Reviewerly/exHarmony huggingface dataset:
Measuring Customer Satisfaction on Software-Based Products and Services: A Requirements Engineering Perspective

Overall customer satisfaction is the most critical quality measure for all bespoke and commercial-off-the-shelf (COTS) software systems deployed in user environments. This study measured the satisfaction of users of a student administration software which was acquired and reconfigured by the IT department at an African university. As a student administration software, the product has all the necessary functionalities. However, most users of the software struggle to effectively and confidently use the software on their own without assistance from the IT department of the university. Although the software is required for capturing and processing students’ academic records by all academic staff, it appears there is a general feeling of boredom by many staff toward the software usage at this university. Therefore, this study aims to investigate the level of satisfaction of users of the software and if there is any correlation between satisfaction of use and user participation in the requirements engineering or reconfiguration process of the software. The study was conducted purely as a private academic initiative undertaken in order to investigate the perceived complaints among the users of the software and to propose professional solution to the problem. The study observed all the necessary steps required for conducting a scientific survey study of this nature including ethical considerations of confidentiality of information obtained from participants, optional user participation in the study (as indicated on the survey instrument), and proper acknowledgment of all literature sources referenced in the present study. The study employed a quantitative approach with a sample of 40 participants who were randomly selected from four different faculty units of the university. The Qualification Weighted Customer Opinion with Safeguard (QWCOS) model was used as initial external measurement (EM) of customer satisfaction. A Flexible Qualification Weighted Customer Opinion with Safeguard (FQWCOS) model was also applied to measure EM. Both results of EM were compared. In all, results from the study suggest that 85% of the sample were not very satisfied with the software, 62.5% will not recommend the software to others, and over 80% of the users did not participate during the requirements reconfiguration of the software before its deployment. There is a significant relationship between participating in requirements configuration and user satisfaction. Furthermore, both QWCOS and FQWCOS yield the same EM values. To improve customer satisfaction on software products and services, a 2D4E framework based on requirements and reconfiguration engineering (RRE) and total quality management (TQM) is proposed in this study.
Maturity Assessment of TOGAF ADM Using Enterprise Architecture Model Analysis and Description Logics

A Maturity Model represents a path towards an increasingly organized and systematic way of doing business. It is therefore a widely used technique valuable to assess certain aspects of organizations, as for example business processes. A maturity assessment can enable stakeholders to clearly identify strengths and improvement points, and prioritize actions in order to reach higher maturity levels. Doing maturity assessments can range from simple self-assessment questionnaires to full-blown assessment methods, such as those recommended by the ISO/IEC TS 33030 or the SEI SCAMPI. A main caveat of these assessments is the resources they encompass. In addition, many times the lack of automation renders benchmarks not possible. Assuming that the wide spread of Enterprise Architecture practices is making the modeling of business domains a fact, and considering the recent state of the art on the representation of those models as ontologies, this paper proposes how existing semantic technology can be used to automate TOGAF ADM maturity assessment of organizations by automating the analysis of enterprise architecture models in ArchiMate.
A Novel Model for Risk Estimation in Software Projects Using Artificial Neural Network

Software projects generally involve more risks due to unexpected negative results. Therefore, the risks encountered in software projects should be detected and analyzed on time, and effective precautions should be taken in order to complete the projects successfully. The aim of this study was to estimate the deviations that may occur in the software project outputs according to risk factors by using artificial neural networks (ANNs). Thus we aimed to minimize loses that may occur in project processes with the developed model. Firstly, a comprehensive and effective list of risk factors was created. Later, a checklist form was prepared for Team Members and Managers. The data collected include general project data and risk factors, and these are the inputs of the model. The outputs of the model are the deviations in the project outputs. MATLAB package program was utilized to develop the model. The performance of the model was measured according to Regression Values and Mean-Squared Error. The model obtained has forty-five inputs, one hidden layer that has fifteen neurons, and five outputs (45-15-5). In addition, the training-R, testing-R, and MSE values of the model were found as 0.9978, 0.9935, and 0.001, respectively. It is seen that the estimation results obtained with the model using the real project data coincide with the actual results largely and the error rates were also very low (close to zero). The experimental results clearly revealed that model performance is high, and it is very effective to use ANNs in risk estimation processes for software projects.
Bearing Fault Classification Using Wavelet Energy and Autoencoder

