Reviewerly/exHarmony huggingface dataset:
Differential Evolution with Level-Based Learning Mechanism

To address complex single objective global optimization problems, a new Level-Based Learning Differential Evolution (LBLDE) is developed in this study. In this approach, the whole population is sorted from the best to the worst at the beginning of each generation. Then, the population is partitioned into multiple levels, and different levels are used to exert different functions. In each level, a control parameter is used to select excellent exemplars from upper levels for learning. In this case, the poorer individuals can choose more learning exemplars to improve their exploration ability, and excellent individuals can directly learn from the several best individuals to improve the quality of solutions. To accelerate the convergence speed, a difference vector selection method based on the level is developed. Furthermore, specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process. A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.
Named-entity recognition for a low-resource language using pre-trained language model

This paper proposes a method for Named-Entity Recognition (NER) for a low-resource language, Tigrinya, using a pre-trained language model. Tigrinya is a morphologically rich language, although one of the underrepresented in the field of NLP. This is mainly due to the limited amount of annotated data available. To address this problem, we introduced the first publicly available NER dataset for Tigrinya. The dataset contains 69,309 tokens that were manually annotated based on the CoNLL 2003 Beginning, Inside, and Outside (BIO) tagging schema. Specifically, we develop a new pre-trained language model for Tigrinya based on RoBERTa, which we refer to as TigRoBERTa. First, It is trained on an unsupervised Tigrinya corpus using Masked Language Modeling (MLM). Then, we show the validity of TigRoBERTa by fine-tuning for a couple of downstream tasks, namely, NER and Part of Speech (POS) tagging. The experimental results show that the method achieved 81.05% F1-score for NER and 92% accuracy for POS tagging, which is better than or comparable to the baseline method based on the CNN-BiLSTM-CRF model.
An Accurate Point Cloud-Based Human Identification Using Micro-Size LiDAR

The demand for safety-boosting systems is increasing, especially nowadays, to limit the rapid spread of COVID-19. Real-time life-logging is an essential application towards tracking infected cases and thus containing the pandemic outbreak. This application raises the need for an accurate human identification technology where cameras cannot be adopted due to privacy. Recently, LiDAR sensor has attracted attention due to its ability to represent the surrounding environment in the form of 3D point cloud. In this paper, we introduce a novel wearable device of a micro-size LiDAR to build an onboard human identification system for life-logging. The system acquires 3D point cloud data of the surrounding environment from which subject-discriminative signatures are extracted. This is achieved by removing noise and background using Spatio-temporal density clustering and fisher vector representations. The extracted features are then used to train a random forest classifier for subject identification. We have implemented and evaluated the proposed system on six different subjects. The results show that the proposed system can effectively remove noise and background and accurately identify subjects with 99.9% accuracy.
The Progress of Business Analytics and Knowledge Management for Enterprise Performance Using Artificial Intelligence and Man-Machine Coordination

This study aims to explore the integration of human-computer interaction (HCI) technology and platform ecosystem in artificial intelligence (AI) environment, thus providing a practical basis for the intelligent development of strategic management of platform ecosystem. With clothing e-commerce as an example, first, the business model of brand clothing is simply analyzed. Then, the fashion knowledge management method is adopted to build the fashion data warehouse. The platform intelligent clothing ecosystem is innovatively put forward through the research of business analytics and management mode of clothing e-commerce industry. The optimized genetic algorithm is used to solve the objective function of the model, and a flexible production scheduling model with multiple constraints and maximum cost-saving is established. Finally, the questionnaire results of voice interaction users are analyzed by HCI customer trust model.
A Comprehensive Survey on Metaheuristic Algorithm for Feature Selection Techniques

In Healthcare industries, patients’ medical records consist of a greater number of features or attributes related to certain health factors and medical terminologies. In these feature set, many attributes are either redundant one or irrelevant to the medical disease that is going to be dealt with it. These redundant and irrelevant feature sets is not going to enhance the final result at any cost and moreover it out bounds the final prediction. Feature selection is the process of selecting feature sets in a neural model that are related to a specific problem in order to aid in the inclusion of relevant features and the exclusion of irrelevant or redundant features, thereby reducing the amount of noisy data and the size of the entire dataset. In this paper, metaheuristic related techniques are taken into account and summarized about its key feature along with how it works. Also, various Feature selections related research works that have been published in last three years are highlighted with its proposed methodology, benefits and limitations. From this, it is inferred that many open challenges and enhancements are still available in the feature selection problem even though many researchers introduced their novelty in this problem.
An unsupervised annotation of Arabic texts using multi-label topic modeling and genetic algorithm

