Reviewerly/exHarmony huggingface dataset:
An Empirical Study on the Influence of Social Interactions for the Acceptance of Answers in Stack Overflow

In knowledge-sharing communities like Stack Overflow (SO), users can post questions, give answers and choose one answer as an accepted answer. The accepted answers will be important references for users when they encounter similar questions. Essentially, posting questions and giving answers is an interactive process occurring among community users, and choosing accepted answers is actually a decision-making process involving multiple factors. Previous works examined the impact on this decision process from the user, question and answer viewpoints. Social interactions between the questioners and answerers, although being popular according to our pre-analysis, have never been considered as a factor that can influence the decisions. To fill this gap, this paper first proposes a comprehensive answer acceptance model that integrates the answer features established by social interactions as well as information of users, questions and answers. We then divide social interactions into two stages and propose a method to calculate the relationship between the questioner and the answerer by analyzing these social interactions. Finally, we investigate the influence of social interactions for the acceptance of answers by performing logistic regression analysis. The results reveal several findings: (1) social-based features explain 16.6 % of the variance explained together, indicating that social interactions have significant and important effects on the acceptance of answers; (2) social interactions that occur after the answer is posted are more influential than these occur before the answer is posted. Based on the findings, we further conduct an online study of 132 SO users, and the respondents report that social interactions have a greater impact on the acceptance of answers than other judgments of answers such as upvotes, downvotes and not accepting answers.
A Study on Image Matting Techniques

Image processing is a wide research area which contains various tasks. Image matting is an image processing tasks. The paper presents a study of various Image Matting approaches. The decomposition of an image into foreground using the opacity and background pixels is known as image matting. Inverse process of image matting is called image composition. It is used for extraction of foreground from an image. It can also produce new images with the help of extracted foreground and different background. It has a variety of applications, starting from image enhancement to extracting useful information from an image.
Computation Model of Data Intensive Computing with MapReduce

It becomes obvious that traditional platforms and processing paradigms can't store and process huge amounts of data. The only solution is to use specially designed ad-hoc platform/architecture based on parallelization that distributes data across large cluster of physical machines. Data Intensive Computing is a subclass of general parallel computing concept which is based on division of large amounts of data into independent parts and processing them in parallel. In the paper the alternative parallelization architectures are reviewed. MapReduce Programming model associated with distributed massive parallel processing of large amount of data is examined. The main objective of this study is to investigate conceptual fundament behind very popular data-drive computation model MapReduce.
Development of Vehicle Domain Controller Based on Ethernet

Abstract The ‘new four modernizations’ of automobiles with the trend of electrification, intelligence, network connection and sharing has brought new challenges to the electrical/electronic architecture of vehicles. The topology of the vehicle has changed from distributed ECU development to the centralized oriented domain controllers and central computers; the function of the whole vehicle has been changed from decentralized software development to the development of a unified software architecture for aggregation. This paper studies the Ethernet-based on-board domain controller and expounds the research method for the unified architecture platform. Based on the SOA model, it discusses the definition of services based on SOME/IP, the software architecture, the hardware topology, and communication layer parameters; and builds a domain controller hardware platform, and simulates the development content in the later stage to form a complete Development process of Ethernet-based on-board domain controller.
Securing the electronic medical record by implementing Advanced Encryption Standard (AES) on the information system of a health service place

Abstract Medical records are very crucial in health services all over the world. This is due to not only to record individual health conditions but also for getting the most appropriate medical services at a local health facility when in mobile conditions. By having a look at the medical records, the local health workers can know the medical history and give their best service in treating the patient. Conventional medical records commonly are written manually and are not flexible for the mobile environment. Therefore, the electronic medical record (EMR) has become mandatory to be implemented to cope with such problems. Another important thing that requires attention is the patient has the right to know the contents of the medical record, especially when having health services at a different hospital or other health services when at mobile conditions. Accessing the EMR from the mobile environment brings a crucial matter, namely, how to secure the contents of EMR and ensure that the access can only be given to the legitimate owner fit as well as other legitimated actors such as the related doctors and the EMR officials. Based on the arisen problem, the contribution of this research is to make the implementation of encryption algorithms that will be used on Secure Electronic Medical Record (SEMR) systems using secure and fast encryption techniques by using Advanced Encryption Standard (AES) on the information system of a health service place.
Explained Artificial Intelligence Helps to Integrate Artificial and Human Intelligence Into Medical Diagnostic Systems: Analytical Review of Publications

