Reviewerly/exHarmony huggingface dataset:
LD Connect: A Linked Data Portal for IOS Press Scientometrics

In this work, we describe a Linked Data portal, LD Connect, which operates on all bibliographic data produced by IOS Press over the past thirty-five years, including more than a hundred thousand papers, authors, affiliations, keywords, and so forth. However, LD Connect is more than just an RDF-based metadata set of bibliographic records. For example, all affiliations are georeferenced, and co-reference resolution has been performed on organizations and contributors including both authors and editors. The resulting knowledge graph serves as a public dataset, web portal, and query endpoint, and it acts as a data backbone for IOS Press and various bibliographic analytics. In addition to the metadata, LD Connect is also the first portal of its kind that publicly shares document embeddings computed from the full text of all papers and knowledge graph embeddings based on the graph structure, thereby enabling semantic search and automated IOS Press scientometrics. These scientometrics run directly on top of the graph and combine it with the learned embeddings to automatically generate data visualizations, such as author and paper similarity over all journals. By making the involved ontologies, embeddings, and scientometrics all publicly available, we aim to share LD Connect services with not only the Semantic Web community but also the broader public to facilitate research and applications based on this large-scale academic knowledge graph. Particularly, the presented scientometric system generalizes beyond IOS Press data and can be deployed on top of other bibliographic datasets as well.
Exploring Truss Maintenance in Fully Dynamic Graphs: A Mixed Structure-Based Approach

Graphs are widely employed in complex system modeling, VLSI design, and social analysis. Mining cohesive subgraphs is a fundamental problem in graph analysis, while implementing cohesive subgraphs requires analysts to not only ensure cohesiveness but also consider the computational intractability. Among a variety of diverse cohesive structures, k-truss exhibits a perfect trade-off between structure tightness and computational efficiency. In a k-truss, each edge is present in at least k-2 triangles. This study aims to contribute to this growing area of truss maintenance in fully dynamic graphs by avoiding expensive re-computation. Specifically, we consider the challenging scenario of batch processing of edge and vertex insertion/deletion and propose efficient algorithms that can maintain the trusses by only searching a very small range of affected edges. Also, our algorithms allow parallel implementations to further improve the efficiency of maintenance. Extensive experiments on both real-world static and temporal graphs illustrate the efficiency and scalability of our algorithms.
Rotation Estimation Based on Anisotropic Angular Radon Spectrum

In this letter, we present the anisotropic Angular Radon Spectrum (ARS), a novel feature for global estimation of rotation in a two dimension space. ARS effectively describes collinearity of points and has the properties of translation-invariance and shift-rotation. We derive the analytical expression of ARS for Gaussian Mixture Models (GMM) representing point clouds where the Gaussian kernels have arbitrary covariances. Furthermore, we developed a preliminary procedure for simplification of GMM suitable for efficient computation of ARS. Rotation between point clouds is estimated by searching of maximum of correlation between their spectra. Correlation is efficiently computed from Fourier series expansion of ARS. Experiments on datasets of distorted object shapes, laser scans and on robotic mapping datasets assess the accuracy and robustness to noise in global rotation estimation.
Catastrophic Forgetting Problem in Semi-Supervised Semantic Segmentation

Restricted by the cost of generating labels for training, semi-supervised methods have been applied to semantic segmentation tasks and have achieved varying degrees of success.Recently, the semisupervised learning method has taken pseudo supervision as the core idea, especially self-training methods that generate pseudo labels.However, pseudo labels are noisy.In semi-supervised learning, as training progresses, the model needs to focus on more semantic classes and bias towards the newly learned classes.Moreover, due to the limitation of the amount of labeled data, it is difficult for the model to "stabilize" the learned knowledge.That raise the issue of the model forgetting previously learned knowledge.Based on this new view, we point out that alleviating "catastrophic forgetting" of the model is beneficial for enhancing the quality of pseudo labels, and propose a pseudo label enhancement strategy.In this strategy, the pseudo labels generated by the previous model are used to rehearse the previous knowledge.Additionally, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the previous and current models.We evaluate our scheme on two general semi-supervised semantic segmentation benchmarks, and both achieve state-of-the-art performance.Our codes are released at https://github.com/wing212/DMT-PLE.
A QoS-Enabled Load Balancing Approach for Cloud Computing Environment Join Minimum Loaded Queue (JMLQ)

