Reviewerly/exHarmony huggingface dataset:
Consumer Semiotics

Although structural semiotics has origins in the dual disciplines of communication science and anthropology, many commercial semioticians limit their practice to the analysis of texts such as advertising, popular media, and cultural phenomena, to the exclusion of consumer research. Some practicing semioticians even advertise that semiotics does not apply to consumer behavior. However, a cursory look at the academic literature makes it clear that the object of semiotics is not limited to textual analysis, but applies to a wide range of human experiences, including social organization (Hodge and Kress 1988), cinema spectating (Metz [1976] 1981), the flow of traffic in a mall (Oswald 2015), and even animal behavior (Sebeok 1972). Furthermore, in the course of twenty years of consulting to blue chip companies, it is clear that the object of semiotics is not limited to textual analysis, but also applies to a wide range of marketing factors including consumer-centered design strategy, cultural branding, and media planning. This chapter illustrates how semiotics can be applied to standard qualitative research methods to gain deeper insights, encourage respondent creativity, and improve the consistency and validity of findings for the client. Christian Pinson contributes an early essay on marketing semiotics research.
Haar’ s Measure using Triangular Fuzzy Finite Topological Group

In this paper, A new approach is used to apply Haar’s measure theory to triangular fuzzy number theory for comprehending and generalizing the uniqueness of invariant measure when there are uncertainty and risk. If T ~ is a triangular fuzzy finite Topological group and X ~ is its subgroup, X ~ also being a triangular fuzzy number, then ) ~ ( (~) T x 
Local Solar Radiation Prediction Using LSTM for Optimizing HEMS Operation Based on Environmental Information and Sky Images

The backwash of a large number of smart houses equipped with photovoltaic power generation systems has led to a decline in the equipment utilization rate of power plants that are interconnected with the commercial grid. A Home Energy Management System (HEMS) that uses both photovoltaic power generation and storage batteries is effective in improving these problems, but highly accurate solar radiation prediction is necessary for optimal operation. In this research, we will create a model for predicting solar radiation using LSTM (long short-term memory), which uses environmental information and sky images as feature value, and investigate the significant differences in each feature value.
PENGEMBANGAN KONTEN E-LEARNING IPS BERBASIS SELF REGULATED LEARNING

Problems found in SMP Negeri 1 Singaraja that is still the lack of learning media used and the results of study IPS students VIII class that many have not completed. This study aims to describe the design of e-learning development in IPS subjects, and also describe product effectiveness e-learning based on self regulated learning on IPS class VIII subjects at SMP Negeri 1 Singaraja. This research is development research and development model which used is Luther model. Data collection methods used were questionnaires and written tests. The results of the study are e-learning of IPS subjects developed valid by: (a) the result of the expert review shows the predicated product is good, (b) individualized trial, small group trials and field trials are showing e-learning predicate with good classification. The effectiveness of using SRL-based e-learning content shows a significant difference between the learners' learning outcomes before learning with the content of e-learning content based on SRL and after learning with the content of e-learning content based on SRL. The learning outcomes based on the mean posttest score were higher than the mean pretest score. The results show that SRL-based e-learning content can improve student learning outcomes.
An Approach of Neural Network For Electrocardiogram Classification

ECG is basically the graphical representation of the electrical activity of cardiac muscles duringcontraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to thisearly detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated bythe cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiologicalparameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG playsa vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of patternrecognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes ofpredefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generatedwaveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters suchas spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includesartificial neural network as a classifier for identifying the abnormalities of heart disease.
Jazz Trombone

Regardless of the genre, there are numerous shared aspects of trombone playing: beautiful, stylistic sound quality; technical solidity; accurate, flexible intonation; and the ability to demonstrate sound musical judgment. Listening and imitation are especially crucial for young trombonists because the trombone is a technically difficult instrument to master, and that cannot be done without good models. The embouchure is relatively easy to form and develop, but the slide takes far more work to play in tune and without glissando. More than any other wind instrument, the trombone requires a player with an excellent ear. This chapter deals with the parts of the big band trombone section and its functions as a unit, followed by a discussion of trombone technical challenges in jazz (particularly with articulation), equipment, and important recordings.
Collaboration of Blockchain and Machine Learning in Healthcare Industry

