Reviewerly/exHarmony huggingface dataset:
Conceptual model of educational blockchain system for diploma indisputability

Diploma verification is a very important issue today. Blockchain technology offers mechanism that can prove diploma validity. Many solutions today are based on publishing diploma, generating cryptographic hash of a document, and publishing these credentials. This can only prove that this diploma document didn't suffer from any modification, but this does not proof that this diploma is valid. This paper presents conceptual model of future system based on blockchain technologies, that could perform diploma verification in a way to verify candidate educational path, and student diploma.
Rethinking “5 bun meonjeo garyeoda 50 nyeon meonjeo ganda”(‘One would die 50 years earlier if he tries to go 5 minutes earlier.’)

The purpose of this paper is to reanalyse the Korean slogan “5 bun meonjeo garyeoda 50 nyeon meonjeo ganda”(a warning against wild driving) within conceptual blending theory, which was analysed in Kwon(2013). The results are as follows. First, the adverb “meonjeo”(‘earlier’) evokes two input spaces. One is the space about arriving at the destination earlier and the other is an alternative space about arriving at the expected time. In the blend there are two people going to the same destination, and their positions can be compared. This is a mirror network. Second, “ga-”(‘go’) of “50 nyeon meonjeo ganda” means ‘die’ by metaphorical blending, which is a single-scope network. Third, the whole slogan is constructed by blending into cause-effect relation of the intention space (“5 bun meonjeo ～”) and the resulting unintended situatiuon space(“50 nyeon meonjeo ～”), which is a double-scope network. Finally, the slogan is understood as a warning through blending with the space of warning frame, which can be regarded as a simplex network.
PoseGate-Former: Transformer Encoder with Trainable Gate for 3D Human Pose Estimation Using Weakly Supervised Learning

Weakly supervised learning for 3D human pose estimation can learn a real human structure, but it generally has lower accuracy on reconstructing 3D poses. In this work, we present a 3D pose estimation model using a Transformer encoder based architecture with a trainable gate, PoseGate-Former. The model is trained using individual images from a weakly supervised learning approach. It can reduce possibility of overfitting on some action categories due to the addition of a trainable gate to the Transformer encoder. We evaluated this model on two benchmark datasets: Human3.6M and HumanEva-I. The experimental results show that this model can obtain substantially better accuracy in all action categories of 3D human poses in the datasets compared with some fully-supervised 3D pose estimation approaches.
Digital Watermarking Scheme for Securing Textual Database Using Histogram Shifting Model

Information security is one of the most important methods of protecting the confidentiality and privacy of internet users. The greater the volume of data, the more the need to increase the security methods for protecting data from intruders. This task can be challenging for researchers in terms of managing enormous data and maintaining their safety and effectiveness. Protection of digital content is a major issue in maintaining the privacy and secrecy of data. Toward this end, digital watermarking is based on the concept of information security through the insertion and detection of an embedded watermark in an efficient manner. Recent methodologies in the research on digital watermarking result in data distortion after embedding the watermark. This scenario can reduce the efficiency of detecting watermarks as well as violate data confidentiality. This study adapts a robust reversible histogram shifting (HS) technique for textual data in relational databases. Furthermore, the study presents a digital watermarking scheme intended for document copyright protection and proof of ownership. The major advantage of the proposed watermarking scheme is that it can protect digital data and preserve data quality. To the best of our knowledge, this research methodology is promising for use in the HS expansion model for watermarking data on non-numeric databases. In addition, the results showed that despite exposing the database to an insertion attacks at 50% and 75% of the watermark, the proposed algorithm can recover more than half of the embedded watermark in all addition and alteration attacks cases. As a result, the watermark information can be retained and restored completely.
Learning Multi-dimensional Parallax Prior for Stereo Image Super-Resolution

Recent years have witnessed great success in image super-resolution (SR). However, how to effectively exploit stereo information for the SR purpose is still challenging. This paper focuses on proposing a general solution to stereo image SR. We propose a novel module named Parallax Multi-Dimensional Attention (PMDA) that could not only be seamlessly integrated into most of existing SISR networks but also explore cross-view information from stereo images. Specifically, a pair of stereo images are fed into two identical SISR networks. The extracted middle features are transferred into PMDA to capture the inherent correlation within stereo image pairs. Finally, the internal-view and cross-view information is mixed by SISR network to generate the final output. We also introduce Self Multi-Dimensional Attention (SMDA) to effectively improve the feature representation capacity of a single image. Based on PMDA and SMDA, we design a stereo image SR model named Progressive Attention Stereo SR (PASR), which illustrates the flexibility of PMDA and performance-boosting guided by PMDA and SMDA. Extensive experiments show its superiority in the aspects of visual quality and quantitative comparison.
Python’s Data Arrangement and Prediction Analysis in Linear Regression Model

