Reviewerly/exHarmony huggingface dataset:
Security Framework for Big Data Usage in Cloud-based e-Learning Application

A variety of businesses, academic institutions, and individual consumers have shown an interest in cloud computing since its inception in the last few years. This is due to the fact that cloud computing users are in a brand-new IT business paradigm. It holds the potential of moving away from a paradigm where businesses have to spend extensively on in-house IT resources in favour of one where they can purchase or rent services managed by a cloud provider and pay as they go for them. There is a significant shortcoming or shortage of finances among smaller educational institutions to effectively use Technology. Education, especially in outlying and impoverished areas, may benefit greatly from cloud computing’s potential to improve both its quality and accessibility. It may provide improved communication and collaboration amongst educators in different places, allowing for more engaging and dynamic classroom activities. Additionally, cloud-based solutions may provide clients/academic institutions with cost savings and exposure to state-of-the-art computing. In this chapter, some of the most significant security challenges that arise when using e-learning applications in conjunction with cloud computing and big data are discussed. In addition, a greater emphasis is placed on the security of middleware applications, which are utilized to connect end-user applications and big data.
Analysis of English automated speaking scoring tests

This study investigated the feasibility of adopting an automatic scoring system (ASS) in a domestic English-speaking education context. Scope, test items, assessment criteria, scoring methods, and reporting strategies of six overseas English-speaking tests utilizing ASSs were examined. Moreover, a comparative analysis was conducted to identify disparities between ASS-based and non-ASS-based speaking tests. Findings were: 1) some ASS-based tests utilized ASS technology throughout the assessment, while others adopted a hybrid scoring system involving human raters; 2) compared to non-ASS-based tests, ASS-based tests used more test items targeting low-level skills such as sound and forms but fewer test items targeting conversation and discourse level skills; 3) pronunciation, fluency, and vocabulary were widely employed as evaluation criteria with sparse use of organization, content, and task completion in most ASS-based tests; 4) differences were minimal in assessment criteria application and score calculation between ASS-based and non-ASS-based tests; and 5) some ASS-based tests provided criteria-specific results and feedback with total scores and proficiency levels.
Approximately Learning Quantum Automata

In this paper, we provide two methods for learning measure-once one-way quantum finite automata using a combination of active learning and non-linear optimization. First, we learn the number of states of a measure-once one-way quantum automaton using a heuristic binary tree representing the different variations of a Hankel matrix. Then we use two optimization methods to learn the unitary matrices representing the transitions of the automaton. When comparing the original automaton with the one learned, we provide a new way to compute the distance on the base of the language of the combined quantum automata. Finally, we show, using experiments on a set of randomly generated quantum automata, which method performs better.
A selective and biased choice of techniques for building a distributed data store

Single-machine data stores cannot support the scale and ubiquity of data today. The Internet applications and services must process a huge number of concurrent requests and events per second. So, they use distributed (or replicated) data stores which store and process data on multiple machines, offering key advantages in performance, scalability, and reliability.
Demo: ComDeX Unveiled Demonstrating the Future of IoT-Enhanced Communities

The rapidly expanding field of Internet of Things (IoT) has necessitated the development of effective and efficient systems for handling the vast quantities of data that are generated. However, the inherent diversity and complexity of IoT environments, coupled with the large volume of data, pose significant challenges to achieving interoperability and efficient data exchange. ComDeX [3], is a novel approach designed to meet these challenges. This demo paper presents the prototype of ComDeX, which is designed to facilitate efficient data exchange in smart communities using a federation of message brokers. The prototype harnesses NGSI-LD and MQTT standards along with an advertisement-based mechanism to enable dynamic data exchange. We detail its implementation and key functionalities, demonstrating its applicability through scenarios that mimic real-world smart communities.
Secure distributed data and event processing at scale: where are we now?

The continuously increasing availability of cloud and edge data center resources is driving our evolution to a data-driven society. Meanwhile, however, the innate sharing of third-party data center resources by users continues to fuel strong security concerns around data and computations handled in and across such infrastructures. While many basic solutions to enforce security have been proposed over the years, both in terms of software mechanisms (cryptographic primitives) and hardware mechanisms (trusted execution environments), they all come with their respective pros and cons. In this talk I will report and reflect on almost a decade of experience of working on the problem of confidentiality-preserving data and event processing. I will cover challenges in terms of guarantees, performance, transparency, or portability and interoperability, discuss tradeoffs therein, solutions, and, finally, open challenges.
Teaching Strategy of Chemistry Concepts Based on Deep Learning

