Reviewerly/exHarmony huggingface dataset:
The Development of Cybersecurity Awareness Measurement Model in the Water Sector

:Cyber-attacks are one of the main threats to information systems, and humans have been identified as the weakest link with regards to information security. This study aims to develop a measurement instrument to evaluate the level of cyber security awareness (CSA) in the water sector in South Africa. There are lots of synergies with regards to cyber system usage across industries, and as a result this study will take a broad base approach in configuring an instrument that can be used to adequately assess the sample space in question. Having a reliable instrument to measure cyber security awareness helps mitigate the failed attempts at preparing employees for imminent cyber disruptions by pin-pointing areas where the training is needed before campaigns can be organised. This study will show that the psychology of employees with respect to cyber security awareness is compartmentalised into three traits: knowledge, attitude, and behaviour. These three traits were assessed under the following eight focus areas to check employee resilience to cyber security: IS policy adherence, Password management, Email use, Internet use, social media use, mobile devices, information handling, and incident reporting. In practice, employees will be required to answer questions formulated under these focus areas to the evaluate their cyber security awareness (CSA) level. The model proposed in this paper was developed to test cybersecurity awareness in the water sector, but can be utilised in other sectors for cybersecurity awareness testing.
Mitigating Global Cyber Risk Through Bridging the National Incident Response Capacity Gap

Cyber-attacks know no borders. Given the globally connected environment, no region or country is secure against cyber-attacks unless the entire world is secure or has cyber capabilities. Yet, whether preventive or reactive, cyber countermeasures require coordination and engagement of various organizations, government bodies, and citizens of different countries. Although a variety of countermeasures exist, Computer Security Incident Response Teams (CSIRTs) have been deemed necessary systems in defending against and preventing cyberattacks, further supporting a nation’s cyber capacity and limiting the harm to citizens, businesses, and governments. Despite calls for establishing CSIRTs at the national level, especially toward protecting critical infrastructure and lives from cyber threats, various discrepancies exist based on a nation’s resources, capabilities, and needs. Limited research delves into the cyber capabilities of low-income countries despite an emphasis on improving global cyber capacity, leading to a need to establish a framework for low-income countries to address the unique needs with lessons learned from existing standards. CSIRTs can improve the cybersecurity posture of countries, so we seek to investigate how low-income countries can better mitigate cyber threats through cyber capacity building, including the creation of CSIRTs. This work-in-progress paper aims to investigate cyber incident response capacity building at the national level in low-income countries and identify challenges they may face in contributing to a more secure global cyberspace. Stemming from this paper, we will conduct a survey of national CSIRTs in low-income countries and conduct semi-structured interviews to further investigate their role. The implications of our research are far-spreading, assisting academics, practitioners, and governments in developing research, processes, and policies to aid low-income countries in their national cyber capacity building.
ONLINE EDUCATION IN PANDEMIC TIMES WITH SPECIFIC REFERENCE TO HIGHER EDUCATION: STUDENTS’ PERSPECTIVES

Education sector presently going through a crisis. The lockdown has restricted classroom teaching and students are completely relying on online content and remote learning features to study these days. The various platforms through which students are interacting with their teachers. Technology can be used as a resource in learning and can open new opportunities in the education sector. The online learning has transformed education. The purpose of this study was to conduct an online survey questions for students regarding student's perception about online classes and opinion about the online classes. The sample consisted of 40 students from colleges and universities in Kolhapur city. Online survey method was used for the purpose of data collection. The findings show that the following areas are important for student satisfaction with online classes, these areas are: quality and timely interaction between student and professor, technical support availability, structured online class modules, and accommodate conduction of online exam.But virtual classrooms will never replace the physical ones.
The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements

The role of evolution operator is to provide the time displacement of wave function through the Hamiltonian of the system. The usage of coordinates representation gives the well-known propagator that is the Green’s function. In this paper it is emphasized that once the propagator is projected onto a scenario of machine learning it would acquire the role of performance in according to the criteria of Tom Mitchell. In this manner from the resulting wave function the probability is simulated presenting noteworthy morphologies in the which the system displays high values of probability for the measurement of distances.
GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network

Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and techniques that support inspiration usually only focus on example browsing with limited user control or similarity-based example retrieval, leading to undesirable design outcomes such as focus drift and design fixation. To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. A quantitative evaluation revealed that the outputs of GANSpiration-based example suggestion approaches are relevant to the input design, and at the same time include diverse instances. A user study with professional UI/UX practitioners showed that the examples suggested by our approach serve as viable sources of inspiration for overall design concepts and specific design elements. Overall, our work paves the road of using advanced generative machine learning techniques in supporting the creative design practice.
A Character String-Based Stemming for Morphologically Derivative Languages

Morphologically derivative languages form words by fusing stems and suffixes, stems are important to be extracted in order to make cross lingual alignment and knowledge transfer. As there are phonetic harmony and disharmony when linguistic particles are combined, both phonetic and morphological changes need to be analyzed. This paper proposes a multilingual stemming method that learns morpho-phonetic changes automatically based on character based embedding and sequential modeling. Firstly, the character feature embedding at the sentence level is used as input, and the BiLSTM model is used to obtain the forward and reverse context sequence, and the attention mechanism is added to this model for weight learning, and the global feature information is extracted to capture the stem and affix boundaries; finally CRF model is used to learn more information from sequence features to describe context information more effectively. In order to verify the effectiveness of the above model, the model in this paper is compared with the traditional model on two different data sets of three derivative languages: Uyghur, Kazakh and Kirghiz. The experimental results show that the model in this paper has the best stemming effect on multilingual sentence-level datasets, which leads to more effective stemming. In addition, the proposed model outperforms other traditional models, and fully consider the data characteristics, and has certain advantages with less human intervention.
Pre-University Teachers’ Digital Competence And Wellbeing: An Israeli And Romanian Comparative Study

In the context of the COVID-19 pandemic, education systems have adopted as a solution a high-speed transfer of face-to-face courses into online or distance ones.Moreover, teachers have been given very little or no guidance or prior training about online synchronous and asynchronous learning, online effective teaching strategies, proper digital gadgets and teaching platforms.There have consistently been reports that teachers have a higher risk of developing psychological disorders in comparison with people from other professional fields.Therefore, the research paper also investigates a concept frequently studied nowadays in connection with mental health, wellbeing-as it enhances the development of personal skills and the realization of personal potential.The study was conducted to assess and compare Israeli and Romanian pre-university teachers' digital competence and wellbeing after three semesters of virtual environment mediated teaching.Two self-reported scales with excellent psychometric value were used for gathering data, namely The Competence Framework for the Digital Competence of Educators (DigCompEdu) and Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS).The results showcase the differences and similarities of the two groups of teachers regarding six areas of digital competencies of educators proposed by the European Framework and wellbeing status.Finally, future research and teacher training issues are discussed.
A new preconcentration technique for the determination of PGMs and gold by fire assay and ICP-OES

Accurate determination of platinum group metals (PGMs) and gold (Au) has always proven to be a difficult task, due to their low concentrations in platiniferous ores. The most common preconcentration technique used in analysis of these metals is fire assay with a flux containing nickel or lead. This technique can be improved by using co-collectors. Here we hypothesise that Fe, Co, and Cu can be used as co-collectors to enhance separation and preconcentration of PMGs and gold by fire assay. To test this hypothesis, geological exploration samples from Siyanda Union Mine (Northam, South Africa) were analysed by an inductively coupled plasma-optical emission spectrometer (ICP-OES) for PGMs (Pt, Pd, and Rh) and Au. A control sample (certified reference material AMIS 0426) was also analysed by the same technique. The PGM and Au recoveries from the control sample ranged from 83% to 105% for all three co-collectors, with relative standard deviations less than 10% for the control sample and 7% for the geological samples. The PGM and Au recoveries by Fe and Co co-collectors were modestly higher than that of the Cu-collector. These results indicate that Fe and Co are better co-collectors than Cu, presumably due to the loss of analyte when using Cu. Taken together, Fe and Co co-collectors can be viable alternatives for analysis of PGMs and gold using the fire assay method.
Incorporating Phrases in Latent Query Reformulation for Multi-Hop Question Answering

