Reviewerly/exHarmony huggingface dataset:
Web Personalisation based on User Interaction : Web Personalisation

The World Wide Web is widely used as a global reference for collecting information. However, various web deployed learning systems provide identical educational resources to users under different profiles. The main objective of this research is to evaluate recommendations of active users/learners by studying their history, utilization likeness as well as differences between the user preferences together with the content of the learning resources. Concepts like Web scraping, Web usage mining for Web personalization are used in this research work to achieve the desired results. User profiling and personalizing the web search results are used to evaluate the links which could be recommended to the active users. Web personalization provides users/learner's relevant information and also the information which is facilitated by the likeness of the users/learners. The purpose of personalization is to make a website more flexible (responsive) towards the user's/learner's requirements. Web mining is used to cover concepts like web content mining as well as web usage mining as well as web structure mining falls under data mining, Personalization also falls under web usage mining. Researchers constantly concentrate on fulfilling the user's/learner's requirements, as all users/learners of different backgrounds have different goals while browsing the web. So search engines must be up to date (aware) with user/learner likeness and preferences so user/learner likeness and preferences should be gathered and used, for this, the concepts like Collaborative Filtering, Document Based and Concept-based profiling can be used.
Gesture Recognition System

Mostly communication in current scenario is happened via vocal sounds with body language gestures. Vocal sound play an important role in communication but at the same time, various body language expressions add more importance to communication. Even in few cases, body language played a very important role like communication between deaf & dumb people or traffic signal etc. In this paper, a method is defined for recognizing Gesture and Posture. This approach is tested on 6 gesture classes and 6 posture classes.
Learning Clustering for Motion Segmentation

Subspace clustering has been extensively studied from the hypothesis-and-test, algebraic, and spectral clustering-based perspectives. Most assume that only a single type/class of subspace is present. Generalizations to multiple types are non-trivial, plagued by challenges such as choice of types and numbers of models, sampling imbalance and parameter tuning. In many real world problems, data may not lie perfectly on a linear subspace and hand designed linear subspace models may not fit into these situations. In this work, we formulate the multi-type subspace clustering problem as one of learning non-linear subspace filters via deep multi-layer perceptrons (mlps). The response to the learnt subspace filters serve as the feature embedding that is clustering-friendly, i.e., points of the same clusters will be embedded closer together through the network. For inference, we apply K-means to the network output to cluster the data. Experiments are carried out on synthetic data and real world motion segmentation problems, producing state-of-the-art results.
Joint Localization Based on Split Covariance Intersection on the Lie Group

This article presents a pose fusion method that accounts for the possible correlations among measurements. The proposed method can handle data fusion problems whose uncertainty has both independent and dependent parts. Different from the existing methods, the uncertainties of the various states or measurements are modeled on the Lie algebra and projected to the manifold through the exponential map, which is more precise than that modeled in the vector space. The correlation is based on the theory of covariance intersection, where the independent and dependent parts are split to yield a more consistent result. In this article, we provide a novel method for the correlated pose fusion algorithm on the manifold. Theoretical derivation and analysis are detailed first, and then, the experimental results are presented to support the proposed theory. The main contributions are threefold: First, we provide a theoretical foundation for the split covariance intersection filter performed on the manifold, where the uncertainty is associated with the Lie algebra. Second, the proposed method gives an explicit fusion formalism on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{SE}(3)$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{SE}(2)$</tex-math></inline-formula> , which covers the most use cases in the field of robotics. Third, we present a localization framework that can work for both single-robot and multirobot systems, where not only the fusion with possible correlation is derived on the manifold but also the state evolution and relative pose computation are performed on the manifold. The experimental results validate the advantage of this approach over state-of-the-art methods.
Few-data guided learning upon end-to-end point cloud network for 3D face recognition

