Reviewerly/exHarmony huggingface dataset:
Manzil: An Interactive Data Visualization Project Exploring Everyday Spaces

This paper intricately documents every moment, that is from the point of realization for the creation to an Arduino-based situated display based on IoT and tangible interactions as a result of movement-based triggers. A classroom project turned pseudo-reality, through ethnomethodology it has been put on record the evolution of research by devoting attention to the observation of commonplaces that are more or less taken for granted in everybody&#x0027;s routines and how on a ride to enrich that experience the team landed on various abstractions that work on collectively producing pillars of social situations. From here on the paper will shape its way from an introduction about situated displays and why the study went ahead with it for their classroom project, to how a story was composed around the mentioned display. Later, an intricate discussion about the implementation, with regards to the Arduino, the permutation combination was put into figuring out the best channels to power the hardware required. Following that, the study focuses on ordinary methods that ordinary people use to analyze their response to a given stimulus. Exploring various variables to analyze positive and negative reactions and building onto a worthwhile experience during the span of a flight of stairs was the most intricately implemented part of the whole project which came right after. Project Manzil is way beyond a public display yet has a horizon to cross ahead in terms of data visualization, and this paper from here on chronicles the journey to reaching a full mount public/situated display for the college.
Attentive Manifold Mixup for Model Robustness

The robustness of deep neural networks becomes more and more significant since the performance of models degrades heavily in real life. The main reason behind that is discrepancy between training and testing distribution. Many state-of-art methods have been proposed to improve model robustness. However, previous methods conduct data augmentation at the input phase, but they only utilize random weights for augmentation, failing to learn an optimal weight combination. Inspired by manifold mixup, Attentive Manifold Mixup network integrates the feature maps of different augmented images and retrieves a learnable ratio between those feature maps by the attention module. We conduct extensive experiments on Cifar-10-C, which is manually added corruptions of noise, blur, weather and digit. AMM shows superior results on public datasets, improving model robustness. We also visualize the attention weights for our methods, providing some underlying interpretation behind the proposed AMM.
Lexicon Ontology driven Concept Lattice Framework for Semantic Web Service Discovery

In the era of Internet, increased usage of e-services have made the web services as an integral part of the World Wide Web. With the proliferation of web services, discovering the relevant set of web services for a user query has become a challenging task. Investigators of the web service domain delivered various solutions to the discovery process by implying manifold techniques like Domain Ontology, Semantic Similarity, Semantic Match making, Machine Learning, Deep Learning and various others as an efficient solution. As an auxiliary solution, this article proposes a novel ideology by the usage of Lexicon Ontology and Formal Concept Analysis for semantic web service discovery. The proposed framework classifies the web services during its registration phase using lexicon ontology and the hierarchy of classified ontology is characterized by Concept Lattice. Whenever a user query is acquired, the relevant web services are extracted from the Concept Lattice based on the ontology derived from the query. The anticipated framework in this article is evaluated by providing queries belonging to different domains to test its robustness. The results gained is evident to prove that the proposed framework performs efficiently when compared to the existing frameworks under the discovery process.
A Multi-Embedded Learning Algorithm for Breast Cancer Diagnosis

Breast cancer growth is likely to be a widespread disease among women in India and around the world. Proper and timely determination is a critical step in recovery and treatment. In any case, it is not a simple but easy task due to some diagnostic weakness using mammograms. Artificial intelligence (AI) techniques can be used to create medical devices that can be utilized as aincredible system for early detection and detection of malignant breast growth that will dramatically improve patient endurance. Therefore, it is sufficient to have the option to decide those impacted by the disease. This test was used for Boruta's feature selection to determine the key points in the design of the AI model. In addition, Random Forest (RF) and Support Vector Machines (SVM) were the two artificial intelligence classifiers used, with the most notable upgrade of 93.2% more, 98% more individually. From the results obtained, SVM method achieves high accuracy rate than the random forest to the point of clarity.
Development of a Simulator for Prototyping Reinforcement Learning-Based Autonomous Cars

