Reviewerly/exHarmony huggingface dataset:
Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle’s optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.
Usefulness of Mobile Assisted Language Learning Application

The focus of this article to examining the District Monitoring Officer (DMO) and Assistant Commissioner (AC)’s perception for Literacy &amp; Numeracy Drive (LND) a mobile-based application that is used in primary schools of public sector in Punjab province, Pakistan to teach student of Grade 03 on a tablet for learning languages and Mathematics. This study aims to gauge the efficacy of LND for its usefulness, usability, accessibility, content, and assessments. Semi-Structured interviews conducted for the purpose of study from the DMO and AC of District Sheikhupura, to measure the effectiveness of LND and evaluate the effectiveness. The result reveals, in its current form, the LND application is not effective and needs improvement in usability, design, content, accessibility, infrastructure, and assessment. Furthermore, DMO and AC recommended that game-based learning consists of an interactive interface, phonics, animations to be developed and adopted. As a more interactive and attractive presentation of the content and variations in the assessment may increase students’ involvement and will make this application more effective and will produce good results.
HopfE

Recently, several Knowledge Graph Embedding (KGE) approaches have been devised to represent entities and relations in a dense vector space and employed in downstream tasks such as link prediction. A few KGE techniques address interpretability, i.e., mapping the connectivity patterns of the relations (symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotation. Other approaches model the representations in higher dimensional space such as four-dimensional space (4D) to enhance the ability to infer the connectivity patterns (i.e., expressiveness). However, modeling relation and entity in a 4D space often comes at the cost of interpretability. We propose HopfE, a novel KGE approach aiming to achieve the interpretability of inferred relations in the four-dimensional space. HopfE models the structural embeddings in 3D Euclidean space. Next, we map the entity embedding vector from a 3D Euclidean space to a 4D hypersphere using the inverse Hopf Fibration, in which we embed the semantic information from the KG ontology. Thus, HopfE considers the structural and semantic properties of the entities without losing expressivity and interpretability. Our empirical results on four well-known benchmarks achieve state-of-the-art performance for KG completion.
Emotional Intelligence as a Vital Indicator of Teacher Effectiveness

The present research will refer to emotional intelligence (EI) with important work-related outcomes such as teacher performance as well as organizational productivity.The initial goal is to explore the effect (role) of emotional intelligence skills in effective teaching, and its effect on the productivity of teacher's performance during the teaching-learning process.Effective teaching and good learning are two integral parts of students' academic success.There are number of crucial factors which play an important role in teacher effectiveness such as teacher's classroom management skills, teacher's subject expertise, knowledge of teaching methods, teacher's professionalism, -how to structure, explain and present the materials, and others.But emotional intelligence (EI), as the fifth component and the most important factor, sets most effective teachers apart from the majority because process of learning involves emotional and intellectual endeavor, struggle, frustration, excitement, also anxiety.Therefore an affective teacher needs an understanding of students' emotional state and causes of their behavior to create an ideal learning environment that encourages positive social interaction, active engagement and strong motivation to learn.In the light of exploration of the role of Emotional Intelligence in effective teaching-learning process, this study asserts that teachers' emotional competence factors contribute to the teacher effectiveness more than any other single factor.For instance, development of supportive teacher-student relationships, effective classroom management strategies, and successful emotional learning program implementation in order to create an ideal classroom climate that is more conducive to learning.All these contributions promote positive developmental outcomes in students' achievements.
AutoHERI

Post-click conversion rate (CVR) estimation is a crucial task in online advertising and recommendation systems. To address the sample selection bias problem in traditional CVR models trained in click space, recent studies perform entire space multi-task learning based on the probability of events in user behavior funnels like "impression-click-conversion". However, those models learn the feature representation of each task independently, and omit potential inter-task correlations that can help improve the CVR estimation performance. In this paper, we propose AutoHERI, an entire space CVR model with automated hierarchical representation integration, which leverages the interplay across multi-tasks' representation learning. It performs neural architecture search to learn optimal connections between layer-wise representations of different tasks. Besides, AutoHERI achieves better search efficiency with one-shot search algorithm, and thus it can be easily extended to new scenarios that have more complex user behaviors. Both offline and online experimental results on large-scale real-world datasets verify that AutoHERI outperforms previous entire space models significantly.
Artistic Virtual Reality

