Reviewerly/exHarmony huggingface dataset:
Teaching and Learning Domain Modeling through Collaboration Patterns: A Controlled Experiment

Domain models in software engineering—often represented as class diagrams—depict relevant classes in a given problem domain along with necessary relationships among those classes. These models are important because they establish links between the requirements of a given system under development and the subsequent phases of the systems development life cycle. Although the teaching of basic concepts related to domain modeling takes only about 1 or 2 hours, proper application of these concepts to a given problem situation is difficult for students studying software engineering. Due to their insufficient domain knowledge of the problem situation and modeling experience, they often produce domain models that may not adequately represent necessary elements as part of the domain models. Analysis patterns can help them by encoding expert knowledge and offering guidance in the modeling process. This article reports the findings from a controlled experiment conducted to study the effects of collaboration patterns on the domain modeling process by students. Specifically, the study investigated the differences in students’ perceptions of the ease of the domain modeling process and quality of models produced, perceived difficulties, and how collaboration patterns help address domain modeling difficulties and the quality of domain models produced. Findings from this experimental study involving students from a software engineering course indicate that although there is no significant difference in subjects’ perceptions between the control and treatment groups, the subjects from the treatment group produced better-quality domain models. Additionally, the qualitative analysis of the feedback collected from the subjects from the control and treatment groups reveals that that having knowledge of patterns is beneficial, as it addresses the difficulties in domain modeling.
Seismic Hazard and Risk in Africa, a Hidden Hazard in an Extreme Vulnerable Context

The IGCP-659 “SEISMOSHAF” project addresses the seismic hazard and risk mitigation, using the IGCP-601 project database on the seismotectonics of Africa. The seismic hazard assessment and risk mitigation will be conducted to comply with the proposals of the Sendai Framework for Disaster Risk Reduction 2015–2030 ( http://www.unisdr.org/we/inform/publications/43291 . Last accessed 30.07.2019). Studies of seismic hazard are vital in terms of understanding potentially damaging earthquakes, which may cause loss of life, injury or other health impacts, property damage, social and economic disruption, or environmental degradation. Seismic hazard assessment is based on the information about the features of excitation of seismic waves at the source, seismic wave propagation (attenuation), and site effect in the region under consideration and combines the results of seismological, geomorphological, geological, geodetic, tectonic investigations, and modeling ( https://igcp-project-659.oaka.fr/ ).
Efficient Planning for Object Search Task Based on Hierarchical POMDP

Searching for a target object in clutter by a robot needs to solve perception uncertainty due to occlusion among objects, the robot has to move objects to gather information about where the target may locate. Meanwhile, we hope that the robot could execute as few actions as possible to get the target. Partially observable Markov decision process (POMDP) is proper to model this task, but it is hard to solve for large scale problems. And action feasibility of robotic manipulation must be identified by motion planning, which is time-consuming. We adopt hierarchical POMDP to reduce the complexity of POMDP planning. Besides, we propose methods to reduce the times of motion planning, the key idea is assigning reward to actions related to feasibility probability, instead of identifying action feasibility at the beginning. Simulations under various scenes show that our methods are effective and efficient.
Vulnerability Analysis of Similar Code

Studying frequent code vulnerabilities in similar code, such as clones, near-duplicates, forked projects, or libraries, can help in the automated detection of security flaws during the software development process. In this work we conduct an empirical study on vulnerabilities in C/C++ code to characterize security flaws and find out if the same vulnerabilities exist in applications that share similar code or have the same business logic/domain. We analyze a code vulnerability dataset including 315 projects with 3284 security issues in 10,880 functions. Our results show that vulnerable functions in 35% of the most occurring CWEs (software weaknesses types) have similar code, and 23% of projects with the same domain/category have the same vulnerabilities. We observe that the most prevalent vulnerabilities in similar code are Use After Free, Improper Access Control, Cryptographic Issues, 7PK - Security Features, DoubleFree, Cross-site Scripting, and Divide By Zero. These vulnerabilities are, however, less frequent compared to other CWEs across all subjects. Our results suggest that automated vulnerability detection tools that work based on code similarity or abstract patterns can be tailored more towards certain CWEs.
FasterBERT with Double Loss Function Based on Batch Data Preprocessing

