Reviewerly/exHarmony huggingface dataset:
Conversational Question Generation in Russian

In this paper, we discuss possibilities of automatic generation of conversational questions in Russian. We are exploring the possibility of using “A Conversational Question Answering Challenge” (CoQA) dataset translated into Russian for training an encoder-decoder model. We review several techniques for improving the quality of questions generated in the Russian language. The results are evaluated manually. Combining a neural network-based approach with a rules-based approach, we develop a system for automatic examination of university students.
Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets

Although large-scale pretrained language models, such as BERT and RoBERTa, have achieved superhuman performance on in-distribution test sets, their performance suffers on out-of-distribution test sets (e.g., on contrast sets). Building contrast sets often re-quires human-expert annotation, which is expensive and hard to create on a large scale. In this work, we propose a Linguistically-Informed Transformation (LIT) method to automatically generate contrast sets, which enables practitioners to explore linguistic phenomena of interests as well as compose different phenomena. Experimenting with our method on SNLI and MNLI shows that current pretrained language models, although being claimed to contain sufficient linguistic knowledge, struggle on our automatically generated contrast sets. Furthermore, we improve models' performance on the contrast sets by apply-ing LIT to augment the training data, without affecting performance on the original data.
Stereo Imaging Using Hardwired Self-Organizing Object Segmentation

Stereo vision utilizes two cameras to acquire two respective images, and then determines the depth map by calculating the disparity between two images. In general, object segmentation and stereo matching are some of the important technologies that are often used in establishing stereo vision systems. In this study, we implement a highly efficient self-organizing map (SOM) neural network hardware accelerator as unsupervised color segmentation for real-time stereo imaging. The stereo imaging system is established by pipelined, hierarchical architecture, which includes an SOM neural network module, a connected component labeling module, and a sum-of-absolute-difference-based stereo matching module. The experiment is conducted on a hardware resources-constrained embedded system. The performance of stereo imaging system is able to achieve 13.8 frames per second of 640 × 480 resolution color images.
Learning Word Representations for Tunisian Sentiment Analysis

Tunisians on social media tend to express themselves in their local dialect using Latin script (TUNIZI). This raises an additional challenge to the process of exploring and recognizing online opinions. To date, very little work has addressed TUNIZI sentiment analysis due to scarce resources for training an automated system. In this paper, we focus on the Tunisian dialect sentiment analysis used on social media. Most of the previous work used machine learning techniques combined with handcrafted features. More recently, Deep Neural Networks were widely used for this task, especially for the English language. In this paper, we explore the importance of various unsupervised word representations (word2vec, BERT) and we investigate the use of Convolutional Neural Networks and Bidirectional Long Short-Term Memory. Without using any kind of handcrafted features, our experimental results on two publicly available datasets showed comparable performances to other languages.
Using gender responsive collaborative learning strategy to improve students achievement and attitude towards learning science in virtual and hands-on laboratory environment

One way of ensuring gender equality in science education is by mainstreaming gender components into the pedagogical delivery of science instruction. Thus, the researchers in this study designed a Gender Responsive Collaborative Learning Strategy (GR-CLS) based on Universal Design for Learning (UDL) principles. The effectiveness of this instructional paradigm on students’ achievement and attitude was determined in a mixed factorial quasi-experimental research design study conducted in a virtual and hands-on laboratory learning environment. Multistage sampling technique was used to select a total sample of 218 secondary school students from same sex and mixed sex schools. The six hypotheses formulated in the study were tested using Means and Analysis of Covariance (ANCOVA). Empirical findings revealed a significant difference in the mean achievement and attitude scores of both male and female students who were exposed to GR-CLS under virtual and hands-on laboratory environment respectively compared to those who were not exposed to GR-CLS. These results indicate that GR-CLS is an effective pedagogical strategy for improving students’ achievement and attitude regardless of gender. Nevertheless, it was discovered that GR-CLS leads to a significant improvement in the achievement of students from same sex schools over mixed sex schools. The study, therefore, recommends the adoption of GR-CLS by science teachers in the planning and implementation of science lessons to create equal opportunities for both male and female students to benefit maximally from learning activities carried out in the laboratory.
Global Information Guided Video Anomaly Detection

Video anomaly detection (VAD) is currently a challenging task due to the complexity of "anomaly" as well as the lack of labor-intensive temporal annotations. In this paper, we propose an end-to-end Global Information Guided (GIG) anomaly detection framework for anomaly detection using the video-level annotations (i.e., weak labels). We propose to first mine the global pattern cues by leveraging the weak labels in a GIG module. Then we build a spatial reasoning module to measure the relevance between vectors in spatial domain with the global cue vectors, and select the most related feature vectors for temporal anomaly detection. The experimental results on the CityScene challenge demonstrate the effectiveness of our model.
THE EFFECTIVENESS OF MOBILE ASSISTED LANGUAGE LEARNING (MALL) ON ESL LISTENING SKILL