Deep-learning-based 3D face recognition methods have developed vigorously in recent years, while the potential of these methods is being exploited in more and more scenarios. In this paper, an end-to-end deep learning network entitled Sur3dNet-Face for point-cloud-based 3D face recognition is proposed. The method uses PointNet, which is a successful point cloud classification solution but performs unexpectedly in face recognition, as the backbone. To adapt the backbone to 3D face recognition, modifications in network architecture and a few-data guided learning framework based on Gaussian process morphable model is supplemented. Instead of mass data in multiple datasets for training, our method takes only Spring2003 subset of FRGC v2.0 for training which contains 943 facial scans and the network is well trained with such a small amount of real data. The processing time to generate face representation is less than 0.15 s. Without fine-tuning on the test set, the Rank-1 Recognition Rate (RR1) is achieved as follows: 98.85% on FRGC v2.0 dataset and 99.33% on Bosphorus dataset, which proves the effectiveness and the potentiality of our method. When facing scenarios with limited resource, the proposed method is expected to give a competitive performance.
Enhanced review-based rating prediction by exploiting aside information and user influence

User-generated reviews greatly supplement the descriptions of items and thereby play an important role in decision making. Researchers have been exploiting these invaluable resources to discover the users’ preferences, model the items’ properties and further provide an explainable recommendation. Legacy strategies seek to quantify the reviews by directly processing the text. However, not all reviews are equally reliable or influential, as the reviews might be generated by different users under various conditions, purposes and habits. Besides, not all reviews given by the users equally contribute to reflecting the users’ preference for the target item since users care about different aspects of different items. In this paper, we propose a novel end-to-end model, named Enhanced Review-based Rating Prediction by Exploiting Aside Information and User Influence (ERP), which differentiates the influence of reviews generated by different users and learns the item-aware user preference with aside information along with their own reviews. On benchmark datasets, our model achieves 1.32% improvements on average in terms of MSE compared to the best result among baselines.
Learning From Imperfect Demonstrations From Agents With Varying Dynamics

Imitation learning enables robots to learn from demonstrations. Previous imitation learning algorithms usually assume access to optimal expert demonstrations. However, in many real-world applications, this assumption is limiting. Most collected demonstrations are not optimal or are produced by an agent with slightly different dynamics. We therefore address the problem of imitation learning when the demonstrations can be sub-optimal or be drawn from agents with varying dynamics. We develop a metric composed of a feasibility score and an optimality score to measure how useful a demonstration is for imitation learning. The proposed score enables learning from more informative demonstrations, and disregarding the less relevant demonstrations. Our experiments on four environments in simulation and on a real robot show improved learned policies with higher expected return.
Semantic Service Clustering With Lightweight BERT-Based Service Embedding Using Invocation Sequences

Service clustering is an efficient method for facilitating service discovery and composition.Traditional approaches based on the self-description documents for services usually utilize service signatures.In Web service composition, service clustering can also be performed by the invocation relationship between services.Therefore, based on the successful development of several embedding techniques for words in several contexts, a novel deep learning-based service embedding using invocation sequences is devised for service clustering.Moreover, many microservices are being created because of the rapid development of the Internet of Things (IoT), and edge, and fog computing.Following these developments, Web service composition based on these environments has emerged in abundance.More efficient lightweight approaches to analyze large numbers of services are necessary for service clustering.Consequently, a lightweight deep learning-based approach for the semantic clustering of service composition is presented to address these requirements.In this paper, we first propose the concept of service embedding to capture semantic information from invocation sequences.Second, we suggest using state-of-the-art neural language sequence models for service embedding and develop a corresponding lightweight Bidirectional Encoder Representations of Transformers (BERT)-based model.Next, combined with K-means clustering, the semantic clustering of service composition is evaluated.Finally, the experimental results show that the clustering process can be effectively performed by the lightweight BERT-based model.
Determining the mixed high-dimensional Bell state of a photon pair through the measurement of a single photon

Recently, a theoretical method and corresponding experiment have shown that the information about entanglement in the state of the qubit photon pair can be retrieved by measuring only one of the photons, even though the state is mixed. In this paper, we investigate this process with the high-dimensional photon pair and more information about the state. We present a method that enables us to determine the state of the photon pair fully. Our method measures only one of the photons while dropping the other one. Importantly, no adjustment of the transformation is required in our method. Moreover, our method is robust to photon loss so that the state can be perfectly determined as long as the photon is not all lost.
Efficient calculation of solar position using rectangular coordinates