Today’s modern industry has widely accepted the intelligent condition monitoring system to improve the industrial organization. As an effect, the data-driven-based fault diagnosis methods are designed by integrating signal processing techniques along with artificial intelligence methods. Various signal processing approaches have been proposed for feature extraction from vibration signals to construct the fault feature space, and thus, over the years, the feature space has increased rapidly. Also, the challenge is to identify the promising features from the space for improving diagnosis performance. Therefore, in this paper, wavelet energy is presented as an input feature set to the fault diagnosis system. In this paper, wavelet energy is utilized to represent the multiple faults for reducing the requirement of number features, and therefore, the complex task of feature extraction becomes simple. Further, the convolutional autoencoder has assisted in finding more distinguishing fault feature from wavelet energy to improve the diagnosis task using extreme learning machine. The proposed method testified using two vibration datasets, and decent results are achieved. The effect of autoencoder on fault diagnosis performance has been observed in comparison to principal component analysis (PCA). Also, the consequence has seen in the size of the extreme learning machine (ELM) architecture.
Approaches to the essential characteristic of students' readiness for professional activity

The article describes the views of scientists on the problem of readiness for professional activity. The author identifies the most common approaches of researchers regarding the characterization of the concept «readiness for professional activity» (personal, functional, systemic, personal-activity, result-activity); summarizes the proposed interpretations of the definition of readiness for professional activity and shows a considerable number of definitions of this concept (readiness as a condition, result, phenomenon, process, quality, purpose, etc.). The analysis of the above formulations shows that they are complementary, but not contradictory, since each of them highlights certain aspects of understanding the essence of such readiness, significantly broadens and deepens the understanding of the complexity and versatility of the «readiness» phenomenon. Students' readiness for professional activity can be understood as a multilevel personal neoplasm, which is formed in several sub-structures of the personality and which are interconnected: the personal component of readiness (substructure of orientation, substructure of mental processes, substructure of biopsychic properties); functional component of readiness (experience substructure). The author analyzes the relationship between the concepts of «preparation», «readiness for professional activity», «professional competence»: the purpose and result of professional training of specialists is their readiness for professional activity, which organically contributes to the formation of professional competence through systematic self-improvement of personality, the accumulation of professional etc. Professional competence is seen not only as a result of professional training, but also as an integration of experience and further self-education, the ability to solve professional problems. The competency approach integrates the above approaches of researchers, penetrates the students' readiness for professional activity and aims to develop professional competence.
An Improvement of the Degradation of Speaker Recognition in Continuous Cold Speech for Home Assistant

Home assistant with speech user interfaces is quite welcomed due to its convenience in recent years. With speaker recognition (SR) technology in this application, personalized services (e.g., playing music, making to-do lists) for different family members become reality. However, the SR accuracy may decline sharply when a family has a cold due to the restriction of hardware and response time. In this paper, we propose a dual model updating strategy based on cold detection to maintain all speaker voice models. In this method, time domain and frequency domain features would be combined to detect continuous cold speech. And then, corresponding models would be selected to determine the identity according to the results of the detection. In order to continuously track SR performance based on data of mobile phone usage, a new mobile phone-based speech dataset (PBSD) which contains voice, phone model, and user's state of physical wellness has been constructed. Besides, the relationship between SR accuracy and users' state of physical wellness also has been analyzed based on a GMM-UBM framework. Finally, to evaluate performance of the proposed method, experiments focused on SR accuracy of 10 speakers from both cold-suffering and healthy states have been conducted. The results demonstrated that the SR accuracy can be improved effectively by the cold detection-based model updating strategy, especially in a cold-suffering circumstance.
Approaches to Construct a Psychological Portrait of Users Based on Analysis of Data in Open Profiles of Social Networks

Most of the free time a modern Internet user spends in various social networks, while publishing various information, news from his life, interested in content, communicating with friends. This paper presents approaches to the construction of psychological analysis on the basis of data posted by the user in the public domain on his page in the social network. The paper describes the algorithms of machine learning and construction of a neural network designed to solve this problem, as well as the results obtained.
Tibetan Location Name Recognition Using Tibetan-Chinese Cross-Lingual Word Embeddings

Location name recognition is an important part of the named entity recognition tasks(NER). For Tibetan Location name recognition, we proposed a method based on Tibetan-Chinese cross-lingual word embedding. We trained Tibetan-Chinese cross-lingual word embedding by using the Tibetan-Chinese bilingual translation word pairs, and proved that the cross-lingual word embedding improves the semantic expression of the monolingual word embedding in semantic similarity experiments. In the BiLSTM-CRF-based Tibetan location name recognition neural network model, we connected the cross-lingual word embedding as the input layer. The experimental results on the Tibetan location name recognition public dataset show that the method of using cross-lingual word embedding effectively improves the performance of Tibetan Location name recognition.
Neural Network Prediction of Gasoline and Diesel Production at Catalytic Cracking Main Fractionator

