Reviewerly/exHarmony huggingface dataset:
The Role of Mobile-assisted Language Learning (MALL) in Enhancing the Writing Skills of Intermediate IEP Students: Expectations vs Reality

Despite extensive research into the effectiveness of mobile-assisted language learning (MALL) in ESL/EFL language learning, there is insufficient evidence to conclude that supplementing conventional teaching methods of the writing process approach with MALL significantly improves the English writing performance of native Arabic speakers. This study investigates the role of mobile-assisted language learning in enhancing the writing skills of forty female intermediate-level Intensive English Program (IEP) students at an intermediate level at Kuwait University. Participants were tested using a variety of mobile applications and websites (i.e. the Microsoft Word application, an English dictionary application, and internet search engines). The researcher utilized questionnaires, observation notes, and other qualitative methods from student assignments for data collection. Results of the data analysis indicate that smartphones are indeed an effective teaching tool, with multiple positive effects on student agency. However, due to its nature and complexity, a cautious approach was used to interpret the findings of this study. The ultimate goal of the research was to compare the traditional approach to the teaching of process writing to a mobile-assisted approach. Study findings are discussed in details and options for future research are explored. Overall, results suggest that learner autonomy is improved by engaging in the writing process both inside and outside of the classroom. The results also suggest implications for increasing motivation and peer collaboration via the use of mobile technology.
CHANCEL: Efficient Multi-client Isolation Under Adversarial Programs

Intel SGX aims to provide the confidentiality of user data on untrusted cloud machines.However, applications that process confidential user data may contain bugs that leak information or be programmed maliciously to collect user data.Existing research that attempts to solve this problem does not consider multi-client isolation in a single enclave.We show that by not supporting such in-enclave isolation, they incur considerable slowdown when concurrently processing multiple clients in different enclave processes, due to the limitations of SGX.This paper proposes CHANCEL, a sandbox designed for multi-client isolation within a single SGX enclave.In particular, CHANCEL allows a program's threads to access both a per-thread memory region and a shared read-only memory region while servicing requests.Each thread handles requests from a single client at a time and is isolated from other threads, using a Multi-Client Software Fault Isolation (MCSFI) scheme.Furthermore, CHANCEL supports various in-enclave services such as an inmemory file system and shielded client communication to ensure complete mediation of the program's interactions with the outside world.We implemented CHANCEL and evaluated it on SGX hardware using both micro-benchmarks and realistic target scenarios, including private information retrieval and product recommendation services.Our results show that CHANCEL outperforms a baseline multi-process sandbox by 4.06 -53.70× on micro-benchmarks and 0.02-21.18×on realistic workloads while providing strong security guarantees.
Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning

Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left ventricle segmentation. Reinforcement learning-based methods in earlier works often rely on learning proper thresholds to perform segmentation, and the segmentation results are inaccurate due to the sensitivity of the threshold. To tackle this problem, a novel DRL agent is designed to imitate the human process to perform LV segmentation. For this purpose, we formulate the segmentation problem as a Markov decision process and innovatively optimize it through DRL. The proposed DRL agent consists of two neural networks, i.e., First-P-Net and Next-P-Net. The First-P-Net locates the initial edge point, and the Next-P-Net locates the remaining edge points successively and ultimately obtains a closed segmentation result. The experimental results show that the proposed model has outperformed the previous reinforcement learning methods and achieved comparable performances compared with deep learning baselines on two widely used LV endocardium segmentation datasets, namely Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset, and Sunnybrook 2009 dataset. Moreover, the proposed model achieves higher F-measure accuracy compared with deep learning methods when training with a very limited number of samples.
Contrastive Multi-document Question Generation

Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.
Time Travel to the Past of Bosnia and Herzegovina through Virtual and Augmented Reality