Autonomous driving is a research field that has received attention in recent years, with increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an autonomous vehicle thoroughly in the physical space, i.e., the so-called ’real world’; therefore, simulators are used in almost all training of autonomous driving algorithms. There are numerous autonomous driving simulators, very few of which are specifically targeted at RL. RL-based cars are challenging due to the variety of reward functions available. There is a lack of simulators addressing many central RL research tasks within autonomous driving, such as scene understanding, localization and mapping, planning and driving policies, and control, which have diverse requirements and goals. It is, therefore, challenging to prototype new RL projects with different simulators, especially when there is a need to examine several reward functions at once. This paper introduces a modified simulator based on the Udacity simulator, made for autonomous cars using RL. It creates reward functions, along with sensors to create a baseline implementation for RL-based vehicles. The modified simulator also resets the vehicle when it gets stuck or is in a non-terminating loop, making it more reliable. Overall, the paper seeks to make the prototyping of new systems simple, with the testing of different RL-based systems.
LDP: A Large Diffuse Filter Pruning to Slim the CNN

In recent years, filter pruning has become one of the most promising methods for CNN compression. However, most of the existing approaches need to prune filters iteratively, which increases the cost and complexity of the pruning process. In this paper, we propose a novel filter pruning strategy to conduct the removal operation only once, which avoids reconstructing new thinner models over and over. To accomplish this, we focus on the information amount of post-features, and replace the iterative removals with equally efficacious but simpler deactivation. The network repeatedly goes through filter deactivation and accuracy recovery, with more and more inefficient filters deactivated, just like a gas diffusing in a container. Therefore the proposed method is named as a Large Diffuse Pruning (LDP). Besides, we simplify the entropy weight calculation procedures by eliminating the repetitive Min-Max Normalization. Extensive experiments on VGGNet and ResNet show that our approach could achieve higher or comparable performance than some of the state-of-the-art methods.
FPGA Based Morse Code Communicator for Visual and Speech Impaired People using Basys-3

People with hearing and visual impairments can communicate effectively through this project. In the FPGA based Morse code, the Basys-3 board is programmed to decode a Morse code communication attempt by the impaired person. The decoded message is then displayed via a seven-segment display connected to the Basys-3 so the caretaker can understand. For individuals with physical limitations, Morse code provides a communication channel that can be used with just one or two switches. Thus, this project provides a communication tool that can be used by visually impaired individuals and those with speech impairments. In this paper, we have tried our best in making the morse code decoder system as mobile as possible considering the fact in mind that the challenged person would mostly need encoders while travelling out to meet new people and the system should be mobile and easily feasible to assemble at any location. Basys-3 being a highly effective programmable board, can effectively compile the morse inputs given, decode at a fast pace and show the output in the seven-segment display at a reasonably high speed for effective communication.
Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation

The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method had been proposed previously for multiple PV systems using motion estimation. This method forecasts the short time ahead PV power generation by estimating the motion between two geographical images of the distributed PV power systems. In this method, the parameter λ, which relates the smoothness of the resulting motion vector field and affects the accuracy of the forecasting, is important. This study focuses on the parameter λ and evaluates the effect of changing this parameter on forecasting accuracy. In the periods with drastic power output changes, the forecasting was conducted on 101 PV systems. The results indicate that the absolute mean error of the proposed method with the best parameter is 10.3%, whereas that of the persistence forecasting method is 23.7%. Therefore, the proposed method is effective in forecasting periods when PV output changes drastically within a short time interval.
A comparison between k-nearest neighbor and jk-nearest neighbor algorithms for signature verification

Signatures are widely used and accepted biometrics used for individual identification. Signatures are categorized as offline and online based on the input method. Online signatures contain more features than the regular offline signature, making them harder to forge. Several algorithms can be used for signature verification, such as the k-nearest neighbor. It is mainly used for one-class classification purposes. In this paper, both k-nearest neighbor and jk-nearest neighbor algorithms are presented, along with a comparison of both algorithms on online signature verification accuracy. The results are conducted using different combinations of verifiers using four different databases and showed that the j k- nearest neighbor classifier outperforms the traditional one-class k-NN classifier by 0.73-10&#x0025; compared to a traditional one-class k-NN classifier.
String Instruments—Bowed

Abstract This chapter provides an introduction to the scientific study of bowed-string instruments, concentrating on aspects that are of direct relevance to performers. Points of similarity and contrast with plucked-string instruments are discussed. A key similarity is in the question of what governs the loudness of any acoustical string instrument. The key contrast is in the manner in which a bowed string vibrates: this is governed by more complicated physics than the vibration of a plucked string, and this has important implications for “playability” and other aspects of performance such as the wolf note. Because of the nature of the subject matter, the organization of the chapter does not separate the background science from the implications for performance: as each successive topic is discussed, implications for performers and teachers are discussed in an integrated way.
Investigation and Analysis on the International Communication Capability of Xinhuanet Russian Version (in Chin.)