The use of mobile technology in learning and teaching English has been on the rise all over the world over the past few decades and hence, has received considerable attention and importance from the academics in recent years. As a result, a number of experimental studies have so far been carried out about the use and effectiveness of mobile phones in the teaching/learning process. However, there have been only a small number of studies on the topic of mobile-assisted listening comprehension. This study basically aims to explore whether Mobile Assisted Language Learning (MALL) is effective in teaching/learning listening to the students of university-level English language programs and could better enhance students’ listening ability. It also endeavors to assess why some MALL strategies/techniques are more effective than the others. For review purpose, the study exclusively used the secondary data available on the broader topic- the use and efficacy of mobile phones in teaching/learning listening skill. The results of this research indicated that the MALL is meaningfully efficacious in teaching/learning ESL/EFL listening skill and using appropriate strategies could positively contribute to bringing about better learning. Besides outlining a brief overview of MALL, the study also attempts to recommend some practical and useful stratagems that ESL/EFL educators can use while designing MALL listening tasks/activities.
Unexpected Information Leakage of Differential Privacy Due to Linear Property of Queries

The differential privacy is a widely accepted conception of privacy preservation and the Laplace mechanism is a famous instance of differential privacy mechanisms to deal with numerical data. In this paper, we find that the differential privacy does not take liner property of queries into account, resulting in unexpected information leakage. In specific, the linear property makes it possible to divide one query into two queries such as $q(D)=q(D_1)+q(D_2)$ if $D=D_1\cup D_2$ and $D_1\cap D_2=\emptyset$. If attackers try to obtain an answer of $q(D)$, they not only can issue the query $q(D)$, but also can issue the $q(D_1)$ and calculate the $q(D_2)$ by themselves as long as they know $D_2$. By different divisions of one query, attackers can obtain multiple different answers for the query from differential privacy mechanisms. However, from attackers' perspective and from differential privacy mechanisms' perspective, the totally consumed privacy budget is different if divisions are delicately designed. The difference leads to unexpected information leakage because the privacy budget is the key parameter to control the amount of legally released information from differential privacy mechanisms. In order to demonstrate the unexpected information leakage, we present a membership inference attacks against the Laplace mechanism.
Automatic classification of sentinel lymph node (SLN) metastases in breast carcinoma whole slide image (WSI) through densenet deep learning network

The evaluation of lymph nodes' metastasis is an important component of Tumor, Node, Metastasis (TNM) breast cancer staging system for better clinical management and treatment.Assessing lymph node metastasis through histologic examination is the most accurate method.This paper proposes significantly advanced and faster image classification Convolutional Neural Network (CNN) model called Densenet-161 for lymph node metastasis.This paper uses pre-processing technique called image thresholding to improve the contrast intensities of the SLN images, which improves the performance of DenseNet.The experimental PCam dataset contains 327,680 patches extracted from 400 Haemotoxylin and Eosin (H&E) stained WSIs of breast cancer with sentinel lymph node sections.The proposed system has generated 94% accuracy for lymph node metastasis classification.Context: Automatic, faster and accurate computational technique implementation for breast cancer sentinel lymph metastases classification in cancer diagnostic pathology.Aims: Automatic sentinel lymph node metastases classification on breast carcinoma WSI through deep learning network, DenseNet-161.Settings and Design: Tumor cells are migrating from a primary metastasize to one or a few lymph nodes, before spreading to other lymph nodes.These few lymph nodes are called as "sentinel" lymph nodes.The status of these sentinel lymph nodes would accurately predict the status of the remaining lymph nodes.Lymph node status assessment is considered to be one of the most important independent prognostic factors in breast cancer.Methods and Materials: Breast cancer metastasis spreads the tumor cells to other parts of the body, which is predominantly through sentinel lymph node.This paper uses an image classification model DenseNet-161 to classify metastaized and normal SLN images.The DenseNets are significantly advantaged over traditional CNNs, by reducing the vanishing-gradient problem, having feature reusage, strengthening feature propagation, having significant reduction in number of parameters and less computation time.The experimental dataset contains Statistical analysis used: The performance of the Densenet-161 is measured by statistic parameter F1 score, training and validation accuracy.Results: The proposed system has generated a training accuracy of 0.9477 and validation accuracy of 0.944, with an F1 score of 0.8406.Conclusions: This model involves extraction of complex information from the medical images dataset, which requires the removal of noise.Even after applying thresholding pre-processing method the noise persists, which requires additional pre-processing before training the model.And by increasing the dataset size through data-augmentation will also improve the accuracy considerably.
General methods and properties to evaluate continuum limits of the 1D discrete time quantum walk

Abstract Models of quantum walks which admit continuous time and continuous spacetime limits have recently led to quantum simulation schemes for simulating fermions in relativistic and nonrelativistic regimes (Molfetta GD, Arrighi P. A quantum walk with both a continuous-time and a continuous-spacetime limit, 2019). This work continues the study of relationships between discrete time quantum walks (DTQW) and their ostensive continuum counterparts by developing a more general framework than was done in Molfetta and Arrighi (A quantum walk with both a continuous-time and a continuous-spacetime limit, 2019) to evaluate the continuous time limit of these discrete quantum systems. Under this framework, we prove two constructive theorems concerning which internal discrete transitions (“coins”) admit nontrivial continuum limits. We additionally prove that the continuous space limit of the continuous time limit of the DTQW can only yield massless states which obey the Dirac equation. Finally, we demonstrate that for general coins the continuous time limit of the DTQW can be identified with the canonical continuous time quantum walk when the coin is allowed to transition through the continuous limit process.
Look It Up: Bilingual and Monolingual Dictionaries Improve Neural Machine Translation.