There have been many algorithms published for calculating the position of the Sun. Most of them are simplified in some way, offering either reduced accuracy or a limited range of valid times. A different approach to the problem is presented here, derived from the approach used by some large astronomical telescope control systems, in which rectangular coordinates are used to transform the celestial coordinates into a direction as seen from a site (or multiple sites) on the surface of the Earth. This reduces the computational complexity for the conversion to topocentric coordinates. For applications in which calculations are repeated at a high rate, such as for solar tracking, this approach also enables a simple interpolation to be used for the Sun’s equatorial position vector, which brings a further dramatic reduction in the computational complexity of the most detailed published algorithms, with a smaller loss of accuracy compared with any of the simpler published algorithms.
Fast and Lightweight Human Pose Estimation

Although achieving significant improvement on pose estimation, the major drawback is that most top-performing methods tend to adopt complex architecture and spend large computational cost to achieve higher performance. Due to the edge device’s limited resources, its top-performing methods are hard to maintain fast inference speed in practice. To address this issue, we proposed the fast and lightweight human pose estimation method to maintain high performance and bear the less computational cost. Especially, the proposed method consists of two parts, i.e., the fast and lightweight pose network (FLPN) for pose estimation and a novel lightweight bottleneck block for reducing computational cost, which can integrate the simple network and lightweight bottleneck into an efficient method for accurate pose estimation. In terms of lightweight bottleneck block, we introduce the structural similarity measurement (SSIM) to refine the appropriate ratio of intrinsic feature maps and reduce the model size. Furthermore, an attention mechanism is also adopted in our lightweight bottleneck block for modeling the contextual information. We demonstrate the performance of the proposed method with extensive experiments on the two standard benchmark datasets by comparing our method with state-of-the-art methods. On the COCO keypoint detection dataset, our proposed method attains a similar accuracy with these state-of-the-art methods, but the computational cost of these top-performing methods is more than 7 times that of ours.
Online Energy-efficient Resource Allocation in Cloud Computing Data Centers

Energy efficiency is a major topic in every scientific field, since being energy efficient means producing more for a smaller cost. Data centers are no exception to this rule as their energy use represents a large portion of the global consumption, and it is needless to say that they ought to perform optimally while being eco-friendly in order to preserve natural resources as much as possible while providing a high quality service for the users. In this paper, we propose an efficient algorithm for allocating users to a pool of servers in an energy-efficient way. Our allocation model emphasizes the critical importance of nondominant resource types such as memory, which usually tend to be wasted by homogeneous allocation approaches. We show that the performance of the algorithm makes it worthy of being used in real-time environments where split-second decisions must be made. We compare our algorithm to the most well-known metaheuristics used in operations research and we show that they do not provide a significant improvement in a reasonable time.
Stitching interferometry using Computer-Generated Hologram (CGH) based on sub-aperture adjustment aberration of 2-D Chebyshev polynomial

Limited by the field of view of the computer-generated hologram (CGH), full-aperture measurement of long cylindrical mirror with large curvature have been challenging. This paper reports a stitching interferometry based on sub-aperture adjustment with an aberration model represented by 2D Chebyshev polynomials. Firstly, a mathematical model under null measurement between the sub-aperture adjustment and its corresponding aberration is established by combining 2-D Chebyshev polynomials, which can correct sub-aperture position deviation. Secondly, on the basis of the adjustment aberration model, a global stitching algorithm based on the self-residual iterative algorithm (SRIA) is proposed. The algorithm uses the global stitching matrix and the sub-aperture spatial position constraint of the adjustment aberration model to iterate repeatedly for sub-aperture position. The experimental results verify the validity and feasibility of the proposed method. Finally, compared with full-aperture measurements using CGH, the proposed stitching method can obtain high-precision smooth stitching surface, and the repeatability is better than 0.7 nm RMS (P-V).
Design of path planning and tracking control of quadrotor

&lt;p style='text-indent:20px;'&gt;In this paper, we first design a motion planning system based on the Batch Informed Trees (BIT*) algorithm for quadrotor and a linear model predictive control (LMPC) is applied to solve the path tracking problem for a quadrotor. BIT* algorithm is used to plan a barrier-free trajectory quickly in an obstructed environment. Then we apply linear model predictive control for the full state quadrotor system model to track the generated trajectory. Finally, the BIT* algorithm simulation case is presented using RVIZ visual interface and some simulation cases are presented using MATLAB / Simulink. The results demonstrate the capability and the effectiveness of the control strategy in fast path tracking and the quadrotor stability, while the desired performance is achieved.&lt;/p&gt;
Improving Deep Multi-modal 3D Object Detection for Autonomous Driving