Even though pre-trained language models like BERT and XLNet have produced significant consequences on a variety of tasks of natural language processing, they are difficult to deploy in practical applications due to their large number of parameters, and huge calculation and memory costs. In order to improve the training speed and inference speed of the pre-trained model, the FasterBERT model is proposed in this paper. This model combines batch data preprocessing method with knowledge distillation. It increases the model to increase the model speed and maintains the original accuracy. Furthermore, double loss function is incorporated during the knowledge distillation process in this paper, so that the model can better learn the knowledge of the teacher model and the knowledge of the real labels. The accuracy of the model is improved. The effectiveness of the FasterBERT model is demonstrated on 12 datasets in this paper. Training speed of the FasterBERT is 1.1-2.2 times higher than FastBERT and 2-43 times higher than BERT. In addition, the accuracy is up to 0.6% higher than that of FastBERT, and the accuracy is better than that of the BERT model on three datasets. The experiments show that the FasterBERT model proposed in this paper performs well on six Chinese datasets and six English datasets. The speed is greatly improved while the accuracy of the task also has a significant effect.
Lung Cancer Detection Using SVM Classification

Cancer is one of the major reasons for death among India where the computer tomography (CT scan) is used to find the cancer, since CT scan is faster, has good clarity, and reliable and it can view organs, tissues, bones, and skeleton structure. Image processing is efficient technique to detect the cancer. In this paper, we implement the image processing technique which includes preprocessing, segmentation, and feature extraction and classification to detect the lung cancer. In preprocessing, we have used three types of filters such as median, adaptive median, and average filer. In segmentation, edge detection, Otsu’s thresholding, and K-means clustering algorithms are used. Then, the segmented image will be subjected to feature extraction using GLCM. For final classification result, binarization process is used.
Implementation of Expanding Hierarchical Operators in GN IDE

Generalized Nets (GNs) are an instrument for modelling and optimization of parallel and concurrent processes. Numerous operators have been introduced over the years to extend the GNs’ modelling power. Generalized Net Integrated Development Environment (GN IDE) – a software tool for creating GN-models and running simulations on such, has been developed to support the research in the GNs’ theory and applications. Recent enhancements in GN IDE involve the integration of GN operators. The implementation in GN IDE of two of the GNs’ hierarchical operators, defined to replace a specified transition or place in a GN-model with an entire GN, is discussed in the present paper. These new features of GN IDE make it possible to easily refine existing GN-models in details.
Classification of Filipino Braille Codes with Contractions Using Machine Vision

Knowledge in Braille is ultimately necessary to maintain learning for the visually impaired. In the Philippines, class attendance has been showing low rates for visually impaired students caused by the shortages of teachers and the absence of the specialized tools intended for teaching them. A proposed solution in addressing this problem is the usage of computers for the automation in the process of the extraction of information in Braille which can facilitate teaching. In recent years, a considerable amount of effort and attention have been devoted to the development of this kind of technology however in languages other than Filipino Braille. Codes in Filipino Braille with its contractions, and even the Filipino language itself has unique features as compared with other languages. In this paper, a system is proposed which uses machine vision in recognizing Filipino Braille codes including one-cell and two-cell contractions. Synthetic Braille images undergo cascade object detection, image processing, extraction of HOG features to develop the three-stage multiclass SVM classifier. Experimental evaluation results reveal a good performance of Filipino Braille classification and translation to texts.
Target tracking by improved ECO

ECO based trackers have achieved excellent performance on visual object tracking. However, Illumination variation and other factors still are challenging research problems in the process of tracking. Moreover, traditional neural networks also face information loss during the transmission process. In this paper, we introduce a new feature fusion (HE, FHOG-Encoder) and update strategy of learning rate. We propose an encoder network to extract features, which consists of two convolutional layers and three residual units. In addition, we design an updating strategy of learning rate, by computing absolute difference of inter-frame pixel, to effectively update sample space model. Experiments on challenging benchmarks OTB-100 are carried out. Experimental results show that our tracker achieves superior performance in some special cases, compared with the original ECO tracker.
Research on Financial Recommendation Algorithm Based on User Interest Evolution and Big Data