Deep learning is a meaningful learning process in which students are fully engaged, successful, and developed around challenging learning topics. Based on deep learning, this paper discusses the teaching strategies of chemical concepts such as summary, analogy, deductive reasoning, and multiple representations, as well as the application cases of strategy implementation, and introduces in detail the application of multiple representation strategies and the requirements of each representation method.
Research and practice on the construction of Chinese medicine knowledge intelligent question answering system based on knowledge graph

Aiming at the problem that people are afraid to choose Chinese medicines, do not know how to choose Chinese medicines and cannot use Chinese medicines correctly because of the lack of scientific understanding of Chinese medicine, a practical intelligent question and answer system of Chinese medicine knowledge was constructed. The system provides people with reliable and convenient Chinese medicine knowledge inspection and intelligent question answering service to improve the role of Chinese medicine knowledge in safeguarding people's health. Firstly, extract and clean the Chinese medicine knowledge in Chinese medicine books, set the extracted Chinese medicine knowledge as a specific format, and build a knowledge graph. Secondly, filter the natural language question, use Aho-Corasick multi-mode matching algorithm for keywords to extract, then use the cosine similarity algorithm to calculate the similarity calculation. Finally, match the question and answer template and generate the answer. Through the system test, it is proved that the system can satisfy the needs of users to obtain reliable Chinese medicine knowledge service quickly.
Video summarization generation with self-attention and random forest regression

Video summarization is used widely in the field of fast browsing and retrieval of videos by generating keyframes or segments to achieve video compression. Existing methods mostly explore based on image content, ignoring the temporal characteristics of videos, resulting in summaries lacking temporal coherence and representativeness. We propose a video summarization network based on an encoder-decoder framework. Specifically, the encoder part extracts features using a convolutional neural network and enhances the weight of key features through self-attention mechanism. The decoder part consists of a bidirectional long short-term memory network fused with a random forest, and adjusts the proportion of the random forest and bidirectional long short-term memory network in the loss function to make the model more stable and accurate in prediction. Experiments were conducted on two datasets and compared with seven other methods, and the comprehensive experimental results prove the effectiveness and feasibility of the proposed method.
Research on Speech Synthesis Based on Prosodic Controllability

In today's highly interactive human-computer world, speech synthesis is widely used in many scenarios, and the requirements for rhyme effects in speech synthesis technology are increasing, so rhyme-controllable models have become a research hotspot. Most current rhyme controllable models are achieved by using a separate neural network to generate reference features, but this approach requires the training of more complex neural network models and the availability of reference audio to achieve display rhyme control. This paper proposes a rhyme-controllable solution based on an end-to-end acoustic model to address the problem of models being unable to precisely control the tone at the word level. The proposed model includes a tone-controllable module, which obtains duration information through the MFA alignment tool and adjusts the tone of the words by using word-level pitch control values and duration information. The acoustic model in this paper is improved by introducing pitch control during the generation of acoustic features and generating more robust audio by combining it with the decoder. In addition, to adjust the overall tone of the audio, a fixed coefficient is multiplied to the pitch values of all frames. Furthermore, this paper also proposes a 48KHz ultra-high-definition audio model by increasing the spectral parameter dimensions and upsampling by a factor.
Prediction of Turkish Sign Language Alphabets Utilizing Deep Learning Method

Sign Language (SL) is an important tool for communicating the hearing-impaired persons. Therefore, many sign languages exist depending on the language of the country. This study proposes using Turkish sign language (TSL) alphabets to exploit the evolution of deep learning (DL) science to convert mimes of sign language to text. Therefore, a new dataset is created with novelty in the type and conditions. The created dataset includes different conditions in backgrounds, lighting, and positions. Then, this paper suggests Convolutional Neural Networks (CNNs) models detect and classify the TSL alphabets signs. Moreover, the research tries to apply different hyperparameters to the developed models to get the best result. The best model of CNN contains five convolutional layers and uses the Adam learning rate method to adjust the accuracy. This model gives 97.80% accuracy after 70 epochs. Consequently, the suggested models with the challenging dataset are an important point for developing the sign language recognition field.
Virtual Reality and Human-Drone Interaction applied to the Construction and Execution of Flight Paths

