, and removes the invalid candidate labels in real time during object locating with the guidance of the candidate labels. Compared with the traditional method, the user study results show that our method significantly improves efficiency and reduces task load for object locating.
KSRG:Knowledge-Aware Sequential Recommendation with Graph Neural Networks

In the actual recommendation scenario, user’s next action is affected by their long-term intrinsic preference and short-term temporal demands. Therefore, how to model user’s long-term and short-term preferences has become the key to improving the accuracy of the recommendation algorithm. However, the current mainstream Markov Chains and Recurrent Neural Networks can not well capture the dynamic interests of users. In view of this, we propose Knowledge-Aware Sequential Recommendation with Graph Neural Networks (KSRG), a novel sequential recommendation framework that takes both semantic information and temporal dynamics into consideration. Specifically, Graph Neural Network (GNN) is utilized to effectively capture the global graph structure information, which reflects user’s long-term interests. In order to capture user short-term preferences, we use relational intensity and Fourier-based temporal decay function to estimate the impact of historical interaction on the target item. Finally, the combined long-term and short-term preferences of users will help to generate a recommendation list. Experimental studies on real-world datasets demonstrate that our method outperforms many strong baselines.
Should All Proposals Be Treated Equally in Object Detection?

The complexity-precision trade-off of an object detector is a critical problem for resource constrained vision tasks. Previous works have emphasized detectors implemented with efficient backbones. The impact on this trade-off of proposal processing by the detection head is investigated in this work. It is hypothesized that improved detection efficiency requires a paradigm shift, towards the unequal processing of proposals, assigning more computation to good proposals than poor ones. This results in better utilization of available computational budget, enabling higher accuracy for the same FLOPS. We formulate this as a learning problem where the goal is to assign operators to proposals, in the detection head, so that the total computational cost is constrained and the precision is maximized. The key finding is that such matching can be learned as a function that maps each proposal embedding into a one-hot code over operators. While this function induces a complex dynamic network routing mechanism, it can be implemented by a simple MLP and learned end-to-end with off-the-shelf object detectors. This dynamic proposal processing (DPP) is shown to outperform state-of-the-art end-to-end object detectors (DETR, Sparse R-CNN) by a clear margin for a given computational complexity. Source code is at https://github.com/liyunsheng13/dpp .
K-Indistinguishable Data Access for Encrypted Key-Value Stores

Key-value store is adopted by many applications due to its high performance in processing big data workloads. Recent research on secure cloud storage has shown that even if the data is encrypted, attackers can learn the sensitive information of data by launching access pattern attacks such as frequency analysis. For this issue, some schemes have been proposed to protect encrypted key-value stores against access pattern attacks. However, existing solutions protect access pattern information at the cost of large storage and bandwidth overhead, which is unacceptable for large-scale key-value stores. In this paper, we devise a K-indistinguishable frequency smoothing scheme for encrypted key-value stores, which can resist access pattern attacks launched by passive persistent adversaries with minimal storage and bandwidth overhead. Then, we propose a dynamic K-indistinguishable frequency smoothing scheme. It can efficiently adapt to the changes in access distribution while ensuring the K-indistinguishable security level and bandwidth efficiency. Finally, we formally analyze the security of our design. Extensive experiments demonstrate that our design achieves high throughput while minimizing storage and bandwidth overhead.
A Self-supervised Graph Autoencoder with Barlow Twins

Self-supervised graph learning has attracted significant interest, especially graph contrastive learning. However, graph contrastive learning heavily relies on the choices of negative samples and the elaborate designs of architectures. Motivated by Barlow Twins, a method in computer vision, we propose a novel graph autoencoder named Core Barlow Graph Auto-Encoder(CBGAE) which does not rely on any special techniques, like predictor networks or momentum encoders. Meanwhile, we set a core view to make maximize agreement between the learned feature information. In contrast to the most existing graph contrastive learning models, it is negative-sample-free.
Recent Approaches on Aspect Based Sentiment Analysis incorporating Deep Learning techniques

"What are the opinions of other people?" is the main factor that sparkles in mind when a decision has to be made let it be for business, life, or service. The surge in people consuming almost everything online and writing comments or reviews has increased the provision for making decisions and analyzing sentiments. The hidden insights of data that user generates are valuable resources to decision-makers and to Government for improving their ideas. The key challenge is to predict accurately the sentiment orientation of review sentences, to classify the expressed mindset or feelings in different ways. A text can be classified based on the aspects of that text like design, quality, value, nature, etc. If such aspect-based categorization of reviews is made, it would help the opinion seekers for better decision making. Recently, deep learning techniques have emerged as an effective means for analysing sentiments based on aspects overcoming the contemporary approaches. We have created a taxonomy for DL approaches in this study. The paper also discusses various deep learning techniques that can create an impact on aspect-based sentiment analysis. This study also analyses the performance of various DL techniques in recent years. It can serve as the foundation for new researchers on ABSA strategies
Image Captioning According to User's Intention and Style

