Reviewerly/exHarmony huggingface dataset:
Blockchain Technology for IoT Security

The Internet of Things (IoT) promoted a common operating picture (COP) through all modern applications. The COP is takes place only accomplished by the developments seen in wireless sensor network devices that have been able to connect through the network, sharing information, and conducting further analysis. In IoT, knowledge sharing and authentication of data through the central server and is hence vulnerable to security and privacy problems. System spoofing, false authentication, less data sharing reliability may take place. As part of IoT, Blockchain (BC) technology is integrated to solve these security and privacy issues by replacing the idea of the central server. This paper discusses the potential protection and privacy threats of IoT component activity and explores how it relates to distributed ledger-based blockchain (DL-BC) technologies. . Blockchain implementations have explicitly been studied here with regard to focused sectors and categories. This paper also discusses Blockchain technology, its contribution, and challenges specific to IoT.
An Optimised Allotment and Tracking Using Django and Opencv

Developments in tracking of objects is one of the key breakthroughs in computer vision in recent years. A video is a continuous flow of frames. By analysing the difference between a frame to its successive, one can estimate the movement of object. In this paper, the movement of person is detected with the help of two cameras facing opposite to each other. The detected persons face is recognized with the faces in the database, his data about the movement is updated in the excel sheet from time to time and the same is applied in the real-world problem of vigilance on invigilators in the examination hall. Examinations are inescapable to conclude a course. Devising the students and watchdogs into the examination halls are the part of proper conduction of Examinations. One of the key things in the administration of examinations is the issuance of invigilations. The faculty act as invigilators. It is arduous to mull over his/her designation and experience while earmarking the invigilations manually. This paper presents a cybernetic and contemplative method of dole out using Django and tracking the movements of each invigilator using Opencv.
Exact inference for a class of hidden Markov models on general state spaces

Exact inference for hidden Markov models requires the evaluation of all distributions of interest – filtering, prediction, smoothing and likelihood – with a finite computational effort. This article provides sufficient conditions for exact inference for a class of hidden Markov models on general state spaces given a set of discretely collected indirect observations linked non linearly to the signal, and a set of practical algorithms for inference. The conditions we obtain are concerned with the existence of a certain type of dual process, which is an auxiliary process embedded in the time reversal of the signal, that in turn allows to represent the distributions and functions of interest as finite mixtures of elementary densities or products thereof. We describe explicitly how to update recursively the parameters involved, yielding qualitatively similar results to those obtained with Baum–Welch filters on finite state spaces. We then provide practical algorithms for implementing the recursions, as well as approximations thereof via an informed pruning of the mixtures, and we show superior performance to particle filters both in accuracy and computational efficiency. The code for optimal filtering, smoothing and parameter inference is made available in the Julia package DualOptimalFiltering.
Providing a Model for Creating Trust and Guaranteeing the Originality of Goods in the Machine Woven Carpet Supply Chain Based on Blockchain

For the past few years, globalization of collaborate industries and consumers has become an outstanding issue in the world.Collaboration between networks of all people/organizations, from creation to product sale as supply chain, is a hot challengingtopic.Planning and risk management, cost control, transparency and traceability are the challenges of supply chain.These challenges are very important topics as they are directly related to security and trust between members is doomed to failure.The same problem appears in carpet industry supply chain.Lack of trust among network members and high potential of cheating in raw material and production process are serious problems in carpet supply chain.We suggest an infrastructure for carpet supply chain based on blockchain.Blockchain can provide security and reliability.Blockchain technology can be used as a decentralized information system which provide an information platform for all supply chain parts.Our proposed system can provide the traceability with trusted information in the entire carpet supply chain, whichwould effectively guarantee the carpet originality, by gathering, transferring and sharing theauthentic data of carpet in production, processing, warehousing, distribution, and selling.Our proposed model remove concerns and make a better commercial carpet market.
An Efficient Framework for Vietnamese Sentiment Classification

With the booming development of E-commerce platforms in many counties, there is a massive amount of customers’ review data in different products and services. Understanding customers’ feedbacks in both current and new products can give online retailers the possibility to improve the product quality, meet customers’ expectations, and increase the corresponding revenue. In this paper, we investigate the Vietnamese sentiment classification problem on two datasets containing Vietnamese customers’ reviews. We propose eight different approaches, including Bi-LSTM, Bi-LSTM + Attention, Bi-GRU, Bi-GRU + Attention, Recurrent CNN, Residual CNN, Transformer, and PhoBERT, and conduct all experiments on two datasets, AIVIVN 2019 and our dataset self-collected from multiple Vietnamese e-commerce websites. The experimental results show that all our proposed methods outperform the winning solution of the competition “AIVIVN 2019 Sentiment Champion” with a significant margin. Especially, Recurrent CNN has the best performance in comparison with other algorithms in terms of both AUC (98.48%) and F1-score (93.42%) in this competition dataset and also surpasses other techniques in our dataset collected. Finally, we aim to publish our codes, and these two data-sets later to contribute to the current research community related to the field of sentiment analysis.
Analisis Hubungan Kepadatan Penduduk dengan Pola Penyebaran COVID-19 Provinsi DKI Jakarta menggunakan Regresi Robust