In the era of data explosion, the value of data has been widely concerned, and the term “big data” has emerged consequently. Big data technology has had a profound impact on China’s social development. With the increasing number of data resources, it is urgent to improve big data analysis technology. How to explore the meaning behind the data, how to analyze the connotation behind the seemingly messy data, etc., have also become a problem, which was widely studied by scientific researchers and educators. Python is an important auxiliary tool for data analysis, with powerful and simple operation, which can meet the preferences of software designers. Therefore, it is very necessary to use Python in big data collection and analysis. This is because the Python data analysis library is fully functional and has strong data screening capabilities and product construction capabilities. This article uses Python as the main tool to analyze different types of data and analyze different types of data to prove the availability of Python in the field of data analysis.
Exploring Academic Leadership of University Teachers under the Chinese Contemporary Philosophy and Social Science (Xin Wen Ke) Construction Innovation Context

Chinese Xin Wen Ke construction innovation is the integration of philosophy and social science, scientific technology, and industry, with the combination of diverse academic subjects and concentrations. Through academic orientation, resources integration, and behavioral modeling, academic leadership of university teachers can be effectively promoted, and the applicative solutions for the emerging obstacles in the process of the combination can be provided. It also can boost the quality of academic leadership of university teachers in relation to orientating values and philosophies, locating the goals, promoting cooperation, and refining achievements. According to recent research, to analyze the contents and structures can foster the quality development in academic leadership of university teachers. This article will draw an overall assessment on different components of academic leadership of university teachers under Chinese Xin Wen Ke construction innovation context, including research reviews and definitions, functions, structures analysis, and cultivation methods, aiming to provide evidence to demonstrate its significance.
States Estimation for Unmanned Surface Vehicles (USVs) Using Dual Unscented Kalman Filters

For future Unmanned Surface Vehicles (USVs), the accuracy of state estimation and speed of tracking are vital in complex environments. However, modelling the USV contains a large number of parameters, some of which are difficult to estimate. This brings a challenging task for USV applications. In this paper, we consider the USV modelling with unknown parameters, and propose a novel Dual Unscented Kalman Filter (DUKF) scheme. To this end, the two parallel UKFs are used for simultaneous estimation of both states and parameters. Simulation results show the superiorities of the DUKF method for USV state estimation, with higher accuracy and faster speed of convergence compared to traditional filter schemes.
Students Perceptions About Teaching in Smart Bahraini Universities

The study aims to investigate the student’s perceptions about teaching in smart Bahraini universities, the researcher conducted his study by using interview of 34 students from universities in the Kingdom of Bahrain. The results revealed that students were satisfied with studying in smart universities, which allow easy communication between the lectures and students, and the results indicated that there are some requirements for smart universities at Bahraini universities must be done, such as smart management, and smart learning environments.
Automated System for Detection of Suspicious Activity in Examination Hall

One among the curses in the education institution of the country is malpractice during written examination, either online or off line. Some of the suspicious activities during written examination includes, copying from neighbouring candidates, other written materials, exchange of answer scripts, etc. These activities lead to physical movement of the face and other parts of the body than the normal writing posture of the candidate(s) seen at the initial stage of the examination. Any deviation to normal posture can be an indicative of suspicious activities. Main objective of this work is to build a real time edge computing-based video analytics and techniques to identify the suspicious activities relating to malpractice case booking as evidence and proofs during written examination. The proposed model includes a low-cost webcam video frame capturing and edge computing for analytics to detect the face and behaviour recognition using the following techniques. Firstly, the CNN is used to detect human faces. Secondly, candidates are recognised using YOLO and features are extracted using Local Binary Patterns Histogram (LBPH). After feature extraction, the activity classification is performed and is used to detect whether the activity is suspicious or not based on the face movement, hand contact detection and body posture using SVM. Lastly, the system will set an alert based on the threshold set for movement pattern of the candidate(s) in the given area focussed by the web cam or any other camera used in an examination centre or for individual online examination for suspicious activities.
The Approach for IoT Malware Detection Based on Opcodes Sequences Pattern Mining

