Reviewerly/exHarmony huggingface dataset:
Fully Immersive Virtual Reality in Logistics Modelling and Simulation Education

With the increasing development and popularisation of information and communication technology, new challenges are posed to higher education in the modernisation of teaching in order to make education and training of students as effective as possible. It is therefore very important to develop and experiment with appropriate development tools, explore their benefits and effectiveness, and integrate them into existing learning strategies. The emergence of a computer-generated digital environment that can be directly experienced, actions that can determine what is happening in it, growth of technological characteristics, and decline in prices of virtual reality hardware leads to a situation that cannot be ignored. This paper investigated users' perceptions on the potential use of fully immersive virtual reality head-mounted displays in a discrete-event simulation of logistics processes. The dynamic nature of virtual environments requires active participation which causes greater engagement, motivation, and interest aided by interaction and challenges.
GPU-BRKGA: A GPU accelerated library for optimization using the biased random-key genetic algorithm

This paper presents a CUDA/C++ library, called GPU-BRKGA, for accelerating the biased random-key genetic algorithm (BRKGA). Our library features an easy to use API that allows designers with little experience in GPU development to accelerate the solution of their optimization problems. It automatically handles the problem-independent modules of BRKGA, and leaves the designer in charge with only the decoding procedure, that is, to convert a random-key vector into a solution to the optimization problem. We compared our solution with two other BRKGA API implementations: the standard CPU-based BRKGA API implementation (brkgaAPI) and another GPU-based BRKGA API from the literature (brkgaCuda). Experimental results show that GPU-BRKGA is up to 28x faster than brkgaAPI and up to 3x faster than brkgaCuda.
A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot

Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of "How clean is clean" is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.
Tunable reservoir computing based on iterative function systems

In this study, a performance-tunable model of reservoir computing based on iterative function systems is proposed and its performance is investigated. Iterated function systems devised for fractal generation are applied to embody a reservoir for generating diverse responses for computation. Reservoir computing is a model of neuromorphic computation suitable for physical implementation owing to its easy feasibility. Flexibility in the parameter space of the iterated function systems allows the properties of the reservoir and the performance of reservoir computation to be tuned. Computer simulations reveal the features of the proposed reservoir computing model in a chaotic signal prediction problem. An experimental system was constructed to demonstrate an optical implementation of the proposed method.
educational role in COVID-19 terms of ethnodesign graphic function in higher education practical activities

The research aims to establish the effectiveness and assessment of the educational role of graphic function of ethnodesign, as well as to determine the feasibility of using didactics outside the classroom practices in art education in the terms of COVID-19. The methodology of study is based on a qualitative approach designed to analyze the results of the research experiment, which provides for comprehensiveness in the study. The method of experiment in work is basic, also applied methods of interviewing and observation. The hypothesis is that in the development of art specialties curricula should be used extracurricular practical classes, where educational components of the graphic function of ethnodesign, as they form creative and educated professionals, actualize educational aspects. The result of the research is the establishment of positive assessment by the participants of educational process of using the graphic function of ethnodesign for educational purposes during practical extracurricular classes since this methodology contributes to the development of creativity and has significant innovative potential in revealing the creative abilities of art students, where students actively influence educational process together. The perspective in the research work is the further implementation of educational and training projects dedicated to the methods of teaching design courses.
Reasoning over Hybrid Chain for Table-and-Text Open Domain Question Answering

Tabular and textual question answering requires systems to perform reasoning over heterogeneous information, considering table structure, and the connections among table and text. In this paper, we propose a ChAin-centric Reasoning and Pre-training framework (CARP). CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering. We also propose a novel chain-centric pre-training method, to enhance the pre-trained model in identifying the cross-modality reasoning process and alleviating the data sparsity problem. This method constructs the large-scale reasoning corpus by synthesizing pseudo heterogeneous reasoning paths from Wikipedia and generating corresponding questions. We evaluate our system on OTT-QA, a large-scale table-and-text open-domain question answering benchmark, and our system achieves the state-of-the-art performance. Further analyses illustrate that the explicit hybrid chain offers substantial performance improvement and interpretablity of the intermediate reasoning process, and the chain-centric pre-training boosts the performance on the chain extraction.
Dual Position Relationship Transformer for Image Captioning

