Reviewerly/exHarmony huggingface dataset:
Simulation of video image detection in leisure sports tourism industry based on convolutional neural network

Due to the continuous development of information technology, emerging technologies such as 5G networks, Turing devices and deep learning are widely used in people's lives. At present, the sports and tourism industry has become a pillar industry supporting the national economy. Sports events have attracted many people's attention, and people are willing to devote themselves to sports activities. Participants and visitors are also increasing day by day. The article also conducts research on the rapid detection of human video behavior, obtains more representative features for this demand. This research is of great help to the development of the industry. This allows the network to manipulate the input video behavior at different scales and extract deeper video information. Therefore, this paper proposes another 3D convolutional neural network model that combines a multi-level pyramid network with an attention mechanism. The visual attributes in the video behavior data set are explicitly learned to refine the classification of similar features. This paper also proposes an integrated neural network based on visual attribute enhancement. It also shows good adaptability, and can complete video data processing based on time series. The extraction of time and space functions is realized by the two-stream neural network method, the two networks learn the corresponding pixel ratios, and it is not easy to lose the clues of the action recognition. This paper also proposes a spatiotemporal convolutional neural network video for human body recognition algorithm, which is applied to the leisure tourism industry according to the specific analysis of the above method, and promotes the development of the industry.
An adaptive moth flame optimization algorithm with historical flame archive strategy and its application

Moth Flame Optimization (MFO) is a new nature-inspired heuristic algorithm, and has successfully been applied in various fields of practical engineering. To enhance exploitation of MFO and avoid dropping into local optimal solution, an adaptive MFO algorithm with historical flame archive strategy is proposed in this paper, which is termed MFO–HFA to avoid ambiguity. In MFO–HFA, to make full use of population history information, the archive consists of historical optimal individuals, which is utilized to preserve the information of better historical flame. Besides, to make full use of the information of top flame information, a top flame randomly matching mechanism is utilized to improve the convergence ability of population. To demonstrate the advantage of MFO–HFA, it is compared with several well-known variants of MFO and some state-of-the-art intelligence algorithms on both 25 benchmark functions of CEC 2005. The experimental results indicate that MFO–HFA outperforms other compared algorithms and has obtained best accuracy. Furthermore, MFO–HFA is used to generate the rules of IDS by NSL-KDD dataset. The test results demonstrate that MFO–HFA outperforms compared algorithms and has gained 96.5% accuracy.
Compressed quantum error mitigation

We introduce a quantum error mitigation technique based on probabilistic error cancellation to eliminate errors which have accumulated during the application of a quantum circuit. Our approach is based on applying an optimal "denoiser" after the action of a noisy circuit and can be performed with an arbitrary number of extra gates. The denoiser is given by an ensemble of circuits distributed with a quasiprobability distribution. For a simple noise model, we show that efficient, local denoisers can be found, and we demonstrate their effectiveness for the digital quantum simulation of the time evolution of simple spin chains.
Identity Retention and Emotion Converted StarGAN for low-resource emotional speaker recognition

In speaker recognition (SR) task, the inconsistency of emotion between enrolled speech and test speech will lead to emotion domain mismatch, which decreases the recognition accuracy and is more serious under low-resource training data condition. To help SR model learn the various emotion distribution when only neutral speech is enrolled, Identity Retention and Emotion Converted StarGAN (IREC-StarGAN) is proposed to generate speech feature in other emotions. Emotion and speaker relevant multi-task loss is designed to retain origin speaker information during adversarial training progress for the generation of emotion feature. To avoid essential speaker or emotion information being contaminated by irrelevant regions, generator uses SC-bottleneck with multi kernel size channels to learn the deep feature of data in various scales. Discriminator adopts densenet with two branches, one of which classifies whether input feature belongs to target emotion domain and the another one focuses on the speaker information. The origin neutral feature will be enrolled with generated feature together to be recognized by Resnet18 for speaker recognition task. The proposed framework obtains the EER of 18.33% on Mandarin Affective Speech Corpus and 8.14% on IEMOCAP, which outperform the existing embedding and GAN methods and reveals the robustness of IREC-StarGAN under low-resource emotion condition.
A Brick-by-Brick Approach to Learning MIPS Microarchitecture

