Reviewerly/exHarmony huggingface dataset:
Digital Transformation in Education

Recent breakthroughs in software and hardware technology have had a significant impact on human social behaviour. In this progressive world, academics and research are one of the fields that record a massive transformation in terms of delivering the contents to the stakeholders. In addition to technological growth, the impact of COVID-19 while the entire world is in lockdown since the beginning of the year 2020 is highly visible not only in education, but in all. The COVID-19 exponentially sped up the digital evolution by transporting physical education into digital. Education is one of the research areas where the twenty-first century's industrial revolution drew a lot of attention. Simultaneously, the infrastructure cost, network connectivity issue, and digital platforms are a few of the challenges for data transportation from originator to the end-users. The economical diversity among the population is another criterion to evaluate digitization and digitalization growth and its impact on developing and developed countries. This chapter presents an educational transformation view in a global scenario from digital data to digital platforms.
An Investigation into Influences of Tweet Sentiments on Stock Market Movements

The increasingly common practice by the public of using social media as an information source and basis for decision making for investment has given social media, such as Twitter, a growing influential role in people's behaviour. We investigate the impact of Tweets on the US stock market using sentiment analysis. We examine how the sentiments in Tweets influence the price movements in S&P 500, an excellent indicator for the US stock market and economy in general. We compared the top five influential Twitter outlets and found WSJ, Bloomberg, Forbes, and Reuters consistently showed similar sentiments. We also discovered a significant two trading days' delay in impacting S&P 500 trends where obvious agreements are found between the above outlets. An enhanced version of SentiStrength has been used where we injected finance-related lexicons underpinned by our investor's ontology to obtain a significantly improved and consistent performance.
Cross-domain image translation with a novel style-guided diversity loss design

Cross-domain image-to-image translation has made remarkable progress in recent years. It aims to map the image from the original image domain to the target domains so that the image can appear in diverse styles. Currently, existing methods are mainly based on Generative Adversarial Networks (GAN). They often employ an auxiliary encoder to extract style features from noises or reference images for the generator to translate new images. However, these approaches are usually feasible for two-domain translation and present low diversity in multi-domain translation since the extracted style features are simply served as additional input to the generator rather than fully utilized. This paper proposes a style-guided image-to-image translation (SG-I2IT) with a novel diversity regularization term named style-guided diversity loss (SD loss), making the best of the extracted style features. In our model, style features not only serve as the generator’s input but also penalize the generator through the new SD loss, thus encouraging the model to capture the image styles better. The effectiveness of our method is demonstrated from two perspectives, noise-based and reference-based image translation. Qualitative and quantitative experiments validate our superiority of the proposed method against the state-of-the-art methods in terms of image quality and diversity. In addition, a user study demonstrates that the proposed method can better capture image styles and translate more realistic images.
A Joint Model of Natural Language Understanding for Human-Computer Conversation in IoT

Natural language understanding (NLU) technologies for human-computer conversation is becoming a hot topic in the Internet of Things (IoT). Intent detection and slot filling are two fundamental NLU subtasks. Current approaches to these two subtasks include joint training methods and pipeline methods. Whether treating intent detection and slot filling as two separate tasks or training the two tasks as a joint model utilizing neural networks, most methods fail to build a complete correlation between the intent and slots. Some studies indicate that the intent and slots have a strong relationship because slots often highly depend on intent and also give clues to intent. Thus, recent joint models connect the two subtasks by sharing an intermediate network representation, but we argue that precise label information from one task is more helpful in improving the performance of another task. It is difficult to achieve complete information interaction between intent and slots because the extracted features in existing methods do not contain sufficient label information. Therefore, a novel bidirectional information transfer model is proposed in order to create a sufficient interaction between intent detection and slot filling with type-aware information enhancement. Such a framework collects more explicit label information from the network’s top layer and learns discriminative features from labels. According to the experimental results, our model greatly outperforms previous models and achieves the state-of-the-art performance on the two datasets: ATIS and SNIPS.
Intelligent English Translation Based on Intelligent Speech Waveform Analysis

In order to improve the effectiveness of an intelligent English translation, this paper combines intelligent speech waveform technology to construct an intelligent English translation system. When the modulation method of each signal component is known, this paper takes the overlapping point as the fitting point set and performs fitting with each data point. Moreover, this paper takes the obtained modulation method parameters as eigenvectors and finally performs clustering to obtain the parameter estimation of each single-component signal. In addition, in the case of overlapping points between each linear English speech waveform dataset, this paper finds the overlapping points and processes the overlapping point sets. The simulation test and data statistical analysis results show that the intelligent English translation method based on intelligent speech waveform analysis proposed in this paper can effectively improve the effect of English translation.
Topical and Non-Topical Approaches to Measure Similarity between Arabic Questions

