Reviewerly/exHarmony huggingface dataset:
Fall Detection Method Based on Pose Estimation Using GRU

Falls are a major cause of injuries or deaths in the elderly over the age of 65 and a factor in social costs. Various detection techniques have been introduced, but the existing sensor base fall detector devices are still ineffective due to user inconvenience, response time, and limited hardware resources. However, since RNN (Recurrent Neural Network) provides excellent accuracy in the problem of analyzing sequential inputs, this paper proposes a fall detection method based on the skeleton data obtained from 2D RGB CCTV cameras. In particular, we proposed a feature extraction and classification method to improve the accuracy of fall detection using GRU. Experiments were conducted through public datasets (SDUFall) to find feature-extraction methods that can achieve high classification accuracy. As a result of various experiments to find a feature extraction method that can achieve high classification accuracy, the proposed method is more effective in detecting falls than unprocessed raw skeletal data which are not processed anything.
Quantitative response of subjective visual recognition to fog concentration attenuation based on image standard deviation

Traffic signs are an essential reminder to drivers and passengers. In the fog environment, the absorption and scattering of water molecules in the propagation medium contribute to the attenuation of beam energy, affect the transmission of visual information, and make people unable to correctly identify traffic information. In this study, we simulated the fog environment in the laboratory, improved the method of defining fog concentration in previous experiments, measured fog concentration with the TSI 8350 EP dust meter, and defined standard deviation based on digital image processing theory to characterize image characteristics. We established a subjective visual evaluation environment in the laboratory, invited participants to participate in subjective evaluation experiments, and obtained the relationship between subjective evaluation and image features in turn. We calculated and obtained the quantitative relationship between subjective evaluation and fog concentration based on the typical time axis of fog concentration and the image's standard deviation. This study's results can enhance people's scientific understanding of the traffic environment, and provide data support for further research.
Text semantic understanding based on knowledge enhancement and multi-granular feature extraction

The neural network model in natural language processing (NLP) has become the mainstream. Bidirectional Long Short-Term Memory Network (BiLSTM) has proved to be a very effective sequence model in text semantic understanding. The Siamese framework has achieved excellent performance in text semantic similarity calculation. However, the feature granularity obtained from BiLSTM is single, and more extensive information cannot be obtained using traditional neural networks. In order to solve these problems, text semantic understanding model based on knowledge enhancement and multi-granular feature extraction (KE-MGFE) is proposed. KE-MGFE integrates knowledge base and three granular text representations, and an attention mechanism is introduced in the sub-networks of the Siamese framework to enhance information interaction. KE-MGFE is formed to capture the deep entity relationships of text and the features of each granularity. Finally, experimental verifications are conducted on two long text datasets. The results clearly show that the proposed model KE-MGFE can achieve higher performance than other latest text similarity calculation methods.
A Study of Blending Ensembles for Detecting Bots on Twitter

In recent years, AI is taking over the world and such rapid advancement has bought many concerns with it, one of them being—deployment of bots on social media platforms to spread propaganda and gain a political advantage. Moreover, nowadays on Twitter, politics has taken the front seat, and hence, it has become more important than ever to tackle this problem by taking a more comprehensive approach. In this work, two blending ensembles are being studied, one of them based on LSTM and the other one based on CNN and will be comparing their performance for a text classification problem on users tweets—which is our primary sub-problem when detecting bots. Alongside, also be shown exactly how insightful the tweet text and metadata may turn out to be for getting a new set of state-of-the-art results as opposed to not considering these at all.
METHODOLOGICAL APPROACHES ON INTERPRETATION CONCEPT "QUALITY" AND "QUALITY OF EDUCATION"

This article focuses on the issues of methodology of the concepts of "quality" and "quality of education" in the relevant regulatory documents and views of the researchers from the ancient – and to our times. The new philosophy of quality is a general category. In addition to products (goods) and services, it generically applies to enterprises, organizations, institutions, their personnel, and management systems. And nowadays, the term "quality" means not only the high quality of goods but also, above all, the way of management, as well as life. In the recent years the issue of the quality of educational services in higher education has gained considerable popularity both in the civil society and in the world. The modern educational paradigm considers this concept as a multifaceted model of social norms and requirements for a personality, an educational environment in which its development and development of a system of education that implements educational tasks at certain stages of study in higher education institutions takes place. It does comprise everything: the quality of the teaching and methodological base, the quality of teaching staff and teaching staff, the quality of providing educational services, the high degree of satisfaction of educational needs of students and, finally, the quality of the subject of learning. The quality of education is a generalized indicator of the development of a society in a particular temporal dimension, and therefore it is considered in the dynamics of changes characterizing the state's progress in the context of the latest world trends. The International Standard also regulates the concept of "quality of education" as a set of properties and characteristics of the educational process that meet the educational needs of consumers of educational services. All these are worldview concepts that have a methodological value to ensure the high quality of education at all stages and levels.
Building A Wheelchair Controlling and Fall Detection System Using Mobile Application