The purpose of this paper is to explore the applications of blockchain in the healthcare industry. Healthcare sector can become an application domain of blockchain as it can be used to securely store health records and maintain an immutable version of truth. Blockchain technology is originally built on Hyperledger, which is a decentralized platform to enable secure, unambiguous and swift transactions and usage of medical records for various purposes. The paper proposes to use blockchain technology to provide a common and secured platform through which medical data can be accessed by doctors, medical practitioners, pharma and insurance companies. In order to provide secured access to such sensitive data, blockchain ensures that any organization or person can only access data with consent of the patient. The Hyperledger Fabric architecture guarantees that the data is safe and private by permitting the patients to grant multi-level access to their data. Apart from blockchain technology, machine learning can be used in the healthcare sector to understand and analyze patterns and gain insights from data. As blockchain can be used to provide secured and authenticated data, machine learning can be used to analyze the provided data and establish new boundaries by applying various machine learning techniques on such real-time medical data.
Homomorphic Encryption

Important types of encryption schemes are those that fall into the homomorphic encryption (HE) category, which allows calculations to be computed directly on the encrypted data, without needing a preceding decryption operation. The most important condition in homomorphic encryption is that the value achieved by decrypting the result obtained by applying the calculations over the encrypted data must be the same as the value achieved by applying the same calculations on the plain data. With these properties, the HE schemes are considered to have great potential because they enable third-party entities to apply functions (therefore, to apply algorithms) on the encrypted data, but without the need for any access of the plain data. In this way, the data is protected and secured while being processed. For a real-life example, suppose you are on vacation into a foreign city and you want to search the Internet, using your phone or another device, for local attractions such as museums, exhibitions, art galleries, and so on. Even this simple search on the Internet may reveal a lot of information about you, such as your exact location, your cultural interests, the time of the search query, and so on. If the search engine used a homomorphic approach, then nothing would be revealed to anyone including the search engine itself, because all of the information and even the search query would be encrypted. The results that you receive would be also encrypted, therefore only you could decrypt them. Homomorphic encryption has applications in many areas, such as finance/business, healthcare, and any domain that works with sensitive data. Further, some formal aspects of homomorphic encryption are given.
Validity Estimation and Improvement Scheme of Safety Certification System for Temporary Equipment and Materials in South Korea

In this study, safety certification system of temporary equipment and materials established in domestic and foreign countries are compared and how to improve it and how to secure the safety are suggested.
Application of Laplacian Mixture Model to Image and Video Retrieval

In this study we present a new approach to feature extraction for image and video retrieval. A Laplacian mixture model is proposed to model the peaky distributions of the wavelet coefficients. The proposed method extracts a low dimensional feature vector which is very important for the retrieval efficiency of the system in terms of response time. Although the importance of effective feature set cannot be overemphasized, yet it is very hard to describe image similarity with only low level features. Learning from the user feedback may enhance the system performance significantly. This approach, known as the relevance feedback, is adopted to further improve the efficiency of the system. The system learns from the user input in the form of positive and negative examples. The parameters of the system are modified by the user behavior. The parameters of the Laplacian mixture model are used to represent texture information of the images. The experimental evaluation indicates the high discriminatory power of the proposed features. The traditional measures of distance between two vectors like city-block or Euclidean are linear in nature. The human visual system does not follow this simple linear model. Therefore, a non-linear approach to the distance measure for defining the similarity between the two images is also explored in this work. It is observed that non-linear modelling of similarity yields more satisfactory performance and increases the retrieval performance by 7.5 per cent. Video is primarily mult-model, i.e., it contains different media components like audio, speech, visual information (frames) and caption (text). Traditionally, visual information is used for the video indexing and retrieval. The visual contents in the videos are very important; however, in some cases visual information is not very helpful for finding clues to the events. For example, certain action sequences such as goal events in a soccer game and explosion in a news video are easier to identify in the audio domain than in the visual domain. Since the proposed feature extraction scheme is based on the shape of the wavelet coefficient distribution, therefore it can also be applied to analyze the embedded audio contents of the video. We use audio information for indexing video clips. A feedback mechanism is also studied to improve the performance of the system.
Human Identification in Smart Home Environment