Cloud computing delivers the on-demand virtualized resources to its consumer for servicing their request on a metered basis. During the high demand of cloud resources the load on system increases that may unbalance the system which affects the quality of service parameters (QoS) adversely that leads to violations of service level agreement (SLA). Role of load balancing is significant in such an environment as it enhances the distribution of workload across multiple devices for example across network links, a cluster of servers, disk drives, etc. The present research work introduced a multi scheduler for balancing the load across the system that aims to optimize the QoS parameters such as response time, resource utilization, and the average waiting time by exploiting these virtual resources in the cloud environment. The performance of the proposed approach analyzed and tested in CloudSim that to optimize these parameters for the current approach. The authors found that our QoS enabled JMLQ approach achieved better results in comparison to our previous JMLQ approach and other variants.
Shaping Visual Representations With Attributes for Few-Shot Recognition

Few-shot recognition aims to recognize novel categories under low-data regimes. Some recent few-shot recognition methods introduce auxiliary semantic modality, i.e., category attribute information, into representation learning, which enhances the feature discrimination and improves the recognition performance. Most of these existing methods only consider the attribute information of support set while ignoring the query set, resulting in a potential loss of performance. In this letter, we propose a novel attribute-shaped learning (ASL) framework, which can jointly perform query attributes generation and discriminative visual representation learning for few-shot recognition. Specifically, a visual-attribute predictor (VAP) is constructed to predict the attributes of queries. By leveraging the attributes information, an attribute-visual attention module (AVAM) is designed, which can adaptively utilize attributes and visual representations to learn more discriminative features. Under the guidance of attribute modality, our method can learn enhanced semantic-aware representation for classification. Experiments demonstrate that our method can achieve competitive results on CUB and SUN benchmarks. Our source code is available at: \url{https://github.com/chenhaoxing/ASL}.
LessonAble: Leveraging Deep Fakes in MOOC Content Creation

This paper introduces LessonAble, a pipelined methodology leveraging the concept of Deep Fakes for generating MOOC (Massive Online Open Course) visual contents directly from a lesson narrative. To achieve this, the proposed pipeline consists of three main modules: audio generation, video generation and lip-syncing. In this work, we use the NVIDIA Tacotron2 Text-to-Speech model to generate custom speech from text, adapt the famous First Order Motion Model to generate the video sequence from different driving sequences and target images, and modify the Wav2Lip model to deal with lip-syncing. Moreover, we introduce some novel strategies to support the use of markdown-like formatting to guide the pipeline in the generation of expression aware (i.e. curious, happy, etc.) contents. Despite the use and adaptation of third parties modules, developing such a pipeline presented interesting challenges, all analysed and reported in this work. The result is an extremely intuitive tool to support MOOC content generation.
Blockchain for Smart City: Opportunities and Future Research Directions

Blockchain for smart cities is a growing prospect and gaining attention from academic and industrical standpoint. The increased population of the world has become a challenge to provide state-of-the-art living standards and resources. Smart cities promote innovative healthcare, transportation, education, security, etc. Blockchain is characterized by facilitating transparency, reliability, data integrity, user access control, immutability, decentralization, audibility. Integrating Blockchain in smart cities has its challenges that require extensive research and development. Smart cities hve been in in motion for last few years in different parts of the world. Still, it has created data security, authentication, sustainability, and vulnerability issues, especially when smart cities are not adequately planned. The application of Blockchain has put forward a lot of improvements in the quality and sustainability of smart cities. In this paper, we have discussed the application of Blockchain across various innovative smart city application domains. Blockchain fundamentals have been discussed before to help the reader gain ideas about Blockchain. This paper also provides future research agendas for blockchain integration in smart cities. The agendas will provide the researchers with helpful research directions.
Methods and Means for Small Dynamic Objects Recognition and Tracking

A literature analysis has shown that object search, recognition, and tracking systems are becoming increasingly popular. However, such systems do not achieve high practical results in analyzing small moving living objects ranging from 8 to 14 mm. This article examines methods and tools for recognizing and tracking the class of small moving objects, such as ants. To fulfill those aims, a customized You Only Look Once Ants Recognition (YOLO_AR) Convolutional Neural Network (CNN) has been trained to recognize Messor Structor ants in the laboratory using the LabelImg object marker tool. The proposed model is an extension of the You Only Look Once v4 (Yolov4) 512 × 512 model with an additional Self Regularized Non–Monotonic (Mish) activation function. Additionally, the scalable solution for continuous object recognizing and tracking was implemented. This solution is based on the OpenDatacam system, with extended Object Tracking modules that allow for tracking and counting objects that have crossed the custom boundary line. During the study, the methods of the alignment algorithm for finding the trajectory of moving objects were modified. I discovered that the Hungarian algorithm showed better results in tracking small objects than the K–D dimensional tree (k-d tree) matching algorithm used in OpenDataCam. Remarkably, such an algorithm showed better results with the implemented YOLO_AR model due to the lack of False Positives (FP). Therefore, I provided a new tracker module with a Hungarian matching algorithm verified on the Multiple Object Tracking (MOT) benchmark. Furthermore, additional customization parameters for object recognition and tracking results parsing and filtering were added, like boundary angle threshold (BAT) and past frames trajectory prediction (PFTP). Experimental tests confirmed the results of the study on a mobile device. During the experiment, parameters such as the quality of recognition and tracking of moving objects, the PFTP and BAT, and the configuration parameters of the neural network and boundary line model were analyzed. The results showed an increased tracking accuracy with the proposed methods by 50%. The study results confirmed the relevance of the topic and the effectiveness of the implemented methods and tools.
Dynamic Programming Through the Lens of Semismooth Newton-Type Methods

