Reviewerly/exHarmony huggingface dataset:
Crafting the Language of Robotic Agents: A vision for electroacoustic music in human–robot interaction

This article discusses the role of electroacoustic music practice in the context of human–robot interaction (HRI), illustrated by the first author’s work creating the sonic language of interactive robotic artwork Diamandini. It starts with a discussion of the role of sound in social robotics and surveys various notable conceptual approaches to robot sound. The central thesis of the article is that electroacoustic music can provide a valuable source of aesthetic considerations and creative practice for the creation of richer and more refined sonic HRIs by giving practitioners new ways to create believable sounding objects, to convey fiction, agency and animacy, and to communicate causality in auditory feedback. To demonstrate this, the authors describe the rationale for treating robot sound design as a compositional process and discuss the implications of the endeavour’s non-linear and site-specific nature. These considerations are illustrated using sound examples and design decisions made throughout the creation process of the robotic artwork. The authors conclude with observations on how the compositional process is affected by this particular application context.
Ensure the dynamic identity and PUF based authenticated key settlement approach for the IoT infrastructure

With technological advancements, the dependency on the Internet of things (IoT) has increased. The spectrum of IoT application domain becomes larger day by day. It converts the existing services into smart services. The evolution brings lot of security challenges hand in hand with IoT paradigms. This is due to the limitation of IoT devices like, low computational capabilities, small storage medium, less power back up and low processing speed, etc. To overcome these limitations, a proper authentication technique must be required to do the trust management in a heterogeneous IoT environment and secure all kinds of traffic. In this research document, a dynamic identity-based authentication approach is introduced for IoT environment, which utilizes very lightweight physical unclonable function, one-way hashing function and exclusive-OR(XOR) function to make the scheme lighter. Broadly accepted the Real or Random (RoR) model and the Burrows Abadi Needham logic (BAN logic) are used here, to check the security and the correctness of the devised protocol. In addition to that, the software simulation tool, Automated validation of Internet security protocols and applications (AVISPA) has proved that, the recommended protocol is safe and secure from various attacks. Furthermore, the comparative performance analysis exhibits the supremacy of the recommended scheme over the analogous schemes w.r.t computational power, memory usage, and communication overhead.
Special aspects of using Big Data in the learning process

The paper examines the special aspects of using Big Data technology in education. The population was made up of 356 third-year university students. To study Big Data technology, a questionnaire was used where respondents rated: cloud technology; apps; Massive Open Online Courses (MOOCs) and digital learning platforms. The study suggested that the education sector is ambitiously applying Big Data technology, both online and offline. All surveyed respondents use apps in Big Data learning and analysis: 73.03% use Moodle, 67.13% use Zoom, 65.17% use Quizlet, 50.84% use Skype, and 35.11% use Slack. MOOCs in education are used by 75% of respondents. Digital learning platforms are used by all respondents. All students use cloud technology. When dealing with Big Data technologies, students preferred apps (8.9 ± 1.33) instead of the cloud (6.9 ± 0.11). Students believe that the important factors for using Big Data in the learning process include: quality of information (85.96%); interest (77.81%); instructor’s support (66.85%). The research findings make it possible to integrate Big Data technology into the learning process, thus improving learning outcomes and providing greater speed in processing reliable and meaningful data.
A Game to Promote Safe Use of Online Media with Mixed Reality Technology

Currently, online media is widely used and accessible to all ages. Careless use of cooperativeness in online media, however, can allow vulnerabilities that lawbreakers exploit. Learning as much as possible about media also requires users to be engaged and not bored. So, to avoid boring players while learning we offer a mixed reality (MR) technology stimulating game named Catcher. Catcher is a story game whereby players investigate accurate facts from varied evidence in 5 different game levels. By giving a hint of the non-player character (NPC) at the story’s beginning, the player finds clues to add more information toward the conclusion of the complete story. Testing Catcher showed the game can improve systematic thinking skills by 40% and media learning by 64%. By choosing MR technology players can connect directly with the game system. This allows them to effectively learn while having safe fun without being bored.
A Visual Analytics Approach to Understanding Gradient Boosting Tree via Click Prediction on Ads