Xinhuanet is a portal of Xinhua News Agency, which is also the most globally influential Chinese website in China. Its Russian version publishes information about China, especially today's China to Russian speakers around the world, provides a window and bridge function in transmitting China's voice, promoting mutual understanding and people-to-people ties. Investigation and analysis of ИА Синьхуа in the Russian-speaking world propagation status, can understand its capabilities and validity of international communication, and also provide reference and experience for relevant media. The key to realizing the capability of news communication is to integrate propagation breadth and validity properly. This survey, is divided into two levels of propagation quantity surveys and propagation values, with data information based on Factiva-Global News Monitoring &amp; Search Engine | DOW Jones, the world's largest digital news database. Starting with six representative keywords about China, this survey investigated the release of relevant content on ИА Синьхуа in the last ten years. The results of the survey showed that, the average display rate in the ИА Синьхуа headlines is 20% of the whole network, which has enhanced the display degree of China on the Internet and achieved the effective coverage of information in a certain extent. Through the comparison and linear regression analysis of the word cloud of high-frequency words with relevant thematic texts in foreign media, it is proved that the frequency of high frequency words in ИА Синьхуа has a significant positive impact on the related values of foreign websites, and the dissemination of which is actively effective. The survey also reflects that there is a problem and deficiency in ИА Синьхуа in terms of international communication, which are mainly reflected in the lack of total volume and continuity of headline dissemination and the difference in focus with foreign media. As an important window for the CIS countries to understand China, ИА Синьхуа needs to further improve the total text volume, especially pay attention to the head- lines design of the text, to make it more in line with the online audience's reading habits. At the same time, in order to continuously improve the external communication power and influence from many aspects such as scale and effect, and continue to provide high-quality information about China for the Russian-speaking world, it is necessary to take into account the information needs and receptive psychology of the audience.
Usability of Instrument for a Farm Product in a Real Farm

Automation of the procedure of the farm products is important to increase the amount of them and to keep the quality even if the farmer who is not a professional grows them. It is desirable that the human and the machine cooperate with each other, since the procedure is often too complex and the shape of the farm products is not constant. It is necessary to evaluate and improve the usability to realize the cooperation. In this paper, the farm product is dealt with the grape that is called as &#x201C;Shine Muscat&#x201D; in Japanese, and it is very delicious but expensive. The box for taking photos of the grape from various directions under the constant light condition is developed, since many photos are necessary for the input data of deep learning to judge whether the harvest season is suitable or not in our project. To take photos, the operator hangs the grape to the mount in the box. This paper describes the usability of hanging mechanism for the grape, and the relation between the usability and the problem in real field. As a result, it is clear that both average and variance of the working time are important to investigate the usability, and that the usability may be correlated with the troubles.
The Modelling of Movement of Multiple Animals that Share Behavioural Features

Abstract In this work, we propose a model that can be used to infer the behaviour of multiple animals. Our proposal is defined as a set of hidden Markov models that are based on the sticky hierarchical Dirichlet process, with a shared base-measure, and a step and turn with an attractive point (STAP) emission distribution. The latent classifications are representative of the behaviour assumed by the animals, which is described by the STAP parameters. Given the latent classifications, the animals are independent. As a result of the way we formalize the distribution over the STAP parameters, the animals may share, in different behaviours, the set or a subset of the parameters, thereby allowing us to investigate the similarities between them. The hidden Markov models, based on the Dirichlet process, allow us to estimate the number of latent behaviours for each animal, as a model parameter. This proposal is motivated by a real data problem, where the global positioning system (GPS) coordinates of six Maremma Sheepdogs have been observed. Among the other results, we show that four dogs share most of the behaviour characteristics, while two have specific behaviours.
Gallery D.C.: Auto-created GUI Component Gallery for Design Search and Knowledge Discovery

