Reviewerly/exHarmony huggingface dataset:
Feature Complement for Visual Tracking Based on Global Feature Comparison

Target tracking is one of the challenging tasks in computer vision. Usually, the center of target origins from the position with the largest response value, and the key to improving tracking performance is to learn reliable feature maps. This paper analyzes the characteristics of the tracking task, designs a global feature comparison function to extract the context, and proposes a feature supplement module based on the global comparison information for further performance improvement. In addition, we also design a template feature update module to supplement template features based on the search area features of the current frame to dynamically adjust model features, improve model generalization capabilities, and avoid model feature fixation. The proposed feature supplement model based on global feature comparison (FSGFC) is evaluated on five visual tracking benchmarks including OTB100, VOT2016, VOT2018, VOT2019 and UAV123. The experimental results show that the model obtains the state-of-the-art performance with a real-time speed.
MobileACNet: ACNet-Based Lightweight Model for Image Classification

Lightweight CNN models aim to extend the application of deep learning from conventional image classification to mobile edge device-based image classification. However, the accuracy of lightweight CNN models currently is not as comparable as traditional large CNN models. To improve the accuracy of mobile platform-based image classification, we propose MobileACNet, a novel ACNet-based lightweight model based on MobileNetV3 (a popular lightweight CNN for image classification on mobile platforms). Our model adopts a similar idea to ACNet: consider global inference and local inference adaptively to improve the classification accuracy. We improve the MobileNetV3 by replacing the inverted residual block with our proposed adaptive inverted residual module (AIR). Experimental results show that our proposed MobileACNet can effectively improve the image classification accuracy by providing additional adaptive global inference on three public datasets, i.e., Cifar-100 dataset, Tiny ImageNet dataset, and a large-scale dataset ImageNet, for mobile-platform-based image classification.
Anchor Link Prediction for Privacy Leakage via De-Anonymization in Multiple Social Networks

Anchor link prediction exacerbates the risk of privacy leakage via the de-anonymization of social network data. Embedding-based methods for anchor link prediction are limited by the excessive similarity of the associated nodes in a latent feature space and the variation between latent feature spaces caused by the semantics of different networks. In this paper, we propose a novel method which reduces the impact of semantic discrepancies between different networks in the latent feature space. The proposed method consists of two phases. First, graph embedding focuses on the network structural roles of nodes and increases the distinction between the associated nodes in the embedding space. Second, a federated adversarial learning framework which performs graph embedding on each social network and an adversarial learning model on the server according to the observable anchor links is used to associate independent graph embedding approaches on different social networks. The combination of distinction enhancement and the association of graph embedding approaches alleviates variance between the latent feature spaces caused by the semantics of different social networks. Extensive experiments on real social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both precision and robustness.
Mining Interesting Negative Sequential Patterns Based on Influence

Negative sequential pattern (NSP) mining can capture frequently occurring and non-occurring behavior information and can play an irreplaceable role in many applications. Most traditional NSP mining algorithms adopt a support measure to discover interesting patterns. However, the support measure does not truly reflect the interestingness of patterns in some cases. In particular, it ignores the effect of the support of every element and the order characteristics among these elements. Hence, an influence measure was proposed to truly reflect the interestingness of patterns. However, the current influence measure is used only in positive sequential pattern (PSP) mining and does not involve NSPs. To address these problems, this study proposes an algorithm, InfI-NSP, to mine interesting NSPs based on influence. First, we modify an existing NSP mining algorithm to efficiently mine NSPs. Second, we modify the influence measure and apply it to NSP mining to mine interesting NSPs. To the best of our knowledge, InfI-NSP is the first algorithm to mine interesting NSPs based on influence. Experiments on real-life and synthetic datasets show that InfI-NSP is effective.
Person Detection Using an Ultra Low-Resolution Thermal Imager on a Low-Cost MCU

Detecting persons in images or video with neural networks is a well-studied subject in literature. However, such works usually assume the availability of a camera of decent resolution and a high-performance processor or GPU to run the detection algorithm, which significantly increases the cost of a complete detection system. However, many applications require low-cost solutions, composed of cheap sensors and simple microcontrollers. In this paper, we demonstrate that even on such hardware we are not condemned to simple classic image processing techniques. We propose a novel ultra-lightweight CNN-based person detector that processes thermal video from a low-cost 32 $$\times $$ 24 pixel static imager. Trained and compressed on our own recorded dataset, our model achieves up to 91.62% accuracy (F1-score), has less than 10k parameters, and runs as fast as 87ms and 46ms on low-cost microcontrollers STM32F407 and STM32F746, respectively.
Explainable Network Pruning for Model Acceleration Based on Filter Similarity and Importance