Employing feature vectors extracted from the target detector has been shown to be effective in improving the performance of image captioning. However, it is considered that existing framework suffers from the deficiency of insufficient information extraction, such as positional relationships; it is very important to judge the relationship between objects. To fill this gap, we present a dual position relationship transformer (DPR) for image captioning; the architecture improves the image information extraction and description coding steps: it first calculates the relative position (RP) and absolute position (AP) between objects, and integrates the dual position relationship information into self-attention. Specifically, convolutional neural network (CNN) and faster R-CNN are applied to extract image features and target detection, then to calculate the RP and AP of the generated object boxes. The former is expressed in coordinate form, and the latter is calculated by sinusoidal encoding. In addition, to better model the sequence and time relationship in the description, DPR adopts long short-term memory to encode text vector. We conduct extensive experiments on the Microsoft COCO: Common Objects in Context (MSCOCO) image captioning data set that shows that our method achieves superior performance that Consensus-based Image Description Evaluation (CIDEr) increased to 114.6 after training 30 epochs and runs 2 times faster, compared with other competitive methods. The ablation study verifies the effectiveness of our proposed module.
An eager splitting strategy for online decision trees in ensembles

Decision tree ensembles are widely used in practice. In this work, we study in ensemble settings the effectiveness of replacing the split strategy for the state-of-the-art online tree learner, Hoeffding Tree, with a rigorous but more eager splitting strategy that we had previously published as Hoeffding AnyTime Tree. Hoeffding AnyTime Tree (HATT), uses the Hoeffding Test to determine whether the current best candidate split is superior to the current split, with the possibility of revision, while Hoeffding Tree aims to determine whether the top candidate is better than the second best and if a test is selected, fixes it for all posterity. HATT converges to the ideal batch tree while Hoeffding Tree does not. We find that HATT is an efficacious base learner for online bagging and online boosting ensembles. On UCI and synthetic streams, HATT as a base learner outperforms HT at a 0.05 significance level for the majority of tested ensembles on what we believe is the largest and most comprehensive set of testbenches in the online learning literature. Our results indicate that HATT is a superior alternative to Hoeffding Tree in a large number of ensemble settings.
Privacy of Web Browsers: A Challenge in Digital Forensics

Web browsers are ubiquitous applications to access public and private applications over the Internet, Intranet, and Extranet. The increased demand for cybersecurity, including data privacy, secrecy, and anonymity, becomes the reason for enhanced privacy and anonymity in common web browsers and specialized web browsers to achieve such purposes. These features are great challenges and obstacles for forensic investigators. In this paper, a collection of research will be analyzed, that have been carried out to identify and collect artifacts of web browsers having secrecy features for examination, validation, and find out potential ways to use the collected information during active investigations. As a result, live forensics can become more relevant and dependable for collecting reasonable artifacts from private browsers. From common browsers using private browsing facilities, even removing web browsers after committing criminal activities can also be identified by analyzing the registry, supporting factual evidence gathering in any Digital Forensic investigation.
Automatic Image Colorization using Deep Learning

Image colorization is a fascinating topic and has become an area of research in the recent years. In this project, we are going to colorize black and white images with the help of Deep Learning techniques. Some previous approaches required human involvement or resulted in the development of desaturated images. We are building a Deep Convolutional Neural Network (CNN) which will be trained on over a million images. The output generated by the model is fully dependent on the images it has been trained from and requires no human help. The images are taken from different sources like ResNet, Reddit, etc. The model will include many hidden layers to make the output more accurate. This will be a fully automatic model and will produce images with accurate colors and contrast. Finally, the goal of this project is to produce realistic and color accurate images that can easily fool the viewer. The viewer wouldn’t be able to differentiate between the photo which the model produced and the real photo. Our project has wide practical applications like historical image/video restoration, image enhancement for better interpretability, frame by frame colorization of black and white documentaries, etc.
Joint Optimization of Kinematics and Anthropometrics for Human Motion Denoising