Questions are crucial expressions in any language. Many Natural Language Processing (NLP) or Natural Language Understanding (NLU) applications, such as question-answering computer systems, automatic chatting apps (chatbots), digital virtual assistants, and opinion mining, can benefit from accurately identifying similar questions in an effective manner. We detail methods for identifying similarities between Arabic questions that have been posted online by Internet users and organizations. Our novel approach uses a non-topical rule-based methodology and topical information (textual similarity, lexical similarity, and semantic similarity) to determine if a pair of Arabic questions are similarly paraphrased. Our method counts the lexical and linguistic distances between each question. Additionally, it identifies questions in accordance with their format and scope using expert hypotheses (rules) that have been experimentally shown to be useful and practical. Even if there is a high degree of lexical similarity between a When question (Timex Factoid—inquiring about time) and a Who inquiry (Enamex Factoid—asking about a named entity), they will not be similar. In an experiment using 2200 question pairs, our method attained an accuracy of 0.85, which is remarkable given the simplicity of the solution and the fact that we did not employ any language models or word embedding. In order to cover common Arabic queries presented by Arabic Internet users, we gathered the questions from various online forums and resources. In this study, we describe a unique method for detecting question similarity that does not require intensive processing, a sizable linguistic corpus, or a costly semantic repository. Because there are not many rich Arabic textual resources, this is especially important for informal Arabic text processing on the Internet.
Evaluation Method of the Metacognitive Ability of Chinese Reading Teaching for Junior Middle School Students Based on Dijkstra Algorithm and IoT Applications

In view of the poor quality of Chinese Reading Teaching in junior middle school, this paper puts forward the metacognitive ability evaluation method of Chinese Reading Teaching for junior middle school students based on Dijkstra algorithm, constructs the Chinese Reading Teaching management system for junior middle school students, optimizes the Chinese reading evaluation indexes for junior middle school students, and optimizes the metacognitive ability evaluation algorithm of Chinese Reading Teaching for junior middle school students combined with Dijkstra algorithm, Finally, the experiment proves that the metacognitive ability evaluation method of Chinese Reading Teaching for junior middle school students based on Dijkstra algorithm has high practicability in the process of practical application, can better ensure the teaching quality, and can fully meet the research requirements.
MSAA-Net: a multi-scale attention-aware U-Net is used to segment the liver

Automatic segmentation of the liver from CT images is a very challenging task because the shape of the liver in the abdominal cavity varies from person to person and it also often fits closely with other organs. In recent years, with the continuous development of deep learning and the proposal of CNN, the neural network-based segmentation models have shown good performance in the field of image segmentation. Among the many network models, U-Net stands out in the task of medical image segmentation. In this paper, we propose a segmentation network MSAA-Net combining multi-scale features and an improved attention-aware U-Net. We extracted features of different scales on a single feature layer and performed attention perception in the channel dimension. We demonstrate that this architecture improves the performance of U-Net, while significantly reducing computational costs. To address the problem that U-Net’s skip connection is difficult to optimize for merging objects of different sizes, we designed a multi-scale attention gate structure (MAG), which allows the model to automatically learn to focus on targets of different sizes. In addition, MAG can be extended to all structures which contain skip connections, such as U-Net and FCN variants. Our structure was extensively evaluated on the 3Dircadb dataset, and the DICE similarity coefficient of the method for the liver segmentation task was 94.42%, with a much smaller number of model parameters than other attentional models. The experimental results show that MSAA-Net achieves very competitive performance in liver segmentation.
Cement Raw Material Reserve Calculation—Geological Sections vs. Structural Maps Approaches, Case Study from Southern Croatia