The feed, temperature, pressure and other conditions of the catalytic cracking main fractionator will have an impact on the output of gasoline and diesel. From the perspective of economic efficiency and technology, it is so necessary to establish a prediction model for the tower process and gasoline and diesel production, because it has important guiding significance for improving the operation process of the tower to maximize the output. The factory model established by the traditional Aspen has a complicated structure, poor portability, and slow operation. Based on this, this paper uses the extreme learning machine (ELM) to establish a gasoline and diesel production prediction model, and uses the bat algorithm (BA) to optimize the weight of the extreme learning machine to improve the prediction accuracy of the model. Through the prediction experiment of gasoline and diesel production in a refinery, it is verified that the extreme learning machine has fast response to gasoline and diesel production forecasts, high algorithm accuracy, small program and strong portability. Furtherly the ELM optimized by bat algorithm has stronger generalization than the unoptimized ELM, which improves the applicability of the forecasting method.
Relative Navigation in Anti-Collision Systems for UAV

In this paper, we describe the design of the onboard anti-collision system for unmanned aerial vehicles (UAV). We assume that all UAV work in the aviation communications network and each of the UAV transmits their location information. The basic condition for the operation of the proposed anti-collision system is an asynchronous communication network. Each network user is precisely determined by the time of broadcasting communications messages. This means that every user who works on this network has information about the time of broadcasting signals to other users. Based on this, we have designed an anti-collision system that will work on the principle of relative navigation.
Quality Of Experience Meets Operators Revenue: Dash Aware Management for Mobile Streaming

In this paper, we apply the recent MPEG-DASH SAND standard for streaming-aware networking elements to develop and investigate an integrated mobile network bandwidth management and resource allocation mechanism. It is used to achieve high quality of experience of the mobile streaming users while jointly assuring high revenue operations by the network operator and by the system's video service providers. The Integrated Manager (IM) leverages its DANE functionalities as defined by SAND standard, namely the capability of using feedback from base stations forming a cluster and from streaming clients, to associate to each user both a dynamic Quality of Experience (QoE) related priority and a static Willing-to-Pay factor related to the user service plan. These two metrics are used by the IM to manage resources belonging to the base stations cluster first at a distributed and then at a centralized level. Specifically, the IM is able to statistically share a bandwidth pool among multiple cells of a single operator, and possibly of multiple operators, to allocate at times excess bandwidth for use in certain cells for the purpose of providing enhanced QoE to identified streams. Simulation results show that the users' need for valuable (higher payoff) bandwidth is of statistical and possibly burst nature so that integrated management and statistical sharing of the bandwidth pool by multiple entities becomes highly effective, when compared to a competitive centralized approach.
Fragment-aided recognition of images under poor lighting and additive impulse noises

On the base of the first-order integral disproportion functions, two algorithms recognizing fragments of standard images are proposed. Due to the disproportion functions techniques, the algorithms have special advantages when processing poorly lighted images, as well as with signals transmitted in the presence of additive impulse noises.
Achieving Abstract Machine Reachability with Learning-Based Model Fulfilment

This paper proposes a probabilistic reachability repair solution that enables abstract machines to automatically evolve and satisfy desired requirements. The solution is a combination of the B-method, machine learning and program synthesis. The B-method is used to formally specify an abstract machine and analyse the reachability of the abstract machine. Machine learning models are used to approximate features hidden in the semantics of the abstract machine. When the abstract machine fails to reach a desired state, the machine learning models are used to discover missing transitions to the state. Inserting the discovered transitions into the original abstract machine will lead to a repaired abstract machine that is capable of achieving the state. To obtain the repaired abstract machine, a set of insertion repairs are synthesised from the discovered transitions and are simplified using context-free grammars. Experimental results reveal that the reachability repair solution is applicable to a wide range of abstract machines and can accurately discover transitions that satisfy the requirements of reachability. Moreover, the results demonstrate that random forests are efficient machine learning models on transition discovery tasks. Additionally, we argue that the automated reachability repair process can improve the efficiency of software development.
Long Short-Term Memory Networks for Earthquake Detection in Venezuelan Regions