Every day the world produces an enormous amount of textual data. This unstructured text is of little use unless it is labeled using a combination of categories, keywords, tags. Humans can never annotate such massive data, and with a growing divide between the daily produced data and those annotated, the only alternative is to mechanize it. Automatic annotation process helps in saving resources in terms of time and cost. The process of multi-label annotation involves associating a document with multiple relevant labels. This paper proposes an unsupervised model to annotate corpus using multi-labels automatically. The model is based on multi-label topic modeling and genetic algorithm (GA). Topic modeling is a technique to extract the hidden topics from text, and the GA is used to find the optimal number of topics. We hyper-tuned the parameters of the topic modeling using two different training methods: variational Bayes and Gibbs sampling. The class imbalance in a corpus can affect the result of topic modeling, where the majority class dominates the minority class. We overcome this problem using the partitioning method. Though the proposed model was developed for the Arabic dataset, it is language neutral. We tested our model on three large Arabic corpora and three large English social media datasets. For the Arabic language, our work being the first work that tackles multi-label annotation, we needed a reference to compare our model. For the Arabic corpus, we compared the result of automatic annotation against humans using crowdsourcing (whose labeling was checked for quality). The analysis of the annotation shows an agreement among models (machine vs. human) of 79.30%. Moreover, for the English dataset, the results are quite competitive.
SEDGN: Sequence enhanced denoising graph neural network for session-based recommendation

Session-based recommendation, which aims at predicting subsequent user actions based on anonymous sessions, plays a significant role in many online services. Based on the graph neural network (GNN), existing methods construct each session as a graph and capture the rich transition relationship between items to generate item representations. However, they neglect natural noise in a session, which is not conducive to making accurate recommendations. In addition, GNN-based methods cannot fully express sequential information of the session, such as the repeated nodes in a session and the starting node of the directed graph. Therefore, we propose a novel method, i.e., sequence enhanced denoising graph neural network (SEDGN). Specifically, we use a gated recurrent unit (GRU) to enhance the graph neural network, thereby addressing the GNN’s deficiency in modeling the sequential information of sessions. In addition, two denoise modules are designed to alleviate the impacts of natural noise by more accurate item representations from the perspective of sequence-structure and graph-structured data. Extensive experiments on five real-world datasets revealed that SEDGN outperforms the most the existed state of art methods and demonstrates that the components in the model are effective.
Why Do Students Prefer Augmented Reality: A Mixed-Method Study on Preschool Teacher Students’ Perceptions on Self-Assessment AR Quizzes in Science Education

Students’ perceptions on AR applications have gained researchers’ interest in the field of ICT-enhanced teaching and learning, especially in recent years. The current study investigates students’ perceptions concerning their learning and immersive experiences gained using AR quizzes with formative self-assessment purposes in a science education university course during one semester. The research followed the mixed-method approach, and the data were collected sequentially by questionnaires and focus group discussions. A descriptive statistical analysis and a thematic analysis were conducted, respectively. Fifty-one (51) students participated in the quantitative data collection procedure and ten (10) of them participated in the focus groups. The results indicate that students are in favor of AR quizzes which justify their stance based on the learning gains and the immersive experiences. AR was underlined to play a significant role by creating an engaging environment of immersion. The findings support the positive stances of students over the combination of AR and formative self-assessment and highlight the role of immersion supported by AR technologies. Additionally, based on the relatively long period of application, the findings create doubts concerning the influence of the novelty effect on students’ positive stances toward AR.
Literary placemaking and narrative immersion in extended reality virtual geographic environments

Virtual geographical environments (VGEs) are being used to represent our sense of place through the application of extended-reality or cross-reality (XR) technologies with a focus on technological advancement and immersion. In light of this, we propose that an expanded and interdisciplinary understanding of the concept of immersion is required to facilitate an understanding of spatial data to a broader audience. The potential of focusing on narrative immersion and literary placemaking in VGEs is discussed to evoke a stronger sense of place and a feeling of presence and belonging in users. This paper seeks to highlight the unique affordances and potentialities of XR narrative style GIS digital representations through an interdisciplinary theoretically lead analysis of outputs created by the Building City dashboards project towards the goal of expanding audiences of non-specialist stakeholders in urban planning processes.
Multi-Classifier Fusion for Open-Set Specific Emitter Identification