Filter-level network pruning has effectively reduced computational cost, as well as energy and memory usage, for parameterized deep networks without damaging performance, particularly in computer vision applications. Most filter-level network pruning algorithms focus on minimizing the impact of pruning on network performance using either importance-based or similarity-based pruning approaches. However, no study has attempted to compare the effectiveness of the two approaches across different network configurations and datasets. To address these issues, this paper compares two explainable network pruning methods based on importance-based and similarity-based approaches to understand their key benefits and limitations. Based on the analysis findings, we propose an innovative hybrid pruning method and demonstrate its effectiveness using several models and datasets. The comparisons with other state-of-the-art filter pruning methods show the superiority of the new hybrid method.
UnseenNet: Fast Training Detector for Unseen Concepts with No Bounding Boxes

Training of object detection models using less data is currently the focus of existing N-shot learning models in computer vision. Such methods use object-level labels and takes hours to train on unseen classes. There are many cases where we have large amount of image-level labels available for training and cannot be utilized by few shot object detection models for training. There is a need for a machine learning framework that can be used for training any unseen class and can become useful in real-time situations. In this paper, we proposed an “Unseen Class Detector” that can be trained within a short time for any possible unseen class without bounding boxes with competitive accuracy. We build our approach on “Strong” and “Weak” baseline detectors, which we trained on object detection and image classification datasets, respectively. Unseen concepts are fine-tuned on the strong baseline detector using only image-level labels and further adapted by transferring the classifier-detector knowledge between baselines. We use semantic as well as visual similarities to identify the source class (i.e. Sheep) for the fine-tuning and adaptation of unseen class (i.e. Goat). Our model (UnseenNet) is trained on the ImageNet classification dataset for unseen classes and tested on an object detection dataset (OpenImages). UnseenNet improves the mean average precision (mAP) by 10% to 30% over existing baselines (semi-supervised and few-shot) of object detection. Moreover, training time of proposed model is $$<10$$ min for each unseen class.
Feature Alignment in Anchor-Free Object Detection

Most anchor-free methods perform object detection using dense recommendation, which assumes that one point can simultaneously conduct accurate category prediction and regression estimation. However, due to different task drivers, valid features for classification and regression may locate at distinct areas in the training phase. This problem is called feature misalignment. To solve it, we propose a new feature alignment method based on anchor-free object detector. Firstly, a global receptive field adaptor (G-RFA) is designed by incorporating the feature pyramid networks (FPN) with the global attention mechanism, and forward features are further fine-tuned with a deformable-subnet (De-Subnet) to remove the influence of redundant contextual information. Then, a new feature filter strategy with a misalignment score is proposed to guide the network to focus on sampling points with aligned features. In addition, we establish mutually independent multi-layer quality distributions to model the priori information of an object on different FPN levels. Equipped with our method, the classification and regression features are aligned, and the generated foreground weight map converges to the centers of classification and regression heatmaps. Experimental results show that without bells and whistles, our method achieves 49.3% AP on MS COCO test-dev under the default 2x training schedule, outperforming related methods. Besides, experiments on PASCAL VOC demonstrate the generalization ability of our method. Code is available at https://github.com/GFENGG/featurealign.
A Novel Ship-Height Measurement System Based on Optical Axes Collinear Stereo Vision

Overheight ships pose serious danger to bridges when sailing along inland rivers. Measuring ship height from a long distance with high precision is a very challenging problem. This article proposes a novel optical axes colinear binocular stereo vision system to meet this goal. The system is equipped with two arrays of cameras, each consisting of a long-focus camera (LFC) and a short-focus camera (SFC), located face-to-face on each side of a river. The SFC, i.e., wide-angle-lens camera, is used for ship detection, while the LFC, i.e., tele lens camera, is used for precise ship-height measurement. Different from conventional binoculars, the optical axis of the two LFCs are arranged to be colinear in our setup. The advantage of this configuration is in twofold. First, it is simple and precise to calibrate the measuring apparatus and the system error since the two LFCs are mutually visible to each other. Second, due to its face-to-face configuration across the river, the entire navigation waterway is under constant monitoring without blind spot; hence, the shortcoming of limited field of view (FOV) of tele lens is overcome with conventional parallel optical axis stereo vision setup. In this work, the detailed error analysis was performed and in-field experiments were well designed. The experimental data matched very well with the theoretical prediction. The proposed system can achieve a ship-height measuring error below 0.1 m for a distance of 1.46 km.
Emotional Speech Synthesis using End-to-End neural TTS models