The concept of virtual reality has transformed from a philosophical concept which implies an intangible reality realized in subjective human experience, into a widely used term that refers to a technologically realized simulated experience. The new technology of virtual reality erases the boundaries of subjective and objective, real and fictional, between cinema, theater, video games, art. Being a play as the basic form of human activity, VR immerses a person in an alternative artistic artificial environment. As a result of the analysis of more than two hundred creative projects using virtual reality, several properties have been identified that contribute to the experience of “virtuality”: unusual experience, activity, naturalism, interaction, emotional engagement. “Unusual experience” is the characteristic that most distinguishes artistic projects (theater, cinema, art, video games) from other non-creative uses of VR. In virtual reality, the role of the audience is becoming more and more active, which is achieved by technical (360-degree perspective or free movement) and artistic means (transformation of the viewer into a character or creator). The use of film and animation techniques expands the boundaries of what is possible in VR. The ability to convey experience at the level of actuality demonstrates the great potential of VR technology in the construction of reality.
Personalized Preference Drift Aware Sequential Recommender System

The user preference patterns are highly dynamic and develop over time. To address the drift of user preference patterns, most of the prior works for sequential recommendation categorize the user preference patterns into different patterns, e.g., short-term and long-term preference. However, the number of user preference patterns is pre-defined and identical for every user, resulting in the drift patterns regardless of the user’s actual drift points. Moreover, existing works recommend the next item by considering the whole historical sequence, which contains the noises from interactions irrelevant to the current user preference pattern. In this work, we propose a model to personalized detects drift of user preference patterns, called PPD. Our proposed method determines the actual drift of user preference patterns by capturing the changes in the characteristics of consecutive items throughout the historical sequence. The detected drift pattern allows PPD to partition the historical sequence into various sub-sequences which contain only a particular preference pattern. As a result, PPD delivers the recommendations relevant to the current user preference pattern by considering only the sub-sequences with similar preference patterns instead of utilizing the whole historical sequence. We conduct the experiments to verify the effectiveness of our proposed method by comparing PPD with the baselines aiming to model the user drift pattern for the recommendation. The experimental results show that our proposed method consistently outperforms the baselines on three benchmark datasets. Additionally, the experiment further shows that PPD delivers superior results when considering only the relevant periods rather than the whole sequence.
A Systematic Review on Screening, Examining and Classification of Breast Cancer

Breast cancer is one of the major causes of women's death. This leads to an immediate requirement for an earlier and accurate diagnosis to lower the mortality rate. Computer-aided detection (CAD) enables clinicians to effectively identify tumors. Herein, images are information sources used for the identification and diagnosis of breast tumors. Many imaging techniques allow clinicians for studying breast anatomy and its analysis. This paper intends to present a study that discusses the emerged algorithms and presents an overview of advancement in the detection and classification of breast tumors. The review also focuses on techniques that emerged for facilitating the classification of different masses using several imaging techniques. The study starts with an overview of several distinct machine learning, deep learning, and other models used for breast cancer diagnosis. Moreover, the study provides an outline of different imaging modalities and different publicly available benchmark databases employed in several research publications for the analysis of breast tumors. As a final point, the study summarizes the current trends and challenges lies in detecting and classifying breast tumors for future researchers.
AI and libraries: trends and projections