Exciting image captioning models are usually individuality-agnostic, and they cannot generate individual description according to the user's intention and language style. To address above problem, this paper proposes a personalized image captioning model by using fine-grained scene control graph and the style control factor to respectively represent user's intention and speaking style. More specifically, we first construct a scene control graph that consists of the object, its attribute and the relationship between it and other objects, and employe the graph flow attention to control the focus of the description. Secondly, propose a style generator to extract user's style pattern from his profile generated based on his gender, age, education level and other information. Finally, enter style factor in the style control module before generating sentences, which enables the language decoder to output a personalized caption for image. The experimental results on MSCOCO and FlickrStyle datasets show that the proposed method can generate personalized and diverse image caption sentences.
Technical Debt Forecasting from Source Code Using Temporal Convolutional Networks

Technical Debt describes a deficit in terms of functions, architecture, or integration, which must subsequently be filled to allow a homogeneous functioning of the product itself or its dependencies. It is predominantly caused by pursuing rapid development versus a correct development procedure. Technical Debt is therefore the result of a non-optimal software development process, which if not managed promptly can compromise the quality of the software. This study presents a technical debt trend forecasting approach based on the use of a temporal convolutional network and a broad set of product and process metrics, collected commit by commit. The model was tested on the entire evolutionary history of two open-source Java software systems available on Github: Commons-codec and Commons-net. The results are excellent and demonstrate the effectiveness of the model, which could be a pioneer in developing a TD reimbursement strategy recommendation tool that can predict when a software product might become too difficult to maintain.
Human Resource Scheduling Control Method Based on Deep Reinforcement Learning

With the increasing importance of human resource management in project process management, human resource scheduling and control methods also need to keep pace with the times. Continuing to use the traditional human resource scheduling control method will waste a lot of potential value of human resources. Therefore, a human resource scheduling control method based on deep reinforcement learning is proposed. By constructing the human resource scheduling control model, the minimum human cost expenditure under various constraints is obtained; Design the deep reinforcement learning algorithm, and design the scheduling algorithm for specific scheduling objectives for the human resources scheduling control center; Create a model-based human resource scheduling management and control evaluation system, and improve the relationship between the comprehensive evaluation value and the advantages and disadvantages of multi project human resource scheduling. Experiments show that this human resource scheduling control method can guide different problems to allocate goals. Considering the time factor, the optimal solution of human resource scheduling and control can be obtained.
Efficient High-Resolution Human Pose Estimation

As a fundamental task of computer vision, human pose estimation (HPE) has achieved significant improvement with the rise of deep learning. However, many existing methods focus too much on model accuracy, leading to high complexity models, which are hard to be deployed especially in computation-limited devices. This paper proposes a lightweight HPE network named efficient high-resolution human pose estimation (EHR-HPE). EHR-HPE network first adopts the high-resolution pattern to acquire accurate heatmaps; then an efficient shuffle block is proposed to reduce the model complexity and boost model performance; finally, the efficient dense connections are designed to further improve model accuracy. Extensive experiment results on two benchmark datasets show that the proposed EHR-HPE network achieves a great tradeoff between accuracy and model complexity. EHR-HPE network can achieve 70.1 mean average precision scores on Common Objects in Context (COCO) test-dev dataset with only 1.7M parameters and 0.91 GFLOPs.
One Size Does NOT Fit All: Data-Adaptive Adversarial Training

Adversarial robustness is critical for deep learning models to defend against adversarial attacks. Although adversarial training is considered to be one of the most effective ways to improve the model’s adversarial robustness, it usually yields models with lower natural accuracy. In this paper, we argue that, for the attackable examples, traditional adversarial training which utilizes a fixed size perturbation ball can create adversarial examples that deviate far away from the original class towards the target class. Thus, the model’s performance on the natural target class will drop drastically, which leads to the decline of natural accuracy. To this end, we propose the Data-Adaptive Adversarial Training (DAAT) which adaptively adjusts the perturbation ball to a proper size for each of the natural examples with the help of a natural trained calibration network. Besides, a dynamic training strategy empowers the DAAT models with impressive robustness while retaining remarkable natural accuracy. Based on a toy example, we theoretically prove the recession of the natural accuracy caused by adversarial training and show how the data-adaptive perturbation size helps the model resist it. Finally, empirical experiments on benchmark datasets demonstrate the significant improvement of DAAT models on natural accuracy compared with strong baselines.
Virtual Canvas for Interactive Learning using OpenCV

