Reviewerly/exHarmony huggingface dataset:
Self-supervised Discriminative Representation Learning by Fuzzy Autoencoder

Representation learning based on autoencoders has received great concern for its potential ability to capture valuable latent information. Conventional autoencoders pursue minimal reconstruction error, but in most machine learning tasks such as classification and clustering, the discrimination of feature representation is also important. To address this limitation, an enhanced self-supervised discriminative fuzzy autoencoder (FAE) is innovatively proposed, which focuses on exploring information within data to guide the unsupervised training process and enhancing feature discrimination in a self-supervised manner. In FAE, fuzzy membership is applied to provide a means of self-supervised, which allows FAE can not only utilize AE’s outstanding representation learning capabilities but can also transform the original data into another space with improved discrimination. First, the objective function corresponding to FAE is proposed by reconstruction loss and clustering oriented loss simultaneously. Subsequently, Mini-Batch Gradient Descent is applied to infer the objective function and the detailed process is illustrated step by step. Finally, empirical studies on clustering tasks have demonstrated the superiority of FAE over the state of the art.
Counter-Mapping with Sounds in the Practices of Postdigital Pedagogy

Abstract The article analyses the methods of postdigital pedagogy based on critical media design (CMD), and collective practices which involve using sound recording and emission as a tool for counter-mapping the problems associated with the history and politics of digitally mediated urbanism. The article provides a detailed account of, and draws on materials from, the Emotional Urban Weather workshop conducted in 2014, in Warsaw, for an international group of professional young researchers, designers, artists and activists. The workshop took place in specially chosen districts of the Polish capital, in which the participants applied various sound design and critical media design strategies in order to address the historical, social, economic and political problems related to this place. Using technological tools capable of recording, emitting and measuring the parameters of sound, they prepared forms of sound intervention in the city. At the same time, this process became an experimental way of ‘opening up’ the problematics of a place by counter-mapping complex socio-cultural issues. Reflection on the workshop and its outcomes have contributed to the development of a postdigital pedagogy. The practices of critical media design (CDM), when combined with field recordings, affective listening and sound intervention, allow various technologies to be incorporated into theoretical critique and new forms of experimental engagement.
Capped Linex Metric Twin Support Vector Machine for Robust Classification

In this paper, a novel robust loss function is designed, namely, capped linear loss function Laε. Simultaneously, we give some ideal and important properties of Laε, such as boundedness, nonconvexity and robustness. Furthermore, a new binary classification learning method is proposed via introducing Laε, which is called the robust twin support vector machine (Linex-TSVM). Linex-TSVM can not only reduce the influence of outliers on Linex-SVM, but also improve the classification performance and robustness of Linex-SVM. Moreover, the effect of outliers on the model can be greatly reduced by introducing two regularization terms to realize the structural risk minimization principle. Finally, a simple and efficient iterative algorithm is designed to solve the non-convex optimization problem Linex-TSVM, and the time complexity of the algorithm is analyzed, which proves that the model satisfies the Bayes rule. Experimental results on multiple datasets demonstrate that the proposed Linex-TSVM can compete with the existing methods in terms of robustness and feasibility.
Biofeedback method for human–computer interaction to improve elder caring: Eye-gaze tracking

Interaction with biofeedback provides exciting opportunities for elderly care because it allows elderly physiological information to be used in novel interaction techniques. Biofeedback interaction faces challenges relating to its design and implementation, despite several contributions in the area. First, it was mainly used to replace traditional devices for interaction, such as touchpads, mouse, or keyboards. Also, few previous interaction techniques have made essential use of physiological information: exploring possibilities that physiological inputs could only develop. This research proposes how the physiological method of eye-gaze tracking could be used to design and develop natural user interaction techniques. A human user's tacit intention to use physiological signals for the domestic area's required activities/requirements may be understood by utilizing nonverbal contact to define the user's intent to use physiological signals for the domestic area's necessary activities/requirements. Traditional gaze monitoring systems depend on explicit infrared lights and high-resolution cameras to achieve good accuracy and robustness. Recent advancements in mobile devices and an increasing interest in capturing everyday human behavior make monitoring eye movements in an unlimited environment all the more promising. The study also discusses interaction design principles from human–computer interaction (HCI), evaluates the new interaction technique between eye-gaze biofeedback with a set of user studies, and proposes ethical considerations appropriate to physiological sensor technology. Eye-gaze movement requires relatively little human effort. The ability to control the gaze persists in most elderly and disabled people with movement impairments, making the gaze a very effective way to communicate with the elderly and disabled with an assistive device.
Decentralized Robust Collision-Avoidance for Cooperative Multirobot Systems: A Gaussian Process-Based Control Barrier Function Approach

