Reviewerly/exHarmony huggingface dataset:
Fintech and Tokenization:  A legislative study in Argentina and Spain about the application of Blockchain in the field of properties

The advent of the Blockchain together with the appearance of the Ethereum platform gave rise to the realization of Szabo's original idea, allowing the implementation of intelligent contracts. Blockchain by itself is a database with certain special characteristics but the potential that this technology acquires with the implementation of intelligent contracts leads us to the conceptualization of intelligent property, internet of things, artificial intelligence, intelligent cities, of physical assets and properties. This last concept also triggers the possibility of issuing tokens linked to undertakings with sustainable development objectives or green tokens. The of digital goods has developed naturally, but the pitfalls -more mental than legal and technological- are present in the of physical assets, and we will address them in this paper. Del Castillo Ionov says: tokenization involves taking an asset and, using blockchain technology, issuing tokens representative of that asset, facilitating its negotiation, the enjoyment of the rights of that asset and its governance. We believe that the of physical assets will allow a new field of action, marketing, transmission, circulation of rights and wealth, as well as a new range of opportunities not only for all legal operators but also for the great mass of capital investors interested in sustainable development, as well as for the home retail investor.
Wavelet Loss Function for Auto-Encoder

In the field of image generation, especially for auto-encoder models, how to extract better features and obtain better quality reconstruction samples by modifying network structure and training algorithms has always been the focus of attention. For example, Variational Auto-Encoder (VAE), which is a very popular auto-encoder model, is a theoretically rigorously derived image generation model. For commonly used auto-encoders, such as VAE, Regularized Auto-Encoder (RAE), and Wasserstein Auto-Encoder (WAE), the mean square error (MSE) is all used as the loss function of the reconstructed process, which makes the blur problem of the reconstruction samples unavoidable. Especially for larger-sized images, the blur phenomenon is more obvious. To solve this problem, Perceptual Loss Function is used in some cases. Although it can improve the image quality to a certain extent, the amount of calculation is large and the image quality improvement in auto-encoder is also relatively limited. For this reason, we try to propose a new loss function, Wavelet loss function, to better generate and reconstruct images. Wavelet transform is applied to the reconstructed image loss function of the auto-encoder, and the frequency characteristics of the decomposed image are used to constrain it. We conducted comparative experiments on two larger-size image datasets (FaceSrub, COIL20) and a small-size image dataset (Fashion_MNIST), and proved the effectiveness of the wavelet loss function. At the same time, we propose a new image quality index: wavelet high-frequency signal-to-noise ratio (WHF-SNR), which can better measure the quality of the reconstructed image of the auto-encoder.
A Novel VLSI Architecture for Barrel Distortion Correction

Fisheye camera has been used in many field such as unmanned aerial vehicle, car reversing displays, endoscope, ect. Because the focal length of fisheye camera is too short, it will cause the barrel distortion. So far, most of the barrel distortion correction VLSI architectures are based on polynomial model or division model, and the parameters of polynomial model and division model are usually obtained from the camera parameters, which means that we can only perform barrel distortion correction on images taken by the same camera. So we have implemented the VLSI architecture of this algorithm [1] which can find out the parameters of polynomial model and division model by images. This algorithm [1] is only applied to the software, but the VLSI architecture has not been implemented yet. Thus, we are the first one who implement the algorithm into the VLSI architecture. Our design contains 102868 gate counts and works with a clock period of 10 ns and operates at a clock rate of 100 MHz by using TSMC's 90 nm cell library.
Report on the WSDM 2020 workshop on state-based user modelling (SUM'20)

The SUM'20 workshop was held at the 13th ACM International WSDM Conference on Web Search and Data Mining (WSDM 2020) in Houston, Texas. The purpose of the workshop was to stimulate the research community to explore open challenges in building systems that can capture the user's state, context and goals, as well as effectively use these for leveraging intelligent user-centric systems in a wide range of applications. The workshop incorporated different plenary sessions and contributed talks. The workshop website and proceedings are available at https://www.k4all.org/event/wsdmsum20.
Few-Shot Classification via Feature Redistribution