Speech synthesis aims at artificially generating human like speech. The advancements in Artificial Intelligence and Natural Language Processing (NLP), particularly deep learning networks, have significantly improved such attempts. Text to Speech Synthesis (TTS) models based on deep neural networks are now able to generate speech samples with high naturality and intelligibilty. Improving the paralinguistic characteristics of speech, like emotion, is also desirable for effective speech synthesis. But availability of suitable emotional speech dataset for neural TTS may be limited. Transfer Learning offers a viable solution for such scenarios of limited resources. In this paper, we present an overview of emotional speech synthesis using end-to-end neural TTS models and compare the performance of Tacotron 2 and FastSpeech 2 for transfer learning based emotional speech synthesis. To generate speech waveform from generated mel spectrogram, HiFi-GAN vocder is used. The synthesized speech samples are evaluated using ScSer speech emotion recognition model. The Tacotron 2 and FastSpeech 2 models reported classification accuracies of 90% and 79% respectively.
Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods

The literature revealed approximately 50% of IT-related projects around the world fail, which must frustrate a sponsor or decision maker since their ability to forecast success is statistically about the same as guessing with a random coin toss. Nonetheless, some project success/failure factors have been identified, but often the effect sizes were statistically negligible. A pragmatic mixed methods recursive approach was applied, using structured programming, machine learning (ML), and statistical software to mine a large data source for probable project success/failure indicators. Seven feature indicators were detected from ML, producing an accuracy of 79.9%, a recall rate of 81%, an F1 score of 0.798, and a ROCa of 0.849. A post-hoc regression model confirmed three indicators were significant with a 27% effect size. The contributions made to the body of knowledge included: A conceptual model comparing ML methods by artificial intelligence capability and research decision making goal, a mixed methods recursive pragmatic research design, application of the random forest ML technique with post hoc statistical methods, and a preliminary list of IT project failure indicators analyzed from big data.
Explaining autonomous driving with visual attention and end-to-end trainable region proposals

Abstract Autonomous driving is advancing at a fast pace, with driving algorithms becoming more and more accurate and reliable. Despite this, it is of utter importance to develop models that can offer a certain degree of explainability in order to be trusted, understood and accepted by researchers and, especially, society. In this work we present a conditional imitation learning agent based on a visual attention mechanism in order to provide visually explainable decisions by design. We propose different variations of the method, relying on end-to-end trainable regions proposal functions, generating regions of interest to be weighed by an attention module. We show that visual attention can improve driving capabilities and provide at the same time explainable decisions.
Research on knowledge extraction in knowledge graph construction

Knowledge Graphs (KGs) are composed of structured information in the form of entities and relations. And the process of extracting entities and relations from data is called Knowledge Extraction. Knowledge extraction is a fundamental task in the field of Natural Language Processing (NLP) and a key part of knowledge graph construction. In this paper, we provide comprehensive research on knowledge extraction in knowledge graph construction. We first introduce the technical architecture of the KGs and the classification of knowledge extraction. Then, we systematically categorize existing works based on the development of knowledge extraction. Finally, we review current open-source tools for knowledge extraction and summarize their advantages and disadvantages.
Data-driven study of the preferences and effects of wearable device visualisation in sport

We present the results of three studies related to the visualisation of sleep data on wearable devices in two forms: smartwatches and fitness bands. We aimed to comprehend the preferences and outcomes of various workout visualizations according to shape. Their usage of wearable technology and their preferences for choosing a certain visualisation were the subjects of an initial questionnaire. The findings demonstrated that visual representations were superior to plain text in terms of boosting people's comprehension and perception of their movement data and that preferences for movement data also affected the choice of visual representation. We examined the effects of smartwatches and fitness wristbands using in-person tests and online simulated visualisations in the pilot research and subsequent perception studies. According to the findings, smartwatches are more adept at visualizing more complicated issues than fitness bands. Due to these restrictions, we are unable to conclude that smartwatches are inherently better than fitness bands, although this poses new issues for further study.
Introduction to path planning in three methods