Data-based machine learning methods have been successfully applied to control system design in recent years. However, safety during the learning and control process is difficult to guarantee due to the inherent uncertainties. In this work, we propose a barrier-function-based robust cooperative collision-avoidance control framework for heterogeneous multirobot systems with model learning. First, a new control barrier function (CBF) design is proposed for cooperative collision-avoidance, which leads to less conservativeness of the feasible control actions. Then, decentralized robust CBF conditions are derived, which incorporate the estimation of the individual model uncertainty using Gaussian process models such that each robot can guarantee safety with a high probability. Finally, a quadratic programming problem is formulated to obtain a controller that is minimally invasive to the nominal controller and satisfies the CBF and velocity constraints simultaneously. The decentralized implementation of the robust collision-avoidance control strategy is explicitly shown and proven. Two simulation examples are given to demonstrate the effectiveness of the proposed control framework.
LMFNet: A Lightweight Multiscale Fusion Network With Hierarchical Structure for Low-Quality 3-D Face Recognition

Three-dimensional (3-D) face recognition (FR) can improve the usability and user-friendliness of human–machine interaction. In general, 3-D FR can be divided into high-quality and low-quality 3-D FR according to different interaction scenarios. The low-quality data can be easily obtained, so its application prospect is more extensive. However, the challenge is how to balance the trade-offs between data accuracy and real-time performance. To solve this problem, we propose a lightweight multiscale fusion network (LMFNet) with a hierarchical structure based on single-mode data for low-quality 3-D FR. First, we design a backbone network with only five feature extraction blocks to reduce computational complexity and improve the inference speed. Second, we devise a mid-low adjacent layer with a multiscale feature fusion (ML-MSFF) module to extract the facial texture and contour information, and a mid-high adjacent layer with a multiscale feature fusion (MH-MSFF) module to obtain the discriminative information in high-level features. Then, a hierarchical multiscale feature fusion (HMSFF) module is formed by combining these two modules mentioned above to acquire the local information of different scales. Finally, we enhance the expression of features by integrating HMSFF with a global convolutional neural network for improving recognition accuracy. Experiments on Lock3DFace, KinectFaceDB, and IIIT-D datasets demonstrate that our proposed LMFNet can achieve superior performance on low-quality datasets. Furthermore, experiments on the cross-quality database based on Bosphorus and the different intensity noise low-quality datasets based on UMB-DB and Bosphorus show that our network is robust and has a high generalization ability. It satisfies the real-time requirement, which lays a foundation for a smooth and user-friendly interactive experience.
A Kalman Filter Framework for Simultaneous LTI Filtering and Total Variation Denoising

This paper proposes a Kalman filter framework for signal denoising that simultaneously utilizes conventional linear time-invariant (LTI) filtering and total variation (TV) denoising. In this approach, the desired signal is considered to be a mixture of two distinct components: a band-limited (e.g., low-frequency component, high-frequency component) signal and a sparse-derivative signal. An iterative Kalman filter/smoother approach is formulated where zero-phase LTI filtering is used to estimate the band-limited signal and TV denoising is used to estimate the sparse-derivative signal.
Dysfunctional trusting and distrusting: Integrating trust and bias perspectives

This paper offers an integrative review of the concept of dysfunctional trust from a trust and bias research perspective. Trust and cognitive/social biases are isomorphically related concepts in their functions as reducers of cognitive effort and facilitators/inhibitors of action. In the case of dysfunctional trust and distrust, bias perspectives contribute theoretically to a framework for the study of the ‘errors’ in decision-making that lead to dysfunctional outcomes of trusting. By reviewing biases and their role in generating trust and the converse, the biasing role of trust within a trust antecedent framework, the review integrates the conceptual linkages between research on bias and heuristics and research on trust, providing a basis for further research and practical applications in educational, business, political, and media domains. The paper makes recommendations for research and practical applications to mitigate the impacts of misinformation, bias in decision-making and dysfunctional trust. Attending to cognitive and other biases in situations involving trust promises to support greater informational resilience by raising metacognitive awareness of bias and trust in human decision-making.
Attend and select: A segment selective transformer for microblog hashtag generation

