Reviewerly/exHarmony huggingface dataset:
In Reply to the Letter to the Editor Regarding “Enhancing Reality: A Systematic Review of Augmented Reality in Neuronavigation and Education”

We performed this meta-analysis to compare the safety of unilateral with bilateral instrumented fusion in 2-level degenerative disorders of the lumbar spine.We conducted an independent and a duplicate electronic database search including PubMed, Embase, and Cochrane Library until January 2020 for randomized controlled trials (RCTs) comparing unilateral pedicle screw fixation with bilateral pedicle screw fixation for multilevel lumbar degenerative disorders. Fusion and complication rates were the primary outcomes analyzed. Analysis was performed in R platform using OpenMeta[Analyst] software.Five RCTs including 215 patients (Unilateral/Bilateral = 106/109) were included in the meta-analysis. There was no significant difference between the 2 groups regarding fusion rate, complication rate, blood loss, duration of hospital stay, functional outcome scores like Visual Analog Scale, Oswestry Disability Index, and Short-Form 36 health survey at the final follow-up. Unilateral pedicle screw fixation was associated with a significant reduction in operation time (P < 0.001). Compared with the open approach, the minimally invasive approach showed a significant difference in terms of factors like operative time, blood loss, hospital stay, Visual Analog Scale, and Oswestry Disability Index (P = 0.004).Our meta-analysis establishes the immediate safety and significant lesser operative period of unilateral pedicle screw fixation in lumbar fusion. However, due to lack of evidence on complications like cage subsidence and adjacent segment disease, unilateral pedicle screw fixation cannot be recommended as an alternative to bilateral pedicle screw fixation for 2-level degenerative spinal disease. Our analysis established the lacunae in literature for high-quality evidence on the subject; hence we recommend further large multicenter studies with longer follow-up to arrive at a conclusion.
GROOVY ASSIGNMENT: Immersive Dome Projection Media

In this Groovy Assignment submission, we present an Immersive Dome Projection Assignment that challenges students to create immersive media content designed and formatted for full dome projection experiences. For this assignment, students create immersive media content through video acquisition, computer imagery renderings and or use of an interactive game engine. The finished media must be in polar, fulldome projection format. The assignment is designed to inspire and motivate creative thinking by challenging students to work within the limitations of dome projection formats, including perspective distortion, projection mapping, and light spill to name a few. While the opportunity to display students work in a full scale projection dome can increase student interest and motivation due to the nontraditional nature of the format, the assignment can be easily scaled and utilized without access to a dome projection facility through the use of virtual dome software, or simulations in rendering software or game engines. Assignment complexity is also scalable, as some approaches to completion involved very little in the way of specialized technical skills (i.e. conversion of immersive photos or video into dome projection format), while more complicated, group project scenarios are also possible by increasing scope and or complexity of content requirements (i.e. computer generated animation, procedural effects, etc.)
[Multi-feature Extraction and Classification of Breast Tumor in Ultrasound Image].

Feature extraction of breast tumors is very important in the breast tumor detection (benign and malignant) in ultrasound image. The traditional quantitative description of breast tumors has some shortcomings, such as inaccuracy. A simple and accurate feature extraction method has been studied.In this paper, a new method of boundary feature extraction was proposed. Firstly, the shape histogram of ultrasound breast tumors was constructed. Secondly, the relevant boundary feature factors were calculated from a local point of view, including sum of maximum curvature, sum of maximum curvature and peak, sum of maximum curvature and standard deviation. Based on the boundary features, shape features and texture features, the linear support vector machine classifiers for benign and malignant breast tumor recognition was constructed.The accuracy of boundary features in the benign and malignant breast tumors classification was 82.69%. The accuracy of shape features was 73.08%. The accuracy of texture features was 63.46%. The classification accuracy of the three fusion features was 86.54%.The classification accuracy of boundary features was higher than that of texture features and shape features. The classification method based on multi-features has the highest accuracy and it describes the benign and malignant tumors from different angles. The research results have practical value.
ANDES at SemEval-2020 Task 12: A jointly-trained BERT multilingual model for offensive language detection

This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research
Phishing URL Detection Using Machine Learning