This paper presents a method for improving students' conceptual understanding of microarchitecture in a MIPS-based Computer Organization and Architecture course. The main goal of this course is for students to learn the functionality of the MIPS microprocessor, i.e., to understand the MIPS instruction set architecture (ISA) and its different microarchitectures (also known as organizations). In contrast to the traditional rote learning of microarchitecture, the proposed method, called the brick-by-brick approach, adopts jigsaw cooperative learning and modifies it specifically for learning microarchitecture. The proposed approach helps students to achieve significant gains in their understanding of microarchitecture concepts. A microarchitecture concept inventory is developed for this case study to assess students' concepts. The formats of concept inventory questions are very fluid and, as such, allow for easy modifications. Analysis of results and statistical tests demonstrate the benefits of the proposed method.
An explorative case study on effects of a makeathon for SDGs on middle school students learning and democratic empowerment

This paper presents an exploratory case study of a Makeathon program called STEAM for Social Good, designed to empower middle school students in India. The program design was based on a set of three design principles developed by the IEEE Pre-University STEM Education team. Students were taught creative problem-solving techniques for addressing societal problems. They learned to design IoT-based prototypes and Android apps. Thirty-four students attended the ten-day training program and participated in the two-day Makeathon in ten teams. Our results show that the program helped develop children's competence in physical computing and app development and enabled them to apply their knowledge to create innovative working prototypes for sustainable development goal challenges. They learned to work with material and time constraints and create working prototypes from concept to completion during the Makeathon. Overall, the children were more confident as students after the program and saw how technology could make a difference.
Design approach centered on the activity or the learning situations: for a learner-centered learning environment (LCLE)

This article deals with the design of TELE (Technology-Enhanced Learning Environment), specifically LCLE (Learner-Centered Learning Environment). It suggests adopting the activity-centered approach in favour of the non-formal learning situation to design an LCLE. After demonstrating that this approach only considers formal learning situations, we describe the different stages of its implementation process. Subsequently, we develop a comparative table of the activity paradigm's formal and non-formal learning situations, allowing us to validate the proposition in question.
Recreation of different educational exercise scenarios for exercise modeling

The content model should be tested using student interactions with the content, and student behavior plays a fundamental role in model evaluation. Using simulations allows the recreation of different educational exercises, and researchers can obtain valuable information before implementing them in a real scenario. This paper presents seven educational exercise scenarios that illustrate different exercise typologies and exercise model visualizations with three characteristics: the number of attempts, grade, and time spent. The results could help detect possible problems in formulating the questions in the proposed scenarios. Additionally, the visualizations show information on the three exercise characteristics, allowing teachers to obtain exercise characteristics and make decisions such as reordering and redesigning to improve the learning process.
Onboard observer-enhanced filter in handling orbital elements estimation of non-cooperative target

The purpose of this paper is to develop an effective method for onboard absolute orbital elements estimation of the non-cooperative target, which may beneficial to collision avoidance. The proposed estimator consists of two parts: the target acceleration observer and the orbital elements filter, where the observer generates partial input for the filter. For the estimator, the data source comes from the orbit determination information of the obstacle-avoidance-satellite and the relative measurement information of the non-cooperative target. These data are fed into an observer to estimate the maneuvering acceleration of the target. Then, the square root cubature adaptive H∞ information filter (SCAHIF) for target-orbital-elements estimation under the complicated noise condition is designed, which is driven by the observer outputs and the target data source. The performance of the overall “observer-enhanced SCAHIF” (OSCAHIF) is evaluated through three scenarios where the target is under zero maneuvering thrust, constant maneuvering thrust, and time-varying maneuvering thrust. Test results indicate that the proposed OSCAHIF provides a feasible solution to real-time absolute orbital elements estimation of the non-cooperative target with the presence of time-variant mixed Gaussian white noise.
Deep Learning-Based Digital Assistant for Farmers in South Africa

A digital assistant is a sophisticated dialogue software that transcends the use of Machine Learning, Artificial Intelligence (AI), and Natural Language Processing (NLP). The main goal of a digital assistant is to assist humans with different tasks depending on their domain. Many of these digital assistants use only English as a means of communication. For many countries, like South Africa, that are multi-lingual this causes a problem, especially for monolingual people in rural areas, as most use their native languages to communicate. This paper proposes a Deep Learning-based digital assistant for rural and urban or commercial farmers in South Africa. The proposed system uses two South African languages, namely English and Zulu. The digital assistant uses Natural Language and Deep Learning algorithms to increase the accuracy of the responses. These results show that the model has a 0.9648 accuracy.
A Vision-Based Approach for the Diagnosis of Digital Asthenopia