In recent years, the area of IoT malware detection has been at the forefront of the security community. Due to lack the latest cybersecurity requirements in IoT devices design, lack of security updates and transparency of security posture, as well as due to the IoT devices specific characteristics, such as heterogeneity of the processor architecture, unsafe deployment of IoT devices, as well as exponential growth in the number of IoT devices - all this leads to an increase in the number of malware targeting IoT devices. The paper presents the new approach for IOT malware detection based on opcodes (operation codes) sequences pattern mining. The proposed approach uses mining of maximal sequential patterns of operation codes from assembly representation of IoT binary executable. Most distinctive of maximal sequential patterns are selected and for each of them estimations of their relevance are performed. Based on these relevance the feature vectors are built which described assembly representation of IoT binary executable. As a classifier Fuzzy Semi-Supervised C-Means classifier was applied. The experimental results shows that the proposed approach allows detecting IOT malware with height effectiveness, with accuracy 99,51 % and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score at level 99,50%.
EPFSTO-ARIMA: Electric Power Forced Stochastic Optimization Predicting Based on ARIMA

With the advance of new technology and management reforms, data sharing has unleashed the full potential for social production during the past decade, especially for enterprise survival. Data poisoning attack is a typical attack faced by data sharing, EPSTO-ARIMA (Electric Power Stochastic Optimization Predicting Based on Autoregressive Integrated Moving Average model) would increase prediction error by generating adversarial shared data, which leads to the failure of the prediction. In response to the EPSTO-ARIMA attack, this paper proposes EPFSTO-ARIMA (Electric Power Forced Stochastic Optimization Predicting Based on Autoregressive Integrated Moving Average model) combined with data sanitization and data grouping. The model was validated by seven sets of data from three datasets. Experimental results indicate that EPFSTO-ARIMA can remedy the flaws of excessive accuracy error caused by the EPSTO-ARIMA. For publicly dataset “Column2”, the proposed EPFSTO-ARIMA achieves 30.44% lower prediction error than EPSTO-ARIMA, respectively. Simultaneously, the terrific results in other datasets have also been ascertained the viability and generalization ability of our proposed EPFSTO-ARIMA.
A Measurement Method of Automobile Rigid Shaft Diameter Based on Machine Vision

Automobile rigid shaft is an indispensable part of automobile. Its diameter is one of the significant parameters that affect its quality. How to measure the automobile rigid shaft diameter quickly and effectively is very important. In this paper, a measurement method based on machine vision is proposed. Because external parameters of the shaft are not easy to calibrate, a multi-section standard shaft with known diameters was installed firstly, and every pixel diameter was calculated in the theoretical image plane by taking an image. Then remove the standard shaft and the measured shaft was installed, which pixel diameter was got in the same way. Finally, the diameter of measured shaft was calculated by the polynomial interpolation method. The results of simulation and experiment show that the automobile rigid shaft diameter can be measured by the method rapidly, and effectively, the error meets the engineering requirements, and this has certain guiding significance for realizing automatic detect of automobile parts.
MatchMaker: Aspect-Based Sentiment Classification via Mutual Information

Aspect-based sentiment classification (ABSC) aims to determine the sentiment polarity toward a specific aspect. In order to finish this task, it is difficult to match a specific aspect with its opinion words since there are usually multiple aspects with different opinion words in a sentence. Many efforts have been made to address this problem, such as graph neural networks and attention mechanism, however come at the cost of the introduced extraneous noise, leading to mismatches of the aspect with its opinion words. In this paper, we propose a Mutual Information-based ABSC model, called MatchMaker, which introduces Mutual Information estimation to strengthen the correlations between a specific aspect and its opinion words without introducing any extraneous noise, thus significantly improving the accuracy when determining the sentiment polarity toward a specific aspect. Experimental results show that our method with Mutual Information is effective. For example, MatchMaker obtains a significant improvement of accuracy over ASGCN model by 3.1% on the Rest14.
Outdoor Assistance for Visually Impaired People Using Machine Learning

Visually impaired and blindness caused by various diseases has been hugely increased because of various problems in the eyes and by born also many people are affected by eye problems to giving solutions for that we choose the project. These people face numerous challenges in playing out their everyday tasks, particularly in self-exploring in a situation that is unusual for them. They can't distinguish the item around them and them unable to recognize the object around them precisely much of the time. With the help of Object detection and classification technique, assist the blind people because during their mobility what are the objects present in front of those people it will be easily identified and giving instructions into the speaker itself. In this paper, our main idea is to detect the object in front of blind persons that detected objects are converted into voice feedback. For object detection, we use Single-Shot Detector Model for Train-Test configuration. then we use Play sound and GTTS python cross-platform libraries for classified labels into voice feedback. This system provides an efficient way of guiding the visually challenged people.
The Economic Impact of Location on a Solar Farm Co-Located with Energy Storage