GUI design is an integral part of software development. The process of designing a mobile application typically starts with the ideation and inspiration search from existing designs. However, existing information-retrieval based, and database-query based methods cannot eﬃciently gain inspirations in three requirements: design practicality, design granularity and design knowledge discovery. In this paper we propose a web application, called Gallery D.C. that aims to facilitate the process of user interface design through real world GUI component search. Gallery D.C. indexes GUI compo-nent designs using reverse engineering and deep learning based computer vision techniques on millions of real world applications. To perform an advanced design search and knowledge discovery, our approach extracts information about size, color, component type, and text information to help designers explore multi-faceted design space and distill higher-order of design knowledge. Gallery D.C. is well received via an informal evaluation with 7 professional designers.Web Link: http://mui-collection.herokuapp.com/.Demo Video Link: https://youtu.be/zVmsz_wY5OQ.
Reconciling of the distances in physical space and latent feature space for complex activity modeling

In the field of human activity recognition (HAR), fundamental difficulties in complex activities recognition (CAR) remain. In this work, to lay a general pipeline for CAR, we try to reconcile the distance of atomic actions of the complex activities in the physical space with that in the latent feature space by utilizing the Variational Auto-Encoder (VAE) and the Uniform Manifold Approximation and Projection (UMAP). The results of this research show that our pipeline can project the atomic actions into a latent feature space with a lower dimension while preserving the global structure reconciled with their global structure in the physical space.
Hybrid Discrete Wavelet Enhancement Model for Brain NCCT Images

NCCT brain images are widely used to diagnose the brain abnormalities. The continued advancement and widespread use of computed tomography in medical science has increased the harmful effect of high dose radiation to patients. Moreover, low dose radiation may result in image deterioration, increase level of noise and artifacts which effects the radiologists' decisions. Different image denoising algorithms may be employed to reduce noise in NCCT images. In this research, a novel HDWN approach has been developed to enhance NCCT image as well as denoise. The proposed method takes into account the inherent properties of noise as well as complementary information of different wavelet coefficients to evaluate the noise in less computing time. Moreover, a directional regularizer has been incorporated to control the uneven pattern of noise and to differentiate image details from noise. Experiments have been performed on real NCCT brain images collected for diagnostic center. The performance metrics PSNR, SSIM, MSE have been used to measure the results. The proposed method outperforms many denoising and image enhancement state of art methods in both quantitative and qualitative measures.
Crowd Exemption Notifier

Crowd management is a challenging problem in the pandemic situation to ensure public safety. To overcome the difficulties, many technologies have been incorporated to cope up with the problems. However, the effectiveness of these techniques is limited due to the density of the crowd changing from low to extremely high depending on the time. We propose a robust feature-based approach to deal with the problem of crowd management for people&#x0027;s security. We have evaluated our method using a dataset and have performed a detailed analysis. The crowd is monitored through a surveillance camera and the image is recognized frame by frame using OpenCV and the recognized frame is processed through the YoloV4 and COCO dataset to get the human identification count. When the crowd exceeds the allocated limit &#x201C;Crowd Exemption Notifier&#x201D; intimates the organizer. This mechanism could help the common people and the government to withstand the widespread viral disease.
Differentially private publication of database streams via hybrid video coding

While most anonymization technology available today is designed for static and small data, the current picture is of massive volumes of dynamic data arriving at unprecedented velocities. From the standpoint of anonymization, the most challenging type of dynamic data is data streams. However, while the majority of proposals deal with publishing either count-based or aggregated statistics about the underlying stream, little attention has been paid to the problem of continuously publishing the stream itself with differential privacy guarantees. In this work, we propose an anonymization method that can publish multiple numerical-attribute, finite microdata streams with high protection as well as high utility, the latter aspect measured as data distortion, delay and record reordering. Our method, which relies on the well-known differential pulse-code modulation scheme, adapts techniques originally intended for hybrid video encoding, to favor and leverage dependencies among the blocks of the original stream and thereby reduce data distortion. The proposed solution is assessed experimentally on two of the largest data sets in the scientific community working in data anonymization. Our extensive empirical evaluation shows the trade-off among privacy protection, data distortion, delay and record reordering, and demonstrates the suitability of adapting video-compression techniques to anonymize database streams.
Measuring the Similarity in 2D: the Case Study of the Extremely Low Frequency Magnetic Field

This paper introduces a new patch-based similarity measure for 2D arrays which is computed by considering not only the value but also the position of the elements in the bi-dimensional arrays. The similarity is evaluated as the mean area of the largest square patches which are in common at the same position in the two arrays. The proposed approach is tested in a case study where considering the position of the patches in the bi-dimensional space is very important. Specifically, it is used as a performance measure in predicting the magnetic field levels generated by a laptop computer and represented as a danger map in the bi-dimensional space. The obtained results are very promising.