Hashtag generation aims to generate short and informal topical tags from a microblog post, in which tokens or phrases form the hashtags. These tokens or phrases may originate from primary fragmental textual pieces (e.g., segments) in the original text and are separated into different segments. However, conventional sequence-to-sequence generation methods are hard to filter out secondary information from different textual granularity and are not good at selecting crucial tokens. Thus, they are suboptimal in generating more condensed hashtags. In this work, we propose a modified Transformer-based generation model with adding a segments-selection procedure for the original encoding and decoding phases. The segments-selection phase is based on a novel Segments Selection Mechanism (SSM) to model different textual granularity on global text, local segments, and tokens, contributing to generating condensed hashtags. Specifically, it first attends to primary semantic segments and then transforms discontinuous segments from the source text into a sequence of hashtags by selecting crucial tokens. Extensive evaluations on the two datasets reveal our approach's superiority with significant improvements to the extraction and generation baselines. The code and datasets are available at https://github.com/OpenSUM/HashtagGen.
Recent Trends in Face Recognition Using Metaheuristic Optimization

Metaheuristic optimization over the last decade has been progressed remarkably and is being utilized in different fields with the aim of improving the performnces and achieved notable successes. Following the efficacy of the optimization usage in different domains, it was later also adopted in biometric systems to improve their accuracy and robustness. In this chapter, we present an unprecendent overview of usage of optimization techniques in face recognition systems. We analyzed various works presented in the literature, which have employed the optimization techniques in different steps of face recognition systems. Then, systematic analysis and findings about different optimization frameworks for face biometrics are introduced. In addition to different optimization techniques applied, we also explore a number of avaiable databases used in face recognition systems with respect to optimization schemes. Finally, open issues and challenges are highlighted and potential future research directions are discussed.
Document image analysis and recognition: a survey

This paper analyzes the problems of document image recognition and the existing solutions. Document recognition algorithms have been studied for quite a long time, but despite this, currently, the topic is relevant and research continues, as evidenced by a large number of associated publications and reviews. However, most of these works and reviews are devoted to individual recognition tasks. In this review, the entire set of methods, approaches, and algorithms necessary for document recognition is considered. A preliminary systematization allowed us to distinguish groups of methods for extracting information from documents of different types: single-page and multi-page, with text and handwritten contents, with a fixed template and flexible structure, and digitalized via different ways: scanning, photographing, video recording. Here, we consider methods of document recognition and analysis applied to a wide range of tasks: identification and verification of identity, due diligence, machine learning algorithms, questionnaires, and audits. The groups of methods necessary for the recognition of a single page image are examined: the classical computer vision algorithms, i.e., keypoints, local feature descriptors, Fast Hough Transforms, image binarization, and modern neural network models for document boundary detection, document classification, document structure analysis, i.e., text blocks and tables localization, extraction and recognition of the details, post-processing of recognition results. The review provides a description of publicly available experimental data packages for training and testing recognition algorithms. Methods for optimizing the performance of document image analysis and recognition methods are described.
An improved tangent search algorithm

The Tangent Search Algorithm (TSA) is a newly developed population-based meta-heuristic algorithm to solve complex optimization problems. It is based on the tangent function, which steers the given solution towards more promising regions of the search space. Though TSA has performed well for many optimization problems, the experimental analyses show that it suffers from the low exploration ability and slow convergence rate. This article proposes an improved TSA algorithm (iTSA). Using two concepts, 'Fitness Weighted Search Strategy' (FWSS) and 'Opposition Based learning' (OBL), iTSA is better in terms of exploration while maintaining the high convergence rate of TSA. • Fitness weighted search strategy (FWSS) is used to increase the exploration ability of TSA. • Opposition based learning (OBL) is used to increase the convergence speed of TSA. • Together, OBL and FWSS into iTSA outperformed the classical TSA and other considered state-of-the-art algorithms. The performance of the proposed iTSA is validated on two sets of test functions: CEC14 benchmark functions and a set of 21 well-known classical benchmark functions. The obtained results are compared with those obtained from the basic TSA and other considered state-of-the-art algorithms.
Structure preserving loss function for single image super resolution

The single image super-resolution (SISR) is a challenging problem due to its ill-posed nature. The main aim of SISR methods is to generate a high-resolution image from a given low-resolution image. Recently, learning methods of SISR based gained popularity due to advanced convolution neural networks (CNN). These networks have been trained using classical loss functions such as absolute and mean square error, resulting in improper network training due to over-fitting. Thus, a robust and better loss function is essential for reduced over-fitting of the network. The proposed method presents a local variance-based loss function to train CNN with reduced loss. The proposed loss function is proved to be more effective than the existing state of art methods. The effectiveness of the proposed loss function has been evaluated using subjective and objective evaluation metrics.
A survey on improving pattern matching algorithms for biological sequences

