Reviewerly/exHarmony huggingface dataset:
The Interspeech Zero Resource Speech Challenge 2021: Spoken language modelling.

We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels. The challenge is based on the Libri-light dataset, which provides up to 60k hours of audio from English audio books without any associated text. We provide a pipeline baseline system consisting on an encoder based on contrastive predictive coding (CPC), a quantizer ($k$-means) and a standard language model (BERT or LSTM). The metrics evaluate the learned representations at the acoustic (ABX discrimination), lexical (spot-the-word), syntactic (acceptability judgment) and semantic levels (similarity judgment). We present an overview of the eight submitted systems from four groups and discuss the main results.
Shifting the Blame? Investigation of User Compliance with Digital Payment Regulations

Users play a crucial role in the majority of successful cyberattacks. Compliance with information security guidelines can lead to more secure digital behavior and thereby reduce the chance of successful attacks. Since customer compliance is especially relevant for banks, the Dutch Banking Association (DBA) has developed and implemented a set of five security guidelines for customers. Each guideline is split into several specific actions that customers need to undertake in order to comply. Failure to comply can lead to a negligence claim and financial losses when falling victim to cybercrime. Such security guidelines are only successful if people are aware of their existence and mostly comply. In a user survey (n = 119) we tested whether this was the case. Results indicate that only a quarter of our sample (24.4%) was aware guidelines existed. When asked about compliance with the five general guidelines, less than a quarter (23.5%) of participants reported following all five guidelines. When asked about compliance with all specified actions needed to comply with these guidelines, only 3.4% reported complete compliance. A more in-depth analysis revealed that awareness of the guidelines did not increase compliance. The findings from this paper support recent findings in the security literature that knowledge and awareness alone do not increase secure digital behavior. Taken together, the low awareness and even lower compliance rates with the DBA security guidelines demonstrated in this study suggest that banks may be unfairly shifting the blame towards their customers.
Regret-Optimal Filtering

We consider the problem of filtering in linear state-space models (e.g., the Kalman filter setting) through the lens of regret optimization. Different assumptions on the driving disturbance and the observation noise sequences give rise to different estimators: in the stochastic setting to the celebrated Kalman filter, and in the deterministic setting of bounded energy disturbances to $H_\infty$ estimators. In this work, we formulate a novel criterion for filter design based on the concept of regret between the estimation error energy of a clairvoyant estimator that has access to all future observations (a so-called smoother) and a causal one that only has access to current and past observations. The regret-optimal estimator is chosen to minimize this worst-case difference across all bounded-energy noise sequences. The resulting estimator is adaptive in the sense that it aims to mimic the behavior of the clairvoyant estimator, irrespective of what the realization of the noise will be and thus interpolates between the stochastic and deterministic approaches. We provide a solution for the regret estimation problem at two different levels. First, we provide a solution at the operator level by reducing it to the Nehari problem. Second, for state-space models, we explicitly find the estimator that achieves the optimal regret. From a computational perspective, the regret-optimal estimator can be easily implemented by solving three Riccati equations and a single Lyapunov equation. For a state-space model of dimension $n$, the regret-optimal estimator has a state-space structure of dimension $3n$. We demonstrate the applicability and efficacy of the estimator in a variety of problems and observe that the estimator has average and worst-case performances that are simultaneously close to their optimal values. We therefore argue that regret-optimality is a viable approach to estimator design.
September 2017 Mexico City Earthquakes

The chapter presents the results on the performance of the SASMEX during the 2017 earthquakes that hit Mexico City. The approach has been the use of a questionnaire-based survey to a sample population of N=2,400. The results are discussed in the context of a systemic disaster management system (SDMS) model. A summary of the key findings are: 1) for the 07 September earthquake, age and occupation were significantly associated with the performance of the SASMEX system, to the extent that young (13-20 years old) participants considered the early warning system as very useful; however, employees considered it as “not at all” useful; 2) for the 19 September earthquake, age and occupation were significantly associated with the SASMEX performance. In particular, the subjects in the over-50 range considered the system as “not at all” useful, and students and employees regarded it as “very useful”; 3) given 1 and 2, it is necessary to further educate the residents of the capital city on the functioning of the SASMEX system (e.g., its strengths and weaknesses, warning time, etc.).
A Proposal of Model of Emotional Regulation in Intelligent Learning Environments

