Reviewerly/exHarmony huggingface dataset:
EyeSayCorrect: Eye Gaze and Voice Based Hands-free Text Correction for Mobile Devices

Text correction on mobile devices usually requires precise and repetitive manual control. In this paper, we present EyeSayCorrect, an eye gaze and voice based hands-free text correction method for mobile devices. To correct text with EyeSayCorrect, the user first utilizes the gaze location on the screen to select a word, then speaks the new phrase. EyeSayCorrect would then infer the user’s correction intention based on the inputs and the text context. We used a Bayesian approach for determining the selected word given an eye-gaze trajectory. Given each sampling point in an eye-gaze trajectory, the posterior probability of selecting a word is calculated and accumulated. The target word would be selected when its accumulated interest is larger than a threshold. The misspelt words have higher priors. Our user studies showed that using priors for misspelt words reduced the task completion time up to 23.79% and the text selection time up to 40.35%, and EyeSayCorrect is a feasible hands-free text correction method on mobile devices.
Deep learning-based digital volume correlation

With the rapid development of 3D volume imaging technology, digital volume correlation (DVC) has already made great progresses and become an indispensable tool in various engineering kingdoms. Inspired by artificial intelligence-related technologies, we develop a novel deep learning-based DVC methodology for 3D deformation analyses with the aid of a set of well-trained neural networks, hereafter referred to as DVC-Net, to implement accurate, precise and robust 3D deformation characterization in an efficient manner in this work. This proposed DVC-Net comprises three sub-networks, i.e., DVC-NetI, DVC-NetS and DVC-NetD, which are used in turn for integer-voxel searching, sub-voxel registration and displacement field denoising. Unlike traditional DVC methods, e.g., subvolume-based local DVC or finite-element method-based global DVC, the proposed DVC-Net neither explicitly relies on correlation criteria nor requires numerical iterative calculations, which greatly reduces computational complexity of the DVC analyses and therefore improves computational efficiency by at least 2∼3 orders of magnitude. Besides, it is suitable for dealing with various practical problems arising in the DVC-related studies such as volumetric images that experience changes in lighting conditions or noisy images. It is anticipated that the proposed DVC-Net can provide an alternative method for high-performance volumetric deformation characterization.
Identity-Based Authentication Technique for IoT Devices

The Internet of Things (IoT) is a broad term used to describe many types of linked equipment that are both intelligent and interconnected through a network. These Internet of Things devices have become an essential part of our everyday lives. These devices, however, are still in the development stage. Because of their low computing capacity, they are susceptible to various cyberattacks like distributed denial of service (DDoS) attacks and denial of service (DoS) attacks. They disrupt the normal functioning of IoT devices and expose sensitive data; therefore, identifying and mitigating cyber-attacks on IoT devices is a critical area of study. In this regard, we proposed an IoT device-specific unique identity-based authentication method in this pa-per. Our proposed approach employs lightweight authentication procedures that use the unique IDs of IoT devices to provide identity - based encryption.
Leonide

Abstract This article presents the longitudinal trilingual corpus of young learners of Italian, German and English called LEONIDE. The corpus consists of L1, L2 and L3 learner texts. L1 texts were written in two languages of schooling (i.e. Italian and German), L2 texts in two languages learned as second languages (i.e. German and Italian), and L3 texts in an additional foreign language (i.e. English). All texts were collected from a group of lower secondary school pupils from the multilingual Italian province of South Tyrol whose development in all three languages was observed over a period of three years. Each text comes with rich metadata as well as manual and automatic annotations.
Moderator effects of mobile users' pedagogical role on science learning: A meta‐analysis

Abstract The advancement of technologies has promoted the increasing popularity and integration of mobile technologies in science education in the past decade. These trends have led to an increased interest among scholars to understand the effects of mobile technologies in science education and whether those effects differ depending on how mobile technologies are used in learning and teaching (eg, student‐led, teacher‐led, collaborative). In this study, we performed a meta‐analysis of 34 studies that directly examined the effects of users' pedagogical role on K‐16 students' achievement in science when engaging in mobile learning (ML). The analysis of the 34 studies yielded an overall significant main effect of ML on K‐16 science learning outcomes. We applied the mixed‐effects model with moderator variables and found that users' pedagogical role significantly moderated the ML effects as a whole. Collaborative and student‐led uses had a statistically significant impact on student science learning, whereas teacher‐led use did not. Findings from this meta‐analysis are consistent with prior research, providing synthesized research‐based evidence of the effects of ML on science learning that holds implications for both mobile curriculum design and mobile technology use. Practitioner notes What is already known about this topic Mobile technology has been increasingly adopted in science learning with great potential to support learning and teaching. Prior meta‐analysis has suggested multiple moderators in measuring the mobile learning effect. Prior empirical studies examined the effect of mobile users' pedagogical role in the specific subject domains (eg, physics) and grade levels (eg, high school). What this paper adds This meta‐analysis is among the first to examine the moderator effect of mobile users' pedagogical roles on ML in K‐16 science education. This study found that mobile technology use is associated with significant science learning outcomes across 34 studies. This study found that the effect of mobile learning was moderated by mobile users' pedagogical roles (ie, who initiated the use). Collaborative use between teachers and students tends to be the most effective. Teacher‐led use tends to be the least effective. Implications for practice This study suggests that we should consider who leads the use of mobile technology when integrating mobile learning in science education. In particular, educators should encourage collaborative and student‐led mobile use for learning and instruction.
Double Mask R-CNN for Pedestrian Detection in a Crowd