Bosnia and Herzegovina (BH) has a very picturesque past. Founded in 11th century, it has always been a crossroads of faiths and civilizations. Extended Reality (XR) technologies can finally take us to time travel into this history, enable us to experience past events and meet historical characters. In this paper, we overview the latest applications we developed that use Virtual Reality (VR) video, Virtual and Augmented Reality (AR) for interactive digital storytelling about BH history. “Nine dissidents” is the first BH VR documentary, tackling a still tricky subject of dissidents in the Socialist Yugoslavia, artists and writers falsely accused, persecuted and still forbidden. “Virtual Museum of Old Crafts” aims to present and preserve crafts intangible heritage through Virtual Reality. “Battle on Neretva VR” is recreating a famous WWII battle offering the users to experience it and meet comrade Tito, the commander of the Yugoslav Liberation Army. “Sarajevo 5D” shows the cultural monuments from Sarajevo that do not exist anymore in physical form using Augmented Reality. Through user experience studies, we measure the user immersion and edutainment of these applications and show the potential of XR for the presentation and preservation of cultural heritage.
Comparative performance of extreme learning machine and Hammerstein–Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of Combretumhypopilinum Diels (Combretaceae)

In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein-Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of Combretum hypopilinum (MECH). Three different performance indicators including; mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and Root mean square error (RMSE) were employed in this research to calculate and determine the performance skills of the models. The obtained results indicated the reliable capability of ELM and HW over MLR model having NSE-values higher than 0.90 in both the calibration and verification stages. The results further demonstrated that, in terms of MAPE and RMSE, ELM and HW models showed higher performance efficiency than the MLR model. Even though HW outperformed the ELM and MLR models in the prediction of I. Whereas, ELM outperformed HW and MLR models in the prediction of C. Overall; the results proved the satisfactory ability of the AI-based models (HW and ELM) for modelling the Intestinal hypermotility and secretory inhibitory effects of MECH.
Partial-DNet: A Novel Blind Denoising Model With Noise Intensity Estimation for HSI

Because of the inevitable noise interference in hyperspectral images (HSIs), the understanding and application of HSIs are seriously restricted. To solve this problem, the research on the data-driven neural network-based denoising method has become a hotspot in recent years. However, for HSIs with inconsistent and mixed noises, the traditional data-driven denoising algorithms have obvious limitations on generalization ability. In order to overcome these drawbacks, a novel partial densenet (Partial-DNet) model with noise intensity estimation is proposed for HSIs blind denoising in this article. In the proposed Partial-DNet model, the noise intensity of each band is estimated in the first and then fused with the observed images to generate the feature maps by introducing the channel attention mechanism. Finally, a novel multiscale neural network is explored to extract the spatial–spectral joint features. The contributions of this article can be summarized as follows: 1) the noise intensity of each band is estimated as a prior to guide the blind denoising framework suit with different data sets adaptively; 2) a Partial-DNet model is proposed to extract multiscale spatial–spectral features more efficient to maintain the details better while denoising; and 3) the experiments on both simulated and real HSI data sets indicate that the proposed denoising framework can be used to remove the inconsistent mixed noises in HSIs adaptively. Compared with the other state-of-the-art denoising methods, HSIs denoised by the proposed Partial-DNet algorithm not only have a higher PSNR index but also have higher classification accuracy under the same situation.
A comparative study on efficient cloud security, services, simulators, load balancing, resource scheduling and storage mechanisms

There are applications in software that can process the user or customer records in hospitals, education sector, government sector, social media and mail communication, etc., which require accessing a single or set of records in a database consisting of millions of records. A basic requirement of those applications is that, the need to access a few data required in a secured manner from the data sets which are very large but simple in structure. Cloud computing provides the computing requirements for these kinds of the new generation of applications involving very large data sets that cannot possibly be handled efficiently without understanding the available cloud computing infrastructures. The objective is to find the cloud security, services, simulators, load balancing, resource scheduling and storage mechanisms which can best suite the need of the cloud environment chosen in an efficient way. This survey would act as a catalyst for the design of cloud environment over the very large data set in the future.
Writing Order Recovery in Complex and Long Static Handwriting