This article aims to study the recommendation algorithm of financial wealth management products based on the Internet. Through the item information of fund wealth management products, it mainly includes wealth management products issued by banks and fund products issued by fund companies as well as user behavior information, combined with traditional collaborative filtering algorithms to obtain a recommendation list, and then add a time series model, consider the seven-day annual interest rate and other time-sensitive attribute factors to improve the accuracy of the recommendation, and finally introduce the current more cutting-edge DIEN model algorithm to get the recommendation result, which integrates the sequence The model is used to simulate the change process of user interest over time, and the attention mechanism is introduced to improve the recommendation effect, thereby providing users with better product choices.
Research on mixed Learning training methods based on educational Technology

This paper is based on the idea of mixed learning and the design model of mixed learning, making full use of the training institutions.Software and hardware environment and human resources, the design and development of a large number of educational teaching resources, and through the construction of network platform provides online learning support services, so as to ensure the quality of online learning and face-to-face learning, saving the time, reduce the cost of learning, improve the efficiency of learning, mobilize the students’ active, so that mix combination training has real and feasible significance. Training activity is a systematic activity, training set the implementation effect of the plan is affected by many factors, only to do a more in-depth and detailed study of the training program.In order to make teachers’ educational technology training do better, the design of educational technology training also needs constant revision and improvement. At the same time, this study combined the machine learning algorithm to make a comparative analysis of the satisfaction of mixed learning and ordinary learning, and verified that the mixed learning mode has better effect.
Study of the Stability Features of Solutions of Systems of Differential Equations

The stability conditions for solutions of systems of ordinary differential equations are considered. The conditions and criteria for the use of partial and external derivatives are proposed. This allows us to investigate the behavior of a function of several variables, without requiring its differentiability, but using only information on partial derivatives. This reduces the restrictions on the degree of smoothness of the studied functions. The use of the apparatus of external derived numbers makes it possible to reduce the restrictions on the degree of smoothness of manifolds when studying the question of the integrability of the field of hyperplanes. Using the apparatus of partial and external derived numbers, it can be shown that the investigation of the stability of solutions of a system of differential equations can be reduced to an investigation of the solvability of a system of equations of a special form.
Latent Class Analysis with Latent Binary Variables: An Application for Analyzing Learning Structures

Analysis of learning structures is discussed with latent class models with latent binary variables. First, a latent class model for dealing with scale patterns of the latent variables is reviewed, and the ML estimation procedure for a structured latent class model for explaining learning or developmental structures is discussed. Numerical examples are given to demonstrate the analysis. Second, a method for evaluating mixed ratios of learning processes in a population is considered. Third, a path analysis in learning structures is discussed, and a numerical example is provided to illustrate the analysis. Finally, a summary of the present chapter and discussions on the latent class analysis with binary latent variables are given.
Enriching Portuguese Word Embeddings with Visual Information

This work focuses on the enrichment of existing Portuguese word embeddings with visual information. The combined text-image embeddings were tested against their text-only counterparts in common NLP tasks. The new embeddings were tested in two different domains - general news and a geosciences. The results show an increase in performance for several tasks, which indicates that visual information fusion for word embeddings can be useful for certain tasks.
Intelligent Speaker Recognition Algorithm Based on SE-Res2Net

Aiming at the problem that it is difficult for traditional speaker recognition algorithms to extract features in text-independent and short-term speech, a speaker recognition algorithm based on SE-Res2Net is proposed. The recognition model of the algorithm is composed of a fully connected Res2Net module and a Squeeze-and-Excitation (SE) module of the Channel Attention Model. The hierarchical connection structure in the Res2Net module can increase the receptive fields of the model and integrate the information of different channels. SE module assigns higher weight to the significant voiceprint feature channels to improve the recognition ability of the model in text-independent scenes. Combined with the self-attention pooling mechanism, the model further emphasizes the characteristic information of voiceprint, so as to solve the problem of insufficient short-term speech information. The experimental results in conventional and truncated data sets show that the SE-Res2Net model outperforms the existing methods in text-independent speaker recognition tasks, and also has the state-of-art performance in short-term speech recognition scenarios.
Influence of digital innovations in education on social responsibility of universities in the condition of a pandemic

The article analyzes the problems of the evolution of the social responsibility of the university in modern conditions, taking into account the impact of digital innovations in education, introduced in universities under the influence of the pandemic. The directions for the development of digital innovations in vocational education are considered, the features of digital competence as a result of digital competencies of all categories of university personnel are highlighted, the key components of responsible digitalization are proposed to enhance the digital maturity of an educational organization in the context of its social responsibility.
Analyzing the Association between Stroke and Chronic Diseases