Policy iteration and value iteration are at the core of many (approximate) dynamic programming methods. For Markov Decision Processes with finite state and action spaces, we show that they are instances of semismooth Newton-type methods for solving the Bellman equation. In particular, we prove that policy iteration is equivalent to the exact semismooth Newton method and enjoys a local quadratic convergence rate. This finding is corroborated by extensive numerical evidence in the fields of control and operations research, which confirms that policy iteration generally requires relatively few iterations to achieve convergence even in presence of a large number of admissible policies. We then show that value iteration is an instance of the fixed-point iteration method and develop a novel locally accelerated version of value iteration with global convergence guarantees and negligible extra computational costs.
Efficient Transfer Learning for Visual Tasks via Continuous Optimization of Prompts

Traditional methods for adapting pre-trained vision models to downstream tasks involve fine-tuning some or all of the model's parameters. There are a number of trade-offs with this approach. When too many parameters are fine-tuned, the model may lose the benefits associated with pre-training, such as the ability to generalize to out-of-distribution data. But, if instead too few parameters are fine-tuned, the model may be unable to adapt effectively for the tasks downstream. In this paper, we propose Visual Prompt Tuning (VPT) as an alternative to fine-tuning for Transformer-based vision models. Our method is closely related to, and inspired by, prefix-tuning of language models [22]. We find that, by adding additional parameters to a pre-trained model, VPT offers similar performance to fine-tuning the final layer. In addition, for low-data settings and for specialized tasks, such as traffic sign recognition, satellite photo recognition and handwriting classification, the performance of Transformer-based vision models is improved with the use of VPT.
Collision Detection for Unions of Convex Bodies With Smooth Boundaries Using Closed-Form Contact Space Parameterization

This paper studies the narrow phase collision detection problem for two general unions of convex bodies encapsulated by smooth surfaces.The approach, namely CFC (Closed-Form Contact space), is based on parameterizing their contact space in closed-form.The first body is dilated to form the contact space while the second is shrunk to a point.Then, the collision detection is formulated as finding the closest point on the parametric contact space with the center of the second body.Numerical solutions are proposed based on the point-to-surface distance as well as the common-normal concept.Furthermore, when the two bodies are moving or under linear deformations, their first time of contact is solved continuously along the time-parameterized trajectories.Benchmark studies are conducted for the proposed algorithms in terms of solution stability and computational cost.Applications of the sampling-based motion planning for robot manipulators are demonstrated.
MSS-WISN: Multiscale Multistaining WBCs Instance Segmentation Network

Accurate segmentation and detection (instance segmentation) of white blood cells (WBCs) from whole slide images remains a challenging task, as the WBCs vary widely in shapes, sizes, and colors caused by different cell subtypes and various staining techniques. In this paper, we propose a novel framework for end-to-end segmentation and detection of WBCs that are on multiple scales and stained by different techniques. We name the framework the multi-scale and multi-staining WBC instance segmentation network (MSS-WISN). The MSS-WISN consists of two parts: 1) a feature extraction network for strengthening the feature expression and minimizing the impact of different staining techniques, and 2) a feature fusion network for highlighting salient features and thereby eliminating the effect of scale variations. To verify the effectiveness of the MSS-WISN, we build a new dataset containing 302 Magenta stained images (collected by Tianjin Medical University) and 242 Wright stained images (from a public dataset). Experiments show that the proposed framework outperforms other state-of-the-art methods in terms of WBC detection and WBC segmentation.
B-PesNet: Smoothly Propagating Semantics for Robust and Reliable Multi-Scale Object Detection for Secure Systems