The order in which the trajectory is executed is a powerful source of information for recognizers.However, there is still no general approach for recovering the trajectory of complex and long handwriting from static images.Complex specimens can result in multiple pen-downs and in a high number of trajectory crossings yielding agglomerations of pixels (also known as clusters).While the scientific literature describes a wide range of approaches for recovering the writing order in handwriting, these approaches nevertheless lack a common evaluation metric.In this paper, we introduce a new system to estimate the order recovery of thinned static trajectories, which allows to effectively resolve the clusters and select the order of the executed pendowns.We evaluate how knowing the starting points of the pen-downs affects the quality of the recovered writing.Once the stability and sensitivity of the system is analyzed, we describe a series of experiments with three publicly available databases, showing competitive results in all cases.We expect the proposed system, whose code is made publicly available to the research community, to reduce potential confusion when the order of complex trajectories are recovered, and this will in turn make the trajectories recovered to be viable for further applications, such as velocity estimation.
Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization

The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of underwater images. Finally, the restored image can be estimated via the image formation model. The results show that the proposed algorithm can output high-quality images, according to qualitative and quantitative analysis.
Just Train Twice: Improving Group Robustness without Training Group Information

Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label. Prior approaches that achieve high worst-group accuracy, like group distributionally robust optimization (group DRO) require expensive group annotations for each training point, whereas approaches that do not use such group annotations typically achieve unsatisfactory worst-group accuracy. In this paper, we propose a simple two-stage approach, JTT, that first trains a standard ERM model for several epochs, and then trains a second model that upweights the training examples that the first model misclassified. Intuitively, this upweights examples from groups on which standard ERM models perform poorly, leading to improved worst-group performance. Averaged over four image classification and natural language processing tasks with spurious correlations, JTT closes 75% of the gap in worst-group accuracy between standard ERM and group DRO, while only requiring group annotations on a small validation set in order to tune hyperparameters.
Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM

Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this article, we seek to develop a novel method to simultaneously estimate network interactions and associations to relevant covariates for count data, and specifically for compositional data, which have a fixed sum constraint. We use a hierarchical Bayesian model with latent layers and employ spike-and-slab priors for both edge and covariate selection. For posterior inference, we develop a novel variational inference scheme with an expectation–maximization step, to enable efficient estimation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of network recovery. We show the practical utility of our model via an application to microbiome data. The human microbiome has been shown to contribute too many of the functions of the human body, and also to be linked with a number of diseases. In our application, we seek to better understand the interaction between microbes and relevant covariates, as well as the interaction of microbes with each other. We call our algorithm simultaneous inference for networks and covariates and provide a Python implementation, which is available online.
Content Based Scientific Article Recommendation System Using Deep Learning Technique

The emergence of the era of big data has increased the ease with which scientific users can access academic articles with better efficiency and accuracy from a pool of papers available. With the exponential increase in the number of research papers that are getting published every year, it has made scholars face the problem of information overload where they find it difficult to conduct comprehensive literature surveys. An article recommendation system helps in overcoming this issue by providing users with personalized recommendations based on their interests and choices. The common approaches used for recommendation are Content-Based Filtering (CBF) and Collaborative Filtering (CF). Even though there is much advancement in the field of article recommendation systems, a content-based approach using a deep learning technology is still in its inception. In this work, a C-SAR model using Gated Recurrent Unit (GRU) and association rule mining Apriori algorithm to provide a recommendation of articles based on the similarity in the content were proposed. The combination of a deep learning technique along with a classical algorithm in data mining is expected to provide better results than the state-of-art model in suggesting similar papers.
ER-AE: Differentially Private Text Generation for Authorship Anonymization

