Reviewerly/exHarmony huggingface dataset:
Heads-up! An alert and warning system for phishing emails

Purpose This study introduces the concept of audiovisual alerts and warnings as a way to reduce phishing susceptibility on mobile devices. Design/methodology/approach This study has three phases. The first phase included 32 subject matter experts that provided feedback toward a phishing alert and warning system. The second phase included development and a pilot study to validate a phishing alert and warning system prototype. The third phase included delivery of the Phishing Alert and Warning System (PAWS TM mobile app) to 205 participants. This study designed, developed, as well as empirically tested the PAWS TM mobile app that alerted and warned participants to the signs of phishing in emails on mobile devices. Findings The results of this study indicated audio alerts and visual warnings potentially lower phishing susceptibility in emails. Audiovisual warnings appeared to assist study participants in noticing phishing emails more easily and in less time than without audiovisual warnings. Practical implications This study's implications to mitigation of phishing emails are key, as it appears that alerts and warnings added to email applications may play a significant role in the reduction of phishing susceptibility. Originality/value This study extends the existing information security body of knowledge on phishing prevention and awareness by using audiovisual alerts and warnings to email recipients tested in real-life applications.
The Analyzing Online Learning Satisfaction and The Use of LLS

This mixed-method approach study is conducted to find out the positive relationship between students’ satisfaction and their LLS use in the context of ICT for ELL. Having wed out for the sake of validity and reliability, 46 sets of both SERVQUAL and SILL questionnaires were then administered to 83 students of telecommunication engineering at Akademi Telkom Jakarta. The result of the study was explained in inferential and descriptive as well as qualitative in nature. The statistical result confirmed the positive effect of online learning satisfaction on the use of LLS. The low correlation coefficient is detected between students’ satisfaction and the use of LLS (r =.235, p &lt;.05); however, the regression model Y=74.3+0.20X is then eligible to estimate and generalize. Thus, by paying attention to SERVQUAL dimensions, the students will more explore their LLS use. The findings provide a greater understanding of students’ satisfaction and LLS use. LLS – CALL integration model could be developed as the implications of the research for further study.&#x0D; Keywords: ICT, Language Learning Strategies, Online Learning, Student Satisfaction, SERVQUAL
Risks of Adopting Automated AIS Applications on the Quality of Internal Auditing

The current study aimed at examining the impact of AIS risks (IT Infrastructure ,Data Entry and Outcome, Internal Operations, Control procedures and tools and Data Security) on quality on internal auditing as a comparison study between Jordan and England. Study adopted quantitative approach and d istributed a questionnaire on (133) internal auditors in Jordan and (331) internal auditors in England. Through SPSS results of study indicated that all adopted risks were found within both samples responded to questionnaire. Both sample also indicated that "internal operations" and "data security" are the most influential risks that may jeopardize quality of internal auditing. Results also indicated differences between impacts of such risks on quality of internal auditing; such risks were attributed to the large population of England compared to Jordan and the intensity of internal auditing in England as higher than Jordan due to the intensity of operations within the country. However, "IT infrastructure" and "data security" appeared to be higher in England compared to Jordan which helped in easing the negative impacts of AIS risks on quality of internal auditing. Study recommended applying restrictions on users to limit the possibility of changing and manipulating data whether by parties inside or outside the organization, in addition to presenting extra efforts in choosing internal auditors in terms of qualifications, experiences and abilities.
On the correspondence between abstract dialectical frameworks and nonmonotonic conditional logics

Abstract The exact relationship between formal argumentation and nonmonotonic logics is a research topic that keeps on eluding researchers despite recent intensified efforts. We contribute to a deeper understanding of this relation by investigating characterizations of abstract dialectical frameworks in conditional logics for nonmonotonic reasoning. We first show that in general, there is a gap between argumentation and conditional semantics when applying several intuitive translations, but then prove that this gap can be closed when focusing on specific classes of translations.
Analisis Kesulitan Belajar Siswa Aspek Motivasi Belajar dan Kondisi Kesehatan Fisik Pada Pembelajaran Daring Mata Pelajaran Biologi Kelas XI SMA Negeri 06 Makassar