To safeguard the privacy and security of IoT systems, specific emitter identification is utilized to recognize device identity with hardware characteristics. In view of the growing demand for identifying unknown devices, this paper aims to discuss open-set specific emitter identification. We firstly build up a problem formulation for open-set SEI by discussing the working mechanisms of radio signals and open-set recognition. And then it is pointed out that feature coincidence is an intractable challenge in open-set SEI. The reason, accounting for this phenome, is that pretrained fingerprint feature extractors are incapable of clustering unknown device features and differentiating them from known ones. Considering that feature coincidence leads to error recognition of unknown devices, we propose to fuse multi-classifiers in the decision layer to improve accuracy and recall. Three distinct inputs and four different fusion methods are adopted in this paper to implement multi-classifier fusion. The datasets collected at Huanghua Airport demonstrate that the proposed method can avoid the coincidence of feature space and achieve higher accuracy and recall.
A new fuzzy cognitive maps classifier based on capsule network

In recent years, classification based on fuzzy cognitive maps (FCM) has received extensive attention, has been applied in many engineering problems, and has proven to work well. However, a host of the existing methods are designed for specific engineering problems, which lack universality and low classification accuracy. In this study, a straightforward and versatile model is proposed to deal with classification. The key of the model is to integrate the capsule network into the inference rules. It forms a new inference rule with a strong coupling coefficient. Weights were learned with a particle swarm algorithm, and the cross-entropy with constraints was used as the cost function. The goal of this research is to maintain the interpretability of the model and enhance its universality and classification performance. The top-down feedback mechanism of the capsule network can meet this requirement. A series of experiments involving public data proved that the proposed method achieves better performance than the previous results obtained using low-level cognitive maps. The comparative analysis also demonstrates the performance of the classification delivered by the proposed model.
Image Dehazing Based on Local and Non-Local Features

Image dehazing is a traditional task, yet it still presents arduous problems, especially in the removal of haze from the texture and edge information of an image. The state-of-the-art dehazing methods may result in the loss of some visual informative details and a decrease in visual quality. To improve dehazing quality, a novel dehazing model is proposed, based on a fractional derivative and data-driven regularization terms. In this model, the contrast constrained adaptive histogram equalization method is used as the data fidelity item; the fractional derivative is applied to avoid over-enhancement and noise amplification; and the proposed data-driven regularization terms are adopted to extract the local and non-local features of an image. Then, to solve the proposed model, half-quadratic splitting is used. Moreover, a dual-stream network based on Convolutional Neural Network (CNN) and Transformer is introduced to structure the data-driven regularization. Further, to estimate the atmospheric light, an atmospheric light model based on the fractional derivative and the atmospheric veil is proposed. Extensive experiments display the effectiveness of the proposed method, which surpasses the state-of-the-art methods for most synthetic and real-world images, quantitatively and qualitatively.
Conversational recommender systems techniques, tools, acceptance, and adoption: A state of the art review

Conversational recommender systems(CRS) have become popular in recent years and have gained the attention of researchers. More emphasis is given to user choices and experiences in recent research studies. This study presents a systematic review and uses PRISMA methodology for the search strategy, study selection, and extraction. The algorithmic aspects and evolution of the most common methods used in the design of conversational recommender system components are discussed to provide a comprehensive picture. The study unveils the major advantages and limitations of the user preference elicitation and recommendation techniques. It also summarizes the recommendation tools and framework for different application domains with the techniques adopted. The study further aimed to find the implementation challenges, and the same is highlighted with the recommended solutions. As the users are the key focus for the conversational recommender systems, this paper also presents the role of effective user acceptance and adoption models. The statistical analysis provided in the study gives an insight into the growing popularity of CRS research areas and their application in various domains. The results of this study provide insights to practitioners, which may help in better CRS implementations and contribute to the literature by providing a comprehensive viewpoint. The study also postulates the topics that need attention and gives future research trends.
Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives

It is common practice to represent spoken languages at their phonetic level. However, for sign languages, this implies breaking motion into its constituent motion primitives. Avatar based Sign Language Production (SLP) has traditionally done just this, building up animation from sequences of hand motions, shapes and facial expressions. However, more recent deep learning based solutions to SLP have tackled the problem using a single network that estimates the full skeletal structure.We propose splitting the SLP task into two distinct jointly-trained sub-tasks. The first translation sub-task translates from spoken language to a latent sign language representation, with gloss supervision. Subsequently, the animation sub-task aims to produce expressive sign language sequences that closely resemble the learnt spatio-temporal representation. Using a progressive transformer for the translation sub-task, we propose a novel Mixture of Motion Primitives (MOMP) architecture for sign language animation. A set of distinct motion primitives are learnt during training, that can be temporally combined at inference to animate continuous sign language sequences.We evaluate on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset, presenting extensive ablation studies and showing that MOMP outperforms baselines in user evaluations. We achieve state-of-the-art back translation performance with an 11% improvement over competing results. Importantly, and for the first time, we showcase stronger performance for a full translation pipeline going from spoken language to sign, than from gloss to sign.
Towards Causal Benchmarking of Biasin Face Analysis Algorithms

Measuring algorithmic bias is crucial both to assess algorithmic fairnessAlgorithmic fairness and to guide the improvement of algorithms. Current bias measurement methods in computer vision are based on observational datasets and so conflate algorithmic bias with dataset bias. To address this problem, we develop an experimental method for measuring algorithmic bias of face analysisFace analysis algorithms, which directly manipulates the attributes of interest, e.g., gender and skin tone, in order to reveal causal links between attribute variation and performance change. Our method is based on generating synthetic image grids that differ along specific attributes while leaving other attributes constant. Crucially, we rely on the perception of human observers to control for synthesis inaccuracies when measuring algorithmic bias. We validate our method by comparing it to a traditional observational bias analysis study in gender classificationGender classification algorithms. The two methods reach different conclusions. While the observational method reports gender and skin color biases, the experimental methodExperimental methods reveals biases due to gender, hair length, age, and facial hair. We also show that our synthetic transects allow for a more straightforward bias analysis on minority and intersectional groups.
Benchmarking of objective quality metrics for point cloud compression

Point cloud is a promising imaging modality for the representation of 3D media. The vast volume of data associated with it requires efficient compression solutions, with lossy algorithms leading to larger bit-rate savings at the expense of visual impairments. While conventional encoding approaches rely on efficient data structures, recent methods have incorporated deep learning for rate-distortion optimization, while inducing perceptual degradations of different natures. To measure the magnitude of such distortions, subjective or objective quality evaluation methodologies are employed. Lately, a remarkable amount of efforts has been devoted to the development of point cloud objective quality metrics, which have been reported to attain high prediction accuracy. However, their performance and generalization capabilities haven’t been evaluated yet in presence of artifacts from learning-based codecs. In this study, we tackle this matter by conducting the first crowdsourcing experiment for point cloud quality reported in the literature, in order to obtain subjective ratings for point cloud models whose topology and color attributes are encoded by both conventional and data-driven methods. Using the subjective scores as ground truth, the performance of a large pool of state-of-the-art quality metrics is rigorously benchmarked, drawing useful insights regarding their efficacy.
Challenges of Blockchain Technology in Context Internet of Things: A Survey

The Internet of Things (IoT) is a combination of communication, information, and embedded technology that truly make up an IoT ecosystem.The designing of the IoT ecosystem that includes different technologies often be complex.This complexity introduces challenges to keeping the IoT secure, protecting the IoT from disclosure to unauthorized parties, ensuring information integrity and so on.This paper presents a comprehensive survey of the challenges and security consideration of the integration of both IoT architectures and blockchain technology which is basically a secure distributed database over a peer to peer architecture.It starts by discussing the characteristics of the IoT, its architectures, their limitations and the role of cloud and fog computing to overcome these limitations.Then it provides an overview of a blockchain technology, its applications, how does it work, and finally the contribution of an integrated blockchain as a solution to security and privacy challenges in IoT.
Old(er) Media and New Musical Affordances in Virtual Reality Experiences

Abstract This chapter explores how recent virtual reality experiences (from 2010 onward) have remediated musical “protocols” of older audiovisual media such as film, video games, and music videos while creating a number of new affordances that draw attention to the newness of VR. Soundtrack and music video protocols in 360° videos such as Lost and Gorillaz’s “Saturnz Barz” emphasize the affordance of looking for a sound source, while the addition of an ambient musical soundtrack to the VR remediation of Google Earth emphasizes the user’s bodily presence in the virtual environment. Critiquing and expanding the notion of VR as a form of what Bolter and Grusin would call “transparent immediacy,” the chapter suggests that the music in these VR applications also facilitates experiences of hypermediacy, foregrounding the apparatus and idiosyncrasies of a new medium.