Purpose This paper aims to focus on the trends and projection for future use of artificial intelligence (AI) in libraries. AI technologies is the latest among the technologies being used in libraries. The technology has systems that have natural language processing, machine learning and pattern recognition capabilities that make service provision easier for libraries. Design/methodology/approach Systematic literature review is done, exploring blogs and wikis, to collect information on the ways in which AI is used and can be futuristically used in libraries. Findings This paper found that uses of AI in libraries entailed enhanced services such as content indexing, document matching, content mapping content summarization and many others. AI possibilities were also found to include improving the technology of gripping, localizing and human–robot interaction and also having artificial superintelligence, the hypothetical AI that surpasses human intelligence and abilities. Originality/value It is concluded that advanced technologies that AI are, will help librarians to open up new horizons and solve challenges that crop up in library service delivery.
Regulating Personal Data Usage in COVID-19 Control Conditions

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Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification

Unsupervised person re-identification (ReID) aims to match a query image of a pedestrian to the images in gallery set without supervision labels. The most popular approaches to tackle unsupervised person ReID are usually performing a clustering algorithm to yield pseudo labels at first and then exploit the pseudo labels to train a deep neural network. However, the pseudo labels are noisy and sensitive to the hyper-parameter(s) in clustering algorithm. In this paper, we propose a Hybrid Contrastive Learning (HCL) approach for unsupervised person ReID, which is based on a hybrid between instance-level and cluster-level contrastive loss functions. Moreover, we present a Multi-Granularity Clustering Ensemble based Hybrid Contrastive Learning (MGCE-HCL) approach, which adopts a multi-granularity clustering ensemble strategy to mine priority information among the pseudo positive sample pairs and defines a priority-weighted hybrid contrastive loss for better tolerating the noises in the pseudo positive samples. We conduct extensive experiments on two benchmark datasets Market-1501 and DukeMTMC-reID. Experimental results validate the effectiveness of our proposals.
A Novel BGCapsule Network for Text Classification

Several text classification tasks such as sentiment analysis, news categorization, multi-label classification and opinion classification are challenging problems even for modern deep learning networks. Recently, Capsule Networks (CapsNets) are proposed for image classification. It has been shown that CapsNets have several advantages over Convolutional Neural Networks (CNNs), while their validity in the domain of text was less explored. In this paper, we propose a novel hybrid architecture viz., BGCapsule, which is a Capsule model preceded by an ensemble of Bidirectional Gated Recurrent Units (BiGRU) for several text classification tasks. We employed an ensemble of Bidirectional GRUs for feature extraction layer preceding the primary capsule layer. The hybrid architecture, after performing basic pre-processing steps, consists of five layers: an embedding layer based on GloVe, a BiGRU-based ensemble layer, a primary capsule layer, a flatten layer and fully connected ReLU layer followed by a fully connected softmax layer. To evaluate the effectiveness of BGCapsule, we conducted extensive experiments on five benchmark datasets (ranging from 10,000 records to 700,000 records) including Movie Review (MR Imdb 2005), AG’s News dataset, Dbpedia ontology dataset, Yelp Review Full dataset and Yelp review polarity dataset. These benchmarks cover several text classification tasks such as news categorization, sentiment analysis, multiclass classification, multi-label classification and opinion classification. We found that our proposed architecture (BGCapsule) achieves better accuracy compared to the existing methods without the help of any external linguistic knowledge such as positive sentiment keywords and negative sentiment keywords. Further, BGCapsule converged faster compared to other extant techniques.
Ytterbium ion trap quantum computing: The current state-of-the-art

We present an overview of contemporary quantum computing with ytterbium ion traps, placing the emphasis on industry implementations. We provide brief, concrete descriptions of various key features, such as trap loading, electronic structure, qubit function, gates, error analysis, and benchmarking. We focus on the underlying science and current technologies to provide readers with a holistic picture of available techniques for using ytterbium in contemporary ion trap designs.
Depth-Aware Dynamic Projection Mapping using High-speed RGB and IR Projectors

In this paper, we propose a strong system for dynamic projection mapping (DPM). It consists of a high-speed 500-fps camera, high-speed 24-bit 947-fps RGB projector, and newly developed 8-bit 2,880-fps infrared (IR) projector. This configuration allows us to capture the depth map and project the depth-aware image at a high frame rate. We also realize 0.4-ms markerless 3D pose tracking using the depth map by leveraging small inter-frame motions under high-frame-rate capture. We exploit these captured data and apply the depth-aware DPM to an entire scene with low latency, in a setting in which tracking-based and modelless mapping are combined and performed simultaneously.
Investigating the impact of development and internal validation design when training prognostic models using a retrospective cohort in big US observational healthcare data