The purpose of this study is to discuss the usage of computer vision in educational applications. In recent years, air writing has become one of the most challenging and exciting research areas in image processing and pattern recognition. The project employs object tracking techniques to construct a motion-to-text converter that might be used as software in the field of education to allow students and teachers to write in the air. The project generates letters on the screen by utilizing computer vision to trace the path of a finger or an object. The resulting text can be utilized for a variety of things, including solving graph-related challenges. Questions on logical reasoning (Mathematics), to write formulas or execute derivations, Kindergarten kids are being taught the alphabet, to replace chalk and board (dustless classroom) in offline classes, etc. For the deaf, it will be a strong way of communication.
Volcano, Earthquake, and Tsunami Hazards of the Cascadia Subduction Zone

Subduction zones produce some of Earth’s most devastating geological events. Recent eruptions of Mount St. Helens and great earthquakes and tsunamis in Japan and Sumatra provide stark examples of the destructive power of subduction-related hazards. In the Cascadia subduction zone, large earthquakes, tsunamis, and volcanic eruptions have occurred in the past and geologic records imply that these events will occur in the future. As the population and infrastructure increase in the region, resilience to these natural hazards requires a detailed scientific understanding of the geologic forces and processes involved, combined with a society motivated to mitigate risks.
A Dense Prediction ViT Network for Single Image Bokeh Rendering

Rendering bokeh effects has become a research hotspot in the field of computational photography. Its essence is to focus on foreground area of interest, and blur other background areas for vision aesthetic requirements. Witness of the great success of vision transformer on dense prediction tasks, in this paper, we further expand the ability of transformer on new task and propose a dense prediction ViT structure for single bokeh image rendering. We leverage vision transformers as the backbone networks and operates on “bag-of-words" representations of image at high levels. Image-like feature representations of different resolutions are aggregated to obtain the final dense prediction. The proposed network has been compared with several state-of-the-art methods on a public large-scale bokeh dataset- the “EBB!" Dataset. The experiment results demonstrate that the proposed network can achieve new state-of-the-art performances on SSIM, LPIPS and MOS criterions. Its predicted bokeh effects are more in line with popular perception. Related source codes and pre-trained models of the proposed model will be available soon on https://github.com/zfw-cv/BEViT .
Definition of the Optimal Generation Mix in Decarbonized Power System

The development of the environmental tendencies in the previous decade led to the alteration of the way in which the perspective of the power systems is seen, where the largest number of adaptations was made to the sector of energy production, in which the role that was occupied by the thermal plants was taken over by the wind and solar plants. However, this change was met with a dose of scepticism, where the variability of the generation power of these plants and the oscillations in their daily production diagram were commonly listed as the arguments against the massive integration of these sources. What was neglected, nonetheless, is the fact that the daily production diagram depends on the type of the source, so the diagrams of the wind and solar plants, for instance, are not identical. Hence, the daily minimums and maximums of generation power of these units would not occur simultaneously, so the unified diagram of both plants would show less oscillations than their individual diagrams. The level of the variations’ reduction would depend on the ratio of the installed capacities of the plants. Therefore, the paper will contain analysis of the test system supplied from the wind and solar plants, with two types of solar panels (monofacial and bifacial) considered. The total installed capacity of these sources will be kept constant, but their share in this amount will be varied in order to define the ratio of the individual installed capacities for which the variations of the total production power on a yearly level is minimal. As the indicator of the variations’ reduction, the minimal mean square deviation of the total hourly production powers from their yearly average will be selected.
Transformed Structured Sparsity With Smoothness for Hyperspectral Image Deblurring

Due to the influence of imaging equipment or environment, a hyperspectral image (HSI) is often unavoidably blurred in the acquisition process, which results in the spatial and spectral information loss of the HSI. The existing HSI deblurring methods can address the problem, however, they neglect the intrinsic structured sparsity and thus reduce the deblurring performance. Aiming at this issue, we propose a new HSI deblurring method based on transformed structured sparsity with smoothness (TSSS). We first use the local piecewise smoothness to obtain the spatial and spectral sparsity of an HSI in the gradient domain. Then, to capture the refined sparsity, we exploit the transform sparsity learning framework to encode the structured sparsity self-adaptively in transform space, where the sparse structures of transformed operators can be depicted by Laplacian scale mixture (LSM), i.e., the sparsity can be expressed as the product of a hidden positive scalar multiplier and a Laplacian vector. The visual and quantitative comparisons of experimental results on three HSI datasets indicate that our method outperforms state-of-the-arts.