In this paper, we propose a novel technique for human motion denoising by jointly optimizing kinematic and anthropometric constraints for a noisy skeleton data. Specifically, we are focused on depth-sensor-based motion capture (D-Mocap) data that are often prone to error, outliers and distortion. To capture human kinematics, we first propose a joint-level Tobit particle filter (TPF) that incorporates a unique observation model to characterize the censored measurement of D-Mocap data. A skeleton-level Differential Evolution (DE) algorithm is then integrated with the sequential Monte Carlo sampling in the TPF, allowing joint-level particles to be re-distributed and re-weighted according to the stability and consistency of skeletal bone lengths as well as the suitability of joint kinematics. This leads to an integrated TPF-DE algorithm that significantly improves the quality of D-Mocap data by making 3D joint trajectories more kinematically admissible and anthropometrically stable. Experimental results on both simulated and real-world D-Mocap show that the errors of joint positions and the bone lengths have been reduced by 30-60&#x0025;, and the accuracy of joint angles has been improved by 40-60&#x0025;. The proposed TPF-DE method outperforms the recent filtering-based and deep learning methods and demonstrate the synergy between the TPF and DE algorithms for effective human motion enhancement.
An Improved Action Recognition Network With Temporal Extraction and Feature Enhancement

Image classification and action recognition are both active research topics in the field of computer vision. However, the development of action recognition is rather slow compared with image classification, due to the difficulties in spatial-temporal information modeling. In this paper, we present TEFE, a deep structure combining temporal extraction with feature enhancement to explore the spatial coherence across temporal dimension. The temporal extraction (TE) module is used to capture the short-term and long-term temporal features. Considering the spatial and temporal cues are fine-grained, we believe such cues (if encoded by well-designed bilinear models) could enhance the representation of actions in videos. The feature enhancement (FE) module approximates bilinear pooling operations, which greatly reduce the amount of parameters exist in original bilinear pooling. Extensive experiments have been conducted on mainstream datasets of human action (Something - Something V1 & V2, and Jester). Experimental results show that our model achieves competitive performances than some existing methods.
Automatic Fall Risk Detection Based on Imbalanced Data

In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society.Regarding accidents that occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss.In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks.We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates.Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting.We assess not only different imbalanced data handling methods but also different machine learning algorithms.After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance.The F1 scores for three different classes, Normal, Fall, and Lying, are 1.00, 0.85 and 0.96, which is comparable to previous research.The experiment shows that our approach is more interpretable with the key feature from skeleton information.Moreover, it can apply in multi-people scenarios and has robustness on medium occlusion.
Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital transformation is structured, which makes it difficult to utilize the advantages of CNN. This paper introduces a novel autoencoder, named Half Convolutional Autoencoder, which adopts convolutional layers to discover the high-order correlation between structured features in the form of Tag Genome, the side information associated with each movie in the MovieLens 20 M dataset, in order to generate a robust feature vector. Subsequently, these new movie representations, along with the introduction of users’ characteristics generated via Tag Genome and their past transactions, are applied into well-known matrix factorization models to resolve the initialization problem and enhance the predicting results. This method not only outperforms traditional matrix factorization techniques by at least 5.35% in terms of accuracy but also stabilizes the training process and guarantees faster convergence.
Correction to: A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition

A Correction to this paper has been published: 10.1007/s13369-021-05881-4
Enabling efficient traceable and revocable time-based data sharing in smart city

