Reviewerly/exHarmony huggingface dataset:
VGG-16-Based Framework for Identification of Facemask Using Video Forensics

In the context of the COVID-19 disease outbreak, organizations such as the universities are at risk of being essentially shut around the world if the overall condition does not improve. The other name for COVID-19 is a serious acute respiratory syndrome, a virus that causes serious respiratory problems. Corona virus-2 is a contagious agent spread through droplets in the air from an affected patient. This spreads easily by direct contact with affected patients or touching the objects which all already touched by the affected patients. Even if there are many vaccines available to defend against COVID-19 across the globe, still there is a high necessity to consider the precautions for avoiding infection. The major aspect for preventing the infection using a facemask that protects a person from entering the virus into the body through the nose and mouth of a person. The other major aspect for preventing the infection by washing hands using and washes or sanitizers. In the present article, the major and popular advanced technique used for image-based detection and classification is the Deep Learning-based VGG-16 technique. The deep learning technology is used in the analysis to identify face mask recognition and determine whether or not the individual is carrying a facemask. VGG-16 is the CNN (Convolutional Neural Network) framework is utilized for the present study. The Kaggle dataset considered consists of 25,000 images with each of the images having 225 × 225 pixels as the resolution, and the proposed model performed with a 96% accuracy.
Personalized recommendation of collective points-of-interest with preference and context awareness

With the popularity of mobile devices, location-based services, such as Foursquare and Facebook, have attracted increasing attention in recent years. Users share their interests anytime, anywhere with their friends on the social network. Point-of-Interest (POI) recommendation, as one of key services on Location-Based Social Networks (LBSNs), can effectively enhance users’ experience especially when they travel in a new city. Previous studies have made great success on POI recommendation by employing geographical influence and user preference. However, it is believed that the human decision on where to visit is very complex and involves the comprehensive factors such as POI popularity, POI location, user trajectory, and time context. In this paper, we propose a collective POIs recommendation framework which leverages the individual latent preference and contextual information. Firstly, to recommend top-K initial POIs, a scoring prediction model is constructed, which considers the influence of similarity, popularity and location of POIs. Furthermore, a next POI recommendation model based on personalized transfer probability is proposed, and the initial POI recommendation is combined to calculate the user’s score on the next POI. Extensive experiments based on real datasets collected from Foursquare demonstrate the proposed framework outperforms the state-of-art ones.
Grading materials for stringed instruments soundboards: An approach considering the orthotropic elastic and damping properties

Choosing or designing materials for stringed instruments soundboards is an issue that garnered the attention of luthiers and scientists for a long time, leading to the definition of descriptors that assess the ability of materials to efficiently radiate sound. This approach often considers the material properties along one direction, neglecting their anisotropic nature or taking it into account with some approximations. The authors propose an alternative approach that considers material anisotropy, soundboard geometry and their effect on the vibration and damping. Two orthotropic descriptors are derived, which include the elastic and damping properties along different directions. These descriptors are used to compare different materials, as well as to grade wood with non destructive tests and to analyse the effect of wood grain orientation. The use of these descriptors can provide useful information to both scientists and luthiers regarding materials choice and composite materials design for the construction of stringed musical instruments.
Scalable anomaly detection in manufacturing systems using an interpretable deep learning approach

Anomaly detection in manufacturing systems has great potential for the prevention of critical quality faults. In recent years, unsupervised deep learning has shown to frequently outperform conventional methods for anomaly detection. However, tuning, deploying and debugging deep learning models is a time-consuming task, limiting their practical applicability in manufacturing systems. We approach this problem by developing a deep learning model that learns interpretable shapes that can be used for anomaly detection in temporal process data. Application of the model to assembly tightening processes in the automotive industry shows a significant improvement in model interpretability and scalability.
Learning-Based Shadow Detection in Aerial Imagery Using Automatic Training Supervision from 3D Point Clouds