With the advent of the pandemic, a sea change has been observed with working professionals having seen their work transition online where dependence on electronic devices have increased with online meetings. The same is true for students as well as younger children who cannot go outside to occupy themselves. Prolonged exposure leaves them susceptible to Computer Vision Syndrome or Digital Eye Strain also termed as Digital Asthenopia. The proposed system recognizes the need to combat digital eye strain and diagnose it using a Vision Based approach by receiving footage of an individual while they are occupied with the use of an electronic device facing a screen and notify them. This is done by the detection of the parameters of blinking rate, redness of the eyes and dryness of the eyes. The classification model that is developed analyzes footage of the human eye and identifies whether or not the individual is suffering from digital eye strain. It is done through a YOLO based Object Detection model where the model was trained over a self-built dataset of stills of the human eye with samples marked as healthy or red eyes and healthy or dry eyes. The calculation of blinking rate and ascertainment of whether it is above a typical rate or not is done by the calculation of Eye Aspect Ratio (EAR) using a dlib model to identify facial landmarks. The dataset collected is initially preprocessed by annotation following which augmentation of the images was done with horizontal and vertical splits as well as 90-degree rotations. The results obtained were the successful identification of blinking rate as well as the accuracy and success of the object detection models that were developed represented by a Mean Average Precision (MAP) of 0.85 and 0.75 for redness and dryness respectively.
Real-Time Suspicious Counter-Flow Detection

In this study, we propose a real-time method for detecting suspicious counter-flow of persons in a video surveillance system. The proposed method uses the YOLO (You Only Look Once) object detection algorithm to detect persons in each frame of the video. A hiding time Kalman filter is then used to predict the position of each person in the next frame, taking into account their movement patterns. Finally, a probabilistic data association algorithm is applied to associate the person detected in each frame with their corresponding tracks. To detect suspicious counter-flow, we define a set of rules based on the direction of movement of the person and their track history. If a person is detected moving in the opposite direction of the expected flow, and if they persist in their counter-flow movement for a certain duration, the system raises an alert. The proposed method offers an effective solution for real-time detection of anomalous person behavior, particularly in crowded environments, and can be used in a variety of applications such as public safety, security, and traffic management. The proposed method is a cutting-edge approach for detecting person counter flow in public or private places using motion analysis, achieving real-time performance on an Intel Core i7 CPU, with varying performance depending on the level of traffic. The observed performance is 14 to 10 frames per second in dense regions (public traffic 35 to 60), 20 to 14 frames per second in moderate regions (public traffic 20 to 35) and more than 20 in sparse regions (public traffic less than 20).
Time Series Forecasting of Ethereum Price by FB-Prophet

Ethereum is one of a technology that allows us to create D-Applications and organizations, keep assets, transact, and communicate without being controlled by a central authority. Investing in cryptocurrencies is now a big business, with tremendous capital flow and billions of industries taking over what was formerly a small market. With this investment, it is critical to grasp the highs and lows of a certain cryptocurrency as well as the output provided by such decisions. Prediction of cryptocurrencies is concrete and needs a thorough comprehension of the daily movement of money. This paper comprises of cryptocurrency price prediction and analysis utilizing the FB-Prophet algorithm, using Ethereum as the cryptocurrency under consideration for analysis and prediction Ethereum. In this paper, we will anticipate the daily closing price series of the Ethereum cryptocurrency using pricing data from previous years (January 2020 to December 2021).
Validating User-Centric Business Process based Service Composition

Now-a-days, service composition is an interesting mechanism in various fields, such as the Internet of Things (IoT), Cloud Computing, and Software-defined networks. Service composition aims to allow interactions among user requirements and their components. Several research studies have been performed on the formal verification to validate the service composition. However, less attention is paid to the service composition validation (completeness and correctness) in the IoT environment using business processes (BPs). Therefore, this paper provides a validated service composition (VSC) approach in the IoT environment. This approach considers the look-up table and Triple Graph Grammar (TGG) rule for the service composition. Look-up table enables to establish a one-to-one relationship between business process (BP) and service composition. This table has limitations in-terms of representation. So, the TGG rule is used to establish the m-to-n relationships. The BP elements are mapped with the service composition, but the backward transformation is not possible. An algorithm is designed for validation purposes. The case study related to healthcare, i.e., clinical decision support system (CDSS), is used to evaluate the proposed approach. Various dynamic features are considered to compare the VSC with the existing approaches. The novelty of this paper is to prove the VSC is complete but not correct. The effectiveness of the VSC is shown in terms of validation and execution time with respect to BPs.
Parallel Corpus Creation for NMT using Web Scraping and Filtering