A large portion of the world population has a physical disability, a large portion of them need a wheelchair for easy movement. Aged citizens and temporarily disabled persons due to accidents or illnesses also need a wheelchair permanently. A conventional wheelchair needs some assistance from other people or some physical effort of the patient. With the advancement in technology, patient support and monitoring system are getting more user friendly and easier to use. In this project, a prototype of a wheelchair along with a fall detection system is developed which is controlled by an android application. This developed prototype can be controlled in a different direction with speed variations, the location of the patient can be monitored by a caretaker or nurse. The fall detection system uses an android device's gyroscope and accelerometer. The command to control the wheelchair is received by the Bluetooth module attached to the wheelchair. The mac address of the module is defined in the android device so that the device does not interact with another patient's wheelchair. A defined keyword is used to initiate an emergency call to the nurse and a notification sends to the nurse when the system detects any fall.
Efficient Implementation of Dirty Words Detection in Decision Tree Model

As an informal language, text with dirty words is very common in online comments. Because of their adverse effects on communication users, text analysis with dirty words must be automated. In this paper, we first crawled past reviews that contained dirty words on many networks. Second, we manually marked 20,000 texts to form a corpus containing dirty words. Then use the data to train our model. We use the Decision tree classifier to detect dirty words contained in Malay text. Empirical research shows that our model works well. The F1-score of labels 0 and 1 reached 98% and 93%, and the precision rate of both is over 95%.
Discretizing Whale Optimization Algorithm to Optimize a Long Short-Term Memory

If previous data can influence data, then the data can be said to have sequential properties. Unlike the data that is not sequential, the randomization of sequential data sequences can change the data. A common neural network model generally cannot distinguish a sequential data from the non-sequential ones. Thus, the recurrent model is made specifically for managing data with sequential properties that must be considered by studying the relationship between data with the previous ones. To create a recurrent model, some parameters should be carefully designed. One of them is the architecture of the model. In this paper, discretizing the whale optimization algorithm (WOA), which is performed by determining the hidden layer number of neurons and dropout an architecture, is proposed to optimize the long short-term memory (LSTM). Evaluations on the large movie review dataset show that the proposed discrete WOA is capable of significantly giving an absolute improvement of the LSTM mean accuracy by up to 1.50% (from 91.23% to 92.73%).
Optimization Of Social Media Comments To Improve Customer Journey Using Machine Learning

The marketing is carried out by using various social media strategies and platforms like Facebook, Instagram, Twitter, Pinterest, LinkedIn, YouTube, etc. these are various platforms that are used for marketing. The audience satisfaction delivers many benefits like loyalty, an increase of being referral, less likely to churn, repeat purchase, buying the product at a premium price. The objective of this project is to analyze customer comments in order to extract product details, issue type, sentiments/emotion using topic modeling which will also showcase keywords fall under particular topics to improve customer satisfaction scores. The customer journey can be analyzed to understand the needs and requirements of customer which when post purchasable of the product also help in understanding customer fulfilment ratio. This project not only helps to understand that promotion through social media is better than the traditional approach but also helps to understand the adaptation of new social media x strategies and their promotion along with customer satisfaction.
Performance Analysis of Steganography Tools

Steganography is the art of hiding secret data in a medium. The medium can be an image, video, or an audio file, which is intended to raise no suspicion. Steganography tools typically generate an identical cover image after concealing the secret data in the original image. In this paper, popular steganography tools that are implemented with different embedding algorithms have been compared based on empirical results. Firstly, a set of diverse cover and secret images has been selected as an input set. A number of image quality metrics are applied to the resulting images for comparison. It is found that OpenStego and S-Tools perform the best among seven steganography tools. This comparative analysis gives an insight into the problems and flaws of those tools. Implications for future research and software development for enhancing steganography tools are also discussed based on the experimentation results.
Detection of Semantically Equivalent Question Pairs