Emotions can influence cognitive development and are key elements to the teachinglearning process.Positive emotions (e.g., engagement) can improve the ability to solve problems, store information, and make decisions.On the other hand, negative emotions (e.g., boredom) reduce the capacity to process information at a deeper level, preventing learning to become effective.Therefore, students' emotions must be regulated to hinder negative and to promote positive emotions during learning.To support the choice of the best intervention to regulate individual emotions, this article proposes an algorithm based on simulated data considering different individual performances in solving Algebra exercises.The results suggest that the proposed model has high success rates (over 90%) in the choice of interventions and may be applied in real scenarios.
A Mobile-Assisted Peer Assessment Approach for Evidence-Based Nursing Education

Students who learn evidence-based nursing can assist the healthcare team to make proper medical decisions and provide patients with valuable advice, thus optimizing the quality of patient care in specific situations. In clinical work, nursing staff members participate in decision making by searching for relevant empirical nursing literature, a basic ability required to enter clinical practice. In traditional instruction, nursing students are taught the Problem, Intervention, Comparison, and Outcome method to learn to use library resources and gather empirical nursing knowledge for decision making. However, it is a challenge for most students to have sufficient practice to make decisions correctly and to have opportunities to perceive medical cases from diverse perspectives. Therefore, we propose a peer assessment-based Problem, Intervention, Comparison, and Outcome approach to help nursing students locate correct evidence and make appropriate decisions about patient care. We performed an experiment with our approach in a nursing university training program. The experimental results reveal that the subjects learning with the proposed approach show better evidence-based nursing knowledge, learning attitude, and critical thinking ability than those learning with the traditional approach.
The Effect of Social Network Use on EFL Learners’ Second Language Achievement: An Investigation into their Attitudes

The efforts were made in the present study to seek two objectives: determining the effect of Telegram as a social network on second language achievement of Iranian foreign language (EFL) learners, and exploring the EFL learner’ attitude toward using Telegram for language learning purposes. To this end, 40 EFL learners were randomly selected and then divided into two groups of experimental and control. The experimental group did their exercises through Telegram as a collective language learning activity at home, while members of the control group did the exercises in their individualistic specific ways. After the treatment, the results of Independent-Samples t-test showed that the experimental group outperformed the control group. The results of interview indicated that EFL learners mostly had never used any kind of social media in a collective manner for language learning. The findings of this study might have some implications for teachers to help them employ social media as an educational tool, for language learners to indicate the advantages of using social media for language learning, and for material developers to design activities in textbooks which need to be done through social media in a collective manner.
Does Emotional Intelligence Prompt Knowledge Sharing Behavior among Academic Staff

The presented paper aims to reveal the direct and indirect relationship between emotional intelligence and knowledge sharing behavior among academic staff in Jordanian universities. Furthermore, it aims to examine the mediation effect of knowledge sharing intention on the relationship between emotional intelligence and knowledge sharing behavior. Convenience sampling was used to collect data from 191 academic staff from private universities in the northern region of Jordan. First, data were tested for validity and reliability. Second, Structural Equation Modeling (SEM) was applied to analyze the model and hypothesis testing. The results indicated that emotional intelligence has a direct and indirect relationship with academic staff knowledge sharing behavior. Furthermore, the results indicated that knowledge sharing intention partially mediates the relationship between emotional intelligence and academic staff knowledge sharing behavior. The limitations of this study include the sampling and cross-sectional design. Hence, future studies can be conducted using a longitudinal design covering other public universities to increase the generalizability of findings. The findings suggest that top management in universities should develop appropriate strategies for improving the emotional intelligence level of academic staff because academic staffs with higher emotional intelligence are more likely to engage themselves in knowledge sharing activities. Furthermore, universities should conduct various training and education programs to improve the level of emotional intelligence among academic staff, thereby fostering knowledge sharing in universities. Furthermore, there is limited literature on the role of emotional intelligence in knowledge sharing behavior among academic staff. The study adds to the extant literature on emotional intelligence and both knowledge sharing intention and knowledge sharing behavior in the context of universities. Besides, the study attempted to investigate the direct and indirect effect of emotional intelligence on knowledge sharing behavior that has not been studied so far.
SCHOOLOGY EFFECTS ON STUDENTS’ WRITING ABILITY