In multi-hop question answering (MH-QA), the machine needs to infer the answer to a given question from multiple documents. Existing models usually apply entities as basic units in the reasoning path. Then they use relevant entities (in the same sentence or document) to expand the path and update the information of these entities to finish the QA. The process might add an entity irrelevant to the answer to the graph and then lead to incorrect predictions. It is further observed that state-of-the-art methods are susceptible to reasoning chains that pivot on compound entities. To make up the deficiency, we present a viable solution, i.e., incorporate phrases in the latent query reformulation method (IP-LQR), which incorporates phrases in the latent query reformulation to improve the cognitive ability of the proposed method for multi-hop question answering. Specifically, IP-LQR utilizes information from relevant contexts to reformulate the question in the semantic space. Then the updated query representations interact with contexts within which the answer is hidden. We also design a semantic-augmented fusion method based on the phrase graph, which is then used to propagate the information. IP-LQR is empirically evaluated on a popular MH-QA benchmark, HotpotQA, and the results of IP-LQR consistently outperform those of the state of the art, verifying its superiority. In summary, by incorporating phrases in the latent query reformulation and employing semantic-augmented embedding fusion, our proposed model can lead to better performance on MH-QA.
Deep learning-based iraqi banknotes classification system for blind people

Modern systems have been focusing on improving the quality of life for people. Hence, new technologies and systems are currently utilized extensively in different sectors of our societies, such as education and medicine. One of the medical applications is using computer vision technology to help blind people in their daily endeavors and reduce their frequent dependence on their close people and also create a state of independence for visually impaired people in conducting daily financial operations. Motivated by this fact, the work concentrates on assisting the visually impaired to distinguish among Iraqi banknotes. In essence, we employ computer vision in conjunction with Deep Learning algorithms to build a multiclass classification model for classifying the banknotes. This system will produce specific vocal commands that are equivalent to the categorized banknote image, and then inform the visually impaired people of the denomination of each banknote. To classify the Iraqi banknotes, it is important to know that they have two sides: the Arabic side and the English side, which is considered one of the important issues for human-computer interaction (HCI) in constructing the classification model. In this paper, we use a database, which comprises 3,961 image samples of the seven Iraqi paper currency categories. Furthermore, a nineteen layers Convolutional Neural Network (CNN) is trained using this database in order to distinguish among the denominations of the banknotes. Finally, the developed system has exhibited an accuracy of 98.6 %, which proves the feasibility of the proposed model.
A Study on the Optimization Simulation of Big Data Video Image Keyframes in Motion Models

In this paper, the signal of athletic sports video image frames is processed and studied according to the technology of big data. The sports video image-multiprocessing technology achieves interference-free research and analysis of sports technology and can meet multiple visual needs of sports technology analysis and evaluation through key technologies such as split-screen synchronous comparison, superimposed synchronous comparison, and video trajectory tracking. The sports video image-processing technology realizes the rapid extraction of key technical parameters of the sports scene, the panoramic map technology of sports video images, the split-lane calibration technology, and the development of special video image analysis software that is innovative in the field of athletics research. An image-blending approach is proposed to alleviate the problem of simple and complex background data imbalance, while enhancing the generalization ability of the network trained using small-scale datasets. Local detail features of the target are introduced in the online-tracking process by an efficient block-filter network. Moreover, online hard-sample learning is utilized to avoid the interference of similar objects to the tracker, thus improving the overall tracking performance. For the feature extraction problem of fuzzy videos, this paper proposes a fuzzy kernel extraction scheme based on the low-rank theory. The scheme fuses multiple fuzzy kernels of keyframe images by low-rank decomposition and then deblurs the video. Next, a double-detection mechanism is used to detect tampering points on the blurred video frames. Finally, the video-tampering points are located, and the specific way of video tampering is determined. Experiments on two public video databases and self-recorded videos show that the method is robust in fuzzy video forgery detection, and the efficiency of fuzzy video detection is improved compared to traditional video forgery detection methods.
Introduction to the Minitrack on Explainable Artificial Intelligence (XAI)

Artificial Intelligence (AI) already reached or even surpassed the task performance of humans in various domains.However, most AI systems are still "black boxes" that are difficult to comprehend -not only for developers, but also for users and decisionmakers.Explainable Artificial Intelligence (XAI) aims at intelligent systems that are highly performant and enable their users to understand, appropriately trust, and scrutinize them.
A Structured Process for Supervising Students’ Final Theses and Projects in Computer Science