As an iterative algorithm consisting of multiple decision trees, gradient boosting decision tree (GBDT) is widely used in problems such as classification and regression prediction. The ensemble decision trees of the algorithm obtain predictive effect by automatically filtering and combining new feature vectors, which contributes to discovering effective feature combinations. However, gradient boosting tree (GBT) is a tedious model, especially the boosting tree approach. It is difficult to interpret the principle of the model due to the characteristic of each tree of the model with weights and the unique structural properties of each decision tree, which is a challenge in many fields that require high interpretation such as financial risk control. In this paper, we design an interactive visual analytic system to solve this problem, to explain the structure and prediction process of the gradient boosting tree model, and to help experts in related fields to perform efficient analysis. We have designed a graphical representation of the feature information and a visual model of the boosting tree to show the basic mechanism of the GBT algorithm in a comprehensive way. The case study is conducted on the dataset of Kaggle competition to prove the effectiveness of the system.
The Aeroplane and Undercarriage Detection Based on Attention Mechanism and Multi-Scale Features Processing

Undercarriage device is one of the essential parts of an aeroplane, and accurate detection of whether the aeroplane undercarriage is operating normally can effectively avoid aeroplane accidents. To address the problems of low automation and low accuracy of small target detection in existing aeroplane undercarriage detection methods, an improved algorithm for aeroplane undercarriage detection YOLO V4 is proposed. Firstly, the convolutional network structure of Inception-ResNet is integrated into the CSPDarkNet53 framework to improve the algorithm’s ability to extract semantic information of target features; then an attention mechanism is added to the path aggregation network algorithm structure to improve the importance and relevance of different features after conceptual operations. In addition, aeroplane and undercarriage datasets were constructed, and finally, the generated partitioned test sets were tested to evaluate the test performance of Faster R-CNN, YOLO V3, and YOLO V4 target detection algorithms. The experimental results show that the improved algorithm has significantly improved the recall rate and the mean accuracy of detection for small targets in our dataset compared with the YOLO V4 algorithm. The reasonableness and advancedness of the improved algorithm in this paper are effectively verified.
Siamese Attention Networks with Adaptive Templates for Visual Tracking

Visual object tracking takes an important role in realistic applications, such as video understanding, unmanned auto vehicles, and autonomous robots. Although the Siamese-based tracker has achieved good performance in tracking tasks, the existing methods using initial template or updating template with simple strategy result in the performance degradation of the model when the target varies in realistic scenarios such as target occlusion, scale variation, and deformation. In this paper, we propose a visual tracking framework with adaptive template update and spatiotemporal attention, named SiamAttnAT. Specially, we propose a historical template selecting strategy and a template adaptively generating method for robust tracking. In addition, we apply the proposed mechanisms to the employed baseline SiamRPN++. Extensive experiments and comparisons with state-of-the-art trackers on short-term and long-term visual tracking benchmarks including VOT2018, OTB-100, UAV123, NFS, and LaSOT show that the proposed framework achieves the outstanding performance with a considerable real-time speed, verifying its efficiency and effectiveness.
Ontology-Based Relational Product Recommendation System

As online shopping has expanded, product recommendations on e-commerce websites have gained significance. Systems for recommending products use information about site navigation and user leave-over to suggest more products. Customers who use a product recommendation system choose better and find items more quickly. On e-commerce websites, collaborative and content-based filtering is used in product suggestion algorithms. Collaborative filtering is driven by user preference similarity and content-based filtering. While content-based filtering groups are related to products, collaboration groups are like-minded individuals. In collaborative filtering, users with similar user profiles are used during the proposal phase; in content-based filtering, users with similar product profiles are found and recommended. These techniques cannot deliver complex commodities and have slow start-up times and small element sets. Users can push the same product if they only like certain things, but they cannot recommend a new product or user who just joined the system because they are not a group member. These approaches cannot capture complex semantic relationships, making them inadequate for recommending complex products. Recent research has focused on incorporating the domain ontology into the proposition process to create a more precise and helpful suggestion. The relational qualities of the product are not covered in this study, only its category and features are. Actually, the ontology of the proposed product should be included in the suggestion system. Relational data is integrated into the recommendation engine in this study using domain ontologies. This was done to research books that people had recommended. Relational data from an online bookseller was used to test the proposed infrastructure.
Image Super-Resolution Based on Frequency Division Generative Adversarial Network

Most supervised super-resolution (SR) algorithms require paired high-resolution (HR) and low-resolution (LR) images as training samples. However, the network structures trained by supervised algorithms don’t adapt to different image degradations and it is difficult to find paired HR and LR images in real world. In this paper, a new unsupervised image SR algorithm based on generative adversarial network (GAN) is proposed. (1) A new network that can be trained with unpaired HR and LR images is proposed. (2) An intermediate process is proposed so that network contains two GANs, the first learns LR degradation, and the second performs SR procedure. (3) The idea of frequency division training is adopted in LR degradation and SR procedures: low frequency of image is preserved and only high frequency is learned. Experimental results on different datasets show that proposed algorithm provide a better balance between visual quality of SR reconstructed images and computational cost when compared with the state-of-the-art SR algorithms.
Performance Comparison of Different Machine Learning Classifiers in Categorizing Bangla News Articles

