Reviewerly/exHarmony huggingface dataset:
Foggy Weather Traffic Detection System Based on UAV Gimbal Camera

To solve the problem that the traditional road traffic monitoring system works poorly under foggy conditions again, an unmanned aircraft traffic detection system is designed. In this paper, the structure and control system of the gimbal is designed based on the stable flying UAV, and the defogging image algorithm is improved. Through the molding process for carbon fiber reinforced epoxy resin composite material to manufacture gimbal-related parts, the structural strength is enhanced, and the degree of structural force deformation is reduced, thus ensuring the accuracy of the control system. By using three-loop control and PID controller to adjust the three-axis gimbal, the gimbal responds accurately and quickly to the body shake and enhances the stability of the camera pose so that clearer images can be obtained. Based on the traditional algorithm, the influence of environmental factors is reduced by combining median filter noise reduction processing, defogging operation, and image enhancement, which improves the ability of the algorithm to cope with complex traffic environments while improving the image recognizability. The UAV traffic monitoring system can accomplish basic traffic detection tasks under general foggy conditions based on the above research.
Reducing Runtime Overhead via Use-Based Migration in Neutral Atom Quantum Architectures

Neutral atoms are a promising choice for scalable quantum computing architectures. Features such as long distance interactions and native multiqubit gates offer reductions in communication costs and operation count. However, the trapped atoms used as qubits can be lost over the course of computation and due to adverse environmental factors. The value of a lost computation qubit cannot be recovered and requires the reloading of the array and rerunning of the computation, greatly increasing the number of runs of a circuit. Software mitigation strategies exist [1] but exhaust the original mapped locations of the circuit slowly and create more spread out clusters of qubits across the architecture decreasing the probability of success. We increase flexibility by developing strategies that find all reachable qubits, rather only adjacent hardware qubits. Second, we divide the architecture into separate sections, and run the circuit in each section, free of lost atoms. Provided the architecture is large enough, this resets the circuit without having to reload the entire architecture. This increases the number of effective shots before reloading by a factor of two for a circuit that utilizes 30% of the architecture. We also explore using these sections to parallelize execution of circuits, reducing the overall runtime by a total 50% for 30 qubit circuit. These techniques contribute to a dynamic new set of strategies to combat the detrimental effects of lost computational space.
Two dimensional successive transformations in computer graphics: Mathematical and matrix notation

Computer graphics has vital role to the advancement of devices and technology. In computer graphics, transformations are used to repositioning, changing orientation, alteration of 2D object, mirror image and slide over. There are some 2D transformation operations which are translation, rotation, scaling, reflection, shearing and some composite transformation along with successive transformation. This paper discussed a comprehensive overview of composition of transformations or successive transformations. Moreover, the mathematical and matrix representation of each successive transformation operations are also mentioned. Mathematical and matrix notation can be helpful to give the accurate result to computer scientists in graphic evaluation through modification.
The DecCert PKI: A Solution to Decentralized Identity Attestation and Zooko’s Triangle

We propose DecCert, a decentralized public key infrastructure designed as a smart contract that solves the problem of identity attestation on public blockchains. Our system allows an individual to bind an identity to a public blockchain address. Once a claim of identity is made by an individual, other users can choose to verify the attested identity based on the evidence presented by an identity claim maker by staking cryptocurrency in the DecCert smart contract. Increasing levels of trust are naturally built based upon the amount staked and the duration the collateral is staked for. This mechanism replaces the usual utilization of digital signatures in a traditional hierarchical certificate authority model or the web of trust model to form a publicly verifiable decentralized stake of trust model. We also present a novel solution to the certificate revocation problem and implement our solution on the Ethereum blockchain. Further, we show that our design solves Zooko’s triangle as defined for public key infrastructure deployments.
RFN: A Random-Feature Based Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Spaces

In supervised learning using kernel methods, we often encounter a large-scale finite-sum minimization over a reproducing kernel Hilbert space (RKHS). Large-scale finite-sum problems can be solved using efficient variants of Newton method, where the Hessian is approximated via sub-samples of data. In RKHS, however, the dependence of the penalty function to kernel makes standard sub-sampling approaches inapplicable, since the gram matrix is not readily available in a low-rank form. In this paper, we observe that for this class of problems, one can naturally use kernel approximation to speed up the Newton method. Focusing on randomized features for kernel approximation, we provide a novel second-order algorithm that enjoys local superlinear convergence and global linear convergence (with high probability). We derive the theoretical lower bound for the number of random features required for the approximated Hessian to be close to the true Hessian in the norm sense. Our numerical experiments on real-world data verify the efficiency of our method compared to several benchmarks.
Rethinking Mobile Interaction Design Within Service