COVID-19 pandemic have been spread around the world since the first outbreak on Desember 2019 in Wuhan, China. DKI Jakarta as one of the highest population density among 34 provinces in Indonesia, has become an endemic area of COVID-19 with the rate of new cases show some fluctuation for each month along 2020. This is a secondary data research which drawn from Health Ministry of Indonesia as well as Center of Statistics for DKI Jakarta. Focus and the scope of this paper is on analyzing the relation between new cases of COVID-19 with population density of Jakarta’s districts. Descriptive and inferential analysis that combined with Robust Regression Test are conducted due to some outliers data. This unbiased method shows a good regression model of spreading new positive cases. M-Estimator Robust Regression with Tukey Bisquare function, shows the best result with the least Residual Standar Error (RSE), that is 0.411. Analysis on statistical test for the chosen model shows that population density has significant impacts on outbreak pattern of COVID-19 in Jakarta. But mobilities and interactions betweeen citizens has also give a great impact.
Aviator: a web service for monitoring the availability of web services

Abstract With Aviator, we present a web service and repository that facilitates surveillance of online tools. Aviator consists of a user-friendly website and two modules, a literature-mining based general and a manually curated module. The general module currently checks 9417 websites twice a day with respect to their availability and stores many features (frontend and backend response time, required RAM and size of the web page, security certificates, analytic tools and trackers embedded in the webpage and others) in a data warehouse. Aviator is also equipped with an analysis functionality, for example authors can check and evaluate the availability of their own tools or those of their peers. Likewise, users can check the availability of a certain tool they intend to use in research or teaching to avoid including unstable tools. The curated section of Aviator offers additional services. We provide API snippets for common programming languages (Perl, PHP, Python, JavaScript) as well as an OpenAPI documentation for embedding in the backend of own web services for an automatic test of their function. We query the respective APIs twice a day and send automated notifications in case of an unexpected result. Naturally, the same analysis functionality as for the literature-based module is available for the curated section. Aviator can freely be used at https://www.ccb.uni-saarland.de/aviator.
On the Efficiency of Machine Learning Models in Malaria Prediction

Malaria is still a real public health concern in Sub-Saharan African countries such as Senegal where it represents approximately 35% of the consultation activities in the hospitals. This is mainly due to the lack of appropriate medical care support and often late and error-prone diagnosis of the disease. For instance, largely used tools like Rapid Diagnosis Test are not fully reliable. This study proposes an extensive study of the efficiency of the most popular machine learning models for the task of Malaria occurrence prediction. We have considered patients from Senegal and have evaluated the overall accuracy of each considered algorithm based on sign and symptom information. Our main result is that machine learning algorithms are promising, in particular Naive Bayesian presents a recall very close to that of a rapid diagnostic test while improving highly its precision by 9%.
Application of user dew agent in hybrid-computing environments

In recent years, research in Dew computing as a new layer in the vertical hierarchy of scalable computing has been developing. Directions for the development of this technology include research into the possibilities of Dew computing and its applications. Our research belongs to the second direction. We use this technology in a package of applied microservices for solving complex problems of the qualitative study of binary dynamic systems based on the Boolean constraint method in a hybrid cloud environment. This environment includes on-premises, high-performance, and cloud resources. In this paper, the usage of Dew computing technology in a hybrid cloud environment is discussed. A user dew agent installed on an on-premises resource is proposed to provide the possibility of the applied microservices package collaboration with cloud services in a hybrid cloud environment. The advantages of using a user dew agent are considered. The architecture, functionality, operation modes, and the installation of the dew agent are presented.
Indoor and Outdoor Surface Measurement of 3D Objects under Different Background Illuminations and Wind Conditions Using Laser-Beam-Based Sinusoidal Fringe Projections