In the digital age, the amount of online Bangla news has increased rapidly. For organizing their news, each news source has its own system for categorization. Because of the diversity, the structure and classification of online Bangla news items cannot always match the needs of an individual. As a result, tackling this issue and categorizing Bangla news items based on the preference of a user is a difficult task. Recommending the proper news category aids users in finding desired items, however, manually categorizing news articles is a lengthy process and cumbersome. It becomes even more challenging when addressing a language with limited resources, such as Bengali, which is one of the world’s most frequently spoken languages. However, just a few ways for categorizing Bangla news articles have been developed, with only few machine learning algorithms being used with restricted resources. Multiple machine learning approaches are highlighted in this study. Using our techniques, we were able to categorize news in nine categories with an accuracy of 93.76% in a dataset including over 400 thousand data sets from the newspaper Prothom Alo. To train diverse supervised learning models, we used dimentionality reduction and a variety of complicated textual properties, such as TF-IDF. Several machine learning classifiers were implemented. The acquired experimental findings demonstrate the recall, F1-score, precision, and accuracy of our implemented models.
Fusion model of Modified Wavelet Transform and Neural Network for medical image compression

Medical imaging is one of emerging fields that has high impact on analysis and diagnosis of diseases. Image compression is introduced in the medical image processing to solve memory and bandwidth requirement problems. In this paper, a hybrid image compression method by integrating Modified Wavelet Transform (MWT) and Neural Network (NN) is proposed for efficient storage as well as transmission of medical images. Primarily, medical images are preprocessed using median filter to suppress noisy data and then decomposed into approximation and detailed coefficients using wavelet transform. Subsequently, detailed coefficients are filtered with proposed thresholding technique. Both approximation and detailed coefficients are divided into sub matrix with the size of n x n and used as input to the NN to perform compression. The image compression performance of the proposed method is evaluated by computing peak signal to noise ratio, compression ratio and storage space and compared with prevailing methods to prove its effectiveness. The empirical findings reveal that the proposed method provides best performance compared to the former methods taken for comparison.
Towards achieving confidentiality in Hyperledger Fabric

Enterprise blockchain use cases have confidentiality requirements when executing cross-organizational business processes or workflows across their corporate boundaries. For instance, only a certain privileged subset of organizations may know of the workflow's existence and is allowed to access sensitive workflow's data assets such as price information. A private or permissioned blockchain such as Hyperledger Fabric (HLF) provides many features to support those enterprise use cases that require confidentiality. Nevertheless, the blockchain architects need to decide when it comes to how to use them, when to use them, and what are the performance and security implications of those design decisions. This work presents a generic methodology to design a secure Hyperledger Fabric (HLF) blockchain architecture with a given security requirements. To achieve that, we developed and used a novel formal description to specify a generic multilateral workflow in the context of an enterprise use case. Furthermore, the security of the resulting architecture is evaluated against an attacker model and relevant countermeasures are presented. In addition, the performance impact of proposed changes is evaluated using the Hyperledger Caliper benchmarking framework.
Survival Analysis of Breast Cancer using Machine Learning

Cancer is a particularly varied ailment that results from pursuing allure, progress, and asperity, which is amazingly troublesome. The most ordinary malignancy-producing structure is the TNM (carcinoma, growth, often major) arrangement that is located generally on dispassionate news like swelling intensity, consideration of spread, etc. Combining phenotypic and microscopic dossiers from tumor victims can result in more specific writings of disease progression and asperity. investigated the accompanying three microscopic datasets (DNA methylation, RNASeq and miRNASeq dossiers) in addition to the clinical dataset to conclude the overall endurance of feeling malignancy sufferers. The machine intelligence algorithms conclude the maximal accuracy of the survival rates.
Design of Intelligent Auxiliary Software in Sino-Russian Cross-Cultural Communication based on Russian Image Character Structure Extraction Algorithm