This study aims to compare two approaches for the reserve calculation of cement raw material by geological sections and structural maps. The first is legally based, and its accuracy is approved by periodical calculation of the exploited material on site. In this research, it was crucial to determine deviation in the calculation approach, i.e., geological section volume calculation versus the volume obtained as a software solution estimated as a number of cells between two structural maps, i.e., maps interpolated at the top and bottom of the analysed lithological unit. Due to complex mineralogy, raw cement needs different energy consumption that directly affects the procedure of cement production, e.g., increases in air pollution through CO2 emissions. The research area was the exploitation field “St. Juraj–St. Kajo,” situated near the town of Split, region of Dalmatia, Southern Croatia. In the deposit, there are seven different lithological units, and all were technologically divided based on their chemical compounds. The lithology included dominantly marl and sandstone with occasional alternations of the conglomerate. Although in the geological sense it is considered a single unit, it partially contains interlayers in the form of lenses such as limy (calcitic) marl, clacisiltite (clayey limestone), and clayey marl. Generally, the knowledge of interlayers’ existence is more important than their exact quantity because it affects expectations of a nonuniform material’s technological quality at the location. For the purpose of the analysed lithological unit calculation, the volumes of the interlayers within were determined as well. Using geological sections for volume calculation is based on the calculation of the block volume (V) between two parallel vertical sections (P1, P2), which is obtained as a product of the mean areas (Paverage) of adjacent parallel sections (P1, P2) multiplied by the distance between them (d). Structural maps represent the calculation of the volume of the analysed object under a function f (x, y) defined by a double definite integral. Comparison of research results encourages the use of software solutions for the volume calculation of cement raw material volume in the future.
Efficient Direct and Immediate User Revocable Attribute-Based Encryption Scheme

Mujawar, Tabassum N. Bhajantri, Lokesh B.Nowadays, many organizations are adopting cloud computing for storing the large amount of organization’s important and private data. Here, it becomes important to mange appropriate access rights to these data as it is stored outside the organization’s boundary and is handled by the third party service providers. The Ciphertext Policy Attribute-based Encryption (CPABE) scheme is the most widely utilized technique that offers encrypted access control. In the existing implementations of CPABE scheme, one of the significant issues that need to be addressed is an efficient revocation mechanism. In this paper a direct and immediate user revocation approach for CPABE scheme is presented. The proposed method offers direct user revocation by maintaining the revocation list and to keep revocation list smaller the validity time is embedded in the user’s secret key. The revoked users are still able to access the previously generated ciphertexts. Hence, ciphertext update process is incorporated and a separate immediate revocation list is maintained so that the revoked users’ access is restricted. Also, in the proposed system the revocation information is embedded in ciphertext as a separate part so the update process of ciphertext is more efficient.
Staring Imaging Attitude Tracking Control Laws for Video Satellites Based on Image Information by Hyperbolic Tangent Fuzzy Sliding Mode Control

This paper studies the staring imaging attitude tracking and control for satellite videos based on image information. An improved temporal-spatial context learning algorithm is employed to extract the image information. Based on this, a hyperbolic tangent fuzzy sliding mode control law is proposed to achieve the attitude tracking and control. Furthermore, the hyperbolic tangent function and fuzzy logic system are introduced into the sliding mode controller. In the experiments, the improved temporal-spatial context learning algorithm is applied for the image information of the space target video sequence captured by Jilin-1 in orbit, where the image information is used as the input of the control loop. Moreover, the proposed method is realized through simulation. Besides, the image change caused by attitude adjustment is achieved successfully, and the target imaging can be located in the center of the image plane to realize the gaze tracking control of the space target effectively.
AI-Assisted College Recommendation System

For an aspiring undergraduate student, choosing which college and courses to apply to is a conundrum. Often, the students wonder which colleges are best suited for them. This issue has been addressed by building an artificial intelligence (AI)-driven recommendation engine. This system is developed using the latest techniques in machine intelligence. The system builds user profiles by explicitly asking questions from users and then maps it with college profiles gathered by web scraping and then generates recommendations based on novel hybrid recommendation techniques. The main aim of this system is to utilize artificial intelligence-based techniques to build an efficient recommendation engine particularly for college students to help them select the college which best suits their interests.
Text Summarization of Legal Documents Using Reinforcement Learning: A Study

Studying and analyzing judicial documents are challenging tasks for the common person. The basic reason for the complexity of documents is their long length with complex language. In this regard, a summarize document is required in simple language that can be understandable to the common person. Manual summarization of legal documents is tedious, and it requires an automatic legal document text summarization technique. In the current scenario, deep learning and machine learning play crucial role in text processing and text summarization. This paper discusses the detailed survey of legal document summarization and the dataset used in various legal documents. Also, discuss the various machine learning models and analyze them evaluation metrics to evaluate the best summarization model. Secondly, this study discusses the quality of summarization with and without t reinforcement learning. The analysis concluded that the rational augmented model [RAG] with deep reinforcement learning outperforms for legal document text summarization with 79.8% accuracy, 70.5% precision, 90.75% recalls, and 75.09% F-measure, respectively. A long short-term memory network model without deep reinforcement learning outperforms 0.93 and 0.98 recall and F-measure, respectively.
Secured E-voting System Through Blockchain Technology

