Reviewerly/exHarmony huggingface dataset:
Earthquake Early Warning ShakeAlert 2.0: Public Rollout

Abstract The ShakeAlert earthquake early warning system is designed to automatically identify and characterize the initiation and rupture evolution of large earthquakes, estimate the intensity of ground shaking that will result, and deliver alerts to people and systems that may experience shaking, prior to the occurrence of shaking at their location. It is configured to issue alerts to locations within the West Coast of the United States. In 2018, ShakeAlert 2.0 went live in a regional public test in the first phase of a general public rollout. The ShakeAlert system is now providing alerts to more than 60 institutional partners in the three states of the western United States where most of the nation’s earthquake risk is concentrated: California, Oregon, and Washington. The ShakeAlert 2.0 product for public alerting is a message containing a polygon enclosing a region predicted to experience modified Mercalli intensity (MMI) threshold levels that depend on the delivery method. Wireless Emergency Alerts are delivered for M 5+ earthquakes with expected shaking of MMI≥IV. For cell phone apps, the thresholds are M 4.5+ and MMI≥III. A polygon format alert is the easiest description for selective rebroadcasting mechanisms (e.g., cell towers) and is a requirement for some mass notification systems such as the Federal Emergency Management Agency’s Integrated Public Alert and Warning System. ShakeAlert 2.0 was tested using historic waveform data consisting of 60 M 3.5+ and 25 M 5.0+ earthquakes, in addition to other anomalous waveforms such as calibration signals. For the historic event test, the average M 5+ false alert and missed event rates for ShakeAlert 2.0 are 8% and 16%. The M 3.5+ false alert and missed event rates are 10% and 36.7%. Real-time performance metrics are also presented to assess how the system behaves in regions that are well-instrumented, sparsely instrumented, and for offshore earthquakes.
Stain Color Adaptive Normalization (SCAN) algorithm: Separation and standardization of histological stains in digital pathology

The diagnosis of histopathological images is based on the visual analysis of tissue slices under a light microscope. However, the histological tissue appearance may assume different color intensities depending on the staining process, operator ability and scanner specifications. This stain variability affects the diagnosis of the pathologist and decreases the accuracy of computer-aided diagnosis systems. In this context, the stain normalization process has proved to be a powerful tool to cope with this issue, allowing to standardize the stain color appearance of a source image respect to a reference image. In this paper, novel fully automated stain separation and normalization approaches for hematoxylin and eosin stained histological slides are presented. The proposed algorithm, named SCAN (Stain Color Adaptive Normalization), is based on segmentation and clustering strategies for cellular structures detection. The SCAN algorithm is able to improve the contrast between histological tissue and background and preserve local structures without changing the color of the lumen and the background. Both stain separation and normalization techniques were qualitatively and quantitively validated on a multi-tissue and multiscale dataset, with highly satisfactory results, outperforming the state-of-the-art approaches. SCAN was also tested on whole-slide images with high performances and low computational times. The potential contribution of the proposed standardization approach is twofold: the improvement of visual diagnosis in digital histopathology and the development of powerful pre-processing strategies to automated classification techniques for cancer detection.
Anomaly detection in surveillance video based on bidirectional prediction

With the development of information technology and the popularization of monitoring network, how to quickly and automatically detect abnormal behaviors in surveillance video is becoming more and more important for public security and smart city. The emergence of deep learning has greatly promoted the development of anomaly detection and much remarkable work has been presented on this topic. However, the existing approaches for anomaly detection generally encounter problems such as insufficient utilization of motion patterns and instability on different datasets. To improve the performance of anomaly detection in surveillance video, we propose a framework based on bidirectional prediction, which predicts the same target frame by the forward and the backward prediction subnetworks, respectively. Then the loss function is constructed based on the real target frame and its bidirectional prediction frame. Furthermore, we also propose an anomaly score estimation method based on the sliding window scheme which focuses on the foregrounds of the prediction error map. The comparison with the state-of-the-art shows that the proposed model outperforms most competing models on different video surveillance datasets.
Elastic Machine Learning Algorithms in Amazon SageMaker

There is a large body of research on scalable machine learning (ML). Nevertheless, training ML models on large, continuously evolving datasets is still a difficult and costly undertaking for many companies and institutions. We discuss such challenges and derive requirements for an industrial-scale ML platform. Next, we describe the computational model behind Amazon SageMaker, which is designed to meet such challenges. SageMaker is an ML platform provided as part of Amazon Web Services (AWS), and supports incremental training, resumable and elastic learning as well as automatic hyperparameter optimization. We detail how to adapt several popular ML algorithms to its computational model. Finally, we present an experimental evaluation on large datasets, comparing SageMaker to several scalable, JVM-based implementations of ML algorithms, which we significantly outperform with regard to computation time and cost.
How Digital Natives Learn and Thrive in the Digital Age: Evidence from an Emerging Economy