This article presents a system architecture that integrates the construction, execution, and monitoring of three-dimensional flight paths for drones using virtual reality (VR). In this work, we propose a system that allows the construction and execution of flight paths by people who lack experience in drone piloting. For this purpose, the integration of two computational modules is introduced into the system architecture. On the one hand, the VR module allows the user to define three-dimensional flight paths in a VR scenario by using Human-Drone interactive techniques, while on the other hand, the Control module performs the supervised execution of these paths in an outdoor scenario using a smartphone that connects to a drone. Therefore, a system based on this architecture was developed and tested through two types of tests. The first one involves real user interaction to evaluate the usability of these computational modules (construction, execution, and runtime monitoring of flight paths). The second type consists of precision tests that are carried out to evaluate the accuracy between the path planned in VR and the one executed in the real scenario.
Detecting Health &amp; Safety Hazards through AI and Edge Computing on Mobile Devices

Safety hazards in working environments introduce significant risks for the health of the people that are active in these environments. The prevention of such incidents becomes a high priority for safety officers. The prevention mechanisms regard compliance with safety standards and auditing of the conditions of the workplaces. This paper, presents an AI based solution that aims to identify safety risks and is able to operate in the edge focusing mobile devices. The presented approach trains an SSD Mobilenet v2 based model with a data set focused on the detection of conditions that might introduce risks for the people and the infrastructure. The trained model has been integrated in a mobile application to utilize the high quality video streams capture by modern smartphones.
Investigation of a Random Number Generator Built into the LabVIEW Graphical Development Environment

A random number generator built into the LabVIEW graphical development environment is being investigated for compliance with the characteristics stated in the documentation. The required number of repetitions for a series of experiments is determined, and a machine experiment is conducted for different sample sizes. For various combinations of parameters, histograms of the distribution of random variables are constructed in order to verify their compliance with a given distribution law. A study of the generator was carried out with the establishment of different bit depth of the output values.
Implementation of Memory Elements and Combinational Logic in the Class of Pyramidal Neural Networks of Fast Learning

The paper considers the use of fast learning pyramidal neural networks for the implementation of memory elements and combinational logic on the example of an encoder and decoder of binary codes. Networks of the class under consideration are representable by linear operators, have a self-similar structure, and are a special case of the fast Fourier transform algorithm. Methods of topological construction of pyramidal networks are given. An algorithm for fast learning of the networks with absolute convergence is presented. It is shown that the pyramidal memory network provides storage and accurate recovery of images similar to storing numbers in random access computer memory. It is noted that pyramid networks belong to the category of deep learning networks.
Deep Learning Based Framework for Multicopter Sensor Data Imputation

The study presented in this paper introduces a novel approach for imputing missing sensor data in multicopters, which enables enhanced safety and reliability by leveraging the multitude of sensors on these aerial vehicles. The proposed approach is based on two deep learning techniques, namely Autoencoders (AE) and Long Short-Term Memory (LSTM) networks. The effectiveness of this approach is evaluated using flight test data from a 2.5 kg hexacopter, and three different scenarios of missing data are considered. To validate the performance of the proposed approach, it is compared against two commonly used imputation techniques: k-Nearest Neighbor (KNN) imputation and Random Forest imputation. The results indicate that the proposed approach outperforms both KNN and Random Forest in terms of the accuracy of imputation. The network has an error of less than 10% when processing signals with six missing sensor readings for a duration of 10 seconds. In contrast, KNN and Random Forest algorithms have an average error of 18% and 26%, respectively. Moreover, the trained model can handle missing data with varying degrees of sparsity, which makes it a more robust and flexible solution. The study also investigates the impact of using initial estimates provided by the Kalman Filter for training the deep learning models. It is observed that incorporating these estimates does not result in any improvement in the imputation accuracy. This suggests that the proposed approach is able to learn the underlying patterns in the data without the need for additional information from the Kalman Filter. Overall, the results of this study demonstrate the potential of deep learning techniques for imputing missing sensor data in multicopters. The proposed approach offers a more accurate and efficient solution than the traditional imputation techniques, and can handle varying degrees of data sparsity. The findings of this study have important implications for the design and operation of multicopters, and could result in enhanced performance and operational effectiveness of these aerial vehicles.
Algorithmic Support for Calculating the Density of the Probability Distribution of a Random Variable at the Output of the Measuring Path

The report presents algorithmic support for calculating the density of the probability distribution of a random variable at the output of the measuring path when changing the conversion characteristics of the measuring blocks. Virtual instruments in the LabVIEW graphical programming environment are created on the basis of the developed algorithms to simulate the first and subsequent measuring blocks of the measuring path.
A Deep Learning based Model for Predicting the future prices of Bitcoin