Knowledge sharing platforms like Quora have millions and billions of questions. With such a vast number of questions, there will be a lot of duplicates in it. Duplicate questions in these sites are normal, especially with the increasing number of questions asked. These redundant queries reduce efficiency and create repetitive data on the data server. Because these questions have the same answers, the user has to write the same content for each of these questions, which is a waste of time. Dealing with this issue would be significant for helping community question answering websites to sort out this problem and deduplicate their database. In this paper, we augment the Siamese-LSTM in two ways to achieve better results than the previous works. First, we augment basic Siamese-LSTM with a dense-layer (Model-1) to observe the improvement. In the second part, Siamese-LSTM is augmented with the machine learning classifier (Model-2). In both scenarios, we observed the improved results when we include the Hand-Engineered features. The proposed model (Model-1) achieves the highest accuracy of 89.11%.
Efficient FPGA Implementation of softmax Layer in Deep Neural Network

Deep neural network (DNN) is an important technology in the field of artificial intelligence. The softmax layer is one of the key component layers for completing multi-classification tasks. The softmax layer contains a large number of exponents and division operations, which causes it to consume a lot of hardware resources in FPGA implementation. This paper presents an efficient FPGA implementation of the softmax layer. For the exponential operation of the softmax layer, the mathematical transformation better than the Coordinate Rotation Digital Computer (CORDIC) algorithm is used to convert to subtraction and 2 exponential operation. The division is converted into a subtraction and shift operation, and added the highest non-zero bit detection processing before the operation to greatly reduce the cycle of the division operation. Experimenting with our softmax architecture on Xilinx ZCU102, the results show that the FPGA implementation of softmax layer can attain the precision of magnitude 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> and greatly accelerate the calculation of the softmax layer.
A Didactic Methodology for Crafting Information Visualizations

Finding or creating the right information visualization solution that meets design goals can be a very challenging task, not only for students but also for visualization experts. In this paper, we introduce a didactic methodology for designing interactive visualizations through hands-on activities. Our approach can assist students and anyone interested in crafting, ranking, and improving new visualization solutions. The suggested approach follows divergent and convergent thinking that motivates designing several low-fidelity prototypes, discussing pros and cons of each in groups, and improving the final solution by incorporating visual and perceptual principles. The methodology is described with teaching course examples for undergraduate and graduate students. We also list observations made when applying the methodology online and offline along with gathered student feedback.
Arbitrary Clutter PHD Filter and Its Implementation

This paper is focus on multiple target tracking with arbitrary clutter process by using the probability hypothesis density (PHD) filter. Based on the Random Finite Set (RFS) framework, the clutter process in the classical PHD filter is modeled as Poisson RFS, which is only reasonable for some scenarios in reality. The PHD filter suitable for arbitrary clutter process has not yet been implemented. In this paper, an arbitrary clutter PHD filter, as well as its particle filter implementation, are derived by using the probability generating functional (PGFL) based on the RFS framework. Simulation results show that the proposed arbitrary clutter PHD filter can be used for multiple target tracking with arbitrary clutter process.
Routing protocols: Key security issues and challenges in IoT, ad hoc, and sensor networks

By 2020, Cisco Inc. predicted that there will be around 50 billion connected devices across the world. The heterogeneity among devices, the variation in the level of security requirement of an object, memory, computing, and processing capability of these devices open up new security threats which were not there in a traditional network. With the advent of smart devices, the world is getting smart, and human life is getting easier day by day; however, these smart devices are encountering numerous privacy and security challenges like secure machine-to-machine (S-M2M) communications, key management, cryptographic solutions, device security, cyberattack, authentication mechanisms, and cloud security. Any security breach in these IoT devices may severely affect human life or any communication system. The traditional security models are lagging behind in defending against these new threats; therefore it is foremost important to identify these issues and resolve them as much as possible. This chapter presents major active and passive attacks and a novel security model to secure the IoT network.
Using semantic field model to create information search engines

Abstract In the web infrastructure of information search, the use of semantic methods is considered to be a new round of the technology development. With the emergence of big data, a relevant issue is processing large amounts of data to extract valuable knowledge, especially for text files in natural language. Practice shows that traditional natural language search engines cannot always extract the necessary data from such data sets, as they do not take into account several subtle aspects of the language used in human speech. To solve this problem, the possibilities of using semantic search engines for text processing are being explored. This paper discusses the possible use of the semantic field model developed by the authors, to create a semantic search engine. Experiments have shown that using this model can improve the search accuracy. This model can be additionally used in creation of interactive dialogue systems.
Efficient Parallel GCD Algorithms for Multicore Shared Memory Architectures