Abstract. This study aims to determine the learning difficulties experienced by students in online learning and the efforts made in overcoming students' learning difficulties in biology during the online learning period. The research method used in this study is a qualitative research method. The population in this study were students of class XI SMA Negeri 06 Makassar which consisted of 6 classes, namely XI MIPA 1, XI MIPA 2, XI MIPA 3, XI MIPA 4, XI MIPA 5, and XI MIPA 6. The instrument used was a Likert scale closed questionnaire. The research data were obtained by distributing questionnaires regarding internal factors of student learning difficulties and conducting interviews with biology teachers in class XI of SMAN 06 Makassar. Based on the research results, there are two aspects that have an influence on learning difficulties on students 'internal factors, namely aspects of learning motivation and aspects of students' physical health conditions. In general, efforts to overcome student learning difficulties are based on the diata aspect, namely, by providing excessive motivation to students, both from teachers and from families. Because, the amount of learning motivation received by students will affect the learning outcomes received. In addition, efforts to prevent learning difficulties caused by physical health factors are by taking a moment to rest after using electronic devices for a long time. As well as the need for self-awareness of the importance of maintaining physical health.Keywords: online learning, student learning difficulties, learning motivation, physical health condition
A qualitative study of Machine Learning practices and engineering challenges in Earth Observation

Abstract Machine Learning (ML) is ubiquitously on the advance. Like many domains, Earth Observation (EO) also increasingly relies on ML applications, where ML methods are applied to process vast amounts of heterogeneous and continuous data streams to answer socially and environmentally relevant questions. However, developing such ML- based EO systems remains challenging: Development processes and employed workflows are often barely structured and poorly reported. The application of ML methods and techniques is considered to be opaque and the lack of transparency is contradictory to the responsible development of ML-based EO applications. To improve this situation a better understanding of the current practices and engineering-related challenges in developing ML-based EO applications is required. In this paper, we report observations from an exploratory study where five experts shared their view on ML engineering in semi-structured interviews. We analysed these interviews with coding techniques as often applied in the domain of empirical software engineering. The interviews provide informative insights into the practical development of ML applications and reveal several engineering challenges. In addition, interviewees participated in a novel workflow sketching task, which provided a tangible reflection of implicit processes. Overall, the results confirm a gap between theoretical conceptions and real practices in ML development even though workflows were sketched abstractly as textbook-like. The results pave the way for a large-scale investigation on requirements for ML engineering in EO.
Differential privacy based classification model for mining medical data stream using adaptive random forest

Abstract Most typical data mining techniques are developed based on training the batch data which makes the task of mining the data stream represent a significant challenge. On the other hand, providing a mechanism to perform data mining operations without revealing the patient’s identity has increasing importance in the data mining field. In this work, a classification model with differential privacy is proposed for mining the medical data stream using Adaptive Random Forest (ARF). The experimental results of applying the proposed model on four medical datasets show that ARF mostly has a more stable performance over the other six techniques.
Significance of Natural Language Processing in Data Analysis Using Business Intelligence

In the current machine-centric world, humans expect a lot from machines right from waking us up. We expect them to do activities like reminding us on traffic, tracking of appointments, etc. The smart devices we have with us are creating a constructive impact on our day-to-day lives. Many of us have not thought about the communication between ourselves and the devices we have and the language we use for communication. Natural language processing runs behind all these activities and is currently playing a vital role with respect to the communication with humans with the use of virtual assistants like Alexa, Siri, and search engines like Bing, Google, etc. This implies that we are talking with the machines as if they are human. The advanced natural language processing techniques have drastically modified the way to discover and interact with data. In the recent world, the same advanced techniques are primarily used in the data analysis using NLP in business intelligence tools. This chapter elaborates the significance of natural language processing in business intelligence.
Using knowledge of human-generated code to bias the search in program synthesis with grammatical evolution

Recent studies show that program synthesis with GE produces code that has different structure compared to human-generated code, e.g., loops and conditions are hardly used. In this article, we extract knowledge from human-generated code to guide evolutionary search. We use a large code-corpus that was mined from the open software repository service GitHub and measure software metrics and properties describing the code-base. We use this knowledge to guide the search by incorporating a new selection scheme. Our new selection scheme favors programs that are structurally similar to the programs in the GitHub code-base. We find noticeable evidence that software metrics can help in guiding evolutionary search.
PAMSGAN: Pyramid Attention Mechanism-Oriented Symmetry Generative Adversarial Network for Motion Image Deblurring