As a generation of ‘digital natives,’ secondary students who were born from 2002 to 2010 have various approaches to acquiring digital knowledge. Digital literacy and resilience are crucial for them to navigate the digital world as much as the real world; however, these remain under-researched subjects, especially in developing countries. In Vietnam, the education system has put considerable effort into teaching students these skills to promote quality education as part of the United Nations-defined Sustainable Development Goal 4 (SDG4). This issue has proven especially salient amid the COVID−19 pandemic lockdowns, which had obliged most schools to switch to online forms of teaching. This study, which utilizes a dataset of 1061 Vietnamese students taken from the United Nations Educational, Scientific, and Cultural Organization (UNESCO)’s “Digital Kids Asia Pacific (DKAP)” project, employs Bayesian statistics to explore the relationship between the students’ background and their digital abilities. Results show that economic status and parents’ level of education are positively correlated with digital literacy. Students from urban schools have only a slightly higher level of digital literacy than their rural counterparts, suggesting that school location may not be a defining explanatory element in the variation of digital literacy and resilience among Vietnamese students. Students’ digital literacy and, especially resilience, also have associations with their gender. Moreover, as students are digitally literate, they are more likely to be digitally resilient. Following SDG4, i.e., Quality Education, it is advisable for schools, and especially parents, to seriously invest in creating a safe, educational environment to enhance digital literacy among students.
Combinational Class Activation Maps for Weakly Supervised Object Localization

Weakly supervised object localization has recently attracted attention since it aims to identify both class labels and locations of objects by using image-level labels. Most previous methods utilize the activation map corresponding to the highest activation source. Exploiting only one activation map of the highest probability class is often biased into limited regions or sometimes even highlights background regions. To resolve these limitations, we propose to use activation maps, named combinational class activation maps (CCAM), which are linear combinations of activation maps from the highest to the lowest probability class. By using CCAM for localization, we suppress background regions to help highlighting foreground objects more accurately. In addition, we design the network architecture to consider spatial relationships for localizing relevant object regions. Specifically, we integrate non-local modules into an existing base network at both low- and high-level layers. Our final model, named non-local combinational class activation maps (NL-CCAM), obtains superior performance compared to previous methods on representative object localization benchmarks including ILSVRC 2016 and CUB- 200-2011. Furthermore, we show that the proposed method has a great capability of generalization by visualizing other datasets.
Use of a Real-Time Locating System for Contact Tracing of Health Care Workers During the COVID-19 Pandemic at an Infectious Disease Center in Singapore: Validation Study

Background In early 2020, coronavirus disease (COVID-19) emerged and spread by community and nosocomial transmission. Effective contact tracing of potentially exposed health care workers is crucial for the prevention and control of infectious disease outbreaks in the health care setting. Objective This study aimed to evaluate the comparative effectiveness of contact tracing during the COVID-19 pandemic through the real-time locating system (RTLS) and review of the electronic medical record (EMR) at the designated hospital for COVID-19 response in Singapore. Methods Over a 2-day study period, all admitted patients with COVID-19, their ward locations, and the health care workers rostered to each ward were identified to determine the total number of potential contacts between patients with COVID-19 and health care workers. The numbers of staff-patient contacts determined by EMR reviews, RTLS-based contact tracing, and a combination of both methods were evaluated. The use of EMR-based and RTLS-based contact tracing methods was further validated by comparing their sensitivity and specificity against self-reported staff-patient contacts by health care workers. Results Of 796 potential staff-patient contacts (between 17 patients and 162 staff members), 104 (13.1%) were identified by both the RTLS and EMR, 54 (6.8%) by the RTLS alone, and 99 (12.4%) by the EMR alone; 539 (67.7%) were not identified through either method. Compared to self-reported contacts, EMR reviews had a sensitivity of 47.2% and a specificity of 77.9%, while the RTLS had a sensitivity of 72.2% and a specificity of 87.7%. The highest sensitivity was obtained by including all contacts identified by either the RTLS or the EMR (sensitivity 77.8%, specificity 73.4%). Conclusions RTLS-based contact tracing showed higher sensitivity and specificity than EMR review. Integration of both methods provided the best performance for rapid contact tracing, although technical adjustments to the RTLS and increasing user compliance with wearing of RTLS tags remain necessary.
Automatic Detection of Offensive Language for Urdu and Roman Urdu