Along with the explosion of mobile access across the globe, service experience has evolved. Now there is a myriad of service settings in which mobile interactive artifacts are used in providing service, ranging from express, shopping to healthcare, and more. Those interactive products in service consequently not only enable to interact with users but also aim to offer high-quality service. In this paper, we find mobile interaction design evolved by taking advantage of service design. To better understand the difference and competence of mobile applications within service, we compare the independence tool mobile applications and the service embedded mobile applications in four paradigms: communication actors, design objective, value, process. There is an obvious feature that from “linear analysis” to “open system analysis”. Then summarizing the benefits of rethinking from a service perspective: sequence thinking of content rhythm; a holistic lens in solving the problem; multi-role stakeholders’ co-creation. Based on service measuring methods, we deduce the evaluation dimensions content of service embedded mobile applications. Finally, To validate the evaluation scheme, we conducted a pilot study in mobile courier applications by collecting and classifying service functions to propose mobile courier applications design constraints from service dimension view. In this way, we try to give a contribution to the development of service-embedded mobile interaction design.
Feature Interactive Convolutional Network with Structure-Aware Information for Knowledge Graph Embedding

Knowledge graph link prediction is an active research topic in knowledge base completion, that is, knowledge graph embedding. Solve the problem of incompleteness of the knowledge graph by realizing the low-dimensional embedding of entities and relations. From the beginning of the shallow and fast addition models such as TransE, TransH, and TransD, to the later DistMult, ComplEx multiplication models, and the recent ConvE based on convolutional networks, they are constantly improving. ConvE uses embedded two-dimensional convolution and multi-layer nonlinear features to model the knowledge graph. The model has achieved good results and can be extended to a larger knowledge graph. However, knowledge graph as a kind of graph structure data, ConvE does not make effective use of this. The recent Graph Convolutional Network (GCN) provides a way to learn the embedding of graph nodes by using the connectivity structure of the graph. In this article, we propose a feature interaction convolutional network with structure-aware information (SAFI), SAFI is composed of the encoder of the weighted graph convolutional network and the decoder of the convolutional network. WGCN uses the node structure, node attributes, and edge relation types of the knowledge graph, and by adding learnable weights, it is used to adjust the amount of information from neighbor nodes during local aggregation, so as to achieve more accurate graph node embedding. The decoder is based on ConvE by introducing three operations: random permutation, chequer reshaping, and circular convolution, which increases the feature interaction capabilities of the model. We have proved the feasibility and effectiveness of SAFI on the benchmark FB15K-237 and WN18RR datasets, and it has about 13% improvement over ConvE on Hits@1, Hits@10, MR and MRR.
The Ψ−asymptotic equivalence of the Lyapunov matrix differential equations with modified argument

Using the notion of strict h −contraction, existence results for Ψ−asymptotic equivalence of two pairs of (Lyapunov) matrix differential equations with modified argument are given.
Research on Intelligent Deformation Monitoring Data Prediction through Particle Swarm Optimization and Support Vector Regression Model

In this paper, particle swarm optimization (PSO) algorithm is used to optimize the parameters of kernel function in support vector regression model (SVR) to analyze and predict deformation monitoring data. The experimental results show that, compared with the results of BP neural network prediction, this method has higher prediction accuracy, better global optimization and higher operation efficiency, which is an effective method for deformation monitoring data prediction.
Fast Out-of-Band Data Integrity Monitor to Mitigate Memory Corruption Attacks

Memory corruption is a root cause of software attacks. Existing defense mechanisms (e.g., DEP, ASLR, CFI, CPI/CPS, and DFI) either offer limited security guarantees or incur high performance overhead. In this paper, we designed and developed a fast out-of-band (OOB) integrity monitor dubbed FastDIM to protect both applications and kernels against memory corruption attacks with less overhead. With FastDIM, a program in question is statically hardened by a compiler module. After that, the integrity of sensitive program data such as control-flow transfers (e.g., code pointers) and security relevant non-control data (e.g., encryption keys) are automatically protected by a monitor at run time. The key differences between FastDIM and related work are in the following aspects: 1) FastDIM offers an OOB monitor that protects the programs independently rather than letting the protected programs verify themselves using inlined reference monitor (IRM); 2) FastDIM extends the concept of shadow stacks originally proposed in CFI to protect not only return addresses but also other sensitive data such as function pointers, vtable pointers, and user-annotated sensitive non-control data. Thus, the protection of FastDIM is beyond control-flow data; 3) FastDIM provides a fast communication mechanism between programs and the monitor, so that the integrity checks are performed efficiently without context switch; and 4) for a better scalability and compatibility, FastDIM does not rely on LTO and Cross-DSO to support applications with dynamically linked libraries. We implemented a Kernel version and a TrustZone version of FastDIM to protect both user programs and Linux/Android kernels. The evaluation results show that the average overhead of FastDIM is 4.4% on SPEC CPU2017 C/C++ benchmarks and around 3% on AnTuTu benchmarks.
A Study on the Factors Affecting the Intention to Adopt of Blockchain-Based Identity Certification Services in the Defense Sector