Motion blur is a common problem in optical imaging, which is caused by the relative displacement between the subject and the camera in the exposure process of the camera.This can result in motion blur of the acquired image, reduce the image resolution and affect the imaging quality.Motion blur image restoration technology uses the existing motion blur image to restore the clear image through the modeling of imaging physical process and mathematical solution without re-photographing the target scene.It has an important application value in the civil and military fields.Solving the problem of motion blur caused by camera jitter and object motion during camera imaging is a very challenging problem.When the popular generative adversarial network model is directly applied to the image blur blind removal task, serious pattern collapse phenomenon will occur.In this paper, we propose a novel motion image deblurring model based on pyramid attention mechanism-oriented symmetry generative adversarial network.This new method does not need to predict the fuzzy kernel of the blurred images, and can directly realize the blind removal of image motion blur.Based on the original CycleGan, the network structure and loss function of the symmetry generative adversarial network are improved.The accuracy of blind removal of motion images is improved, and the stability of the network is greatly enhanced in the case of limited samples.The generative network adopts the encoding and decoding structure, and introduces the feature pyramid attention mechanism.The combination of multi-scale pyramid features and attention mechanism can capture more rich advanced features to improve the model performance.In the experiment, the RMSProp algorithm is used to optimize the network training.Finally, a clear image is obtained through network adversarial training between generative and discriminant network.Experimental results on the related image blur benchmark datasets show that the restoration quality of the proposed method is higher in terms of subjective and objective evaluation.Meanwhile, the restoration results can achieve better results in subsequent object detection tasks.
Etiometry’s T3 Heuristic Evaluation

Five students and a Human Factors Psychology professor participated in a heuristic evaluation of clinical decision support software, called T3, to help improve usability. T3 gathers physiological monitoring data from a variety of sources, e.g. pulse oximeters, electrocardiographs, and ventilators, and uses the data to compute two novel risk indexes. To effectively support clinical decision-making, T3 should present the patient data it collects, as well as the risk indexes that it computes, in an easy to understand interactive display. The evaluation identified 38 potential usability issues, about half of which were deemed ‘not an issue’ or rated as minor. The team developed 11 recommendations for changes to help improve usability of T3. Given the volume and complexity of the information that T3 displays, this is a short list, suggesting relatively good usability. However, none of the evaluators has the clinical training or experience of typical users; it is possible that clinical users would identify additional usability issues.
SIKAP BAHASA DAN PEMBELAJARAN BAHASA INDONESIA DI PERGURUAN TINGGI

A positive attitude is an attitude of enthusiasm towards the use of the language (the language used by the group / speech community where he is). Conversely, if these characteristics have disappeared or weakened from a person or from a group of members of the speech community, it means that a negative attitude towards a language has hit that person or group of people. Law Number 24 of 2009 Article 25 states that Indonesian is a national identity, national pride, a means of unifying various ethnic groups, as well as a means of communication between regions and regional cultures. Therefore, as a user of Indonesian, you should have a sense of pride in using Indonesian. However, in the scope of higher education, a positive attitude towards language is not fully owned by most students. Awareness of loyalty, pride in owning, and maintaining the Indonesian language seems to be lacking. This is because students tend to be more confident when using a foreign language compared to their own language. In the context of learning Indonesian, this task is only borne by Indonesian language teachers and lecturers. This paradigm should be changed because getting used to using Indonesian properly and correctly will reap maximum results in increasing student academic achievement. Understanding Indonesian in accordance with good and correct Indonesian language rules is needed for students to have a positive attitude in using Indonesian. A positive attitude in Indonesian can be shown in the form of language loyalty, language pride, and awareness of language norms.
Living with Earthquakes: Educating masses through earthquake awareness: North East (NE) India perspective

Earthquake is one of the most frightening and destructive phenomena of nature. The northeast India region, as well as its adjoining South-Asian neighbours including Myanmar and Bangladesh, is tectonically and seismically most active. The region categorized under the highest level of seismic hazard potential: Zone V, of the seismic zonation map of India, has experienced nearly 22 large (M ≥7.0) and two great earthquakes (M S ∼ 8.7) in the past 130 years. All these earthquakes caused wide-spread damage over the region. In the recent past, with rapid urbanization combined with a significant population rise as compared to those times when these great/large earthquakes occurred, the seismic vulnerability index has increased manifold. The situation demands widespread dissemination of seismic hazard and preparedness information via community engagement and highlighting on potentially tragic consequences of earthquakes by conducting extensive mock drill exercises &amp; earthquake awareness programmes. In this paper, the role and efforts of the statuary bodies in the region, such as National Disaster Management Authority (NDMA) and CSIR – Northeast Institute of Science and Technology (NEIST) and societal program of Academy of Scientific and Innovative research, to mitigate and minimize seismic hazard by extensive dissemination of earthquake information, via scientific scenario and impact assessment, is holistically compiled.
Integer-Wise Functional Bootstrapping on TFHE: Applications in Secure Integer Arithmetics