In recent years, unethical behavior in the cyber-environment has been revealed.The presence of offensive language on social media platforms and automatic detection of such language is becoming a major challenge in modern society.The complexity of natural language constructs makes this task even more challenging.Until now, most of the research has focused on resource-rich languages like English.Roman Urdu and Urdu are two scripts of writing the Urdu language on social media.The Roman script uses the English language characters while the Urdu script uses Urdu language characters.Urdu and Hindi languages are similar with the only difference in their writing script but the Roman scripts of both languages are similar.This study is about the detection of offensive language from the user's comments presented in a resourcepoor language Urdu.We propose the first offensive dataset of Urdu containing user-generated comments from social media.We use individual and combined n-grams techniques to extract features at character-level and word-level.We apply seventeen classifiers from seven machine learning techniques to detect offensive language from both Urdu and Roman Urdu text comments.Experiments show that the regression-based models using character n-grams show superior performance to process the Urdu language.Character-level tri-gram outperforms the other word and character n-grams.LogitBoost and SimpleLogistic outperform the other models and achieve 99.2% and 95.9% values of F-measure on Roman Urdu and Urdu datasets respectively.Our designed dataset is publically available on GitHub for future research.
FLASH: Fast and Robust Framework for Privacy-preserving Machine Learning

Abstract Privacy-preserving machine learning (PPML) via Secure Multi-party Computation (MPC) has gained momentum in the recent past. Assuming a minimal network of pair-wise private channels, we propose an efficient four-party PPML framework over rings ℤ 2 ℓ, FLASH, the first of its kind in the regime of PPML framework, that achieves the strongest security notion of Guaranteed Output Delivery (all parties obtain the output irrespective of adversary’s behaviour). The state of the art ML frameworks such as ABY3 by Mohassel et.al (ACM CCS’18) and SecureNN by Wagh et.al (PETS’19) operate in the setting of 3 parties with one malicious corruption but achieve the weaker security guarantee of abort . We demonstrate PPML with real-time efficiency, using the following custom-made tools that overcome the limitations of the aforementioned state-of-the-art– (a) dot product , which is independent of the vector size unlike the state-of-the-art ABY3, SecureNN and ASTRA by Chaudhari et.al (ACM CCSW’19), all of which have linear dependence on the vector size. (b) Truncation and MSB Extraction , which are constant round and free of circuits like Parallel Prefix Adder (PPA) and Ripple Carry Adder (RCA), unlike ABY3 which uses these circuits and has round complexity of the order of depth of these circuits. We then exhibit the application of our FLASH framework in the secure server-aided prediction of vital algorithms– Linear Regression, Logistic Regression, Deep Neural Networks, and Binarized Neural Networks. We substantiate our theoretical claims through improvement in benchmarks of the aforementioned algorithms when compared with the current best framework ABY3. All the protocols are implemented over a 64-bit ring in LAN and WAN. Our experiments demonstrate that, for MNIST dataset, the improvement (in terms of throughput) ranges from 24 × to 1390 × over LAN and WAN together.
A study of higher education students' self-perceived digital competences for learning and everyday life online participation

Purpose An increasing amount of research and debate has emerged over the last few years, emphasising the need for developing digitally competent, literate, able, skilled, capable people within a constantly changing technological and online environment. Existing definitions and perspectives in this area go beyond the use of technological tools or media for the creation of a digital literacy mindset, which develops throughout one's life. However, Higher Education strategies have not yet caught up with this agenda. Design/methodology/approach A student survey with Library and Information Science students from three higher education institutions in Scotland, Ireland and Greece was conducted as a basis of empirical data to support the theoretical propositions of the study. The survey centred on the technical and higher-level digital competences of students and drawing from students' self-perceived digital competences for learning and for the everyday life digital context, addressing e-leisure, e-learning, e-democracy, e-government and e-health activities. The survey critically enabled students to assess digital competences from their perspectives as digital participants. Findings Students' self-assessment of digital competences were lacking in a number of areas, which involved the development of information literacy, digital creation, digital research and digital identity management. In addition, students' digital competences were found to be linked to previous experiences within the everyday life digital environment. The higher the self-perceived digital competence levels of students were on the basis of dealing with everyday life digital tasks, the more likely they were to also develop high self-perceived digital competence in other digital areas related to their education. Originality/value Higher education has not fully embraced digital competences as a core, fundamental literacy which addresses both technology mastery and a digital citizenship mindset. As emerging models begin to challenge traditional teaching and learning paradigms, with global connectivity and personalised approaches, existing digital divides may be further accelerated. This requires revisiting digital competences with emphasis on the diversity of the contexts where it develops and of the learners involved, in the overall continuum of learning for life.
Ensemble feature selection in high dimension, low sample size datasets: Parallel and serial combination approaches

