Reviewerly/exHarmony huggingface dataset:
Siamese network with a depthwise over-parameterized convolutional layer for visual tracking

Visual tracking is a fundamental research task in vision computer. It has broad application prospects, such as military defense and civil security. Visual tracking encounters many challenges in practical application, such as occlusion, fast motion and background clutter. Siamese based trackers achieve superior tracking performance in balanced accuracy and tracking speed. The deep feature extraction with Convolutional Neural Network (CNN) is an essential component in Siamese tracking framework. Although existing trackers take full advantage of deep feature information, the spatial structure and semantic information are not adequately exploited, which are helpful for enhancing target representations. The lack of these spatial and semantic information may lead to tracking drift. In this paper, we design a CNN feature extraction subnetwork based on a Depthwise Over-parameterized Convolutional layer (DO-Conv). A joint convolution method is introduced, namely the conventional and depthwise convolution. The depthwise convolution kernel explores independent channel information, which effectively extracts shallow spatial information and deep semantic information, and discards background information. Based on DO-Conv, we propose a novel tracking algorithm in Siamese framework (named DOSiam). Extensive experiments conducted on five benchmarks including OTB2015, VOT2016, VOT2018, GOT-10k and VOT2019-RGBT(TIR) show that the proposed DOSiam achieves leading tracking performance with real-time tracking speed at 60 FPS against state-of-the-art trackers.
Hazard Trend Identification Model Based on Statistical Analysis of Abnormal Power Generation Behavior Data

In order to solve the problem of abnormal data identification for key indicators with the deepening and development of power enterprise reform, this paper proposes a method of dangerous trend identification model based on a statistical analysis of abnormal power generation behavior data. The method includes a data access scheme, feature extraction scheme, and anomaly detection algorithm. The experimental results show that the proportion of users whose electricity consumption behavior conforms to the peak period electricity consumption &gt; normal period electricity consumption &gt; valley period electricity consumption exceeds 90%. More than 85% of users’ electricity consumption behavior is in line with the proportion of electricity consumption that is less than 0.25 in millet hours. The proportion of users whose fluctuation coefficient of electricity consumption in the valley period is less than 1 exceeds 85%, and 99.9% of users’ fluctuation coefficient of electricity consumption in the valley period is less than 5, which proves that abnormal power generation behavior data and abnormal power consumption data can bring early warning to some dangerous power consumption behaviors. The statistical analysis model of abnormal power generation behavior data can play a positive role in the identification of dangerous trends.
Digital Transformation of the University as a Means of Framing Eco-Environment for Creativity and Creative Activities to Attract and Develop Talented and Skilled Persons

The purpose of this article is to present the results of the ongoing study of the University digital transformation on the basis of a comprehensive theoretical model. The article describes a conceptual model of the University digital transformation formed based on the comprehensive quantitative and qualitative analysis of potential ecosystem participants in line with the requirements of changing external conditions. The authors list the current results of the transformation and strategic plans considering any achievements adjustment. The National University of Science and Technology “MISiS” (NUST MISiS) has been used as an experimental basis for the research. The key achievement of NUST MISiS such as a digital ecosystem is described in this article. The digital environment of NUST MISiS considers the needs of the University staff and students and contributes to achieving the strategic goals.
An Improved Clustering Algorithm for Multi-Density Data

The clustering method divides a dataset into groups with similar data using similarity metrics. However, discovering clusters in different densities, shapes and distinct sizes is still a challenging task. In this regard, experts and researchers opt to use the DBSCAN algorithm as it uses density-based clustering techniques that define clusters of different sizes and shapes. However, it is misapplied to clusters of different densities due to its global attributes that generate a single density. Furthermore, most existing algorithms are unsupervised methods, where available prior knowledge is useless. To address these problems, this research suggests the use of a clustering algorithm that is semi-supervised. This allows the algorithm to use existing knowledge to generate pairwise constraints for clustering multi-density data. The proposed algorithm consists of two stages: first, it divides the dataset into different sets based on their density level and then applies the semi-supervised DBSCAN algorithm to each partition. Evaluation of the results shows the algorithm performing effectively and efficiently in comparison to unsupervised clustering algorithms.
The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM

Graph Convolutional Neural Network (GCN) is widely used in text classification tasks. Furthermore, it has been effectively used to accomplish tasks that are thought to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and it is not good at capturing local information. The Bidirectional Encoder Representation from Transformers (BERT) has the ability to capture contextual information in sentences or documents, but it is limited in capturing global (the corpus) information about vocabulary in a language, which is the advantage of GCN. Therefore, this paper proposes an improved model to solve the above problems. The original GCN uses word co-occurrence relationships to build text graphs. Word connections are not abundant enough and cannot capture context dependencies well, so we introduce a semantic dictionary and dependencies. While the model enhances the ability to capture contextual dependencies, it lacks the ability to capture sequences. Therefore, we introduced BERT and Bi-directional Long Short-Term Memory (BiLSTM) Network to perform deeper learning on the features of text, thereby improving the classification effect of the model. The experimental results show that our model is more effective than previous research reports on four text classification datasets.
What can we learn from recommendations of early-career engineers? Assessing computing and software engineering education using a career monitoring survey

This paper presents an analysis of the skills and professional competencies that recent graduates from computing and software engineering programmes recommend for current students. Previous studies have not investigated the viewpoints of early-career engineers, and the current study addresses this research gap. The data used in this study comes from nationwide career monitoring surveys for former university students who graduated five years earlier. We analyzed the responses to questions about the skills and competencies needed in the software or computing jobs and compared them with the satisfaction and career paths of the respondents. According to the results, three types of skills and competencies are paramount: Soft skills in general, programming skills, and the practical experience gained during university studies. A logistic regression analysis revealed that soft skills are recommended by those who are most satisfied with their careers. Practical skills are more likely to be recommended if the respondent is less satisfied with their studies. Based on the findings, we concluded that the responses from the career monitoring survey could be used as an indicator of how well studies prepare graduates for the industry.
Towards Integrated Graduate Skills for UK Computing Science Students

In preparing computing science students for industry, degree content often focuses on technical skills such as programming. Such skills are essential for a successful post-study career in industry and is popular with students. However, industry notes that students are often limited in what can be referred to as graduate attributes or transferable skills. Such skills include effective teamwork, communication, and critical thinking amongst others. Similar gaps have also been demonstrated for computing science students more specifically, resulting in industry developing their own training programmes for graduates.
A comparison of distance metrics for the multi-objective pathfinding problem

Abstract Pathfinding, also known as route planning, is one of the most important aspects of logistics, robotics, and other applications where engineers must balance many competing interests. There is a significant challenge in pathfinding problems with multiple objectives because many paths can map to the same objective value. Such multi-modal solutions cannot easily be found in multi-objective optimisation algorithms, which are typically geared towards selection mechanisms in the objective space. A niching approach for preserving good diverse solutions in the decision space is proposed in this paper, which is tailored for pathfinding problems. The criteria used to compare the solutions within the decision space are path similarity metrics, which we extend from a previous study, and are used instead of the well-established crowding distance. In two variations, we investigate the proposed meta-heuristic approach on a range of benchmark instances and compare the methodology to a deterministic optimisation approach.
An Anchor-Free 3D Object Detection Approach Based on Hierarchical Pillars

Object detection in the 2D domain is well developed owing to the wide application of CMOS image sensors and the great success of deep learning technologies in recent years. However, under circumstances such as autonomous driving, the variation of weather conditions and light conditions makes it impossible to perform reliable detection using regular 2D image sensors. 3D data generated by a Lidar or Radar is more robust to such environments, hence serving as an essential complement to 2D data in such scenarios. Well-established anchor-based detectors in the 2D domain suffer from time-consuming anchor configuration and cannot be exploited directly to process 3D data. This paper proposes an anchor-free network that encodes the raw point cloud into a hierarchical pillar representation to locate objects. Without predefined anchors and NMS postprocessing, our method directly predicts the center points and box properties to accomplish the detection task efficiently. In addition, a PCA-based initialization for the convolutional kernel is proposed to accelerate the training process. Experiments are implemented on the KITTI benchmark, and our method can achieve competitive performance with other anchor-based methods. Comprehensive ablation studies further verify the validity and rationality of each part of the proposed method.
Learning Stereo Depth Estimation with Bio-Inspired Spike Cameras

Bio-inspired spike cameras, offering high temporal resolution spike streams, have brought a new perspective to address common challenges (e.g.,high-speed motion blur) in depth estimation tasks. In this paper, we propose a novel problem setting, spike-based stereo depth estimation, which is the first trail that explores an end-to-end network to learn stereo depth estimation with transformers for spike cameras, named Spike-based Stereo Depth Estimation Transformer (SSDEFormer). We first build a hybrid camera platform and provide a new stereo depth estimation dataset (i.e.,PKU-Spike-Stereo) with spatiotemporal synchronized labels. Then, we propose a novel spike representation to effectively exploit spatiotemporal information from spike streams. Finally, a transformer-based network is designed to generate dense depth maps without a fixed-disparity cost volume. Empirically, it shows that our approach is extremely effective on both synthetic and real-world datasets. The results verify that spike cameras can perform robust depth estimation even in cases where conventional cameras and event cameras fail in fast motion scenarios.
BE-Net:Boudary-Enhanced Networks for Accurate Gland Segmentation