People moved their life as automatic systems, like smart home. But here major issues is security, the sensitive personal information have been raised the privacy concerns such as (modifying the data, unauthorized access of data) among peoples would like to share their personal information are only utilized for their benefits, rather than being utilized for malignant purpose. Person Identification in smart home with real time is the major challenging in monitoring system. Capturing the still and live images with high efficiency is the complex task. This system provides the efficiency in higher rate with quick response. Data are lively gathered and lively compared with the data, so the process is undergoing the HAAR and the Convolutional Neural Network (CNN). Capturing and labeling the images is done with the live processing then the live video processing is taken the frames of images. So, the data are not stored for long time, and compared the still and live frames in the known frame databases it matches the features and notify the system with two categories as known and unknown.
Optimization-Based Effective Feature Set Selection in Big Data

Of late, the data mining has appeared on the arena as an ideal form of knowledge discovery crucial for the purpose of providing appropriate solutions to an assortment of issues in a specified sphere. In this regard, the classification represents an effective method deployed with a view to locating several categories of anonymous data. Further, the feature selection has significantly showcased its supreme efficiency in a host of applications by effectively ushering in easier and more all-inclusive remodel, augmenting the learning performance, and organizing fresh and comprehensible data. However, of late, certain severe stumbling blocks have cropped up in the arena of feature selection, in the form of certain distinctive traits of significant of big data, like the data velocity and data variety. In the document, a sincere effort is made to successfully address the prospective problems encountered by the feature selection in respect of big data analytics. Various tests conducted have upheld the fact that the oppositional grasshopper techniques are endowed with the acumen of effectively extracting the requisite features so as to achieve the preferred outcome Further, enthusing experimental outcomes have revealed the fact only a trivial number of hidden neurons are necessary for the purpose of the feature selection to effectively appraise the quality of an individual, which represents a chosen subset of features.
Ownership Identification of Multimedia Content Prediction on a Large Scale

This article prepared with three main objectives, first it is to review the large size of multimedia file protection, second for ownership identification of the multimedia content and finally for implementing this setup for cloud based setup instead of web based service architecture. Therefore many of the infrastructure developed for multimedia content to enable the security against the file handling services for managing the better ownership identification. On private and/or public clouds, the device can be deployed. Our framework has two new components: (i) method of producing 3-D image signatures, and (ii) distributed multimedia object matching engine. The signature method produces stable and accurate 3-D video signatures capturing the depth signals in those videos as well as computationally efficient processing and comparison and having limited space. The corresponding distributed engine is highly scalable and designed to support different multimedia objects. We have introduced and deployed the proposed system on two clouds such as private and amazon based clouds.
Exploring and explaining properties of motion processing in biological brains using a neural network

Visual motion perception underpins behaviours ranging from navigation to depth perception and grasping. Our limited access to biological systems constrain our understanding of how motion is processed within the brain. Here we explore properties of motion perception in biological systems by training a neural network (‘MotionNetxy’) to estimate the velocity image sequences. The network recapitulates key characteristics of motion processing in biological brains, and we use our complete access to its structure explore and understand motion (mis)perception at the computational-, neural-, and perceptual-levels. First, we find that the network recapitulates the biological response to reverse-phi motion in terms of direction. We further find that it overestimates the speed of slow reverse-phi motion while underestimating the speed of fast reverse-phi motion because of the correlation between reverse-phi motion and the spatiotemporal receptive fields tuned to motion in opposite directions. Second, we find that the distribution of spatiotemporal tuning properties in the V1 and MT layers of the network are similar to those observed in biological systems. We then show that compared to MT units tuned to fast speeds, those tuned to slow speeds primarily receive input from V1 units tuned to high spatial frequency and low temporal frequency. Third, we find that there is a positive correlation between the pattern-motion and speed selectivity of MT units. Finally, we show that the network captures human underestimation of low coherence motion stimuli, and that this is due to pooling of noise and signal motion. These findings provide biologically plausible explanations for well-known phenomena, and produce concrete predictions for future psychophysical and neurophysiological experiments.
3D shape measurement based on the novel non- aligned phase-coding method