Schoology is a learning management system for students to create, control, and share the content of their learning resources. Also known as teaching media for the virtual learning experience, this course management system provides tools to administer classroom activity for the blended learning activity. The present study was aimed to analyze schoology effects on students’ writing ability, and to investigate the students’ interest towards schoology in the writing process. There were sixty students from the first semester students of English Department at STKIP PGRI Bandar Lampung in 2018/2019 academic years. The samples composed of two groups that consist of the experimental and control group. Schoology was used in the experimental class, and the conventional method was used in the control class. The writing test and questionnaire administered to accomplish the objective of the research. Paired and independent sample t-test was used for analyzing the quantitative data. The findings of the quantitative data revealed the use of schoology was more effective than the use of non-schoology. It was determined by the mean score from both of the groups (82 > 72).  The difference (df) of both mean scores was statistically significant based on the t-test value at 0.05 significance level, and the probability value was smaller than the significant level (0.000 < 0.05).  In summary, the result of hypothesis testing found that t-value = 5.993 was higher than t-table = 2.000 (p=0.05, df =58). Ha was accepted, and Ho was rejected. In addition, the result of questionnaire analysis confirms that schoology platform was attractive for teaching and learning writing subject.
Another brick in the wall: An exploratory analysis of digital forensics programs in the United States

We present a comprehensive review of digital forensics programs offered by universities across the United States (U.S.). While numerous studies on digital forensics standards and curriculum exist, few, if any, have examined digital forensics courses offered across the nation. Since digital forensics courses vary from university to university, online course catalogs for academic institutions were evaluated to curate a dataset. Universities were selected based on online searches, similar to those that would be made by prospective students. Ninety-seven (n = 97) degree programs in the U.S. were evaluated. Overall, results showed that advanced technical courses are missing from curricula. We conclude that most degree programs evaluated offer legal/cyber law & ethics, investigative processes, and lab & forensic operations courses. The courses offered the least were memory forensics, Internet of Things (IoT) forensics, and program & software forensics. The data shows that some universities with the Forensic Science Education Programs Accreditation Commission (FEPAC) accreditation are lacking instruction in timely digital forensics topics such as memory forensics (0%), hardware security (0%), program & software forensics (0%), and ethical hacking (0%). Investigative processes (100%), network forensics (100%), lab & forensic operations (100%), and a senior design/capstone project (100%) are offered at all FEPAC accredited universities in digital forensics and digital evidence. Undergraduate degree programs with the National Centers of Digital Forensics Academic Excellence (CDFAE) designation had over a 50% offering rate for 11 out of the 22 courses we evaluated. However, memory forensics (0%) and IoT forensics (12.5%) were largely underrepresented. Our work provides an overview of the current state of digital forensics programs and discusses the importance of these courses to educate the next digital forensics workforce.
Lecture Information Service Based on Multiple Features Fusion

Information service is always a hot topic especially when the Web is accessible anywhere. In university, lecture information is very important for students and teachers who want to take part in academic meetings. Therefore, lecture news extraction is an important and imperative task. Many open information extraction methods have been proposed, but due to the high heterogeneity of websites, this task is still a challenge. In this paper, we propose a method based on fusing multiple features to locate lecture news on the university website. These features include the linked relationship between parent webpage and child webpages, the visual similarity, and the semantics of webpages. Additionally, this paper provides an information service based on a main content extraction algorithm for extracting the lecture information. Stable and invariant features enable the proposed method to adapt to various kinds of campus websites. The experiments conducted on 50 websites show the effectiveness and efficiency of the provided service.
Improve SegNet with feature pyramid for road scene parsing

Road scene parsing is a common task in semantic segmentation. Its images have characteristics of containing complex scene context and differing greatly among targets of the same category from different scales. To address these problems, we propose a semantic segmentation model combined with edge detection. We extend the segmentation network with an encoder-decoder structure by adding an edge feature pyramid module, namely Edge Feature Pyramid Network (EFPNet, for short). This module uses edge detection operators to get boundary information and then combines the multiscale features to improve the ability to recognize small targets. EFPNet can make up the shortcomings of convolutional neural network features, and it helps to produce smooth segmentation. After extracting features of the encoder and decoder, EFPNet uses Euclidean distance to compare the similarity between the presentation of the encoder and the decoder, which can increase the decoder’s ability to restore from the encoder. We evaluated the proposed method on Cityscapes datasets. The experiment on Cityscapes datasets demonstrates that the accuracies are improved by 7.5% and 6.2% over the popular SegNet and ENet. And the ablation experiment validates the effectiveness of our method.
Solving N-Queen Problem by Genetic Algorithm using Novel Mutation Operator