Artificial intelligence-based medical systems can by now diagnose various disorders highly accurately. However, we should stress that despite encouraging and ever improving results, people still distrust such systems. We review relevant publications over the past five years, to identify the main causes of such mistrust and ways to overcome it. Our study showes that the main reasons to distrust these systems are opaque models, blackbox algorithms, and potentially unrepresentful training samples. We demonstrate that explainable artificial intelligence, aimed to create more user-friendly and understandable systems, has become a noticeable new topic in theoretical research and practical development. Another notable trend is to develop approaches to build hybrid systems, where artificial and human intelligence interact according to the teamwork model.
Online FAQ Chatbot for Customer Support

Chatbots and conversational systems are becoming a prominent research area, and many businesses are starting to leverage on their capability to handle basic communication tasks. With a vast variety of available frameworks for chat-bot development from tech giants, business organizations can build their own systems quickly and conveniently. However, these frameworks often lack a proper set of holistic tools to build a chatbot that is manageable, adaptable to learn, and scalable. Hence, frequently, additional machine learning mechanisms are needed to improve performance. In this paper, we demonstrate a chatbot system that uses machine learning to answer Frequently Asked Questions (FAQs) from our school website. The system includes different types of user query and a vector similarity analysis component to handle long and complex user queries. In addition, the Google’ s DialogFlow framework is used for intention detection.
Model of Normal University Students’ Education Quality Based on Artificial Neural Networks

Abstract As a main form of checking out learning and teaching effects, exam has traditionally been used in schools. Accurate and objective assessment of students’ learning effects through examination performance is an important part of evaluation of students’ education. This paper proposes a comprehensive evaluation model of evaluating examination performance based on quality of examination paper, explores the application of artificial neural network technology in students’ comprehensive education evaluation, focusing on BP neural network, some works can also be introduced by Python. The process is to compose input vector of BP neural network using the four evaluating indicators of quality of examination paper, namely reliability, validity, difficulty and discrimination, and raw score, namely the students’ score of examination paper. Take valuation namely the quantitative value of learning effect as the output vector of BP neural network. Design a reasonable network structure and training sample, put the training sample in the network for processing till until systematic error meets the specified requirements. By so, the obtained network model is the required comprehensive evaluation model of examination performance. At last, the paper analyzes the simulation of the evaluating model’s feasibility using MATLAB software, obtains satisfying results, and proves the feasibility of the proposed network model.
Research on the Development Status of Applied Electronic Technology in Intelligent Terminal Design

With the continuous improvement of the current level of science and technology, all kinds of intelligent terminal (IT.) devices appear in people's life, and has been well used, also provides help for people's life and work. The ability of IT. to be more functional is closely linked to the use of electronic technology (ET). In this paper, we will first understand the function of IT., clarify the characteristics of IT. devices, and finally analyze the embodiment of electronic key technologies applied to IT. To provide reference for relevant researchers.
A New Sentiment Analysis Model for Mixed Language using Contextual Lexicon

Sentiment analysis is one of the most active research areas in Natural Language Processing since early 2000. To date, sentiment analysis has been applied to various domains such as product, movie, sport and political reviews. Previously, the research in sentiment analysis area only concentrated on mining a single language. Nevertheless, due to the growth of multiple language usage in the form of writing and speaking, sentiment analysis activities have become more challenging. Many opinion keywords carry different polarities when they are used in different context, posing huge challenges to this field of research. Furthermore, in a social media environment where users tend to mix up languages, a lot of ambiguous content is present which makes a post difficult to be classified. Thus, this paper is a foray into the sentiment analysis for the context of mixed language.
Continuous Learning for Question Answering

We consider the problem of answering natural language questions over a Knowledge Graph, in the case of systems that must evolve over time in a production environment. One of the key issues is that we can expect that the system will receive questions that cannot be answered with the current state of the Knowledge Graph. We discuss here the challenges we need to address in this scenario and the expected behavior of this kind of Lifelong learning system. We also suggest a first task to address this problem and a possible procedure to build a benchmark.
Bipartite Network of Interest (BNOI): Extending Co-Word Network with Interest of Researchers Using Sensor Data and Corresponding Applications as an Example