In this study, both theoretical analysis and experimental validation are carried out for 3D surface measurement under different indoor/outdoor environmental conditions via combining the projected laser-beam-based sinusoidal optical signal, the optical filtering technique, and the single-shot approach based on Fourier transform profilometry. The designed optical signal generator used in this work is capable of ensuring that the projected fringe pattern is monochromatic, higher-contrast, time-invariant, and truly sinusoidal. The proposed and developed optical setup of 3D surface measurement is portable and is used for in-situ experiments of 3D surface measurements that have been carried out under different sunlight illuminations. The experimental results indicate that accurate reconstructions of measured objects with even or varying surface reflectivity can be obtained under windy conditions and strong environmental illuminations such as the background illuminance of 5600–35,000 Lux. The generated fringe-pattern signal is not sensitive to vibrations from environmental influences including the effects of the wind, which has overcome the outdoor-measurement restrictions of the traditional interferometric system and the profilometry approaches based on phase-shifting methods.
Capsule Networks for Classifying Conflicting Double-Handed Classical Dance Gestures

A challenging application of computer vision is the classification of gestures in classical dance. Classical dance is one of the most accepted forms of arts in India. Due to the complexity of various gestures used to convey unique meaning to the audiance, computational analysis of the dance gestures is a complex task. Hence, it requires efficient machine learning techniques to solve this problem. In this paper, we introduce a classification model for identifying a subclass of hastas called Samyuta hastas (double-handed gestures which preserve hierarchy) where the mudras or hastas are unique hand gestures that imply some actions and ideologies. A classification technique that preserves the object location within an image is necessary for this problem. Hence, we use capsule networks, which maintain equivariance in an image and can analyze the hierarchy and order of objects within the image. A dataset comprising of around 2400 images distributed evenly among six classes of Asamyutha hastas is used for the work. The classification was performed by the deep learning pipelines using typical convolutional neural networks (CNN), transfer learning, and the proposed capsule network (hasta CapsNet). The results show that since CNN loses the location information of an object due to its pooling layers that extract only the vital features, it tends to wrongly classify the double-handed gestures, whereas the capsule networks have achieved better accuracy on the dataset
Motivational Elements in Computer Games for Learning Mathematics

One of the main drawbacks of delivering new teaching lessons in e-learning systems is the lack of motivation for using those systems. This paper analyses which elements of computer games for learning mathematics have a beneficial effect on intrinsic motivation and give students continuous feedback in order to improve the learning process. While the control group has access to the basic version of the educational computer game, the experimental group uses the version enriched with additional motivational elements which include enhanced graphics for indulging in the game, messages of support while playing the game, and the possibility to compare results with fellow peers in terms of trophies and medals won.
Genetic Algorithm and BP Neural Network for College Physical Education Teaching Evaluation

Physical education is considered as the essential part of health plan and also reflects the higher education system. A teacher with physical education is accountable to teach physical education and/or health education to the students. This study has proposed a new approach for teaching evaluation with college physical education in universities. By combining neural network with genetic algorithm (GA), the approach that we proposed can improve the general traditional BP network, such as global convergence and training time. The model uses MATLAB software for empirical research work. According to MATLAB simulation and experiments, it was revealed that the combining neural network with GA algorithm has effective application prospects for the teaching evaluation with college physical education in universities.
Improving the Ability of Deep Neural Networks to Use Information from Multiple Views in Breast Cancer Screening.

In breast cancer screening, radiologists make the diagnosis based on images that are taken from two angles. Inspired by this, we seek to improve the performance of deep neural networks applied to this task by encouraging the model to use information from both views of the breast. First, we took a closer look at the training process and observed an imbalance between learning from the two views. In particular, we observed that layers processing one of the views have parameters with larger gradients in magnitude, and contribute more to the overall loss reduction. Next, we tested several methods targeted at utilizing both views more equally in training. We found that using the same weights to process both views, or using modality dropout, leads to a boost in performance. Looking forward, our results indicate improving learning dynamics as a promising avenue for improving utilization of multiple views in deep neural networks for medical diagnosis.
A Geo-spatial approach for quantifying rooftop photovoltaic energy potential in Karnal smart city, Haryana - A case study

Solar energy is one of the best sustainable forms of renewable energy. India has a cumulative installed capacity of 9.23 GW of grid-connected solar power and set an ambitious target of attaining 100 GW of solar capacity by 2022, including 40 GW of grid-connected rooftop solar installations well. The present study demonstrates the Geospatial technology to estimate the potential of solar photovoltaic on the rooftops of Karnal city. The satellite data of Sentinel -2 and World View-II data was interpreted so as to extract the building footprints. Digital Elevation Model (DEM) data derived from ALOS (Advanced Land Observing Satellite) PALSAR (2019) data was used to calculate the Global Horizontal Irradiance (GHI). It was calculated that the average annual GHI varied between 0.79-5.9 kWh/m2/day. The study revealed maximum GHI (462 kWh/m2) was recorded during the monsoon season. It was estimated that the seasonal energy generation capacity in urban area was minimum (268.4MWh) in the winter season, while the maximum (2632.4MWh) energy generation capacity was observed during the monsoon season. In the case of the industrial area, the minimum seasonal energy generation capacity was found to be 23.9 MWh in winter while the maximum of 234.8 MWh during the monsoon season. If solar panels installed on the proposed rooftops, an estimated energy potential of 5.9 GWh would be generated in the study area.
Problems of distance learning for schoolchildren