Deep learning in image classification tasks needs extensive data to get representative features. It becomes an emerging problem when facing a few labeled samples to classify novel categories. To this end, previous works focus on meta-learning (ML) and metric learning to leverage large resemble tasks to adapt to new tasks with few labeled examples. In this letter, we introduce a few-shot-learning (FSL) method based on feature redistribution. Our proposed method trained a more generalized embedding model which can provide great representative features with few labeled data. Then we transformed feature data layout through box-cox transformation into Gaussian distribution or Gaussian-like distribution. To measure the similarity between the class center and unlabeled samples, we proposed a metric method using Sinkhorn mapping initialized with the correlation coefficient. Our approach shows great performance on the CUB dataset, CIFAR dataset, and MiniImageNet dataset for 5 shot tasks by extensive comparisons to related baseline methods.
The Effect of Internet-Based Learning Media on High School Student Achievement

Along with the development of a very rapid era, especially in countries that have developed such as America, Europe, Japan and others. These countries are countries that have advanced in the field of technology, technology has a very important role in human life because with the existence of technology humans can easily relate to one another. Now technology is increasingly sophisticated, the most popular technology today is the internet because with the internet there is a lot of valuable information that we can easily retrieve, the internet is one of the very fast and inexpensive communication tools. The internet is a large library in which there are millions of billions of behaviors. information in the form of text in the form of electronic media besides the internet is known as cyberspace, because almost all aspects of life in the real world are on the internet such as sports, politics, entertainment and so forth, besides that the internet also has a very important role in the world of education because of the existence the internet can add to our knowledge and can increase student motivation. With the use of the internet in the world of education so that students can have a commitment to actively learn and have technical abilities, especially in the field of education, therefore the internet can facilitate teaching and learning well. With the use of the internet in the world of education so that students can have a commitment to actively learn and have technical abilities, especially in the field of education, therefore the internet can facilitate teaching and learning well. Keywords: Internet, Learning media, Achievement
Non-destructive fibroblast NIH-3T3 spheroid classification using convolutional neural network

Cellular spheroid is a complex aggregate of cells, and imaging is an important step in understanding how cell behavior operates and give references about possibilities according to the cells in the spheroid formation. Identifying structures in these images is manual and time-consuming and have a high rate of variability inter experts. An automated identification can solve these problems. The aim of this work is to present a study of Convolutional Neural Network (CNN) applied to live cells identification in NIH-3T3 spheroid. Four different CNN architectures are exploited in this paper: AlexNet, ResNet18, GoogLeNet, and VGG 16 with batch normalization. Many experiments were performed to get the best architecture involving data augmentation, hyperparameter tuning, and transfer learning using ImageNet. The experiments identify up to five different structures in a spheroid image, where the AlexNet achieved the best performance considering the F1-score as the evaluation metric. The use of CNN for this kind of identification opens the possibility of following the spheroid’s behaviour when cultured in more complex images.
Assessment of Information Security Vulnerabilities in Common Seismological Equipment

Abstract Modern seismic and Global Navigation Satellite Systems stations are nowadays equipped with Internet of Things devices that acquire, process, and transmit various geophysical parameters in near-real time. This technological advance has introduced a new threat paradigm for common seismological devices. Such threats can be assessed with standard information security methods and practices. This article aims to identify security weaknesses, describe weak security points and potential attacks on such environments, and anticipate the countermeasures needed. Real tests and attacks have been applied to demonstrate the lack of data encryption and user authentication processes, the risks posed by unencrypted communication protocols, unsafe practices regarding settings and passwords, and poor design implementations. All these factors may impact and possibly disrupt the daily operation of seismic observatories because they can lead to falsifying data, altering configurations, or producing malicious false alarms. These in turn may cause unnecessary public concern or distrust, financial losses, or even national security issues. For all these reasons, several countermeasures and solutions are also proposed and evaluated to address each of the identified vulnerabilities.
Risk structuring of the quality system of additional professional education of a higher educational institution

The article discusses the basics of structuring educational risks in the quality management system of additional professional education (QMS APE) of higher education institutions. The article analyzes approaches to the identification of a criterion feature of the risk structure in the context of risk-oriented thinking in the management of processes in the QMS of an educational organization. The expediency of structuring educational risks on the basis of a variety of processes in the modular-hierarchical model of the QMS of a higher education institution is justified, which allows extending this approach to the entire set of processes of an educational organization. We propose an algorithm for structuring educational risks based on the identification of process parameters, where the risk of implementing the process is expressed through the formulation of planned results, and the process parameters (input, output, control effects, resource provision, etc.) are considered as sources of risk under conditions of uncertainty. We consider the adaptation of the proposed approach to the design of supporting processes of the QMS APE on the example of the process «Recruitment». It is argued that the proposed approach to risk structuring can be used in relation to the QMS processes of a higher educational institution.
“Text neck pain” and Hand held devices