Bachelor’s and Master’s theses are an opportunity for students to dive deep into a research question and gain in-depth knowledge about a research topic. At the end of a curriculum, theses act as a kind of bridge between the educational stage and work or further study. Thesis supervision is a resource-intensive task and often a critical factor for the quality of a thesis. After decades of experience with different methods, we have developed a concept for continuous thesis supervision and applied it to two specific degree programs at our university, the Bachelor's program in Computer Science and Digital Communication and the Master's program in Software Design and Engineering. Compared to traditional supervision methods, this concept has led to a higher adherence to deadlines and to a higher quality of the final theses. In this paper, we present our findings on different types of supervisors and our concept for continuous thesis supervision, which can be adapted to each identified supervisor type. This concept comprises several methods, intended to inspire other supervisors to choose the set of methods that best suits their needs. We also describe how this concept can be adapted for project-based courses.
Reinforcement Learning based Sequential Controller for Mobile Robots with Obstacle Avoidance

Obstacle avoidance and path planning play substantial roles in mobile robot applications. This paper has two main parts: first, an object detection algorithm based on YOLO-v4 with custom classes and depth camera in real-time has been implemented in Robot Operating System (ROS) to resolve robot obstacle avoidance issues. Then, controlling of the robot was discussed by dividing the path into three parts with their specific PID or PD controllers. The optimum parameters of each controller are then calculated by Reinforcement Learning (RL). For ensuring the precision of the obstacle avoidance method, the accuracy of distance is evaluated by the significant result of real thorough objects. Then for evaluating the controllers, the desired-and the traveled path were compared and the positional error was calculated.
A Systematic Literature Review : the Role of Classical Test Theory and Item Response Theory in Item Analysis to Determine the Quality of Mathematics Tests

This research aims to: 1) find out the role of Classical Test Theory and Item Response Theory in item analysis, and 2) find out the role of Classical Test Theory and Item Response theory on the previous research results regarding item analysis to determine the quality of mathematics tests. This research is qualitative research that uses Systematic Literature Review (SLR) as a research method. The research data is collected by documentation. The population of this research is all of the articles from the mathematics journal in the Sinta Ristekbrin database. While the sample is the articles that are obtained from screening. From this research, it can be seen that CTT and IRT play an important role in item analysis, both are commonly used theories and each has its own advantages and disadvantages. The selection of theories to be used in item analysis must consider the advantages and disadvantages of each theory. The indicators used in IRT are validity, reliability, parameter a (distinguishing power), parameter b (level of difficulty), and parameter c (false guess). Based on the results of data analysis, it is known that 8 out of 10 articles use indicators of validity, reliability, level of difficulty, and discriminatory power; 7 out of 10 articles used descriptive quantitative methods; all articles tell about the test form used except article 7 (A7) and article 8 (A8); the number of samples taken affects the implementation of each theory; and only 1 in 10 articles, namely article 10 (A10), provides an overall test quality conclusion
Awareness of the Concept of Social Engineering in Jeddah

Significant advances have been seen in the field of cybersecurity in various institutions globally. However, there are still challenges to setting standards and rules to avoid related problems, due to the use of social engineering. Thus, raising awareness of social engineering methods is considered vital to mitigate the negative impact of social engineering. The aim of this study is to investigate the level of awareness of the concept of social engineering in the city of Jeddah. This study was conducted using an online questionnaire to collect and analyze data. It has been found that there is a lack of awareness, and this issue must be addressed before it escalates.
Correlation and prediction of high-cost information retrieval evaluation metrics using deep learning

Introduction. To reduce cost of the evaluation of information retrieval systems, this study proposes a method that employs deep learning to predict the precision evaluation metric. It also aims to show why some of existing evaluation metrics correlate with each other while considering the varying distributions of relevance assessments. It aims to ensure reproducibility of all the presented experiments. Method. Using data from several test collections of the Text REetrieval Conference (TREC) we show why some evaluation metrics correlate with each other, through mathematical intuitions. In addition, regression models were used to investigate how the predictions of the evaluation metrics are affected by queries or topics with variations of relevance assessments. Lastly, the proposed prediction method employs deep learning. Analysis. We use coefficient of determination, Kendall's tau, Spearman and Pearson correlations. Results. This study showed that the proposed method performed better predictions than other recently proposed methods in retrieval research. It also showed why the correlation exists between precision and rank biased precision metrics, and why recall and average precision metrics have reduced correlation when the cut-off depth increases. Conclusions. The proposed method and the justifications for the correlations between some pairs of retrieval metrics will be valuable to researchers for the predictions of the evaluation metrics of information retrieval systems.