As an alternative means of certification due to the abolition of public certificates in 2020, interest in certification services that can verify the identity of the distributed environment with blockchain technology, a key technology of the fourth industrial revolution, is increasing. Although various services are provided due to active activities in the private sector, the defense sector is insufficiently reviewing technology, so it is time to study factors that affect the acceptance of blockchain-based identity certificate services. This study considered and presented a research model for integrated technology acceptance models to demonstrate the factors of blockchain-based identification service system characteristics and service characteristics on acceptance intentions for the defense sector. Through this research model, it is meaningful to verify the research model and analyze acceptance factors to activate blockchain-based identification services in the defense field.
A Training-Based Blind Underwater Image Quality Evaluation Metric

Due to the unique image formation principle and the limitations of imaging devices, underwater images usually suffer from low contrast, color degradation, and blurring effects, which seriously hinder the interpretation of the image content. Additionally, several underwater image enhancement and restoration (UIER) algorithms have been proposed to improve the quality of underwater images. However, their performances vary greatly under different underwater scenarios. Therefore, establishing an effective quality evaluation metric plays an irreplaceable role in assessing the underwater image quality as well as evaluating the performances of UIER algorithms. In this paper, we propose a novel training-based blind underwater image quality elevation metric. Technically, the proposed metric extracts and fuses three groups of features covering naturalness, color, and contrast. Moreover, observing that the severity of color cast is highly related to the chroma of the image, we develop an underwater color factor feature to accurately estimate the severity of color distortion. Experimental results demonstrate the superior performance of the proposed method and its ability to evaluate the performance of UIER algorithms.
Interactive Object Segmentation with Inside-Outside Guidance

This work explores how to harvest precise object segmentation masks while minimizing the human interaction cost. To achieve this, we propose a simple yet effective interaction scheme, named Inside-Outside Guidance (IOG). Concretely, we leverage an inside point that is clicked near the object center and two outside points at the symmetrical corner locations (top-left and bottom-right or top-right and bottom-left) of an almost-tight bounding box that encloses the target object. The interaction results in a total of one foreground click and four background clicks for segmentation. The advantages of our IOG are four-fold: 1) the two outside points can help remove distractions from other objects or background; 2) the inside point can help eliminate the unrelated regions inside the bounding box; 3) the inside and outside points are easily identified, reducing the confusion raised by the state-of-the-art DEXTR [1] in labeling some extreme samples; 4) it naturally supports additional click annotations for further correction. Despite its simplicity, our IOG not only achieves state-of-the-art performance on several popular benchmarks such as GrabCut [2], PASCAL [3] and MS COCO [4], but also demonstrates strong generalization capability across different domains such as street scenes (Cityscapes [5]), aerial imagery (Rooftop [6] and Agriculture-Vision [7]) and medical images (ssTEM [8]). Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shiyinzhang/Inside-Outside-Guidance.</uri>
Video Deep Learning Classification for Autonomous Vehicle Navigation

Video action classification and vehicle navigation are essential capabilities for autonomous vehicles. Recurrent Neural Networks (RNNs) are critical for predicting the action from prior video frames as well as inferring/classifying the motion/action in the surroundings, be it structured highways or open terrains. Coupled with pre-processing the sequence of video frames using a multitude of methods, from Deep ConvNets’ transfer learning, to Discrete-Fourier Transform (DFT), RNN can become more powerful for these applications. In this work, we identify the building blocks of an effective video-navigation system, and then show an example of its performance on example action datasets to illustrate its potential performance.
Magical-Decomposition: Winning Both Adversarial Robustness and Efficiency on Hardware