Deploying energy storage (ES), alongside renewable generation, is vital to decarbonise electricity grids. A key aspect of deploying these is choosing a suitable location, which is both geographically feasible and economical. Previous studies identify locations with suitable geographies; here we focus on the economic impact of location. We explore how the maximised profits, determined using a mixed integer linear programming (MILP) optimisation model, of a solar farm co-located with ES vary in different regions around Great Britain (GB) as a case study. Real solar generation data is used, along with a weather model, to accurately represent forecast and actual output. For solar farms without ES profits are higher in locations with greater solar irradiance. However for sites with ES we find greater profit variation, primarily due to different distribution charges. For the majority of GB, ES does not add sufficient value to offset its high upfront costs and is not worth adding to solar sites. Additionally, it is currently uneconomical to add ES to most existing solar farms, despite many studies highlighting the grid benefits this would bring. We recommend that distribution network operator and market pricing better reflects the value which ES can bring to the electricity system.
Design, Implementation, and Characterization of a Proper Face Mask Usage Classification Model

The COVID-19 pandemic has prompted several interventions attempting to curb the viral spread, in which one of the most prominent measures is the wearing of face masks. While monitoring compliance to the protocol remains a challenge during the pandemic, several models have been developed to detect the presence of face masks in each person’s face. However, a common limitation in present models is the lack of accounting for the proper usage of face masks. This study fills in those gaps by incorporating a multinomial face mask detection model to not only detect the presence of a face mask, but also the correctness in wearing it. The model is based on pretrained deep neural network architecture, fine-tuned using the Flickr Face HQ dataset and the MaskedFace-Net dataset containing 3000 multi-angled images for each of the three categories: no mask, incorrectly worn mask, and correctly worn mask. The modelling involved preprocessing of images, face detection, mask detection, and classification, which achieved an accuracy of 99.11%, and a false discovery rate of 0.67%. This study can be significant in aiding the efforts of containing COVID-19, as determining the correctness of face mask usage can significantly improve surveillance in public spaces and prompt better contact tracing methods.
The Present Situation of Emergency Rescue of Intelligent Rescue Device

Modern high-tech warfare has made the soldiers in the battlefield more dangerous, more complex injuries, but also caused a great difficulty in immediate treatment. In order to use intelligent control technology to treat the wounded in the battlefield, this paper briefly summarizes the characteristics of modern war and the status quo and development focus of intelligent treatment of the wounded, and put forward a kind of Intelligent Rescue System (IRS), offer a new idea of intelligent treatment for the future war.
Study of Equities using Signed Social Network

Stock prices in the financial market are highly unpredictable; yet, there is usually some correlation between equities. These correlations can be positive or negative, causing equities to move in the same direction or in opposite directions. In this paper correlation analysis is performed on the Indian stock market to build a well-balanced portfolio. We have taken advantage of the fact that stocks have positive or negative correlation to create a signed social network of stocks using a modified winner-takes-all strategy [3]. We then used the FEC algorithm [9] to determine communities with positive intra-links and negative inter-links from this network. Our study revealed that various sub-sectors such as the pharmaceutical, IT, financial sector, and so on form communities within the network. We present a few different strategies to build portfolios by selecting companies from the determined communities that have only positive, only negative, or both negative and positive linkages. We use the Sharpe ratio[5] to assess the performance of these portfolios, and found that having negatively correlated stocks enhances portfolio balancing significantly.
Diagnostic Code Group Prediction by Integrating Structured and Unstructured Clinical Data

Diagnostic coding is a process by which written, verbal and other patient-case related documentation are used for enabling disease prediction, accurate documentation, and insurance settlements. It is a prevalently manual process even in countries that have successfully adopted Electronic Health Record (EHR) systems. The problem is exacerbated in developing countries where widespread adoption of EHR systems is still not at par with Western counterparts. EHRs contain a wealth of patient information embedded in numerical, text, and image formats. A disease prediction model that exploits all this information, enabling accurate and faster diagnosis would be quite beneficial. We address this challenging task by proposing mixed ensemble models consisting of boosting and deep learning architectures for the task of diagnostic code group prediction. The models are trained on a dataset created by integrating features from structured (lab test reports) as well as unstructured (clinical text) data. We analyze the proposed model's performance on MIMIC-III, an open dataset of clinical data using standard multi-label metrics. Empirical evaluations underscored the significant performance of our approach for this task, compared to state-of-the-art works which rely on a single data source. Our novelty lies in effectively integrating relevant information from both data sources thereby ensuring larger ICD-9 code coverage, handling the inherent class imbalance, and adopting a novel approach to form the ensemble models.