The greatest common divisor (GCD) is used for numerous applications in number theory, modular arithmetic, encryption algorithms such as RSA, random number generation, and solving linear Diophantine equations. High-performance algorithms, which are efficiently and accurately find the GCD of two integers and n (>2) integers, are needed in the modern world for many applications in science and engineering. Parallel hardware and parallel programming solve such emerging challenges that are not possible in a serial world. Modern desktop and laptop computers are equipped with multicore processors with shared memory architecture. In this paper, we develop novel efficient parallel n integers GCD algorithms for multicore shared memory architecture. The brute force, divide-and-conquer, linear recursive and finding minimum first techniques are adopted in our novel algorithms to reduce the size and the complexity of the n integers GCD problem. Various working models of OpenMP, such as the thread-centric, loop-centric and task-centric models are utilized, which promised a more natural way of exploiting and expressing regular and irregular algorithms. A comprehensive performance analysis applies to prove the efficiency of the proposed algorithms.
Unsupervised Data Labeling on Graphs by Self‐Assignment Flows

Abstract This paper extends the recently introduced assignment flow approach for supervised image labeling to unsupervised scenarios where no labels are given. The resulting self‐assignment flow takes a pairwise data affinity matrix as input data and maximizes the correlation with a low‐rank matrix that is parametrized by the variables of the assignment flow, which entails an assignment of the data to themselves through the formation of latent labels (feature prototypes). A single user parameter, the neighborhood size for the geometric regularization of assignments, drives the entire process. By smooth geodesic interpolation between different normalizations of self‐assignment matrices on the positive definite matrix manifold, a one‐parameter family of self‐assignment flows is defined. Accordingly, our approach can be characterized from different viewpoints, e.g. as performing spatially regularized, rank‐constrained discrete optimal transport, or as computing spatially regularized normalized spectral cuts. Regarding combinatorial optimization, our approach successfully determines completely positive factorizations of self‐assignments in large‐scale scenarios, subject to spatial regularization. Various experiments including the unsupervised learning of patch dictionaries using a locally invariant distance function, illustrate the properties of the approach.
Programmable quantum processor implemented with superconducting circuit

Abstract A quantum processor might execute certain computational tasks exponentially faster than a classical processor. Here, using superconducting quantum circuits we design a powerful universal quantum processor with the structure of symmetric all-to-all capacitive connection. We present the Hamiltonian and use it to demonstrate a full set of qubit operations needed in the programmable universal quantum computations. With the device the unwanted crosstalk and ZZ -type couplings between qubits can be effectively suppressed by tuning gate voltages, and the design allows efficient and high-quality couplings of qubits. Within available technology, the scheme may enable a practical programmable universal quantum computer.
A first Step towards a Protection Profile for the Security Evaluation of Consensus Mechanisms

While numerous studies deepen the security issues of the distributed consensus mechanisms, the lack of a normalized reference for their security evaluation makes it difficult to exploit their conclusions. In this paper, the author applies the rigorous standardized methodology of the Common Criteria to consensus mechanisms and defines the security problem to introduce a Protection Profile focused on a peer node as physical device, in its blockchain environment. The aim is to focus on the security assessment of a physical peer device in a distributed network. The further goal is to succeed in defining shared relevant evaluation metrics.
GLAGAN image inpainting algorithm based on global and local consistency

Image inpainting is an important part in the field of image processing, and its purpose is to complete the damaged area according to the pixel information not lost in the image. At present, although the image inpainting algorithm based on deep learning can repair the missing area, there are still problems that the effective information of the far area cannot be obtained, and the edge of the repair area is blurred or even distorted. In view of the above problems, this paper proposes a GLAGAN image inpainting algorithm based on global and local consistency, so that the repaired image can be semantically consistent globally and locally. In the generation network, the dilated convolution is used to initially repair the missing area, and the cross attention module is used to obtain the correlation between the repaired area and the known area, and the feature weight is calculated to further repair the damaged image. Then through the global discriminator and local discriminator to conduct adversarial training to improve the consistency of the image repair results. Experimental results show that the repair effect of the algorithm is more real and natural, and it has been further improved in both subjective evaluation and objective evaluation.