All services provided by robots to humans are based on navigation control. Navigation control includes positioning and navigation. Path planning is a key part of navigation. The navigation control algorithm is at the heart of determining the behaviour of the robot. The navigation control module includes global path planning and local path planning. Global path planning is the creation of a feasible path from the start point to the target point using an existing electronic map as a standard. Local path planning, also known as local obstacle avoidance, is the process by which sensors scan for unknown obstacles during the robot's operation and redefine a local path around the obstacles towards the target point. This paper describes some of the main algorithms that are more widely used in motion planning, including sampling-based search methods such as RRT and its range of optimisation methods. Each of these methods has its own search process and results. There is also a search algorithm called the Markov decision process model, which we tried to combine with RRT but failed due to different application areas.
F-SRAF: a small target detection method based on attention mechanism and feature enhancement

In target detection, small target detection is always a thorny problem, because there is little pixel information of small target in a picture. However, in the feature extraction phase of most target detection network architectures, the deep feature maps lack information on the edge or geometric details of small targets. Although the shallow feature map is rich in geometric details, it lacks the semantic features of small objects. To solve the above problems, this paper proposes a small target detection model F-SRAF, which combines feature enhancement and attention mechanism. Specifically, firstly, the feature enhancement module is used to extract credible geometric details by combining the shallow high-resolution features with the deep super-resolution semantic features. Then, the channel attention mechanism is added to the deep super-resolution semantic features to enhance the features useful to the target and suppress the background features. Finally, the related detail feature map learned by the network is fused with the semantic feature map added with the channel attention mechanism to output a high-resolution feature map with semantic information. In the experiment of this paper, our method has achieved good results on MS COCO dataset and Tsinghua-Tencent 100K dataset. At the same time, it is proved that this detection algorithm can enhance the semantic information of shallow feature map and has a good detection effect for small targets.
Trajectory-text retrieval model based on graph neural network

Cross-modal retrieval has been widely used in the Vision-Language field and has achieved many results, but there is a lack of research in the trajectory-text field. At the same time, the current popular cross-modal retrieval models not only lack fine-grained semantic alignment between different modalities, but also ignore the influence of the grammatical structure of the text on the retrieval effect. To solve the above problems, this paper proposes a dual-stream trajectory text retrieval model combined with graph neural network, combining local and global two cross-modal interaction methods: (1) Local alignment, encoding trajectory points and words respectively after passing through the masking module. Semantic alignment. (2) Global alignment, introducing momentum contrastive learning to achieve trajectory and text retrieval learning. Experimental results show that this hierarchical matching method not only retains the efficient performance of the dual-stream model, but also has higher accuracy than other cross-modal retrieval models, and its R@1 value on the dataset is improved by 3.2%-4.7%.
Design and implementation on data management system of ordnance support work with certification

Based on the B/S architecture, a design scheme was proposed to develop a data management system of ordnance support work with certification. Containing such hardware components as printing and attendance devices, the system could not only realize the management of the ordnance support work with certification, but also support the immediate production of a work pass in the form of IC card, and realize the daily attendance management of technical support personnel. An information-based solution was therefore given for the problems of work with certification encountered by the ordnance support forces.
Model compression method combining multi-factor channel pruning and knowledge distillation

Deploying deep learning models in embedded terminals is essential for applications with real-time reasoning requirements. In order to make the model run efficiently in the embedded end with limited resources, we propose a model compression method combining multi-factor channel pruning and knowledge distillation. In the process of network sparsity, this method uses the double factors of the BN layer to improve the pruning standard and guides the local pruning of the model according to the new standard to ensure the compression rate. In order to further improve the accuracy, we use the knowledge transfer method in the idea of knowledge distillation to fine-tune the model and use the more continuous parameter distribution of the student model to ensure accuracy. We use several deep learning models to test. The experimental results show that the proposed model compression method has the advantages of fewer parameters and higher accuracy, which reduces the resources occupied by the model application to the embedded end. More importantly, this method not only realizes the efficient operation of various models in the embedded end, but also ensures the high accuracy of the model.
A lightweight delegated private set intersection in the internet setting for the cloud computing environments

Due to the storage and computing ability of cloud technology, many protocols are suitable for deployment on cloud servers. Private set intersection (PSI) is practical technology in data mining, similar document detection and so on. In some cloud-based IoT system and mobile devices have poor ability to calculate the intersection. In this scenario, we design a protocol make the part of the computing intersection execute on the cloud server, called Efficient-DPSI, which supports flexible parameters. Experimental results show that our Efficient-DPSI protocol is more efficient with existing related delegated PSI protocols.