Most of privacy protection studies for textual data focus on removing explicit sensitive identifiers.However, personal writing style, as a strong indicator of the authorship, is often neglected.Recent studies, such as SynTF, have shown promising results on privacy-preserving text mining.However, their anonymization algorithm can only output numeric term vectors which are difficult for the recipients to interpret.We propose a novel text generation model with a two-set exponential mechanism for authorship anonymization.By augmenting the semantic information through a REINFORCE training reward function, the model can generate differentially private text that has a close semantic and similar grammatical structure to the original text while removing personal traits of the writing style.It does not assume any conditioned labels or paralleled text data for training.We evaluate the performance of the proposed model on the reallife peer reviews dataset and the Yelp review dataset.The result suggests that our model outperforms the state-of-the-art on semantic preservation, authorship obfuscation, and stylometric transformation.
A Binocular Vision-Based 3D Sampling Moiré Method for Complex Shape Measurement

As a promising method for moiré processing, sampling moiré has attracted significant interest for binocular vision-based 3D measurement, which is widely used in many fields of science and engineering. However, one key problem of its 3D shape measurement is that the visual angle difference between the left and right cameras causes inconsistency of the fringe image carrier fields, resulting in the phase mismatch of sampling moiré. In this paper, we developed a phase correction method to solve this problem. After epipolar rectification and carrier phase introduction and correction, the absolute phase of the fringe images was obtained. A more universal 3D sampling moiré measurement can be achieved based on the phase match and binocular vision model. Our numerical simulation and experiment showed the high robustness and anti-noise ability of this new 3D sampling moiré method for high-precision 3D shape measurement. As an application, cantilever beams are fabricated by directed energy deposition (DED) using different process parameters, and their 3D deformation caused by residual stresses is measured, showing great potential for residual stress analyses during additive manufacturing.
Automated Cervical Digitized Histology Whole-Slide Image Analysis Toolbox

Cervical intraepithelial neoplasia (CIN) is regarded as a potential precancerous state of the uterine cervix. Timely and appropriate early treatment of CIN can help reduce cervical cancer mortality. Accurate estimation of CIN grade correlated with human papillomavirus type, which is the primary cause of the disease, helps determine the patient's risk for developing the disease. Colposcopy is used to select women for biopsy. Expert pathologists examine the biopsied cervical epithelial tissue under a microscope. The examination can take a long time and is prone to error and often results in high inter-and intra-observer variability in outcomes.We propose a novel image analysis toolbox that can automate CIN diagnosis using whole slide image (digitized biopsies) of cervical tissue samples. The toolbox is built as a four-step deep learning model that detects the epithelium regions, segments the detected epithelial portions, analyzes local vertical segment regions, and finally classifies each epithelium block with localized attention. We propose an epithelium detection network in this study and make use of our earlier research on epithelium segmentation and CIN classification to complete the design of the end-to-end CIN diagnosis toolbox.The results show that automated epithelium detection and segmentation for CIN classification yields comparable results to manually segmented epithelium CIN classification.This highlights the potential as a tool for automated digitized histology slide image analysis to assist expert pathologists.
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights

Automatic speech recognition (ASR) in Sanskrit is interesting, owing to the various linguistic peculiarities present in the language.The Sanskrit language is lexically productive, undergoes euphonic assimilation of phones at the word boundaries and exhibits variations in spelling conventions and in pronunciations.In this work, we propose the first large scale study of automatic speech recognition (ASR) in Sanskrit, with an emphasis on the impact of unit selection in Sanskrit ASR.In this work, we release a 78 hour ASR dataset for Sanskrit, which faithfully captures several of the linguistic characteristics expressed by the language.We investigate the role of different acoustic model and language model units in ASR systems for Sanskrit.We also propose a new modelling unit, inspired by the syllable level unit selection, that captures character sequences from one vowel in the word to the next vowel.We also highlight the importance of choosing graphemic representations for Sanskrit and show the impact of this choice on word error rates (WER).Finally, we extend these insights from Sanskrit ASR for building ASR systems in two other Indic languages, Gujarati and Telugu.For both these languages, our experimental results show that the use of phonetic based graphemic representations in ASR results in performance improvements as compared to ASR systems that use native scripts.