Phishing attack is used to obtain the information like username, password, bank account details, and credit card details. It is a most popular cybercrime today. Phishing attacks also affect the online payment sector financial institution, file hosting or cloud storage, and many others. Phishing attack always targets to these Web sites which are related to the online payment sector and Web mail. Many techniques are used to prevent the phishing attack like blacklist, Heuristic, visual similarity, and machine learning. Blacklist technique is most commonly used because it is easy to implement, but this technique cannot detect a new phishing attack. So, now, machine learning is most efficient technique to detect the phishing attack and this technique is able to detect all drawback of other phishing detect techniques. So this research work is completely based on machine learning algorithms which are logistic regression, decision tree, random forest, and SVM to detect phishing Web site. In this research work, select the best model on the basis of six analysis factors.
A Method of UML Sequence Diagram Verification Based on a Graph Grammar

With the increasing scale and complexity of software, the description of software structure plays a more and more important role. In order to ensure the quality of the software and improve the reliability of the software, it is a feasible way to combine the relevant parts of the software structure with the formal methods. In this paper, based on an existing context-sensitive temporal graph grammar, the formal method for UML sequence diagrams is proposed. Then, new productions are presented, which could be used to verify and analyze the temporal semantics in UML sequence diagrams. Finally, a case study of ATM withdrawal operation is provided to show how the formal method works.
Classification of Biomedical Literature in Hypertension and Diabetes

Textual information gives us more clear information as it is presented using words and characters, which is easy for humans to understand. To extract this kind of information, text mining was introduced as new technology. Text mining is the process of extracting non-trivial patterns or knowledge from text documents or from textual databases. The purpose of this research paper is to perform and compare keyword extraction using statistical and linguistic extraction tools for 120 text documents related to hypertension and diabetes disease. In order to draw this comparison, RStudio, a statistical-based tool and TerMine, a linguistic-based tool have been used to demonstrate the process of extracting the specified keyword from the biomedical literature. Thus, classification evaluation using Naïve Bayes classifier is carried out in order to evaluate and compare the performance of the statistical and linguistic approaches using these tools. Experimental results show the result of the comparison and the difference between both tools in executing extraction keywords.
SIAMESE NETWORK COMBINED WITH ATTENTION MECHANISM FOR OBJECT TRACKING

Abstract. After the development of deep learning object tracking methods in recent years, the fully convolutional siamese network object tracking algorithm SiamFC has become a more classic deep learning object tracking algorithm. In view of the problem that the accuracy of the tracking results of SiamFC will be reduced in the case of complex backgrounds, this paper introduces the attention mechanism based on the SiamFC, which performs channel and spatial weighting on the feature maps obtained by convolution of the input image. At the same time, the backbone network model of CNN in the algorithm is adjusted, then the siamese network combined with attention mechanism for object tracking is proposed. It can strengthen the effectiveness of the results of feature extraction and enhance the ability of the network model to discriminate targets. In this paper, the algorithm is tested on the OTB2015, VOT2016 and VOT2017 datasets, and compared with multiple object tracking algorithms. Experimental results show that the algorithm in this paper can better solve the complex background problem in object tracking, and has certain advantages compared with other algorithms.
Breast Cancer Recurrence Prediction in Biopsy Using Machine Learning Framework

There are numerous toxic and lethal substances that are available in the environment due to the rapid industrial growth and excessive application of pesticides. These substances get into the human food chain majorly through air, water, and soil. This paper presents a case study investigating the organochlorine pesticide levels in women experiencing the malignant and benign growth of breast cancer disease in order to evaluate the exposure against the chemicals and its association with the risk of breast cancer among women. After obtaining the blood and adipose tissue samples, levels of 51 chemicals including DDT, its metabolites, and isomers of HCH among 50 women each with the malignant and benign growth of breast cancer disease are measured. The levels of the chemicals in women with malignant growth of breast cancer are compared with benign cases and a prediction model is built using popular ensemble machine learning framework. The proposed framework is an optimized version of Random Forest algorithm in which feature selection is implemented and the process is incorporated with the preprocessing filters. The proposed framework for breast cancer prediction successfully achieved a prediction accuracy of 90.47%, which is found to be better than the standard classifiers like SVM, neural network, etc.
Means of Research Activity in the Formation of Bachelor’s Research Competence