TFHE is a fast fully homomorphic encryption scheme proposed by Chillotti et al. in Asiacrypt’ 2018. Integer-wise TFHE is a generalized version of TFHE that can encrypt the plaintext of an integer that was implicitly presented by Chillotti et al., and Bourse et al. presented the actual form of the scheme in CRYPTO’ 2018. However, Bourse et al.’s scheme provides only homomorphic integer additions and homomorphic evaluations of a sign function. In this paper, we construct a technique for operating any 1-variable function in only one bootstrapping of the integer-wise TFHE. For applications of the scheme, we also construct a useful homomorphic evaluation of several integer arithmetics: division, equality test, and multiplication between integer and binary numbers. Our implementation results show that our homomorphic division is approximately 3.4 times faster than any existing work and that its run time is less than 1 second for 4-bit integer inputs.
Training Teachers For Leadership In Education

The article discusses the problem of leadership in education, as well as the range of professional competencies necessary for a teacher to become an effective leader in the classroom, at school, and in the local community.The authors assume that leadership is a professional and personal characteristic of any teacher, and not just the head of an educational organization.Therefore, it is necessary to train future teachers for leadership at the university and through the professional development.The article presents the results of an empirical study aimed at revealing the attitude of teachers to leadership in education as a professionally important quality, as well as to various ways of training teachers for effective leadership.The authors analyze the results of an online survey of teachers and heads of educational organizations.The data obtained show that the responding teachers would like to develop their leadership qualities and consider leadership important for their professional activities.The results of the questionnaire confirmed the need for systematic and purposeful training of teachers for leadership both at the university and in the system of professional development.The authors come to the conclusion that it is necessary to train two categories of leaders in education at the university level: teachers of an educational organization and educational managers who perform leadership functions in this organization.
A hard fork in the road: developing an effective regulatory framework for public blockchains

Over the past decade, public blockchains have emerged as innovative, technology platforms capable of highly secure and sophisticated peer-to-peer online transmission of data between unknown parties. However, public blockchains challenge traditional regulatory models that assume centralised, single points of control facilitated via established intermediaries. In such a context, the present paper analyses the potential liability attaching to blockchain participants and considers how privacy laws apply to blockchain transactions, given the immutable nature of blockchain records. The paper then recommends reforms to strengthen blockchain regulation, including statutory recognition of fiduciary obligations for elite developers with super-user access to allow recourse for loss resulting from hard forks. A ‘hard fork’ is a radical change to a network’s protocol that leads to formerly valid blocks and transactions becoming invalid or vice-versa. Finally, the paper considers changes to privacy laws to permit ongoing consent and cross-border disclosure of data to accommodate blockchain operations.
An Elderly Monitoring System Based on Multiple Ultra-Sensitive Vibration and Pneumatic Sensors

In recent years, aging of the population has become a major social problem. Various types of sensor networks have been applied to elderly monitoring for addressing the care problems of the elderly living alone. We have also proposed elderly monitoring systems based on wireless sensor network devices. However, vision-based sensors can also cause a mental burden on the elderly’s privacy. Furthermore, the number of sensors must be reduced, if possible. Therefore, this study proposes an elderly monitoring system composed of two vibration sensors placed on the floor and a pneumatic sensor placed on the bed. Because both sensors include considerable measurement noise, we propose a human behavior estimation method that includes anomaly detection from time-series measurement data using an autocorrelation coefficient. Finally, we discuss the effectiveness and usability of the proposed system through several experimental results. The accuracy of walking detection reaches 94.4%, while the error of heartbeat detection is 3.01 bpm.
Person Re-Identification Based on Partition Adaptive Network Structure and Channel Partition Weight Adaptive

This article noticed that the feature of the block in human body image is noticeable, some algorithms in pedestrian re-identification are based on partitioning the human body to calculate the similarity between pedestrians images.However, it is not easy to find a proper method to partition the pedestrian image.This article proposes a method partition adaptive network structure (PANS) to automatically determine the partition scheme.This method can save much work to find a suitable partitioning scheme, and the effect of the automatic partitioning scheme is better than that of the manual partitioning scheme.In addition, this paper proposes a pedestrian re-recognition method based on channel partition weight adaptive (CPWA).We obtained better results on three public pedestrian re-identification data sets compared with the baseline.This method, combined with the automatic partitioning scheme proposed in this article, can improve results.We have done experiments on the three public data sets of market-1501, DukeMTMC-Reid, and CUHK03, proving the superiority of the two methods proposed in this article.