Traditional co-word networks do not discriminate keywords of researcher interest from general keywords. Co-word networks are therefore often too general to provide knowledge if interest to domain experts. Inspired by the recent work that uses an automatic method to identify the questions of interest to researchers like “problems” and “solutions”, we try to answer a similar question “what sensors can be used for what kind of applications”, which is great interest in sensor- related fields. By generalizing the specific questions as “questions of interest”, we built a knowledge network considering researcher interest, called bipartite network of interest (BNOI). Different from a co-word approaches using accurate keywords from a list, BNOI uses classification models to find possible entities of interest. A total of nine feature extraction methods including N-grams, Word2Vec, BERT, etc. were used to extract features to train the classification models, including naïve Bayes (NB), support vector machines (SVM) and logistic regression (LR). In addition, a multi-feature fusion strategy and a voting principle (VP) method are applied to assemble the capability of the features and the classification models. Using the abstract text data of 350 remote sensing articles, features are extracted and the models trained. The experiment results show that after removing the biased words and using the ten-fold cross-validation method, the F-measure of “sensors” and “applications” are 93.2% and 85.5%, respectively. It is thus demonstrated that researcher questions of interest can be better answered by the constructed BNOI based on classification results, comparedwith the traditional co-word network approach.
Discovering Research Areas from Patents: A Case Study in Autonomous Vehicles Industry

Seven research areas introduced by the `Autonomous Systems' research lab provide research areas required to enable the autonomous vehicle industry. For ensuring the validity of the research areas with the baseline, we apply Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) on the US patents containing `Autonomous Vehicles' to identify keywords and research areas of relevant technologies. Keyword clustering and TF-IDF are repeatedly applied to the retrieved keywords to further filter out irrelevant words. Coherence values for LSA and LDA are evaluated to determine an adequate number of topics that need to be generated. We found that results from LSA provide a list of technologies already included in the baseline while topics from LDA provide associated keywords to support defining each technology. We conclude the numbers and topics provided by the baseline model closely represent the industry of autonomous vehicles but the identified topics from us provide a significant extension in research areas. The resulting research areas may provide overviews and guidelines on the autonomous vehicles industry for researchers and institutes.
Blockchain based Smart Contracts in Automation of Shipping Ports

Shipping is an integral part of international trade which facilitates ninety percent of the world's goods and cargo movement transported by sea with over seventy percent as containerized cargo. Freight transport enables economic growth, provides access to markets, connects producers and consumers, determines trade competitiveness, supports effective integration into global supply chains, and contributes to social progress and inclusive development. Around the world, shipping documents are still prepared and processed mainly in old paper style. This result processes happening within shipping ports to be highly paper-dependent and time-consuming. As it involves multiple participants with opposing interests, shipping documents are easily vulnerable to paperwork manipulation. Blockchain technology provides a distributed, shared, and append-only ledger in which new records are added through consensus among participants invoked by smart contracts. This paper is the application of smart contracts in shipping port processes to digitize and automate paperwork filings. Smart contracts are written inspired by the shipping ports processes and deployed into Ganache private blockchain thus incorporating immutable nature, trust, decentralization and privacy of the blockchain into the entire shipment process. Automation will reduce the possible human errors and manipulation, also conserving time resources reducing cost, and improving speed.
Character Modifier Combinations Recognition in Sinhala Handwriting

Sinhala script is categorized as a segmental writing system and therefore consonant-vowel sequences are identified as a unit. Depending on the vowel and the consonant combination, the diacritic can attach above, below, following or preceding the consonant. The identification of characters with the modifier symbols is important in recognizing generally written Sinhala text and has not been addressed sufficiently in the existing research. Sinhala handwriting recognition is much difficult compared to the other popular languages due to the complexity of the shapes of the characters and the presence of the modifier symbols. This paper discusses on a projection profile, distance profile, partial distance profile and contour based approach to identify characters along with character modifiers in Sinhala script. The proposed method has given an average recognition rate of 75% for 283 character modifier combinations. The proposed solution can be used to identify Sinhala handwritten text with a proper segmentation mechanism.
Entity relationship extraction based on bi-channel neural network

It is of extremely important research value to identify the hidden relationship between the entities and extracted entities in this paper. Based on the two-channel convolutional neural network, the bi-directional long-short term memory network and multi-head attention mechanism have been added by this paper, and a relational extraction model of the two- channel neural network has been proposed. After the training of word vector, the resulting feature fusion is further classified, and finally the experimental verification is conducted on the SemEval-2010 Task 8 data set. The validation results have shown that this method has a good relationship extraction effect.
On Multi-Scalar Multiplication Algorithms for Register-Constrained Environments