Objective:The initial objective of this study was to describe the association of musculoskeletal disorders (MSDs) with physical activities.Methods: A prospective study of 400 individuals was obtained using the self-assessment application, Your Physical Health, developed by physical health insights (PHI).The study was designed to assist individuals in avoiding MSDs related to their activities.Results: Analysis of the data demonstrated multiple associations between MSDs and individual and activity factors.Of interest was the strong association between head and neck pain and the used of hand held devices. Conclusions:This study found that individuals reported a higher incidence of hand and neck pain with increasing use of hand held devices.It is important to remember that non-specific pain is not a specific medical diagnosis.
Enhancing Realism and Presence with Active Physical Reactions in Augmented Reality

This paper proposes a novel method to directly interact with physical content through virtual objects instead of directly using a physical proxy. The physical objects can be virtually triggered and then perform active reactions that conform to general physical laws and user expectations. We designed and implemented a prototype system to enable users to operate a virtual hammer to hit cups with different amounts of water inside. The cups could produce different audio and vibration feedback and water waves in response to the virtual hammer’s collisions. We conducted a pilot study and found that the system could enhance the realism and presence in AR interaction, compared with using a physical proxy.
SENTIMENT ANALYSIS OF CUSTOMER REVIEWS ON E-COMMERCE SITES

The world we see nowadays is getting more digitalized. In this digitalized economy, Electronic commerce (e-commerce) is playing a vital role in every aspect of human life. E-commerce is a business model that lets firms and individuals buy and sell things over the internet. Thus, e-commerce has become a launching platform for many new inventions. In this project, we are taking up the task of helping the local traders in selling new model smartphones. We performed sentiment analysis using a supervised learning method of machine learning on a large-scale Amazon data like customer reviews about mobile phones and product description by the manufacturers. The information obtained after sentiment analysis using machine learning will not only result in increasing sales of local retailers but also make them competitive.
Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data

Parkinson's Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. The high-resolution longitudinal activity data collected by smartphone applications nowadays make it possible to conduct remote and convenient health assessment. However, out-of-lab tests often suffer from poor quality controls as well as irregularly collected observations, leading to noisy test results. To address these issues, we propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild. The proposed method first synchronizes discrete activity tests into multimodal features at unified time points. Next, it distills and enriches local and global representations from noisy data across modalities and temporal observations by two attention modules. With the proposed mechanisms, our model is capable of handling noisy observations and at the same time extracting refined temporal features for improved prediction performance. Quantitative and qualitative results on a large public dataset demonstrate the effectiveness of the proposed approach.
Seismic diffusivity: The influence of fracture networks on the patterns of induced seismicity.

&amp;lt;p&amp;gt;It has been long understood that the injection of fluids into the subsurface, a common practice in several industries, often leads to seismic activity by altering the fluid pressures and stress states acting along fault structures. In some cases, this puts the people and infrastructure located nearby at considerable risk. The effective mitigation of this potential hazard relies heavily on understanding the physical mechanisms controlling the behavior of injection-induced seismicity. Here, we aim to better understand the spatiotemporal patterns of the seismicity through the concept of triggering fronts (i.e. the propagating front where the onset of seismicity occurs). Previously, triggering fronts have been studied mostly in the context of homogenous porous media. Here, field scale simulations of fluid injection into fractured rock are modeled as linear, uncoupled fluid flow. While injection-induced seismicity is certainly affected by poroelastic stressing and nonlinear hydraulic parameters of the rock, the focus of this study is to understand the impact of a discrete fracture network on patterns of seismicity. Therefore, poroelastic and nonlinear effects are ignored. Results indicate that the pathways of high permeability within the fracture network greatly influence the migration of the triggering front. While the triggering front clearly follows a diffusive process as expected, the corresponding diffusivity is found to be distinct from the effective hydraulic diffusivity of the domain. We, therefore, call this diffusivity the seismic diffusivity of the fractured rock. Understanding seismic diffusivity may help us better interpret datasets of injection-induced seismicity and potentially forecast the patterns of injection-induced seismicity in well-characterized formations.&amp;lt;/p&amp;gt;
ALGORITMA KNN UNTUK KLASIFIKASI KEMATANGAN BUAH APEL BERDASARKAN TEKSTUR