Summary Pattern matching is a highly useful procedure in several stages of the computational pipelines. Furthermore, some research trends in this research domain contributed to growing biological databases and updated them throughout time. This article proposes an comparison and analysis of different algorithms for match equivalent pattern matching like complexity, efficiency, and techniques. Which algorithm is best for which DNA sequence and why? This describes the different algorithms for various activities that include pattern matching as an important aspect of functionality. This article shows that BM, Horspool, ZT, QS, FS, Smith, and SSABS methods employ the bad character preprocessing function. In addition, BM, SSABS, TVSBS, and BRFS methods are using two approaches in the preprocessing stage, which decreases the preprocessing time. Furthermore, KR, QS, SSABS, BRFS, and Shift‐Or are not recommended for the long pattern, whereas ZT, FS, d‐BM, Raita, and Smith are not recommended for the short pattern. This is because they are time‐consuming and certain algorithms, such as ZT and DCPM, use a lot of time and space during the matching and search process, while others, such as d‐BM and TSW, save space and time. Although DCPM, BRFS, and QS are quicker than other algorithms, FLPM, PAPM, and LFPM rank highest in terms of complexity time.
Multi-level Taxonomy Review for Sign Language Recognition: Emphasis on Indian Sign Language

With the phenomenal increase in image and video databases, there is an increase in the human-computer interaction that recognizes Sign Language. Exchanging information using different gestures between two people is sign language, known as non-verbal communication. Sign language recognition is already done in various languages; however, for Indian Sign Language, there is no adequate amount of work done. This article presents a review on sign language recognition for multiple languages. Data acquisition methods have been over-viewed in four ways (a) Glove-based, (b) Kinect-based, (c) Leap motion controller, and (d) Vision-based. Some of them have pros and cons that have also been discussed for every data acquisition method. Applications of sign language recognition are also discussed. Furthermore, this review also creates a coherent taxonomy to represent the modern research divided into three levels: Level 1 Elementary level (Recognition of sign characters), Level 2 Advanced level (Recognition of sign words), and Level 3 Professional level (Sentence interpretation). The available challenges and issues for each level are also explored in this research to provide valuable perceptions into technological environments. Various publicly available datasets for different sign languages are also discussed. An efficient review of this article shows that the significant exploration of communication via sign acknowledgment has been performed on static, dynamic, isolated, and continuous gestures using various acquisition methods. Comprehensively, the hope is that this study will enable readers to learn new pathways and gain knowledge to carry out further research work in the domain related to sign language recognition.
Cloud Computing Database and Travel Smart Platform Design Based on LSTM Algorithm

Information technology has played a key role in the development of the tourism planning service industry and has now become an important foundation for the survival and rapid development of the industry. In this context, due to the fast updating and popularization of information technology, it has greatly promoted the development of the tourism industry. In order to meet the current public demand for tourism information, this paper integrates cloud computing, VR technology, and big data analysis technology to build a smart platform for intelligent perception tourism information services. The system can obtain tourism information through mobile Internet terminals. Among them, the database of the smart tourism planning platform is the most important module. Aiming at the many difficulties in adapting data encryption in cloud storage applications, this article designs an adaptive CloudCrypt data encryption system based on cloud computing technology and proposes dynamic JavaScript dynamic analysis and automatic identification of data technology, through adaptive different cloud applications to obtain data encryption protection. CloudCrypt is suitable for typical cloud applications, such as mail and storage. The entry cost of the system is extremely low; it can fully guarantee the security of the tourism information platform database and can integrate wireless sensor networks into the tourism information platform. The network system composed of sensor nodes activates detection, calculation, and communication modules through wireless communication and has the advantages of low cost, low power consumption, and fast networking speed. In this paper, through the construction of integrated wireless sensor technology and cloud computing database, it is applied to the construction of a tourism smart platform, thereby promoting the development of smart travel technology.
Aesthetic evaluation of Asian and Caucasian photos with overall and attribute scores

Nowadays, the portrait image aesthetic attribute evaluation is extremely valuable in the research area, but there is no either specialized portrait image aesthetic attribute dataset or specialized portrait image aesthetic dataset. At the same time, the lightweight image aesthetic attribute evaluation model is also valuable in the market. In this paper, we propose two datasets about portrait image aesthetic attribute scores and portrait image overall aesthetic scores. We also design two models to predict attribute scores and overall scores. We use Knowledge Distillation(KD) and train our feature extraction model on a large-scale dataset to make the evaluation models lightweight. In the experiments, our models have good performance in experiments and extremely highly practical value. The parameter quantity of our models is all less than 6 M and the Spearman’s Rank Correlation Coefficient (SRCC) of results are above 0.75. These models provide aesthetic references to portrait images, which can give some photographic suggestions for color, lighting, and composition.