Shadows, motion parallax, and occlusions pose significant challenges to vision tasks in wide area motion imagery (WAMI) including object identification and tracking. Although there are many successful shadow detection approaches that work well in indoor scenes, close range outdoor scenes, and spaceborne satellite images, the methods tend to fail in intermediate altitude aerial WAMI. We propose an automatic shadow mask estimation approach using self-supervised learning with-out manual labeling to provide a large amount of training data for deep learning-based aerial shadow extraction. Analytical ground-truth shadow masks are generated using 3D point clouds combined with known solar angles. FSDNet, a deep network for shadow detection, is evaluated on aerial imagery. Preliminary results indicate that training using automated shadow mask self-supervision improves performance, and opens the door for developing new deep architectures for shadow detection and enhancement in WAMI.
Adaptive Visual Servoing for Obstacle Avoidance of Micro Unmanned Aerial Vehicle with Optical Flow and Switched System Model

A vision-based adaptive switching controller that uses optical flow information to avoid obstacles for micro unmanned aerial vehicles (MUAV) is proposed in this paper. To use the optical flow to indicate the distance between the MUAV and the environment, we propose an algorithm with multi-thread processing such that the optical flow information is obtained reliably and continuously in the entire camera field of view. The flying behavior of considered MUAV is regarded as a switching system when considering different flying modes during the mission of obstacle avoidance. By the required flight direction for obstacle avoidance specified by the detected optical flow, an adaptive control scheme is designed to track the required trajectory in switching modes. The simulation result shows the tracking performances of the adaptive control with the switching system. The experiment of the whole system is completed to verify the obstacle avoidance capability of our system.
Path Planning of Mobile Robots in Warehouse Logistics Relying on Computer Multimedia 3D Reconstruction Technology

Because traditional methods are difficult to solve the problems related to the path planning of logistics robots, this study proposes a method of using computer multimedia 3D reconstruction technology to realize the path planning of warehouse logistics robots. Without fully considering the accurate movement path between points, according to the warehouse logistics robot, it is judged whether the starting point is at the exit. The planning problem of the movement path is converted into a TSP problem and a TS-TSP problem. Finally, the analysis of experimental results shows that the method proposed in this study converges faster than traditional algorithms and can quickly obtain the global optimal solution. At the same time, the warehousing logistics robot requires less path planning time and has strong practical application.
Multilayer Decision-Based Fuzzy Logic Model to Navigate Mobile Robot in Unknown Dynamic Environments

The investigation into mobile robot navigation under uncertain dynamic environments is of great significance. This paper seeks to solve the current problems which are the difficulty to plan in indeterminate ever-changing environments, the problem of optimality, failure in complex situations, and the problem of predicting the obstacle velocity vector. The objective of this study is to propose a multilayer decision-based fuzzy logic model to find the solution for robot navigation through a safe path while preventing any types of barriers and to understand the non-collision mobile robots’ movement in an unknown dynamic environment. In this study, the prediction and priority rules of a multilayer decision are used by the fuzzy logic controller to improve the quality of the next position with regard to its path length, safety, and runtime. The results of comparison studies revealed a considerable improvement in failure rate and path length. Outcomes show that the suggested method displays attractive features, for instance, great stability, great optimality, zero failure rates, and low running time. The average path length for all test environments is 13.11 with 0.47 a standard deviation that provides 89% of an average optimality rate. The average running time is about 5.31 s with a 0.25 standard deviation.
Multilabeling Indonesian Toxic Comments Classification Using The Bidirectional Encoder Representations of Transformers Model

The main goal of this study is to create a language model to identify and classify multi-label on toxic comments that exist in social media. However, toxic comments classification faces a problem: one single toxic comment can have more than one class or label. Therefore, traditional algorithms such as Naive Bayes, SVM, or else cannot handle this type of classification efficiently because to analyze toxic comments, a model needs to understand language context. For that reason, we use BERT (Bidirectional Encoder Representation from Transformers), which can learn language bidirectionally. Two models (bert-base-multilingual-uncased and indobert-base-p1) will be trained and compared. The data used in this study is acquired from Twitter and will go through a pre-processing phase that includes case folding, data cleaning, normalization, and non-alphanumeric removal before it is used to train BERT. The model's hyperparameters are three epochs, 2e-5 learning rate, and a batch size of 32. The best test accuracy scores acquired from the two models in this paper are toxic: 92%, severe toxic: 92%, obscene: 91%, insult: 85%, threat: 93%, dan identity hate: 90%.
Exploring the Microbiome Analysis and Visualization Landscape

Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts. Both the reviewing process and workshops are based on domain characterization methods to facilitate communication and collaboration between researchers from different disciplines. We identify several research questions related to microbiomes, and describe how different analysis methods and visualizations help in tackling them.
Public Reaction to Scientific Research via Twitter Sentiment Prediction

Social media users share their ideas, thoughts, and emotions with other users. However, it is not clear how online users would respond to new research outcomes. This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications. Additionally, we investigate what features of the research articles help in such prediction. Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles.
Combined stochastic-deterministic predictive control using local-minima free navigation

This work proposes a model predictive control approach of a wheeled mobile robot based on a local-minima free navigation function. The constructed navigation function includes information on a goal location and obstacles. Novel conservative navigation is introduced that is simple to compute and yields convergent control behavior. To solve the optimization problem the combined optimization is proposed by a fixed candidate set and particle swarm optimization. The efficiency of the proposed approaches is validated on simulations, by experimental results on the Husky A200 mobile robot and comparisons to Stable Sparse RRT planner.
Russian-English dataset and comparative analysis of algorithms for cross-language embedding-based entity alignment

Abstract The problem of data fusion from data bases and knowledge graphs in different languages is becoming increasingly important. The main step of such a fusion is the identification of equivalent entities in different knowledge graphs and merging their descriptions. This problem is known as the identity resolution, or entity alignment problem. Recently, a large group of new entity alignment methods has emerged. They look for the so called “embeddings” of entities and establish the equivalence of entities by comparing their embeddings. This paper presents experiments with embedding-based entity alignment algorithms on a Russian-English dataset. The purpose of this work is to identify language-specific features of the entity alignment algorithms. Also, future directions of research are outlined.
Supervised Multimodal Emotion Analysis of Violence on Doctors Tweets

With the advent of novel coronavirus pandemic doctors, health workers, and the government too, are trying their best of their capacity to deal with contemporary situations. It is genuine that when a person’s close one is lost, they will react vociferously but accusing the doctors and workers and harming them is also morally indignant as the person saving so many lives his/her own life is in danger. With the boom of technology and how the world has come so close on social media, many social media users are expressing their views in either the support or opposition of the saviors of this pandemic, the doctors and the health care workers. These views of people are enough to create a good or bad impression of any doctor in minds of people and can even create a hostile behavior for that doctor by others, analyzing the stand of the person towards the ongoing violent situation towards workers using a multimodal emotional analysis combining both visual and textual data. This paper uses a Multimodal Transformer model which combines both visual and textual data is the sole purpose of this paper. Apart from the main aim, the paper will also explain whether in social media more information has been carried out by a text or more information can spread through images posted on social media. The paper will explain the use of appropriate loss function for imbalanced data also.
Tools for adaptation of a mobile application to the needs of users with cognitive impairments

The existing approaches and principles to the creation of adaptive software systems to help people with cognitive impairments are analyzed. Based on the analysis of the subject area, an ontological model has been designed, which provides the ability to dynamically determine the parameters of the software. A method for adapting a mobile application has been implemented and a software system has been developed on its basis that allows to customize the functionality and graphical interface, depending on the user&#x0027;s health disorders. The time parameters of the performance of the personalized user settings formation are determined, depending on the increase in the number of entities instances.
An Entropy-Based Approach for Identifying User-Preferred Camera Positions

Viewpoint Quality (VQ) metrics have the potential to predict human preferences for camera placement. With this study, we introduce new VQ metrics that incorporate entropy, and explore how they can be used in combination. Our evaluation involves three phases: (1) creating a database of isosurface imagery from ten large, scientific data sets, (2) conducting a user study with approximately 30 large data visualization experts who provided over 1000 responses, and (3) analyzing how our entropy-based VQ metrics compared with existing VQ metrics in predicting expert preference. In terms of findings, we find that our entropy-based metrics are able to predict expert preferences 68% of the time, while existing VQ metrics perform much worse (52%). This finding, while valuable on its own, also opens the door for future work on in situ camera placement. Finally, as another important contribution, this work has the most extensive evaluation to date of existing VQ metrics to predict expert preference for visualizations of large, scientific data sets.
Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry

Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes. However, labeled data are often scarce in many real-world applications, which poses a significant challenge when building accurate soft sensor models. Therefore, this paper proposes a novel semi-supervised soft sensor method, referred to as ensemble semi-supervised negative correlation learning extreme learning machine (EnSSNCLELM), for industrial processes with limited labeled data. First, an improved supervised regression algorithm called NCLELM is developed, by integrating the philosophy of negative correlation learning into extreme learning machine (ELM). Then, with NCLELM as the base learning technique, a multi-learner pseudo-labeling optimization approach is proposed, by converting the estimation of pseudo labels as an explicit optimization problem, in order to obtain high-confidence pseudo-labeled data. Furthermore, a set of diverse semi-supervised NCLELM models (SSNCLELM) are developed from different enlarged labeled sets, which are obtained by combining the labeled and pseudo-labeled training data. Finally, those SSNCLELM models whose prediction accuracies were not worse than their supervised counterparts were combined using a stacking strategy. The proposed method can not only exploit both labeled and unlabeled data, but also combine the merits of semi-supervised and ensemble learning paradigms, thereby providing superior predictions over traditional supervised and semi-supervised soft sensor methods. The effectiveness and superiority of the proposed method were demonstrated through two chemical applications.
Isomorphic Cipher Reduction

All ciphers are a form of the substitution cipher. Variations of ciphers, such as the P cipher, block ciphers, and product ciphers have been introduced in an effort to hide the patterns that filter through the obscuring process. Still, if the right parameters are used, it is possible to substitute an equivalent substitution cipher for most encryption algorithms and then use a property of substitution to greatly simplify the analysis and decryption of even block product ciphers. In this paper we also identify and explain exceptions to this reduction. The purpose of applying this cipher reduction is to strengthen security by eliminating the weaknesses that reduction identifies.
A deep reinforcement learning-based agent for negotiation with multiple communication channels

Agent-based negotiation aims at automating negotiation process on behalf of humans to save time and efforts. While successful, the research of automated negotiation focuses on communication merely through offer exchange (e.g., following alternating offer protocol). As many real-world settings involve linguistic channel to express intentions, ask questions, discuss plans and so on, the information bandwidth is therefore restricted and grounded in the action space of negotiation. To bridge the gap, this work proposes the MCAN (Multiple Channel Automated Negotiation) agent that is based on deep reinforcement learning (DRL). It is capable of negotiating using both offers and linguistic communication channels with autonomous agents or human players. Specifically, the agent leverages Parametrized Deep Q-Network (P-DQN) that provides solution for a hybrid discrete-continuous action space, thereby learning a comprehensive negotiation strategy which integrates linguistic communication skills and bidding strategies. The experimental results show that the MCAN agent can successfully complete negotiations with both proposal and linguistic communication, and it outperforms baseline agents in terms of averaged utility.
Utilizing SBERT For Finding Similar Questions in Community Question Answering

The BERT model was fine-tuned to give state-of-the-art results in sentence-pair regressions. However, this model requires that both questions are fed into the network, which leads to a massive computational overhead. Instead of computing on n pairs of sentences, SBERT was proposed to learn sentence representation by computing on only one query question. This model was proven to work effectively on semantic textual similarity (STS). In this paper, we explore SBERT model for question retrieval in Community Question Answering. Results show that SBERT decreases slightly in performance compared to BERT4ECOMMERCE. However, This model reduces the effort for finding the most similar question from 795 seconds with BERT to about 0.828 seconds with SBERT, while maintaining the accuracy from BERT.
A Disentangled Representations based Unsupervised Deformable Framework for Cross-modality Image Registration

Cross-modality magnetic resonance image (MRI) registration is a fundamental step in various MRI analysis tasks. However, it remains challenging due to the domain shift between different modalities. In this paper, we proposed a fully unsupervised deformable framework for cross-modality image registration through image disentangling. To be specific, MRIs of both modalities are decomposed into a shared domain-invariant content space and domain-specific style spaces via a multi-modal unsupervised image-to-image translation approach. An unsupervised deformable network is then built based on the assumption that intrinsic information in the content space is preserved among different modalities. In addition, we proposed a novel loss function consists of two metrics, with one defined in the original image space and the other in the content space. Validation experiments were performed on two datasets. Compared to two conventional state-of-the-art cross-modality registration methods, the proposed framework shows a superior registration performance.Clinical relevance-This work can serve as an auxiliary tool for cross-modality registration in clinical practice.