Abstract N-Queen is a well-known NP-Hard problem. In N-Queen problem, the challenge is to place n queens on n*n chess board such that no two queens can attack each other. The problem can-not be solved using traditional algorithms. Genetic algorithm is a well-known optimization technique. The problem has to be represented in genetic form to solve it using genetic algorithm. In this paper a new mutation operator is proposed to solve N-Queen problem. The proposed algorithm is applied on some instances of N-Queen problem and it is observed that the proposed performs better as compared to other existing algorithms.
A Denoising Method for Light Field Imaging Sensor Based on Spatial-Angular Collaborative Encoding Network

Light field (LF) imaging sensors based on micro-lens array are susceptible to numerous noise pollution when collecting raw 4D LF data due to their own special structural design, which affects visual perception and subsequent applications such as depth estimation. Unfortunately, existing 2D image denoising methods are difficult to directly apply to 4D LF images. To this end, this paper proposes a new LF denoising method based on spatial-angular collaborative encoding network, considering the inherent 4D structure of LF image. Specifically, the convolutions in the spatial and angular branches are first constructed to extract specific 2D spatial and 2D angular features from noisy LF data. Then, a tailored spatial-angular collaborative encoder is designed to co-process spatial-angular features and improve the expression ability of features. After that, the spatial-angular feature fusion module is constructed to fuse the extracted features. Finally, the denoised LF image is reconstructed by a residual prediction module integrating attention mechanism. In particular, the proposed method simultaneously reconstructs all sub-aperture images of LF in one forward inference, so as to preserve the angular consistency of the denoised LF image. Extensive experimental results show that the proposed method outperforms the state-of-the-art methods in both subjective visual perception and objective quality evaluation. Furthermore, the proposed method preserves the parallax structure well, which is beneficial for subsequent LF applications.
Gaze Interactive and Attention Aware Low Vision Aids as Future Smart Glasses

We present a working paper on integrating eye tracking with mixed and augmented reality for the benefit of low vision aids. We outline the current state of the art and relevant research and point to further research and development required in order to adapt to individual user, environment, and current task. We outline key technical challenges and possible solutions including calibration, dealing with variant eye data quality, measuring and adapting image processing to low vision within current technical limitations, and outline an experimental approach to designing data-driven solutions using machine learning and artificial intelligence.
Author Identification Based on NLP

The amount of textual content is increasing exponentially, especially through the publication of articles; the issue is further complicated by the increase in anonymous textual data. Researchers are looking for alternative methods to predict the author of an unknown text, which is called Author Identification. In this research, the study is performed with Bag of Words (BOW) and Latent Semantic Analysis (LSA) features. The “All the news” dataset on Kaggle is used for experimentation and to compare BOW and LSA for the best performance in the task of author identification. Support vector machine, random forest, Bidirectional Encoder Representations from Transformers (BERT), and logistic regression classification algorithms are used for author prediction. For first scope that have 20 authors, for each author 100 articles, the greatest accuracy is seen from logistic regression using bag-of-words, followed by random forest, also using bag-of-words; in all algorithms, bag-of-words scored better than LSA. Ultimately, BERT model was applied in this research and achieved 70.33% accuracy performance. For second scope that increase the number of articles till 500 articles per author and decrees the number of authors till 10, the BOW achieves better performance results with the logistic regression algorithm at 93.86%. Moreover, the best accuracy performance is with LR at 94.9% when merged the feature together and it proved that it is better than applied BOW and LSA individual, with an improvement by almost 0.1% comparing with BOW only. Ultimately, BRET achieved result by 86.56% accuracy performance and 0.51 log los.
Credit Card Fraud Detection Using Different ML and DL Techniques

Swift increase in Financial hoax due to the growth in communication systems and technology has caused reduction of loads of money across the globe because of which major challenges are faced in Credit card fraudulence detection which are-arguably the profiles of hoax and regular characters change continuously. Secondly, datasets of credit card frauds are somewhat indicative and unsymmetrical. Credit card fraudulence detection is mainly impacted by the collection of factors, evaluation approach on datasets and the processes used for detecting. This paper signifies the analysis of various methods used to ascertain their performance in the fraud detection and they are logistic regression, Naïve Bayes, K-nearest neighbor, support vector machines (SVM), decision tree, random forest classification, k-means and keras on the credit card dataset. The methods are applied on the refined dataset and the project is executed in Python. The techniques are assessed on the basis of accuracy and f1-score. The outcome indicates the utmost precision of random forest classification (RFC) along with other applied techniques and the comparative study proves that random forest classification (RFC) performs better than other methods implemented.