Feature selection in high dimension, low sample size (HDLSS) data is always an important data pre-processing task. In the literature, the concept of ensemble learning has been applied to improve single feature selection methods, the so-called ensemble feature selection techniques. The most widely used approach is to combine multiple feature selection methods and their selection results via some sort of aggregation function in a parallel manner. Another ensemble strategy is based on the serial combination approach where the selection results of the first feature selection stage are used as input for the second stage of feature selection to produce the final output. The aim of this paper is to fully explore the performance of parallel and serial combination approaches for ensemble feature selection over HDLSS data. In particular, we strive to answer two research questions: whether parallel and serial based ensemble feature selection can outperform single feature selection and which combination approach is the better choice for ensemble feature selection. The experimental results based on comparing nine parallel and nine serial combinations, as well as three single baseline feature selection methods, including principal component analysis (PCA), genetic algorithm (GA), and C4.5 decision tree, show that ensemble feature selection performs better than single feature selection in terms of classification accuracy. However, there are no significant differences in performance between the single best baseline method (i.e. GA) and the top three parallel and serial combinations. On the other hand, the serial combination approach produces the largest feature reduction rate.
A ChatBot for Learning Chinese: Learning Achievement and Technology Acceptance

ChatBot has potential as a language learning tool, especially for learning Chinese vocabulary. This study aimed to investigate the impact of using a newly developed ChatBot to learn Chinese vocabulary by comparing how it works in different learning environments and to explore the ChatBot with reference to the Technology Acceptance Model (TAM). This study was conducted with 58 students divided into two independent groups. The control group used ChatBot in a one-on-many classroom. The experimental group applied the ChatBot in one-on-one tutor sessions. A pretest and a posttest were used to measure the effect of the ChatBot, while TAM was explored through questionnaire and interview. Data analysis includes a paired-sample t test, analysis of covariance, and levels of effect. The results indicated that the ChatBot significantly improved the students’ learning achievement and that having a one-on-one environment may lead to better outcome than what could be achieved in a classroom. The TAM model was tested using partial least square. The result showed that perceived usefulness was the predictor of behavioral intention, whereas perceived ease of use was not. The students agreed that the ChatBot benefited their learning of Chinese vocabulary, with several adjustments need to be made for further progress.
Own Data? Ethical Reflections on Data Ownership

Abstract In discourses on digitization and the data economy, it is often claimed that data subjects shall be owners of their data. In this paper, we provide a problem diagnosis for such calls for data ownership : a large variety of demands are discussed under this heading. It thus becomes challenging to specify what—if anything—unites them. We identify four conceptual dimensions of calls for data ownership and argue that these help to systematize and to compare different positions. In view of this pluralism of data ownership claims, we introduce, spell out and defend a constructive interpretative proposal: claims for data ownership are charitably understood as attempts to call for the redistribution of material resources and the socio-cultural recognition of data subjects. We argue that as one consequence of this reading, it misses the point to reject claims for data ownership on the grounds that property in data does not exist. Instead, data ownership brings to attention a claim to renegotiate such aspects of the status quo .
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization

Adversarial examples cause neural networks to produce incorrect outputs with high confidence. Although adversarial training is one of the most effective forms of defense against adversarial examples, unfortunately, a large gap exists between test accuracy and training accuracy in adversarial training. In this paper, we identify Adversarial Feature Overfitting (AFO), which may cause poor adversarially robust generalization, and we show that adversarial training can overshoot the optimal point in terms of robust generalization, leading to AFO in our simple Gaussian model. Considering these theoretical results, we present soft labeling as a solution to the AFO problem. Furthermore, we propose Adversarial Vertex mixup (AVmixup), a soft-labeled data augmentation approach for improving adversarially robust generalization. We complement our theoretical analysis with experiments on CIFAR10, CIFAR100, SVHN, and Tiny ImageNet, and show that AVmixup significantly improves the robust generalization performance and that it reduces the trade-off between standard accuracy and adversarial robustness.