The internal validation of prediction models aims to quantify the generalisability of a model. We aim to determine the impact, if any, that the choice of development and internal validation design has on the internal performance bias and model generalisability in big data (n~500 000).Retrospective cohort.Primary and secondary care; three US claims databases.1 200 769 patients pharmaceutically treated for their first occurrence of depression.We investigated the impact of the development/validation design across 21 real-world prediction questions. Model discrimination and calibration were assessed. We trained LASSO logistic regression models using US claims data and internally validated the models using eight different designs: 'no test/validation set', 'test/validation set' and cross validation with 3-fold, 5-fold or 10-fold with and without a test set. We then externally validated each model in two new US claims databases. We estimated the internal validation bias per design by empirically comparing the differences between the estimated internal performance and external performance.The differences between the models' internal estimated performances and external performances were largest for the 'no test/validation set' design. This indicates even with large data the 'no test/validation set' design causes models to overfit. The seven alternative designs included some validation process to select the hyperparameters and a fair testing process to estimate internal performance. These designs had similar internal performance estimates and performed similarly when externally validated in the two external databases.Even with big data, it is important to use some validation process to select the optimal hyperparameters and fairly assess internal validation using a test set or cross-validation.
A Multi-Robot Balanced Coverage Path Planning Strategy for Patrol Missions

This paper presents a multi-robot balanced coverage path planning (MCPP) strategy for patrol missions. In the entire path-based multi-robot path assignment problem, conventional studies allow path redundancy and divide the path itself according to the number of robots. However, more collisions between robots can happen due to path redundancy. Therefore, a strategy of equally assigning a path to each robot is proposed without path redundancy. First, given a map, a graph for robot movement is constructed. A problem similar to the traveling salesman problem is solved in the graph. As a result, an entire path is generated. Subsequently, unique paths are extracted from the whole path and combined according to the number of robots. Since the combined paths are not evenly divided, the paths are compensated in a way that minimizes the maximum length of ones. Simulations were performed by varying the number of robots. Through performance comparison, it showed that the paths were divided well using the proposed approach.
A Hyperledger Fabric-based Blockchain Architecture to Secure IoT-based Health Monitoring Systems

Although blockchain is a promising technology that can bring significant benefits into current centralized IoTbased health monitoring systems in order to address security challenges, the resource-constrained IoT devices of these systems cannot afford complex and heavyweight operations due to their limited processing power, storage capacity, and battery life.Therefore, in this paper, we propose a Hyperledger Fabric-based blockchain architecture to: i) enhance security in IoT-based health monitoring systems, ii) achieve better storage handling due to the limited storage capacity of sensors and gateways, iii) facilitate decentralized accountability, and iv) eliminate single point of failure.
A Hybrid Multi-Feature Semantic Similarity Based Online Social Recommendation System Using CNN

Modern organizations are keen to work towards their customer needs. To achieve this, analyzing their activities and identifying their interest in any entity becomes important. Every user has been identified as the most important factor in point of organization, and they never give up even a single user. Several approaches have been discussed earlier, which use artificial intelligence to mine the users and their interest in the problem. However, the deep learning algorithms are identified as most efficient in identifying the user interest but suffer to achieve higher performance. Towards this issue, an efficient multi-feature semantic similarity-based online social recommendation system has been proposed. The method uses Convolution Neural Network (CNN) to train and predict user interest in any topic. Each layer has been identified as a single interest, and neurons of the layers are initialized with huge data set. The neuron estimates the Multi-Feature Semantic Similarity (MFSS) towards each interest of the user. Finally, the method identifies the single interest for the user by ranking each interest to produce recommendations to the user. The proposed algorithm improves the performance of recommendation generation with less false ratio.