A basic but expensive operation in the implementations of several famous public-key cryptosystems is the computation of the multi-scalar multiplication in a certain finite additive group defined by an elliptic curve. We propose an adaptive window method for the multi-scalar multiplication, which aims to balance the computation cost and the memory cost under register-constrained environments. That is, our method can maximize the computation efficiency of multi-scalar multiplication according to any small, fixed number of registers provided by electronic devices. We further demonstrate that our method is efficient when five registers are available. Our method is further studied in detail in the case where it is combined with the non-adjacent form (NAF) representation and the joint sparse form (JSF) representation. One efficiency result is that our method with the proposed improved NAF n-bit representation on average requires 209n/432 point additions. To the best of our knowledge, this efficiency result is optimal compared with those of similar methods using five registers. Unlike the previous window methods, which store all possible values in the window, our method stores those with comparatively high probabilities to reduce the number of required registers.
Performance of Machine Learning and Deep Learning on Arabic Handwritten Text Recognition

This paper proposes machine learning and deep learning techniques for recognizing Arabic handwritten text. Finally, this paper introduces a comparative study between them in term of their performance. Actually, the classification of Arabic handwritten text plays a vital role in the computer vision domain, where traditional machine learning techniques and deep learning techniques are commonly used by researchers. In this paper, both machine learning and deep learning techniques are proposed and evaluated for recognizing Arabic handwritten text. Several experiments were carried out using both machine learning and deep learning on two different databases the AHCR and ADBase. The AHCR contains 28000 images of handwritten Arabic alphabet letters written by 100 writers. While the ADBase contains 70,000 images of handwritten Arabic digits written by 700 writers. The experimental results on both databases have demonstrated that the performance of the deep learning outperforms machine learning.
The Driver's Attention Level

Road accidents are directly proportional to the number of cars on the market. Without car safety systems, this number will keep rising. The main factor for the accidents are drowsiness and fatigue. These can be detected by analysing images with the driver so, an example of a driver monitoring system may include a monitoring camera, mounted in front of the driver. A method based on machine learning and computer vision can be a solution to solve the problem of driver safety. The objectives of our work includes an analysis of different approaches of driver monitoring systems and the implementation of a system based on convolutional neural networks which analyze the images coming from a monochrome infrared monitoring camera placed in front of the driver seat. The goal of this work is to decide if the driver is attentive or not (attentive) on the road. Our research was done by implementing a classifier based on AlexNet architecture and return one of the 6 attention classed. To improve the system accuracy, the face was detected using DNN Face Detector (using OpenCV approach). The final system is able to detect when the driver is not paying attention to the road, based on existing test data.
A Monocular Vision Relative Displacement Measurement Method Based on Bundle Adjustment Optimization and Quadratic Function Correction

Abstract In order to meet the requirements of high-precision target displacement measurement, this paper proposed a measurement method based on monocular vision measurement system by which the advantages of simple structure, convenient debugging, easy to install and calibrate is recognized. This method first uses the bundle adjustment algorithm with conditional constraints to optimize the solution results of the orthogonal iterative algorithm, then calculates the relative poses of the optimization results, finally establishes an error correction model to perform secondary optimization on the relative poses. The experimental results show that the method proposed in this paper can reduce more than 54% average error of displacement measurement results, and the error between the final displacement measurement results and the actual displacement values is controlled at 0.4mm level, which proves that the method has good practical value.
Automation of Malarial Cell Count and Stage Classification Using Morphological Operations and Variable Optimization Using Hyper-Parameter Tuning

Malaria, caused by the parasite plasmodium, infects the cells of the host and multiplies using their resources. One of the best ways to test malaria involves forming blood smears and examining the slides. Malarial cell count detection is a crucial process in classifying the extent of malarial infection. We have devised and tested an algorithm that automates the process of malarial cell count detection and classifies the images according to stage of malaria present. Images from various patients were obtained and tested. We used morphological operations to demarcate malaria infected cells, and hyper-parameter tuning was employed to optimize the algorithm for better accuracy. The proposed algorithm for malarial cell count detection is capable of automating the cell count process and produces accuracy levels greater than 90% in finding the correct count of parasite infected cells in the sample and greater than 90% accuracy in correctly classifying the stage of malaria.