The article raises the problems of the post-quarantine state of the education system at school, considers the organization of «inverted» distance learning in educational organizations, which can be useful in adapting to the extreme conditions of an urgent transition to distance learning. The types and methods of providing students with motivational materials for self-study of the topic and organization of feedback are described, an overview of educational p latforms, digital and distance technologies used is provided. The organization of distance learning in the form of simplified and updated versions is proposed, tools and an algorithm for the development and editing of video lessons are analyzed, the essence of the Augmented Reality application is described, which solves the problem of student involvement in the process of mastering educational material. In addition, ways of solving emerging problems of distance learning are given.
Towards Understanding Learning in Neural Networks with Linear Teachers

Can a neural network minimizing cross-entropy learn linearly separable data? Despite progress in the theory of deep learning, this question remains unsolved. Here we prove that SGD globally optimizes this learning problem for a two-layer network with Leaky ReLU activations. The learned network can in principle be very complex. However, empirical evidence suggests that it often turns out to be approximately linear. We provide theoretical support for this phenomenon by proving that if network weights converge to two weight clusters, this will imply an approximately linear decision boundary. Finally, we show a condition on the optimization that leads to weight clustering. We provide empirical results that validate our theoretical analysis.
Sensitizing students to information security and privacy awareness with analogue gamification

Many studies and investigations in the area of information security reveal a careless handling of sensitive data and insufficient information security awareness.Despite increasing digitization, which has a huge impact on society as a whole, we tend not to pay enough attention to the risks associated with it.But research shows that the mere transfer of knowledge is not enough to cause behavioural changes.Rather, a systemic approach that can bring about the necessary emotionalization and reflection through analogue game-based learning is helpful.
Robust active contours based on local threshold preprocessing fitting energies for fast segmentation of inhomogenous images

This letter presents a robust active contour model driven by the local threshold preprocessing fitting energies to actualize fast segmentation of inhomogenous images. First, the local threshold preprocessing is carried out to compute two local intensity means. Second, these two means are sorted and serve as the local area fitting centres, which makes the contour move along the internal or external target boundaries. Finally, the above local area fitting centres are used to build the model's energy functional based on the symmetric cross entropy. Experimental results of the real inhomogenous images confirm that the presented model can segment inhomogenous images much faster and is robust to the setting up of the original contour.
Multi-input Recurrent Independent Mechanisms for leveraging knowledge sources: Case studies on sentiment analysis and health text mining

This paper presents a way to inject and leverage existing knowledge from external sources in a Deep Learning environment, extending the recently proposed Recurrent Independent Mechnisms (RIMs) architecture, which comprises a set of interacting yet independent modules.We show that this extension of the RIMs architecture is an effective framework with lower parameter implications compared to purely fine-tuned systems.
The Lipschitz Constant of Self-Attention

Lipschitz constants of neural networks have been explored in various contexts in deep learning, such as provable adversarial robustness, estimating Wasserstein distance, stabilising training of GANs, and formulating invertible neural networks. Such works have focused on bounding the Lipschitz constant of fully connected or convolutional networks, composed of linear maps and pointwise non-linearities. In this paper, we investigate the Lipschitz constant of self-attention, a non-linear neural network module widely used in sequence modelling. We prove that the standard dot-product self-attention is *not* Lipschitz, and propose an alternative L2 self-attention that *is* Lipschitz. We derive an upper bound on the Lipschitz constant of L2 self-attention and provide empirical evidence for its asymptotic tightness. To demonstrate the practical relevance of our theoretical work, we formulate invertible self-attention and use it in a Transformer-based architecture for a character-level language modelling task.
Trading Application Based on Blockchain Technology

Blockchain is a growing distributed list of records that are linked using cryptographic hashes. Its robustness, simplicity, immutability and trust are basis for development of interesting innovative business solutions. Considering some use-case scenarios within the financial sector, we developed a Blockchain based trading application placed on the Cosmos network. Cosmos is a decentralized network of independent, scalable, and interoperable Blockchains, creating the foundation for new economy. Our work describes the application functionalities for buying, selling and renting goods. It shows the easiness of trading, while eliminating the third parties and demonstrates the disruptive potential of the Blockchain technologies.