This article discusses the application of research activities as a means of forming the research competence of a bachelor. Research the concept of research competence is the General concept of “research”. The role of research competence of the bachelor is determined. The result of the study is a certain territorial system concept and objective education. The emerging levels of professional competence of the future bachelor’s mathematics teacher coincide with the level of students’ performance of educational and methodological tasks: low level-the lack of a system of knowledge of a reproductive nature about the methodological features of teaching certain topics of mathematics, about the age characteristics of students; inability to make educational and methodological questions even on a given sample; intermediate level-a system of acquired and existing knowledge, skills and basic professional qualities in the learning process, which is able to solve educational and methodological issues in standard situations with the help of a teacher or other persons through level 1 tasks using individual approaches based on the model; a high level of methodological literacy — readiness for independent and creative release training tasks in the changed conditions on all major types of educational-methodical assignments with the use of special, generic methods or their own methods of instructional activities in accordance with accepted standards and norms and the implementation of educational-methodical tasks of the second and third levels. We can conclude that research is a phenomenon of human culture, the essence of which is the knowledge of the world around us. The result of the research is objective knowledge reflected in the system of concepts in a certain area.
Adaptive MoD Chatbot: Toward Providing Contextual Corporate Summarized Document as Suggestions and Reported Issue Ticket Routing

After advent of Internet and cloud computing technology, many of the software solutions are hosted in cloud infrastructure (a.k.a SaaS) to enable customers to pay for services as they use. Having this situation, the software non-functional requirements such as ‘availability’ and ‘reliability’ have become critical as customers expect \(24\times 7\) support for their hosted applications. In such scenario, chatbot or conversational AI becomes more critical to act as the first point of contact to support customer’s queries to reduce multiple round trips between DevOps (Cloud infrastructure support team) to an extent possible. Currently, Team of MoD (manager on duty) is the first point of contact to support for any upcoming customer queries and if in need will be percolated down to team of developer on duty (DoD) for speedy issue resolution. In this paper, we are trying to build a novel solution of adaptive chatbot in providing contextual Q&A responses with built-in natural language template generation, building technical document corpus specific knowledge base, building document text summarization to fit into chatbot real estate, creating corporate technical document indexing to provide important document links and summarized text as response, creating support tickets for the issue reported with automated ticket routing classification to enable speedy resolution of customer queries by DoD with the techniques from information retrieval, information extraction, NLP, NLG, text summarization, text classification and ontology. This paper proposes customized text ranking algorithm by ensuring more weightage is given to document sentences having n-gram linked entities from ontology knowledge base.
Enveloped Inverted Tree Recursive Hashing: An Efficient Transformation for Parallel Hashing

Security and performance are two of the most important concerns for cryptographic hashing algorithms, presenting a compelling challenge, since there seems to be a trade-off between achieving high speed on one hand and robust security on the other. However, with the advances in computer architecture and semiconductor technology, it is possible to achieve both by adopting parallelism. This paper presents a novel transformation based on the recursive tree hashing to parallelize and speed up typical hashing algorithms. The proposed transformation, called Enveloped Inverted Tree Recursive Hashing (EITRH), has three steps: “message expansion,” “parallel reduction,” and “hash value generation.” It improves upon the accuracy and the speed of hash code generation. Also proposed are some algorithms using the EITRH transformation for high-speed hashing on multiple cores. The security analysis of EITRH framework demonstrates its multi-property preservation capabilities. Discussion of EITRH w.r.t. performance benchmarks suggests its potential to achieve high speed in practical implementation.
High education and human resources in the Russian Arctic region: problems and particular qualities of development

Abstract The article presents a description of the history of high education development in the Arctic zone of the Russian Federation, provides an overview of the priority areas of popular professions for the region as a whole, and for its individual subjects; analyzes the main indicators that determine the opportunities for training in higher education institutions and identifies the distinctive features of the functioning of higher education in the Arctic region. A number of recommendations are proposed for certain areas of improving the system of training in higher education institutions of the studied region, taking into account its geographical location.
Effectiveness of GeoGebra in Teaching Grade 10 Mathematics

This study investigated the effectiveness of using GeoGebra in teaching Grade 10 Mathematics. The study involved 66 Grade 10 students. A total of 34 students were in the control group and 32 students in the experimental group. The researcher utilized quasi-experimental design (for the control group and the experimental group). The research used pre-test and post-test approach. The control group was taught using the conventional way while the experimental group was taught using GeoGebra software. The results showed significant difference between the control and the experimental group (t=-4.05, p = 0.0001). The gain score of the respondents showed significant difference (t = -8.28, p<0.0001). The findings of this study would prove teachers the opportunity to use GeoGebra software in their teaching mathematics that will enhance student’s performance in mathematics.
Interpretation and Segmentation of Chart Images Using h-Means Image Clustering Algorithm