Stroke is a neurological disease that occurs when brain cells die due to lack of oxygen and nutrition. If a stroke is detected within a few hours, it can increase the chance of preventing complications. The main causes of death for modern people revolve around various chronic diseases like heart disease, hypertension and stroke. In this paper, we use the data of chronic diseases in the United States to analyze the situation of stroke caused by chronic diseases. The analysis of the basic patient data may be used to predict the high risk of stroke. This work focuses on using different data mining techniques to make different prediction modules. Four data mining techniques, namely, Random Forest, K-Nearest Neighbors, Logistics Regression and Decision Tree, are used to make predictions and the results are compared with each other using various evaluation matrix. This analysis methodology is suitable for examining data mining algorithms with health data.
Privacy Enhanced Registered Devices for Fine-Grained Access Control

Aadhaar is one of the largest biometric identity systems in the world which aims to assign a unique digital identity to each resident of India. A resident can authenticate himself using his Aadhaar number and a biometric. Biometric is a very sensitive data, and registered devices were introduced to eliminate the use of stored biometrics. Each registered device is supposed to have a unique identity and to follow suggested best practices to ensure security. With the proliferation of digital services, the use of registered devices is expected to grow more in near future. Although registered device is a good initiative, attribute-based access and communication may not be very efficient at present. In the present world of ubiquitous computing, IoT and 5G, a device may need to provide an assurance that it possesses a certain set of requisite attributes rather than some serial or a model number. Furthermore, owner of the device may not want to reveal device identity to protect his privacy since device identity may be correlated with owner’s identity. In this paper, we present an efficient scheme of privacy enhanced attribute-based registered devices for fine-grained access control using attribute-based signature.
Usability Cost-Benefit Analysis for Information Technology Applications and Decision Making

Usability is an important quality attribute for information technology (IT) applications. However, integrating usability design and evaluation as an integral part of the development processes in information technology development organizations is still a challenge. This chapter gives an overview on the usability cost-benefit analysis models and provides some example cases of the importance of usability. These models and cases can be used by usability professionals to motivate the organizational management to provide resources for usability work and to integrate usability work as part of the development process. The target audience for this chapter are professionals and researchers working in the field of IT, managers in IT development organizations, as well as managers in organizations acquiring and using IT.
Bridgebase: A Knowledge Graph Framework for Monitoring and Analysis of Bridges

Infrastructural budgets on bridge maintenance and monitoring always demands for figuring out optimised ways which could perform rehabilitation work efficiently. In this work, we try to create a pipeline for (1) transforming inventory data to a knowledge graph which supports more readability by defining a question answer system (2) converting bridge nodes from the knowledge graph into embeddings based on sparse knowledge about bridge ratings and, (3) clustering similar bridges based on parameters to perform maintenance work easily. In this work, a knowledge graph framework is presented, then the nodes of the knowledge graph get converted to latent-vectors using Deepwalk. The conversion of bridge nodes to a latent vector transformation leverages essential knowledge graph structure, which makes it easy to cluster (by selecting appropriate features based on the formed graph structure) using Density Based Clustering (DBScan). The main objective of this work is to cluster similar bridge vectors, with less knowledge about the dataset and bridge properties, hence making it appropriate for streaming consumption. We create a real time bridge monitoring system (visualiser) with any employed structure. The efficiency of bridge node embedding is evaluated based on National Bridge Inventory dataset and the F1-scores were obtained with varied concentrations of unlabeled data. The obtained results showed how Deepwalk can perform better bridge node embedding (higher F1 scores) than other algorithms which show lower accuracy with sparse knowledge. As the knowledge graph gets stored in graph database systems, it opens up an interface for easy integration with Internet of Things applications.
Implementation of Artificial Neural Network to Recognize Numbers from Voice

Speech recognition is a subjective phenomenon which also an important part of human–machine interaction which still faces a lot of problem. The purpose of this work is to investigate and apply the artificial neural network (ANN) to recognise numbers using voice. In this work, MATLAB neural network toolbox is used to create, train and simulate the ANN. The dataset consisted a voice from ‘one’ to ‘five’ undergo windowing process to view a short time segment of a longer signal and analyse its frequency content and then being filtered by using a band-pass filter to remove the unwanted noise and been converted into histograms as an input for the network. From the experiments, the highest accuracy level obtained is 72.5% by using histograms as Feature Extraction. .