Object detection in real-world applications such as autonomous driving scenarios is a challenging issue since objects often occlude each other. 3D object detection has achieved high accuracy and efficiency, but detecting small object instances and occluded objects are the most challenging issues to deploy detectors in crowded scenes. Our main focus in this paper is deep multi-modal based object detector in an automated driving system with early fusion on 3D object detection utilizing both Light Detection and Ranging (LiDAR) and image data. We aim at obtaining highly accurate 3D localization and recognition of objects in the road scene and try to improve the performance. In this regard, our basic architecture follows an established two-stage architecture, Aggregate View Object Detection-Feature Pyramid Network (AVOD-FPN), one of the best among sensor fusion-based methods. AVOD-FPN has yielded promising results especially for detecting small instances. Moreover, another main challenging issue in autonomous driving is detecting the occluded objects. So we try to address this difficulty by integrating attention network into the multi-modal 3D object detector. Experiments are shown to produce state-of-the-art results on the KITTI 3D sensor fusion-based object detection benchmark.
Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.
Event Detection with Entity Markers

Event detection involves the identification of instances of specified types of events in text and their classification into event types. In this paper, we approach the event detection task as a relation extraction task. In this context, we assume that the clues brought by the entities participating in an event are important and could improve the performance of event detection. Therefore, we propose to exploit entity information explicitly for detecting the event triggers by marking them at different levels while fine-tuning a pre-trained language model. The experimental results prove that our approach obtains state-of-the-art results on the ACE 2005 dataset.
Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings

Most state-of-the-art instance segmentation methods have to be trained on densely annotated images. While difficult in general, this requirement is especially daunting for biomedical images, where domain expertise is often required for annotation and no large public data collections are available for pre-training. We propose to address the dense annotation bottleneck by introducing a proposal-free segmentation approach based on non-spatial embeddings, which exploits the structure of the learned embedding space to extract individual instances in a differentiable way. The segmentation loss can then be applied directly to instances and the overall pipeline can be trained in a fully-or weakly supervised manner. We consider the challenging case of positive-unlabeled supervision, where a novel self-supervised consistency loss is introduced for the unlabeled parts of the training data. We evaluate the proposed method on 2D and 3D segmentation problems in different microscopy modalities as well as on the Cityscapes and CVPPP instance segmentation benchmarks, achieving state-of-the-art results on the latter.
GloBug: Using global data in Fault Localization

Fault Localization (FL) is an important first step in software debugging and is mostly manual in the current practice. Many methods have been proposed over years to automate the FL process, including information retrieval (IR)-based techniques. These methods localize the fault based on the similarity of the reported bug report and the source code. Newer variations of IR-based FL (IRFL) techniques also look into the history of bug reports and leverage them during the localization. However, all existing IRFL techniques limit themselves to the current project's data (local data). In this study, we introduce Globug, which is an IRFL framework consisting of methods that use models pre-trained on the global data (extracted from open-source benchmark projects). In Globug, we investigate two heuristics: (a) the effect of global data on a state-of-the-art IR-FL technique, namely BugLocator, and (b) the application of a Word Embedding technique (Doc2Vec) together with global data. Our large scale experiment on 51 software projects shows that using global data improves BugLocator on average 6.6% and 4.8% in terms of MRR (Mean Reciprocal Rank) and MAP (Mean Average Precision), with over 14% in a majority (64% and 54% in terms of MRR and MAP, respectively) of the cases. This amount of improvement is significant compared to the improvement rates that five other state-of-the-art IRFL tools provide over BugLocator. In addition, training the models globally is a one-time offline task with no overhead on BugLocator's run-time fault localization. Our study, however, shows that a Word Embedding-based global solution did not further improve the results.
Research on Sports Balanced Development Evaluation System Based on Edge Computing and Balanced Game

Sports can promote physical and mental health and the development of personality. How to build a balanced development evaluation system for sports and find a quality education suitable for the school are particularly important. In this article, we use edge computing technology to design a balanced development framework for sports. The framework will guide students to actively participate in physical exercise and develop sports to a higher, more comprehensive level. Then, the equilibrium game model is used to analyse the evaluation system of the balanced development of college sports. The research results show that the university sports balanced development evaluation system has good application prospects. The empirical analysis results verify its accuracy and reliability.