Model compression is one of the most preferred techniques for efficiently deploying deep neural networks (DNNs) on resource- constrained Internet of Things (IoT) platforms. However, the simply compressed model is often vulnerable to adversarial attacks, leading to a conflict between robustness and efficiency, especially for IoT devices exposed to complex real-world scenarios. We, for the first time, address this problem by developing a novel framework dubbed Magical-Decomposition to simultaneously enhance both robustness and efficiency for hardware. By leveraging a hardware-friendly model compression method called singular value decomposition, the defending algorithm can be supported by most of the existing DNN hardware accelerators. To step further, by using a recently developed DNN interpretation tool, the underlying scheme of how the adversarial accuracy can be increased in the compressed model is highlighted clearly. Ablation studies and extensive experiments under various attacks/models/datasets consistently validate the effectiveness and scalability of the proposed framework.
Simulated Workbench Design for Characterisation and Selection of Appropriate Outlier Ensemble Algorithm

Outlier ensembles can enhance the performance of outlier detection algorithms. However, identification of an appropriate outlier ensemble algorithm for the given application is a difficult task. In order to achieve this function, the performance features of outlier ensembles with gathered data types containing a certain set of outliers have been characterised and their ensemble performance analysed in this paper. The proposed methodology suggests comparing data and anomalous features of a given real time application to the data set available in the proposed workbench. Users can use the results of experiments conducted using the workbench to identify highly efficient algorithms for their application without undertaking further analysis once matching data is located in the workbench. Therefore, this research paper act as a recommendation system that enables data scientists from various fields to pick an effective outlier ensemble for their application.
An Efficient Approach for Predicting the state of Financial Inclusion by Addressing Class Imbalance using the Oversampling Technique

The development of any nation can be improved by addressing financial inclusion issues. In many nations, there are challenges in getting people financially included in the banking system. As an example, Africa faces enormous challenges that affect development and livelihood due to poor financial inclusion reported in the Zindi financial inclusion dataset. The literature revealed that various machine learning techniques have been utilized to predict the state of financial inclusion but the performance of those approaches needs to be improved. In this paper, we have proposed an efficient approach for predicting the state of financial inclusion by addressing class imbalance issue in the Zindi dataset. So, to handle the issue oversampling has been done using Synthetic Minority Oversampling Technique. Further, we have utilized RandomForest, XGBoost, DecisionTree, Logistic Regression, Support Vector Machine K-Nearest Neighbors, GradientBoosting, HistogramGradient Boosting, and AdaptiveBoosting for predicting how likely a person will have a bank account. The holdout cross-validation method has been used to split the training dataset randomly into 90% train and 10% test set. The result was collected and further compared with some existing approaches, which indicates that our approach outperforms the existing state-of-the-art approach. The Random Forest was optimal among adapted models based on F1-score, precision, recall, and accuracy metrics which are 91.556%, 91.357%, 91.736%, and 91.541% respectively.
Beyond Vision: A Semantic Reasoning Enhanced Model for Gesture Recognition with Improved Spatiotemporal Capacity

Gesture recognition is an imperative and practical problem owing to its great application potential. Although recent works have made great progress in this field, there also exist three non-negligible problems: 1) existing works lack efficient temporal modeling ability; 2) existing works lack effective spatial attention capacity; 3) most works only focus on the visual information, without considering the semantic relationship between different classes. To tackle the first problem, we propose a Long and Short-term Temporal Shift Module (LS-TSM). It extends the original TSM and expands the step size of shift operation to model long-term and short-term temporal information simultaneously. For the second problem, we expect to focus on the spatial area where the change of hand mainly occurs. Therefore, we propose a Spatial Attention Module (SAM) which utilizes the RGB difference between frames to get a spatial attention mask to assign different weights to different spatial positions. As for the last, we propose a Label Relation Module (LRM) which can take full advantage of the relationship among classes based on their labels’ semantic information. With the proposed modules, our work achieves the state-of-the-art performance on two commonly used gesture datasets, i.e., EgoGesture and NVGesture datasets. Extensive experiments demonstrate the effectiveness of our proposed modules.
XR based Interaction: Leveraging on Virtual Digital Twin for Efficient Exploration with Small FOV Augmented Reality Glass

In this poster, we propose to take advantage of the digital twinned environment to interact more efficiently in spite of the limited sized FOV of the AR glasses. Using the digital twin of the environment, “magical” VR interaction techniques can be applied, as visualized and overlaid through the small window, while still maintaining the spatial association to the augmented real world. As an example, we consider the use of amplified movement within the corresponding VR space - in an attempt to provide effectively larger view with the same AR field of view but with less amount of physical movements. The results of the pilot experiment with the proposed method have in-dicated that the interaction performance was partly improved, while the spatial understanding with regards to the augmentation space maintained. The work illustrates the concept of and potential for XR based interaction where the user can leverage on the advantages of both VR and AR mode operations.