For chart analysis, it is necessary to segment regions of interest from the background for a better understanding of distinctive pieces of charts. Many different methods, such as thresholding, clustering, compression-based methods, etc., are proposed for image segmentation. For accurate representation, decoding of graphical data or visual contents is needed for a readable and understandable data pattern for a computing machine. The existing works for classification of charts have relied on local and handcrafted features that lack in dealing with a massive amount of visual data. That could include substantial variabilities of complex data from within-class and between-classes of data. Therefore, we propose a segmentation method using histogram to segment the charts into a region of interest for feature extraction and classification. H-means algorithm uses histogram to decide the number of clusters by extracting peaks as ROIs for each chart image and segments foreground from the background. An image database of info charts having 5280 images covering ten categories of chart images is introduced to the research community.
The Application of Artificial Intelligence in English Teaching

The birth and development of science and technology is to better serve mankind. The development of artificial intelligence technology is becoming more and more mature, especially in the application of English teaching, which promotes the reform, development and modernization of English teaching. This paper analyzes the connotation of AI technology and the application of artificial intelligence technology in English listening, writing, translation and oral teaching.
The Organization of Software Teams in the Quest for Continuous Delivery: A Grounded Theory Approach

Context: Continuous delivery practices accelerate time to market and improve customer satisfaction. Although recent related work suggests that organizations employing continuous delivery should promote a collaborative culture among different IT teams, there is no substantial literature tackling how organizations should organize their teams to excel in continuous delivery. Objective: In this study, we investigate how organizations pursuing continuous delivery organize their development and operations teams. Method: We collected and analyzed data from interviews with 46 IT professionals, following Grounded Theory guidelines. Results: After a careful analysis, we identified four patterns of organizational structures: (1) siloed departments, (2) classical DevOps, (3) cross-functional teams, and (4) platform teams. The main contribution of this study is a taxonomy that organizes these structures along with their properties. This taxonomy is our theory for organizing software teams in the context of continuous delivery.
A DCT Fractional Bit Replacement Based Dual Watermarking Algorithm for Image Authentication

Abstract: Watermarking is a process of embedding a message inside a digital signal like an image, video or text. It is used for several key reasons such as authenticity verification, ownership recognition and hidden communication. In this paper, we discuss about image watermarking, where secret messages are stored in images. Introduction: We propose a dual watermarking approach, which is based on Discrete Cosine Transform, Discrete Wavelet Transform and Singular Value Decomposition methods. This paper considers one watermark as robust and other water mark as fragile. Method: The robust watermark is embedded in Discrete Wavelet Transform- Singular Value Decomposition - domain and is used to transmit hidden messages. The fragile watermark is embedded in Discrete Cosine Transform domain and is used for verification of secret message of the robust watermark. The proposed algorithm is tested in the experimental results section and shows promising results against denoising, rotation, translation and cropping attacks. Result: The results show that the performance of the proposed algorithm in terms of mean squared error, structural similarity and peak signal to noise ratio is S4considerable as compared with the existing methods. Discussion: We present the comparison results with Himanshu et. al. in table 10, from which we can see that our method performs better with gaussian noise and rotational attack only lacking with Salt and Pepper noise. Fig. 7 and Fig. 8, in terms of resulting PSNR shows the variation of noise variance and degree of rotation. From the graphs it is evident that out method performs better against Gaussian and rotational attack. Conclusion: In this paper a dual watermarking method is proposed in which one watermark is fragile which is called as authentication watermark whereas the other watermark is robust and is called as the information watermark. The authentication watermark is embedded in the fractional part of DCT domain in the cover image and the information watermark is embedded in the diagonal vector of the LL sub-band.
Text-based Localization of Moments in a Video Corpus

Prior works on text-based video moment localization focus on temporally grounding the textual query in an untrimmed video. These works assume that the relevant video is already known and attempt to localize the moment on that relevant video only. Different from such works, we relax this assumption and address the task of localizing moments in a corpus of videos for a given sentence query. This task poses a unique challenge as the system is required to perform: (i) retrieval of the relevant video where only a segment of the video corresponds with the queried sentence, and (ii) temporal localization of moment in the relevant video based on sentence query. Towards overcoming this challenge, we propose Hierarchical Moment Alignment Network (HMAN) which learns an effective joint embedding space for moments and sentences. In addition to learning subtle differences between intra-video moments, HMAN focuses on distinguishing inter-video global semantic concepts based on sentence queries. Qualitative and quantitative results on three benchmark text-based video moment retrieval datasets - Charades-STA, DiDeMo, and ActivityNet Captions - demonstrate that our method achieves promising performance on the proposed task of temporal localization of moments in a corpus of videos.