Aiming at the difficulty of feature extraction and the limitation of NMS (nonmaximum suppression) in crowded pedestrian detection, a new detection network named Double Mask R-CNN based on Mask R-CNN with FPN (Feature Pyramid Network) is proposed in this article. The algorithm has two improvements: firstly, we add a semantic segmentation branch on the FPN to strengthen the feature extraction of crowded pedestrians; secondly, we design a rule to estimate the pedestrian visibility of detected image according to the human keypoints information, and this rule can cover binary mask on the image whose pedestrian visibility is less than a certain threshold. Then we input the masked image into the network to locate occluded pedestrians. Experimental results on the CrowdHuman dataset show that the log-average miss rate (MR) of Double Mask R-CNN is 13, 12% lower than the best results of other mainstream networks. Similar improvements on WiderPerson dataset are also achieved by the Double Mask R-CNN.
Entropy-based discrimination between translated Chinese and original Chinese using data mining techniques

The present research reports on the use of data mining techniques for differentiating between translated and non-translated original Chinese based on monolingual comparable corpora. We operationalized seven entropy-based metrics including character, wordform unigram, wordform bigram and wordform trigram, POS (Part-of-speech) unigram, POS bigram and POS trigram entropy from two balanced Chinese comparable corpora (translated vs non-translated) for data mining and analysis. We then applied four data mining techniques including Support Vector Machines (SVMs), Linear discriminant analysis (LDA), Random Forest (RF) and Multilayer Perceptron (MLP) to distinguish translated Chinese from original Chinese based on these seven features. Our results show that SVMs is the most robust and effective classifier, yielding an AUC of 90.5% and an accuracy rate of 84.3%. Our results have affirmed the hypothesis that translational language is categorically different from original language. Our research demonstrates that combining information-theoretic indicator of Shannon's entropy together with machine learning techniques can provide a novel approach for studying translation as a unique communicative activity. This study has yielded new insights for corpus-based studies on the translationese phenomenon in the field of translation studies.
An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel

Breast cancer (BC) is the most common malignant cancer in women. A predictive model is required to predict the 5-year survival in patients with BC (5YSPBC) and improve the treatment quality by increasing their survival rate. However, no reports in literature about apps developed and designed in medical practice to classify the 5YSPBC. This study aimed to build a model to develop an app for an automatically accurate classification of the 5YSPBC.A total of 1810 patients with BC were recruited in a hospital in Taiwan from the secondary data with codes on 53 characteristic variables that were endorsed by professional staff clerks as of December 31, 2019. Five models (i.e., revolution neural network [CNN], artificial neural network, Naïve Bayes, K-nearest Neighbors Algorithm, and Logistic regression) and 3 tasks (i.e., extraction of feature variables, model comparison in accuracy [ACC] and stability, and app development) were performed to achieve the goal of developing an app to predict the 5YSPBC. The sensitivity, specificity, and receiver operating characteristic curve (area under ROC curve) on models across 2 scenarios of training (70%) and testing (30%) sets were compared. An app predicting the 5YSPBC was developed involving the model estimated parameters for a website assessment.We observed that the 15-variable CNN model yields higher ACC rates (0.87 and 0.86) with area under ROC curves of 0.80 and 0.78 (95% confidence interval 0.78-82 and 0.74-81) based on 1357 training and 540 testing cases an available app for patients predicting the 5YSPBC was successfully developed and demonstrated in this study.The 15-variable CNN model with 38 parameters estimated using CNN for improving the ACC of the 5YSPBC has been particularly demonstrated in Microsoft Excel. An app developed for helping clinicians assess the 5YSPBC in clinical settings is required for application in the future.
Feeding the Trolling: Understanding and Mitigating Online Trolling Behavior as an Unintended Consequence