Bitcoin was introduced in 2009 and is the earliest cryp- tocurrency in the world. It has gained immense popularity and has attracted a huge consumer base owing to its ever-increasing market capitalization. This has led to many traders and investors being interested in knowing the future prices of these cryptocurrencies to gain profits. Researchers have contributed several works in the field of predicting the future cryptocurrency but with very low accuracy. The aim of this paper is to propose a bitcoin price prediction model which will help predict the future prices of bitcoin. Different deep-learning models are involved in the proposed prediction model namely Gated Recurrent Unit(GRU), Long Short-Term Memory(LSTM), Bidirectional GRU (BiGRU) and Bidirectional LSTM (BiLSTM). The performance analysis of the different models shows that BiGRU is able to predict the future bitcoin prices with the lowest Mean Absolute Error Percentage(MAPE) score of 3.41
Pin Conservation System Using Illusion Methodology

Personal Identification Number (PIN) verification has become an essential component of modern security systems for various applications, including financial and mobile devices. However, traditional PIN-based verification is vulnerable to shoulder-surfing and other speculative attacks. In this paper, we propose an innovative PIN-based verification method known as Deception PIN (IPIN), which utilizes a split keypad with different digit orderings to confuse potential attackers. Furthermore, to counter shoulder-surfing attacks, we introduce a dynamic keypad that changes with every validation attempt. The core of our work is the Perceivability computation, which enables us to assess the visibility of images, including the half-and-half keypads. We also explore the possibility of using the Perceivability computation to evaluate other types of images. However, the limits of the Perceivability computation must be appropriately set to ensure its effectiveness. Our proposed IPIN method provides an additional layer of security that enhances the protection of sensitive information in various applications.
Real-time detection of Lung cancer using CNN

One of the leading causes of mortality worldwide is Lung cancer, and early identification is important for successful treatment. Deep Learning Technologies Convolutional Neural Networks (CNNs) have recently exhibited significant guarantees in medical image refining. This research paper offers a real-time lung cancer recognition method based on a CNN. The suggested method takes a digitised chest CT picture as input and goes through several preprocessing protocols before submitting it to the CNN. The CNN model was trained on a huge dataset of chest CT pictures categorised as malignant or non-cancerous. The suggested approach detected lung cancer with good accuracy and sensitivity using CT pictures. The real-time system can process CTT pictures in a couple of seconds, which can be a significant advantage in clinical settings where a timely diagnosis is crucial. This study shows the accuracy and efficiency of a lung cancer diagnosis can be improved using CNN Deep learning technology.
Exploring Social Virtual Worlds for Virtual Communication

Social Virtual Worlds have begun to offer great potential for communication in recent years. The recent development of SVWs, VR and blockchain has led to the metaverse. SVW is all about creating an immersive virtual social environment that simulates a three dimensional social environment for communication, which is primarily based on personal presence and social presence. In this research we connect users in a surreal virtual environment and establish voice communication in order to make them feel each other's presence. Users can interact and hangout in the virtual world.
Hand sign detection using deep learning single shot detection technique

Communication with deaf and dumb people has always been difficult because very few people understand sign language, and the communication gap is growing, resulting in a lack of social interaction and missed opportunities, such as a technical interview. Various works had previously been done to solve this problem, but the previous work in this area had some flaws, such as the inability to interpret the signs in real time. This work was not able to solve this problem properly. To overcome this communication barrier, we have created a realtime hand sign language detection model that can detect many hand signs and Indian alphabet sign language in real time and interpret their meaning for us. We built this model with the TensorFlow object detection API, a Zoo-trained TensorFlow model, OpenCV, and several Python libraries. We have created and used our own data set using OpenCV and the camera. We have created a diverse and large data set to increase the accuracy of the model. The end result of the paper is that our trained model can understand the sign that a person is making through our camera and interpret its meaning.
A Word's Difficulty Level Classification Model Based on Random Forest Algorithm and K-Means Clustering Algorithm

In recent years, COVID-19 has brought a huge impact on people’s life, making online activities a new trend, which promotes the rapid development of mini-games. A mini-game called "Wordle" came out currently has been brought into focus. Players are required to solve the puzzle by guessing a five-letter word in six tries or less, receiving feedback with every guess. We use machine learning algorithms, random forests and K-means clustering respectively to classify solution word (EERIE as an example) by difficulty based on previous data. According to our model, "EERIE" is verified to belong to the medium difficulty category in both algorithms. Specially, we find that compared with K-means clustering algorithm, random forest algorithm has a higher accuracy in word difficulty classification.