Parallel corpus is a critical resource for various Natural Language Processing (NLP) tasks including neural machine translation (NMT). For utilizing in the translation of low resource languages using NMT models, a good and sizable parallel corpus is needed. In this paper, details of the method designed and experimented to implement extraction of parallel texts in multiple languages from websites using a web scraping method is presented. For extracting information from the websites some websites allow us to use Application Programming Interfaces (APIs) provided by them. But for some websites, APIs are not available to extract the data. In such situations, we have to apply web scraping techniques to get information from such websites. Moreover, scraping textual data as-it-is is much easier. However, identifying and scraping parallel texts in multiple languages is more challenging and involves language identification, mapping, filtering etc. We explained how we implemented web scraping method to gather parallel text data from websites. Specific and structured methodology and techniques for identification of parallel text data and related methods are described. We also explained how cleaning is done on the parallel text data to get a good and clean parallel corpus which would be suitable to be applied in NMT.
Priority Queue-based Extractive Text Summarizer

Text summarization simply means creating a summary from a text document while retaining its main ideas and points of contention. Text summarization is completely aimed to generate a coherent and precise synopsis of the text verbal document. Generating a coherent summary for a large text is a time-consuming task, this paper proposes a novel generic text summarizer for the English language that can accept a maximum input of 160-170 words and generate a summary of 60-80 words, which retains the original context of the input text. This model utilizes a heap queue algorithm for text summarization. The heap queue helps in preserving the phrases from an input text, by skimming the top-scoring sentences, making it easier to be extracted in terms of importance. The input text is tokenized aptly, with all the stop words removed. Further word frequency is calculated, which is used to calculate sentence score, the words are joined together to form a coherent sentence, a summary that uses the summary’s highest-scoring sentences. The model is tested using various scoring methods available and has obtained an accuracy of 86 percent. It is also observed that the cosine similarity for the model-generated output and manual reference summary is 0.86. The proposed model is a generic text summarizer that can be used for any type of data summarization, irrespective of its domain.
Face Mask Wearing Condition Classification using YOLOv5 trained with Hybrid Dataset

Face mask detection was not a major needed application of computer vision till 2020. After the deadly COVID-19 disease spread worldwide, several measures were taken to stop the virus from further spreading. Wearing face masks is the simplest and best precautions one can take to reduce the risk of getting infected from the virus.In crowded places like airports, malls and big companies masks are mandatory for the safety of everyone. Governments made mask compulsory in public places and had to ensure that everyone wore mask properly. Computer vision and deep learning can be used to detect people not wearing mask automatically. Many papers have discussed or proposed different models to detect face mask. Most of the models are trained to detect with and without mask. In this paper third category i.e. improper mask has been added which detects the case where mask is not worn properly. The model used for mask detection is YOLOv5 (You Only Look Once Version 5) which is the latest(still being upgraded) version of YOLO. YOLO is a type of Single Shot Detector and is very fast in object detection. This model was trained with hybrid data-set which contains 1000 images of each class that is ‘with mask’, ‘without mask’ and ‘improper/partial mask’ which makes total of 3000 images. The paper presents performance of YOLOv5 in detecting face mask when trained with the hybrid dataset.
Convolutional Sequence to Sequence Learning for English-Khasi Neural Machine Translation

The convolutional neural network is relatively prevalent for visual recognition tasks. Implementing CNN for neural machine translation is still challenging compared to the predominant approach such as recurrent neural networks and transformers. This paper presents an empirical study of the convolutional sequence to sequence learning model to translate English to Khasi. The approach uses convolution for both encoders and decoders with multi-step attention. The study also includes a model using a transfer learning approach on which the parent model is trained using the English-French dataset. The quantitative and qualitative results are significant thus suggesting that the convolutional approach for neural machine translation tasks can perform remarkably well as other tasks using convolution neural networks in the case of English-Khasi language pair.
A multilingual clause boundary detection approach for Assamese and Bishnupriya Manipuri

Clause boundary detection is an important step in various natural language processing tasks like dependency parsing, machine translation etc. A complex sentence in any language may contain more than one clause. The boundaries should be identified for capturing various dependency relations and for identifying the arguments of the verbal components. We present here, the task of identification and classification of clauses in Assamese and Bishnupriya Manipuri text. To the best of our knowledge, not much work has been done on clause boundary identification for these two languages, which makes this task more important. We have built a rule-based system using linguistic cues such as coordinating conjunct, subordinating conjunct etc. We additionally considered POS tags of neighbouring words as features into the existing conjunction-based approach, which can also be applied to the languages that belongs to the same language family and have certain similarities. Experimental results show that our approach achieves a satisfactory result.