As a kind of three-dimensional(3-D) shape measurement technology, the phase-coding method was widely used because of its high robustness. However, due to the interference of nonlinear and noise, error may appear in the edge of the fringe, which leads to 2π phase jump errors in the unwrapped phase map. To alleviate this problem, a new method based on non-aligned phase-coding was proposed, in which the phase difference of sinusoidal fringe patterns and fringe order patterns is a fixed value. Before the wrapped phase is unwrapped, the fringe order is re-corrected by the wrapped phase to make them aligned. Since the wrapped phase and the corrected fringe order are strictly aligned, the errors are eliminated. Finally, several groups of comparative experiments are carried out to demonstrate the robustness and effectiveness of the proposed method.
A novel IPSO technique for path navigation and obstacle avoidance

Path navigation using meta-heuristic optimisation is a popular research topic in the field of autonomous vehicle path planning. In this paper, an improved particle swarm optimisation (IPSO) technique has been presented for unmanned vehicle path navigation which is the enhanced version of existing PSO technique. IPSO is used to solve the convergence speed problem which is a general practice in optimisation algorithms. The modified fitness function of the proposed algorithm is designed in such a manner that causes to converge the algorithm fast. IPSO technique is dependent upon the working of modified fitness function for the path optimisation in an unknown environment. After several iterations, the best cost is computed which is utilised to find the optimised path in mobile robot path navigation. The simulation results clearly indicate that IPSO is competent enough when compared with the existing optimisation methods (genetic algorithm, invasive weed optimisation (IWO)).
FT Image Processing

For medical research, approximately 115 million animals are needed every year.In medical imaging, rodents are used to test possible applications and procedures for diagnoses of anatomical and physiological diseases.However, working with living animals increases the complexity of an experiment.Accurate experimental planning is essential in order to fulfill the 3R rules (replace, reduce, refine).Especially in tracer-based imaging modalities, such as magnetic particle imaging (MPI), where only nanoparticles give a positive contrast, the anatomical structure of the rodent is not visible without co-registration with another imaging modality.This leads to problems in the experimental planning, as parameters such as field of view, rodent position, and tracer concentration have to be determined without visual feedback.In this work a 3D CAD rat model is presented, which can be used to improve the experiment planning and thus reduce the number of animals required.It was determined using an anatomy atlas and 3D printed with stereolithography.The resulting model contains the most important organs and vessels as hollow cavities.By filling these with appropriate tracer materials, the phantom can be used in different imaging modalities such as MPI, MRI or CT.In a first MPI measurement, the phantom was filled with superparamagnetic nanoparticles.The tracer concentrations in the different organs were determined assuming a steady state that mimics an even distribution of particles in the blood of a living rat.The perfusion of the organs was imitated by calculating the ratio of the blood volume contained in the respective organ to the entire organ volume of the model.Finally, a successful visualization of all organs and vessels of the phantom was possible.This enables the planning of the experiment and the optimization of the experimental parameters for a region of interest, where certain organs would be localized in a living animal.
O SERVIÇO SOCIAL NO ÂMBITO HOSPITALAR EM TEMPOS DE PANDEMIA DA COVID-19

This article has as objective to analyze the public strategies towards the discourses about the use of masks, various present modalities in acts of language and to provide the description of the materials and the analysis of what is really efficient regarding the use of masks.The methodological basis used was the bibliographic review consulted on impact databases by the scientific community.The databases chosen as consultation sources were Web of Science and PubMed.For the selection of texts and their analysis, the steps of an integrative review literature (IRL) were developed, as suggested by Souza, Silva, & Carvalho, (2010), seeking publications about COVID-19 and individual protection masks.As the search keys were pandemic, individual protection masks, Personal protective equipment -PPE and COVID-19 for descriptors and scientific data on the subject were addressed.The result for the analysis for this study makes it possible to assess the type of material most suitable for this protective equipment production, the use of masks by the Brazilian population during the pandemic and the public strategies used in this context.We conclude that the use of a mask depends not only on the type of material used but also on isolation and hygiene measures.Thus, it was verified how emerging public strategies help in the use and greater adherence of protection masks by the population.Although some actions have been implemented in Brazil, the citizen's disobedience and irresponsibility regarding the proper use of protective masks persists.