Trolling is a form of consumer misbehavior that involves deliberate, deceptive, and mischievous attempts to provoke reactions from other online users. This research draws on actor-network theory to explore the assemblages of human and non-human entities that allow and perpetuate online trolling behaviors. By taking a practice-focused multi-sited ethnographic research approach, the research shows that online trolling is often an unintended consequence of interactions between human and non-human entities that are joined in the performance of trolling behavior. These entities include: troll(s), target(s), a medium of exchange, audience(s), other trolls, trolling artifacts, regulators, revenue streams, and assistants. Some of these actors (i.e., troll, target, medium) are playing a role in initiating, and other actors are (un)intentionally sustaining trolling by celebrating it, boosting it, facilitating it, and normalizing it. The findings highlight the role of nontraditional actors in the performance of misbehaviors and suggest that effective management of online consumer misbehaviors such as trolling will include managing the socio-technical networks that allow and fuel these misbehaviors.
Efficient semi-external depth-first search

• A novel and efficient semi-external DFS algorithm EP-DFS is presented. • EP-DFS requires simpler CPU calculation and less memory space. • A novel index is devised to reduce the disk random accesses. • Extensive experiments are conducted on both real and synthetic datasets . As graphs grow in size, many real-world graphs are difficult to load into the primary memory of a computer. Thus, computing depth-first search (DFS) results (i.e., depth-first order or DFS-Tree) on the semi-external memory model is important to investigate. Semi-external algorithms assume that the primary memory can at least hold a spanning tree T of a graph G and gradually restructure T into a DFS-Tree, which is nontrivial. In this paper, we present a comprehensive study for the semi-external DFS problem. Based on a theoretical analysis of this problem, we introduce a new semi-external DFS algorithm called EP-DFS with a lightweight index N + - index . Unlike traditional algorithms, we focus on addressing such a complex problem efficiently with fewer I/Os, simpler CPU calculations (implementation-friendly), and less random I/O accesses (key-to-efficiency). Extensive experimental evaluations are performed on both synthetic and real graphs, and experimental results confirm that the proposed EP-DFS algorithm markedly outperforms existing algorithms.
Geography Education in a Collaborative Virtual Environment: A Qualitative Study on Geography Teachers

The presented study aspires to utilize the gradually validated immense potential of collaborative immersive virtual environments (CIVEs) in higher education when designing and conducting geography lessons. These particular lessons focused on hypsography. A Research through Design approach and relevant qualitative methodology were used as we engaged two groups of domain experts (experienced geography teachers) to validate both the learning scenarios and the virtual environment we used. The lessons were administered via eDIVE—a novel platform for collaborative learning and teaching in virtual reality of our own design. The teachers underwent a hypsography virtual lesson and were randomly divided into two groups to be compared, which differed in the level of structure given to the lesson (one group received detailed instructions on what task they were to solve, while the other was given a free hand in exploring the environment and activities it afforded). The teachers’ experiences were then summarized in a post-lesson reflection and a subsequent focus group following the tasks. The participants’ expertise allowed insights to be gained into their first-hand experience as students, as well as their expert view of the lesson from an educational point of view. Virtual reality’s implementation into teaching practice was the key topic of the discussion.
Image Captioning Using Deep Learning

The process of generating a textual description for images is known as image captioning. Now a days it is one of the recent and growing research problem. Day by day various solutions are being introduced for solving the problem. Even though, many solutions are already available, a lot of attention is still required for getting better and precise results. So, we came up with the idea of developing a image captioning model using different combinations of Convolutional Neural Network architecture along with Long Short Term Memory in order to get better results. We have used three combination of CNN and LSTM for developing the model. The proposed model is trained with three Convolutional Neural Network architecture such as Inception-v3, Xception, ResNet50 for feature extraction from the image and Long ShortTerm Memory for generating the relevant captions. Among the three combinations of CNN and LSTM, the best combination is selected based on the accuracy of the model. The model is trained using the Flicker8k dataset.
Live migration of virtual machine memory content in networked systems

Enterprises are increasingly outsourcing their computing needs to cloud computing. This dependency on cloud services requires uninterrupted and high availability of services. However, achieving the availability of services is not always possible because cloud services may need to be temporarily suspended for several reasons. These include real-time maintenance of cloud infrastructure, load balancing of services, deployment of countermeasures against cyber attacks, power management, proactive fault tolerance, or resource utilization, etc. The unavailability of critical cloud services may negatively impact the business model of cloud providers because clients would likely be unhappy with frequent service interruptions. One solution to this service interruption is real-time Live Machine Migration (LVM) — a virtualization technique in network-based systems involving migrating virtual machines (VMs) from one host to another. The key objective of LVM is to avoid or minimize the interruption of application services running on a VM during the migration from a source to a destination host server. To achieve this objective, some challenges need to be tackled. An optimal LVM approach first needs to address the challenge of simultaneously minimizing downtime, network resource usage for migration, and total migration time. Several optimization techniques have been proposed for enhancing the performance of LVM. This paper provides a comprehensive review of the LVM challenges particularly focusing on memory content migration and the proposed solutions to overcome the identified challenges and their limitations. The paper also points out some open research issues and future directions towards optimizing LVM.
Side-Channel Attack Counteraction via Machine Learning-Targeted Power Compensation for Post-Silicon HW Security Patching