The morphology of glands is the gold standard for pathologists to assess the degree of benign and malignant adenocarcinoma in medical diagnosis. Accurate segmentation of glands is indispensable for a computer-aided diagnosis system. However, heterogeneity exists in glandular morphology between benign and malignant, and mutual adhesion of glands adds to the difficulties in determining glandular boundaries. This paper proposes a boundary-enhanced network (BE-Net) that repeatedly uses boundary information to segment glands accurately. The network adopts an encoder-decoder structure that uses the introduced cascaded feature integration module (CFIM) as a decoder to advance aggregate features for explicit boundary refinement. In addition, we also propose a context-augmented attention module (CAAM) to refine implicit boundary and segment the gland boundary accurately through the repeated use of boundary information to improve the overall model's segmentation performance. The experiments demonstrate that the proposed method significantly outperformed other state-of-the-art methods on the 2015 MICCAI Gland Challenge dataset.
Parallel Edge-Image Learning for Image Inpainting

The primary goal of image inpainting is to fix holes in a damaged image with natural contents. A key challenge is that a damaged image contains complex structures in differ-ent ways, with each consisting of its configuration of edges and spatial dependencies. As a result, filled images often converge to unnatural and implausible results. Currently, the edge-image inpainting methods adopt two stages to recover edges and images successively, which suffer from feature in-consistency and error accumulation. This leads us to present a parallel edge-image learning framework that explicitly char-acterizes these internal configurations in a single stage. The framework introduces a dual parallel network-based decoder to generate the image and the edges concurrently, leading to feature consistency at the semantic level. Also, a new cross-fire mechanism aims to exchange edge-image information in the decoder, avoiding error accumulation. Empirical evaluations on benchmark datasets suggest that our approach out-performs the state-of-the-art methods on image inpainting.
Automatic Label Injection Into Local Infrastructure LiDAR Point Cloud for Training Data Set Generation

The representation of objects in LiDAR point clouds is changed as the height of the mounting position of sensor devices gets increased. Most of the available open datasets for training machine learning based object detectors are generated with vehicle top mounted sensors, thus the detectors trained on such datasets perform weaker when the sensor is observing the scene from a significantly higher viewpoint (e.g. infrastructure sensor). In this paper a novel Automatic Label Injection method is proposed to label the objects in the point cloud of the high-mounted infrastructure LiDAR sensor based on the output of a well performing &#x201C;trainer&#x201D; detector deployed at optimal height while considering the uncertainties caused by various factors described in detail throughout the paper. The proposed automatic labeling approach has been validated on a small scale sensor setup in a real-world traffic scenario where accurate differential GNSS reference data where also available for each test vehicle. Furthermore, the concept of a distributed multi-sensor system covering a larger area aimed for automatic dataset generation is also presented. It is shown that a machine learning based detector trained on differential GNSS-based training dataset performs very similarly to the detector retrained on a dataset generated by the proposed Automatic Label Injection technique. According to our results a significant increase in the maximum detection range can be achieved by retraining the detector on viewpoint specific data generated fully automatically by the proposed label injection technique compared to a detector trained on vehicle top mounted sensor data.
Multiple Carrier UAV Path Planning Based on Hybrid A

Unmanned aerial vehicles (UAVs) are becoming more and more important in modern warfare, and future carrier UAVs will also be indispensable for the improvement of aircraft carrier combat capabilities. Taking into account the future trend of unmanned combat and the ease of dealing with the environment such as path planning in the hangar, the carrier UAV in this article needs to consider reversing motion. Based on this, a mathematical model for path planning is established and a hybrid A-star algorithm is introduced. The path planning problem of shipborne UAV. In addition, the previous multi-carrier UAV path planning did not consider the impact of the dispatch process, so this paper proposes a conflict avoidance strategy for multi-carrier UAV path planning. The simulation results show that the hybrid A* is effective in the path planning of carrier UAVs, and the proposed multi-carrier UAV conflict avoidance strategy can fully meet the given constraints. It shows that the method proposed in this paper can effectively cope with the path planning task of carrier UAV.
Dual Selection Network for Video Object Detection