Abstract An apple is a fruit that is widely planted in mountainous or cold regions, for example in Malang. Apples are fruits that have many colors, there are green, yellow, and red colors. With a variety of colors that make consumers feel confused whether the apples to be eaten are sweet or sour. Because almost most consumers do not know the type of apple that will be purchased. Sometimes the type of apple that will be purchased is green, but because it does not know ripe or raw, it is wrong to choose. In order to help consumers in knowing the level of maturity of apple again, a system was made. With the aim to be able to classify the level of maturity of the apple again. So that the system created will display information whether the apple is more ripe or raw. The system will process the image of the apple again and take the GLCM texture features (intensity, contrast, energy, smoothness, entropy, skewness). And the process of determining fruit maturity using the KNN method. The system was built using the matlab tool, with 200 datasets, consisting of 130 training datasets, and 70 testing datasets. In applying the KNN algorithm to determine the maturity level of apples, the accuracy results are 51.4%. With output data that is not in accordance with the target number of 34 data and according to the target number of 36 data.
Document Skew Detection and Correction Using Ellipse Shape

This paper presents a method for reconstructing skew images. The basic idea of the proposed approach is to compute the inertia ellipse of each connected component of the document image, then find the orientation of theses ellipses, and finally select the most frequent angle. This paper aims to resolve the disadvantages of low accuracy and lack of robustness of the existing approaches. The high performance of the algorithm shows in the experimental results, such that the algorithm has the ability in detecting the angle of skew for different document with Varity levels of complexity, as example poor quality documents images and different layout (journal, handwritten, newspapers, books, historical). These documents include figures, border noise, formulas, diagrams and different languages (English, Arabic, Chinese and Japanese document).
Robust Image Watermarking Using DWT and Artificial Neural Network Techniques

Content in this digital world plays an important role which is available in the form of text, image, video, etc. So protecting this from unauthorized access is an important issue. Hence researcher works, in this field from last few decades and proposed number of methods. The proposed work offers a robust and fast algorithm for embedding watermark in an image. In the proposed work, DWT feature was used where embedding was done in LL band. Security of data was improved by Inverse S-order before embedding data. Embedded data is transformed into training vector for neural network model, while corresponding watermark bit is desired output. Embedded data is passing in neural network for learning which gives a corresponding watermark bit. Sothis involvement of neural network for watermark detection increases the robustness of the work. Real set of images was utilized for performing the experiment. Evaluation parameters show that proposed work has enhances PSNR, SNR, NC parameters as compared to existing methods.
Evaluation of Customer Satisfaction about Telecom Operators in Turkey by Analyzing Sentiments of Customers through Twitter

Sentiment analysis is the process of determining whether a piece of writing is positive, negative or neutral. Text analysis based sentiment analysis consolidates natural language processing models and machine learning techniques to determine sentiment scores to the entities, topics, themes and categories within a phrase or, sentence. Furthermore, customer satisfaction is an evaluation of how products and services supplied by a company satisfy or exceed customer expectation. In this work, we propose to analyze customer satisfaction of three big telecommunication operators which are Turkcell, Turk Telekom, and Vodafone in Turkey by utilizing sentiment analysis of customers of them. For this purpose, Twitter social media platform is used for the purpose of gathering the related tweets that are mentioned with hashtags by the customers of operators. In order to improve the system performance, various pre-processing models are used such as removing punctuation marks, stop-words elimination, removing tags, URLs filter, stemming. Finally, sentiment of users is evaluated through machine learning algorithms namely, random forest, support vector machine (SVM), multilayer perceptron (MLP), k-nearest neighbors (k-NN), naive Bayes (NB), and decision tree. The experiment results present remarkable classification performance with accuracy of over 80 percent for all telecom operators. Thus, this study can inspire telecommunications companies to analyze customer satisfaction through the social media platform.
Investigation of adsorption activity of poly(acrylamide-styrene)/bentonite nanocomposite for efficient removal of manganese ions from aqueous solution

Removal of heavy metals which are produced from industrial and agricultural processes is essential due to their adverse effects on the environment.In the present work, polyacrylamide-polystyrene/ bentonite nanocomposite was easily synthesized through in situ polymerization and used for the removal of manganese ions from aqueous solutions.The properties of the nanocomposite synthesized by field emission scanning electron microscopy, transmission electron microscopy, Fourier transformation infrared spectroscopy, X-ray diffraction, Brunauer-Emmett-Teller and thermogravimetric analysis techniques.Then, at the optimum values (pH = 6, contact time = 12 h, adsorbent value = 5 g and manganese ion concentration = 150 ppm), the manganese ions (cations) in the solution were adsorbed by the nanocomposite adsorbent polyacrylamide-polystyrene/bentonite and the amount of absorption was measured by atomic absorption, which was 84.2% and 82.1% for analyze and real sample, respectively.