This paper emphatically expounds the importance of encoding and decoding of Russian-language image character structure extraction algorithm in cross-cultural communication, and takes Russian-Chinese language as an example to list the similarities and differences conveyed by non-linguistic information in Russian-Chinese communication. sex. This paper starts with the goal of Russian teaching in colleges, firstly obtains the required dot matrix graphics of Russian letters, and establishes a Russian font set to meet the needs of data display and framing during interface design; The sequence relationship of alphabetic place. Put forward the countermeasures of Russian cultural knowledge teaching in the context of Sino-Russian cross-cultural communication, in order to provide a certain reference value for university Russian teaching.
Performance Appraisal and Automatic Scoring System for College Counselors Based on Kmeans Clustering

The optimal solution is output as the result to the Kmeans algorithm as the initial clustering center, and the proposed linear distance model is used to complete the clustering. Combined with the theory of target management, focusing on the job requirements and responsibilities of the counselors, the counselors’ performance appraisal objectives were determined, the counselor performance appraisal system was established, and the first-level indicators and the second-level indicators and their weights were determined by using Del Illegal and Analytic Hierarchy Process (AHP). This paper constructs a performance appraisal system for local undergraduate college counselors based on management by objectives, and has carried out a pilot implementation in a college. The behavior anchoring method is used to determine the scoring standards of each index, which solves the problem of inconsistent scoring standards for different assessment subjects in the past. In the assessment results, the assessment results of the three dimensions are independently evaluated by category. This paper has a certain practical significance and reference value for the optimization research of the counselor’s performance appraisal scheme under the background of institutional reform in a university.
Neuro-Symbolic Word Embedding Using Textual and Knowledge Graph Information

The construction of high-quality word embeddings is essential in natural language processing. In existing approaches using a large text corpus, the word embeddings learn only sequential patterns in the context; thus, accurate learning of the syntax and semantic relationships between words is limited. Several methods have been proposed for constructing word embeddings using syntactic information. However, these methods are not trained for the semantic relationships between words in sentences or external knowledge. In this paper, we present a method for improved word embeddings using symbolic graphs for external knowledge and the relationships of the syntax and semantic role between words in sentences. The proposed model sequentially learns two symbolic graphs with different properties through a graph convolutional network (GCN) model. A new symbolic graph representation is generated to understand sentences grammatically and semantically. This graph representation includes comprehensive information that combines dependency parsing and semantic role labeling. Subsequently, word embeddings are constructed through the GCN model. The same GCN model initializes the word representations that are created in the first step and trains the relationships of ConceptNet using the relationships between words. The proposed word embeddings outperform the baselines in benchmarks and extrinsic tasks.
Character-Based Value Factorization For MADRL

Abstract Value factorization is a popular method for cooperative multi-agent deep reinforcement learning. In this method, agents generally have the same ability and rely only on individual value function to select actions, which is calculated from total environment reward. It ignores the impact of individual characteristics of heterogeneous agents on actions selection, which leads to the lack of pertinence during training and the increase of difficulty in learning effective policies. In order to stimulate individual awareness of heterogeneous agents and improve their learning efficiency and stability, we propose a novel value factorization method based on Personality Characteristics, PCQMIX, which assigns personality characteristics to each agent and takes them as internal rewards to train agents. As a result, PCQMIX can generate heterogeneous agents with specific personality characteristics suitable for specific scenarios. Experiments show that PCQMIX generates agents with stable personality characteristics and outperforms all baselines in multiple scenarios of the StarCraft II micromanagement task.
A Hybrid Face Recognition Approach Using Local Appearance and Deep Models

Visible and thermal face recognition are highly important topics in computer vision. Existing face recognition models generally focus on facial images in the visible domain. However, they fail in the lack of the light and become non-functional at night. In addition, the performance of the models decreases in the case of occlusion. This work aims to build a hybrid two-branch pipeline that detects, aligns, represents, and recognizes a face from either thermal or visible domains using both a local appearance-based and a deep learning-based method. In addition, we present a fusion scheme to combine the outputs of these methods in the final stage. The recent state-of-the-art deep learning-based face recognition approaches mainly focus on eye region for identification. This leads to a performance drop when these models are confronted with occluded faces. On the other hand, local appearance-based approaches have been shown to be robust to occlusion as they extract features from all parts of the face. Therefore, in order to enable a high-accuracy face recognition pipeline, we combine deep learning and local appearance based models. We have conducted extensive experiments on the EURECOM and ROF datasets to assess the performance of the proposed approach. Experimental results show that in both domains there are significant improvements in classification accuracies under various facial appearances variations due to the factors, such as facial expressions, illumination conditions, and occlusion.