Multi-scale object detection is a research hotspot, and it has critical applications in many secure systems.Although the object detection algorithms have constantly been progressing recently, how to perform highly accurate and reliable multi-class object detection is still a challenging task due to the influence of many factors, such as the deformation and occlusion of the object in the actual scene.The more interference factors, the more complicated the semantic information, so we need a deeper network to extract deep information.However, deep neural networks often suffer from network degradation.To prevent the occurrence of degradation on deep neural networks, we put forth a new model using a newly-designed Pre-ReLU, which inserts a ReLU layer before the convolution layer for the sake of preventing network degradation and ensuring the performance of deep networks.This structure can transfer the semantic information more smoothly from the shallow to the deep layer.However, the deep networks will encounter not only degradation, but also a decline in efficiency.Therefore, to speed up the two-stage detector, we divide the feature map into many groups so as to diminish the number of parameters.Correspondingly, calculation speed has been enhanced, achieving a balance between speed and accuracy.Through mathematical demonstration, a Balanced Loss (BL) is proposed by a balance factor to decrease the weight of the negative sample during the training phase to balance the positives and negatives.Finally, our detector demonstrates rosy results in a range of experiments and gains an mAP of 73.38 on PASCAL VOC2007, which approaches the requirement of many security systems.
A Graph-Based Multi-view Clustering Approach for Continuous Pattern Mining

Today’s smart monitoring applications need machine learningMachine learning models and data mining algorithms that are capable of analysing and mining the temporal component of data streamsData stream. These models and algorithms also ought to take into account the multi-sourceMulti-source nature of the sensor data by being able to conduct multi-view analysis. In this study, we address these challenges by introducing a novel multi-view dataMulti-view data streamData stream clustering approach, entitled MST-MVS clustering, that can be applied in different smart monitoring applications for continuous pattern miningPattern mining and data labelling. This proposed approach is based on the Minimum Spanning TreeMinimum spanning tree (MST) clustering algorithm. This algorithm is applied for parallel building of local clustering models on different views in each chunk of data. The MST-MVS clustering transfers knowledge learnt in the current data chunk to the next chunk in the form of artificial nodes used by the MST clustering algorithm. These artificial nodes are identified by analyzing multi-view patterns extracted at each data chunk in the form of an integrated (global) clustering model. We further show how the extracted patterns can be used for post-labelling of the chunk’s data by introducing a dedicated labelling technique, entitled Pattern-labelling. We study and evaluate the MST-MVS clustering algorithm under different experimental scenarios on synthetic and real-world data.
Duplicate Image Representation Based on Semi-Supervised Learning

For duplicate image detection, the more advanced large-scale image retrieval systems in recent years have mainly used the Bag-of-Feature ( BoF ) model to meet the real-time. However, due to the lack of semantic information in the training process of the visual dictionary, BoF model cannot guarantee semantic similarity. Therefore, this paper proposes a duplicate image representation algorithm based on semi-supervised learning. This algorithm first generates semi-supervised hashes, and then maps the image local descriptors to binary codes based on semi-supervised learning. Finally, an image is represented by a frequency histogram of binary codes. Since the semantic information can be effectively introduced through the construction of the marker matrix and the classification matrix during the training process, semi-supervised learning can not only guarantee the metric similarity of the local descriptors, but also guarantee the semantic similarity. And the experimental results also show this algorithm has a better retrieval effect compared with traditional algorithms.
A Novel Method to Perceive Self-Vehicle State Based on Vehicle Video by Image Similarity Calculation

Perceiving self-vehicle state based on vehicle information can provide key information for unmanned driving and improve vehicle safety monitoring ability. However, existing studies mainly perceive the vehicle state using out-of-vehicle sensors, positioning systems and in-vehicle sensors, and these methods have their own limitations. In recent years, video image processing has been introduced to transportation research. Despite this and the popularity of vehicle videos, self-vehicle state perception based on vehicle videos captured by the drive recorder remains an unworked area. Therefore, this paper proposed a novel method to perceive self-vehicle state which contains &#x201C;move&#x201D; and &#x201C;stop&#x201D; by calculating the image similarity of the static region between two adjacent video frames. The static region extraction is based on You Only Look At CoefficenTs (YOLACT) instance segmentation model, which can avoid the interference of surroundings like cars and pedestrians. We acquired actual tram vehicle videos to validate our method which can accurately perceive the state and state transition continuously and real-timely in different complex scenes at any time, even if it stops and restarts within only 3 seconds. The approach gives a new thought and inspiration for studies of videos and illustrates that based on vehicle videos we can not only obtain the vehicle&#x2019;s environment information but also perceive the self-vehicle state. And the proposed approach can be an alternative for estimating self-vehicle state when traditional methods are not available.