Election is the heart and soul of any democratic country. Hence, it is mandatory that elections should be conducted in a safe and secure environment. Also it is the duty of voters to present themselves on the Election Day. However, there are various security issues with present-day election processes like fake voting and vote tampering. Also, there is an issue of low voter turnout especially during a pandemic. Such issues can be solved by blockchain technology. The architecture of blockchain technology makes it difficult to alter data, or alter votes, and attack on the blockchain itself which makes it secure and reliable. Furthermore, voters could cast their vote from the comfort of their home which is helpful in a pandemic which leads to a healthy voter turnout. Blockchain could very well be the future of how a country conducts its elections.
Validating global horizontal irradiance estimated by McClear model under clear-sky and all-sky conditions in Pakistan

The availability of accurate and reliable solar radiation data is very important to explore solar energy potential for commercial power plants. This study presents the validation of global horizontal irradiance (GHI) from McClear model under clear-sky and all-sky conditions against measured data for complex topography of Pakistan. The McClear GHI shows good agreement with measured GHI under clear-sky conditions for nine stations and good correlation was achieved with correlation coefficient ranging from 0.972 to 0.992. The relative mean bias error (rMBE) and relative root mean square error (rRMSE) using GHI data having temporal resolution of 10 minutes was ranging from −2.3 to 6.4% and 3.6 to 7.8% respectively. A seasonal statistical analysis shows variation of errors during seasons, significant variation of errors was observed for monthly statistical analysis, the overall errors for all stations were maximum in January and minimum in June. Cloud fraction (CF) from three state-of-the-art datasets (ERA-5, MERRA-2 and MODIS) was incorporated into McClear clear-sky GHI (GHI MC,clear-sky ) to derive commercially resourceful all-sky GHI data (GHI MC,all-sky ) using a noval approach. The rMBE of GHI MC,all-sky using CF from ERA-5, MERRA-2 and MODIS ranges from 0.1 to 8.4%, 0.1 to 14.1% and 0.2 to 16.8% respectively. The application of ERA-5 cloud fraction shows good correlation with correlation coefficient ranging from 0.871 to 0.966 and gave best results as rMBE for all stations is less than 4% except one station, the long-term GHI MC,all-sky data can be used for the solar resource assessment of Pakistan in the absence of measured data.
Using tiny artifacts to answer big questions: Machine learning, microdebitage, and household spaces at Tamarindito

The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vulnerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take multiple hours spread over several days to complete. Because of this, microdebitage analysis is typically completed in very small areas of sites due to the intensive time and labor commitment required. Recently, however, my colleagues and I have developed a novel, interdisciplinary method that combines dynamic image analysis and machine learning to analyze microdebitage taken from soil samples at archaeological sites. Analyses of experimental microdebitage demonstrated that microdebitage could be accurately and efficiently identified within archaeological soil samples using this method. In the present study, we apply these methods to soil samples taken from the Maya Capital of Tamarindito in Guatemala to verify whether these methods remain accurate when applied to archaeological contexts.
Detumbling motion planning for the space manipulator with grasped targets and flexible appendages in the post-capturing phase

This paper proposes a detumbling motion planning algorithm for free-flying space manipulator with a grasped tumbling target in the post-capturing phase. This algorithm can not only collision-freely guide the space manipulator and the target to terminal stationary states but also suppress the residual vibration of the flexible appendage on the space manipulator. First, considering the avoidances of self-collisions and motion singularities, a smooth detumbling path is planned for the space manipulator by a proposed smoothing rapid random tree star algorithm (SM-RRT*). Second, a quintic polynomial function is implemented to generate a continuous detumbling trajectory along the detumbling path. Then, with the object of minimizing the residual flexible vibrations and the constrains of joint acceleration limits, an optimization model is established to refine the detumbling trajectory. Finally, the optimization model is solved by an improved particle swarm optimization algorithm (PSO), where a potential field term is included in the generation of the particle velocity to enhance the computational efficiency. Simulation results validate the effectiveness of the proposed detumbling motion planning algorithm.