Some off-the-shelf video object detection methods usually enhance the degraded proposal features of target frames by aggregating the proposal features from support frames. However, the proposals generated by region proposal network may not be accurate, resulting in inaccurate proposal features and limited performance. To mitigate this, we propose a novel dual selection network (DSNet) for video object detection, which contains two successive stages: selecting proposals that fit objects more closely, and selecting proposal features that are more conducive to feature aggregation. Correspondingly, the proposal selection module (PSM) aims to select better proposals by exploiting their boundary information, and the selective aggregation module (SAM) aims to select better proposal features for aggregation. Consequently, DSNet can generate more robust proposal features through the novel dual selection mechanism implemented by PSM and SAM. Extensive experiments show that our DSNet obtains 83.7% mAP and achieves superior performance over several state-of-the-art methods.
Disentangled Visual Representations for Extreme Human Body Video Compression

Recent years have witnessed the great promise of deep neural video compression codecs. However, there are still unprecedented challenges ahead when the videos are expected to be encoded with extremely low bitrate. Motivated by recent attempts of layered conceptual image compression, we make the first attempt to leverage the disentangled visual representations for extreme human body video compression. More specifically, to capture the main structure, we adopt the inferred human pose keypoints as the structure code of each frame, thereby deriving the motion information from structure codes of adjacent frames for further compression. To better exploit the texture redundancy, all frames share the same texture codes by incorporating the proposed texture contrastive learning to ensure texture consistency within a video. Two branches are consequently transmitted in a separable manner, and the generator synthesizes the reconstructed video with the combination of all decoded representations at the decoder side. Both qualitative and quantitative experimental results demonstrate that the proposed scheme can produce perceptually pleasing reconstruction results in ultra-low bitrates far below that can be reached by other video codecs.
Contrastive Class-Specific Encoding for Few-Shot Object Detection

In this paper, we propose a new few-shot object detection (FSOD) framework that introduces a new contrastive branch to extract the class representation of images, which improves the generalization performance of the detection model for novel classes. Additionally, we investigate the effectiveness of both self-supervised and supervised contrastive losses for class-specific encoding in our framework. Experimental results on the benchmark datasets indicate that our proposed method archives the state-of-the-art performance compared with existing FSOD methods.
Kernel Regression for Matrix-Variate Gaussian Distributed Signals Over Sample Graphs

Recent advances of kernel regression assume that target signals lie over a feature graph such that their values can be predicted with the assistance of the graph learned from training data. In this paper, we propose a novel kernel regression framework whose outputs follow a matrix-variate Gaussian distribution (MVGD) such that the kernel matrix can be viewed as the column covariance matrix of outputs, and the hyperparameters of a chosen kernel can be optimized using gradient methods. Furthermore, in contrast to the state-of-the-art kernel regression algorithms over graph (KRG), a sample graph of target outputs is introduced to work with regression coefficients and hyperparameters of a chosen kernel in our algorithms. The proposed KRG framework is decomposed into two stages, including the estimation of row and column covariance matrices of MVGD and graph learning along with the estimation of regression coefficients. Numerical approaches are developed to tackle the corresponding optimization problems. Experimental results over synthetic and real-world datasets demonstrate that the performance of the proposed algorithms is superior to that of the state-of-the-art methods.
TC-Net: Transformer-Convolutional Networks for Road Segmentation

In this paper, we propose a lightweight backbone network called Transformer-Convolutional Networks (TC-Net) for ef-ficient road segmentation. Conventional road segmentation methods employ data fusion to enrich key features, so that more branches are required in the network which increases the parameter size of the model and the difficulty of deploy-ment on edge devices. On the contrary, our TC-Net fully uti-lizes the interconnection between various regions on the input image to enhance the feature representation ability while re-ducing the amount of parameters of each branch. The key components of our TC-Net are the Transformer-Conv (TC) and PatchMerging-Conv (PC) modules. Specifically, the TC module applies convolution to optimize the process of cross-window connections, and the PC module flexibly adjusts the scale of feature maps to further reduce the computational cost. Extensive experiments on the KITTI dataset show that our method greatly reduces the amount of parameters while achieving comparable performance with state-of-the-arts.
Applicability of Fitts’ law to interaction with touchscreen: review of experimental results

Fitts’ law models human psychomotor behaviour and can be used to predict the time required to complete a movement task. Although originally proposed for physical apparatus, Fitts’ law has been adopted to study how human beings use various computer input devices to perform onscreen pointing tasks. Touchscreens are now used in smartphones, tablets and other digital devices. The applicability of Fitts’ law to the interaction with touchscreen has been studied for both stationary computers and mobile devices. Researchers have been studying this problem for about forty years, but the body of work remains small and there is no consensus on whether Fitts’ law is valid for touch-based interaction. This paper reviews studies reporting positive-, null- and negative results on the applicability of Fitts’ law to interaction with touchscreen and proposing modifications to Fitts’ law especially for modelling interaction with touchscreen.