Counteracting side-channel attacks has become a basic requirement in secure integrated circuits handling physical or sensitive data through cryptography, and preventing information leakage via power and electromagnetic (EM) emissions. Over time, the implementation of protection techniques against power analysis and EM attacks has progressively moved from design-specific (i.e., requiring redesign for their reuse [1]–[3]) to design-reusable frameworks [4]–[10], facilitating reuse with no modifications across designs, system security verification, and reducing the area/power overhead through reuse of existing silicon infrastructure across secure design IPs on the same die. Accordingly, embedding protection into regulators has been extensively explored to degrade the attack SNR and increase the minimum traces to key disclosure (MTD) via current equalization [4], a switching regulator with randomized loop control [5], a digital LDO (DLDO) with noise injection [6], a DLDO with randomized thresholds and AES transformations [7], a DLDO based on an edge-chasing quantizer [8], current-domain signature attenuation [9] and an additional time-varying transfer function [10]. Such protections allow design reuse and some degree of power-security flexibility, but have common limitations in that: 1) they indiscriminately compensate the entire large-signal power rather than focusing on small-signal information-sensitive power contributions, preventing power overhead reductions, 2) the level of protection is set at design time, and cannot improve after chip fabrication (no learning), 3) they cannot adapt to mitigate newly discovered side-channel vulnerabilities and attacks. Indeed, power overhead and security upgrade-ability over time are crucial in energy-autonomous systems with long lifespans and in applications where device replacement is expensive or unfeasible (e.g., IoT, implantables).
Bidirectional Encoder Representation from Transformers (BERT) Variants for Procedural Long-Form Answer Extraction

Extracting information from large verbose documents is a gruelling task which requires patience and huge amounts of effort. Lengthy documents like Portable Document Formats (PDFs) contain tonnes of information including tables, figures, etc. which makes it hard to retrieve specific pieces of textual content like step-by-step instructions or procedures. To the best of our knowledge, this work is the maiden effort to efficiently extract such long-form procedural answers from the aforementioned information sources. The proposed approach retrieves succinct responses from the relevant PDFs for a given user query using a transformer model embedded with attention mechanism. This model is trained using a self-made dataset namely, Pro-LongQA, consisting of carefully crafted procedure based questions and answers. A comparative study of one of the state-of-the-art transformer models, namely, Bidirectional Encoder Representation from Transformers (BERT) and its different variants such as RoBerta, Albert and DistilBert for the task of long-form question answering is performed. Among which, BERT and RoBerta proved to be the best performing models for this task with an accuracy of 87.2&#x0025; and 86.4&#x0025; respectively.
Multilingual speech recognition for GlobalPhone languages

In this paper, we present our investigations towards the development of multilingual automatic speech recognition (ML ASR) systems using the GlobalPhone database. In addition to GlobalPhone, we have included 4 Ethiopian languages (Amharic, Oromo, Tigrigna and Wolaytta), as well as Uyghur and English in our investigation. In order to see the impact of language relatedness in ML ASR training, we have analyzed both phonetic overlap and morphological complexity of the languages. Deep Neural Network based ML ASR systems have been developed using ML mix, transfer and multitask learning approaches. Relative word error rate (WER) reductions up to 33.21% have been achieved as a result of using resources of other languages in ML acoustic model training. Our experimental results show that languages with small amounts of monolingual training data benefit a lot from ML training. Moreover, using phonetically related languages in ML training is more beneficiary than using phonetically less related languages. It seems that the nature of the corpus (single or mixed domain, noisy or noise free, etc.) has also an impact in ML training although it is not as important as the phonetic relatedness of the languages.
The non-linear nature of the cost of comprehensibility

Abstract A key challenge in Artificial Intelligence (AI) has been the potential trade-off between the accuracy and comprehensibility of machine learning models, as that also relates to their safe and trusted adoption. While there has been a lot of talk about this trade-off, there is no systematic study that assesses to what extent it exists, how often it occurs, and for what types of datasets. Based on the analysis of 90 benchmark classification datasets, we find that this trade-off exists for most (69%) of the datasets, but that somewhat surprisingly for the majority of cases it is rather small while for only a few it is very large. Comprehensibility can be enhanced by adding yet another algorithmic step, that of surrogate modelling using so-called ‘explainable’ models. Such models can improve the accuracy-comprehensibility trade-off, especially in cases where the black box was initially better. Finally, we find that dataset characteristics related to the complexity required to model the dataset, and the level of noise, can significantly explain this trade-off and thus the cost of comprehensibility. These insights lead to specific guidelines on how and when to apply AI algorithms when comprehensibility is required.