Reliable earthquake detection and location algorithms are necessary to properly catalog and analyze the continuously growing seismic records. This paper reports the results of applying Long Short-Term Memory (LSTM) networks to single-station three-channel waveforms for P-wave earthquake detection in western and north central regions of Venezuela. Precisely, we apply our technique to study the seismicity along the dextral strike-slip Boconó and La Victoria - San Sebastián faults, with complex tectonics driven by the interactions between the South American and Caribbean plates.
Real-time Shape Estimation for Flexible Unmanned Air Vehicle via Kalman Filtering

As modern aircraft designs with flexible airframes become susceptible to dynamic coupling between rigid body and structural modes, challenges in aircraft design, modeling, and control increase significantly. Active modal suppression control is required to ensure dynamic stability across desired flight envelope. Closed-loop shape control, which takes advantage of the airframe flexibility to optimize aerodynamic shape for minimal drag, is also an important technology to be developed. One of the critical pieces of technology required for structural mode and shape control is shape estimation of the structure in real-time, which can serve as a feedback signal. In this paper, a Kalman filter-based shape estimation approach for a small flying wing unmanned air vehicle (UAV) is described. The UAV features a set of distributed sensors including small, light-weight inertial measurement units (IMUs) along its wings and center-body, as well as cameras that record and process visual information on wing-tip deflections. A linear Kalman Filter is designed using a linear aeroelastic vehicle-dynamic model for state propagation and IMU measurement data for measurement updates. The filter estimates wing-tip deflection and twist while camera data, which is available at a different sampling rate and is independent of the theoretical model, is used for validating the estimation. Data obtained from flight tests conducted for system identification purposes are used to validate the performance of the filter. Finally, blended estimates of wing-tip heave and twist are obtained via weighted averaging of filter estimates and visual data from the cameras.
Countering Adversarial Examples by Means of Steganographic Attacks

Deep learning models are now used in multiple contexts, including safety critical applications. However, it has been proven that small adversarial alterations to the input can undermine the performances of the model, leading to unreliable results, while being hardly visible to a human observer. Image watermarking share similarities with this field: a small information is embedded inside the media, aiming at being not perceivable but robust. Many attacks have been developed to remove watermarks. In this paper, we evaluate the effectiveness of multiple image transformations to remove adversarial perturbations from images. Our experiments on the MNIST dataset for a Projected Gradient Descent-based adversary demonstrate that many transformations can result in a significant gain in accuracy when classifying adversarial examples, while not degrading the quality of the images when the adversary is not present/non significant.
An Intelligent Control System for Service Robots

this research presents investigation of the typical steps required for successful completion of the typical tasks of the personal service robots. It proposes how those steps can be structured and integrated within a self-learning expert system. There have been conducted real experiments on a personal service robot. The results of those experiments concluded that by using an intelligent control mode the overall user experience is improving. With the incorporated mode the robot will be easily operated by an elderly or inexperienced people.
The Effectiveness of Cyber Security Frameworks in Combating Terrorism in Zimbabwe

This study investigated the effectiveness of Zimbabwe's cyber security frameworks in combating terrorism. Media reports and scholarly evidence have constantly shown that cybercrime has led to loss of life, money, security, damage to property. There is a lot of concern among citizens of African countries such as Zimbabwe, Kenya, and Nigeria that their governments are not equipped with effective cyber security systems to combat these cyber threats. It is on the basis of these concerns that this study was carried out with the hope of helping to close policy and knowledge gaps affecting the effectiveness of the cyber security frameworks of Zimbabwe. The study used qualitative methodology and a case study research design was utilised. The study was conducted in Harare the capital city of Zimbabwe. Key informant interviews and documentary search were used to collect data. Among the findings of the study were that cyber fraud and theft are some of the significant cyber security threats in Zimbabwe; that Zimbabwe lacks established legislation and other regulatory institutions for cyber-security. Among the recommendations are; that the Ministry of Justice, Legal and Parliamentary Affairs enacts cyber security laws in Zimbabwe to combat cyber terrorism.
A Textual Password Entry Method Resistant to Human Shoulder-Surfing Attack

Textual password is one of the most widely used authentication methods today. However, entering password in public is vulnerable to shoulder-surfing attacks. The attacker can observe or use the device to record the authentication session to obtain the password. Then the account is invaded and that will cause loss of data and property to the user. In this paper, we propose a new method MapPass for human shoulder-surfing resistant textual password entry by significantly increasing the limitation of cognitive ability of the attacker. Besides, we put forward the concept of attack alert, that is, the system can detect the failed shoulder-surfing attack and timely remind the user. We add this function to the method MapPass to improve the security of the method. Additionally, we analyze the security and usability of the proposed method.