With the assistance of emerging techniques, such as cloud computing, fog computing and Internet of Things (IoT), smart city is developing rapidly into a novel and well-accepted service pattern these days. The trend also facilitates numerous relevant applications, e.g., smart health care, smart office, smart campus, etc., and drives the urgent demand for data sharing. However, this brings many concerns on data security as there is more private and sensitive information contained in the data of smart city applications. It may incur disastrous consequences if the shared data are illegally accessed, which necessitates an efficient data access control scheme for data sharing in smart city applications with resource-poor user terminals. To this end, we proposes an efficient traceable and revocable time-based CP-ABE (TR-TABE) scheme which can achieve time-based and fine-grained data access control over large attribute universe for data sharing in large-scale smart city applications. To trace and punish the malicious users that intentionally leak their keys to pursue illicit profits, we design an efficient user tracing and revocation mechanism with forward and backward security. For efficiency improvement, we integrate outsourced decryption and verify the correctness of its result. The proposed scheme is proved secure with formal security proof and is demonstrated to be practical for data sharing in smart city applications with extensive performance evaluation.
Selecting ChatBot Platform for Health Enterprise Training: A Fuzzy AHP Approach

This study departs from the issue on how to select Chabot platform to deliver virtual training in a health enterprise environment. In-house training is aimed at improving man power knowledge and skill required to achieve health enterprise objectives. During Covid-19 pandemic, traditional face-to-face training has been difficult to conduct due to many health-related limitations. In order to deal with such issues, medical team at health enterprise seeks to apply Chatbot platform that might be used to deliver in-house training via online appropriately. Considering many aspects to be considered in selecting several Chatbot platforms, Fuzzy Analytic Hierarchy Process is proposed in this study to deal with the issue. It is finally recommended to use Carik platform to deliver in-house training in the corporation.
Deep Learning-Based Imbalanced Classification With Fuzzy Support Vector Machine

Imbalanced classification is widespread in the fields of medical diagnosis, biomedicine, smart city and Internet of Things. The imbalance of data distribution makes traditional classification methods more biased towards majority classes and ignores the importance of minority class. It makes the traditional classification methods ineffective in imbalanced classification. In this paper, a novel imbalance classification method based on deep learning and fuzzy support vector machine is proposed and named as DFSVM. DFSVM first uses a deep neural network to obtain an embedding representation of the data. This deep neural network is trained by using triplet loss to enhance similarities within classes and differences between classes. To alleviate the effects of imbalanced data distribution, oversampling is performed in the embedding space of the data. In this paper, we use an oversampling method based on feature and center distance, which can obtain more diverse new samples and prevent overfitting. To enhance the impact of minority class, we use a fuzzy support vector machine (FSVM) based on cost-sensitive learning as the final classifier. FSVM assigns a higher misclassification cost to minority class samples to improve the classification quality. Experiments were performed on multiple biological datasets and real-world datasets. The experimental results show that DFSVM has achieved promising classification performance.
Survey of Pre-trained Models for Natural Language Processing

Deep learning has developed rapidly. The pre-training technology of natural language processing has also made great progress. Early natural language processing used static pre-training techniques, such as Word2Vec, GLoVe, and other word vector methods to segment text. However, this segmentation method does not consider, and the context cannot solve the problem of polysemy. The BERT model has made many typical downstream tasks a typical improvement, which has greatly promoted technological development in the field of NLP and has since entered the epoch of dynamic pre-training technology. On this basis, we analyze the shortcomings of current pre-training technology and finally looks forward to the future development trend of pre-training technology.
Semantic Role Labeling Integrated with Multilevel Linguistic Cues and Bi-LSTM-CRF

Chinese Semantic Role Labeling (SRL) is the core technology of semantic understanding. In the field of Chinese information processing, where statistical machine learning is still the mainstream, the traditional labeling methods rely heavily on the parsing degree of syntax and semantics of sentences. Therefore, the labeling precision is limited and cannot meet the current needs. This paper adopts the model based on a bidirectional long short-term memory network combined with the Conditional Random Field (Bi-LSTM-CRF). In the feature processing stage, pooling technology is combined with sampling and selecting multifeature vector groups to improve the performance of the sequence labeling model. Lexical, syntactic, and other multilevel linguistic features are integrated into the training to realize in-depth improvement of the original labeling model. Through several groups of experiments, the precision of model annotation in this paper has been significantly improved combined with linguistic-assisted analysis, which proves that it can optimize the annotation performance of the model by integrating relevant linguistic features into the model based on Bi-LSTM-CRF and sampling and extracting multifeature groups; the evaluation of F increases to 82.18 percent.