Despite advances in neural machine translation (NMT) quality, rare words continue to be problematic. For humans, the solution to the rare-word problem has long been dictionaries, but dictionaries cannot be straightforwardly incorporated into NMT. In this paper, we describe a new method for attaching dictionary definitions to rare words so that the network can learn the best way to use them. We demonstrate improvements of up to 3.1 BLEU using bilingual dictionaries and up to 0.7 BLEU using monolingual source-language dictionaries.
HARMONIZING CULTURAL DIPLOMACY AND COMMERCIALIZATION OF TRADITIONAL CULTURAL EXPRESSIONS: CASE STUDY ON I LA GALIGO THEATRICAL PERFORMANCE

Purpose: Indonesia is protecting Traditional Cultural Expressions (TCEs) by communal Copyright Law. In the case of I La Galigo, performers used one of Bugis's ancient literary works on theatre. Bugis people believe that there was a misappropriation of Bugis culture. This paper evaluates how cultural diplomacy gives benefit to the Bugis people and how to do Copyright law support I La Galigo theatrical performers. Methodology: From a methodological standpoint, this paper used both a normative and ethnography-legal research approach in data collection. Main Findings: Any society in Indonesia including the Bugis people has the freedom to preserve and develop their cultural values. Implications/Applications: The importance of harmonizing implementing legislation in protecting performers of traditional cultural expressions is very much needed. TCEs inscription as defending protection, its dissemination including cultural diplomacy, and its utilization become three chains supporting each other for the treatment of TCEs in the future. Novelty/Originality of this study: The research of I La Galigo theatrical performance in legal particularly traditional cultural expressions perspective has never been done by other researchers.
On Quantum Computing for Mixed-Integer Programming

Quantum computing (QC) is emerging as a new computing resource that could be superior to conventional computing (CC) for certain classes of optimization problems. However, in principle QC can only solve unconstrained binary programming problems, while mixed-integer linear programming (MIP) is of most interest in practice. We attempt to bridge the gap between the capability of QC and real-world applications by developing a new approach for MIP. The idea is decomposing the MIP into binary programming and linear programming (LP) problems, which are respectively solved by QC and conventional computing. We formalize a decomposition approach that ensures that with a sufficient number of back and forth iterations, the algorithm can reach the optimal solution of the original MIP problem. The algorithm is tested on a 2000Q D-Wave quantum processing units (QPU) and is shown to be effective for small-scaled test cases.
Students’ Confidence Instrument Analysis in Poetry Learning through Rasch Model

Background: Students' low confidence becomes the reasons for conducting this research whereas it is one of the most important skills needed to develop ability and potency. Objective: This research aims to obtain information about Students' confidence levels in Bahasa Indonesia's subject, especially in poetry learning. Materials and Methods: This research employed a cross-sectional study with the quantitative approach. Participants of this research were 45 fifth grade students in Cibeureum Elementary School and 50 th grade students in Kotabaru Elementary School 2019/2020 academic year. The self-report questionnaires were used as a research instrument to measure Students' confidence levels with the cluster sampling technique. Data were analyzed using the Partial Credit Model (PCM). These parameters are identified with the category coefficients and the scoring function of the Rasch model for polychotomous responses in which the latent trait is assumed uni-dimensional. Results: The trial results showed that as many as 13 items proved to be compatible with PCM instruments, the reliability of the instrument was 0.74, the item difficulty index started from -0.36 to 0.36 which meant that all items in the category were sufficient. Conclusion: This development instrument is valid and really measures the effect of student confidence on poetry learning, so it can be used to measure student confidence in poetry learning.
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees

(Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating the maximum likelihood of mixture models or incomplete data problems. A major challenge facing this popular technique is how to effectively preserve the privacy of sensitive data. Previous research on this problem has already lead to the discovery of some Differentially Private (DP) algorithms for (Gradient) EM. However, unlike in the non-private case, existing techniques are not yet able to provide finite sample statistical guarantees. To address this issue, we propose in this paper the first DP version of (Gradient) EM algorithm with statistical guarantees. Moreover, we apply our general framework to three canonical models: Gaussian Mixture Model (GMM), Mixture of Regressions Model (MRM) and Linear Regression with Missing Covariates (RMC). Specifically, for GMM in the DP model, our estimation error is near optimal in some cases. For the other two models, we provide the first finite sample statistical guarantees. Our theory is supported by thorough numerical experiments.
Automatic Detection of Usability Smells in Web Applications running in mobile devices

Currently, the use of mobile devices, mainly smartphones, has been gradually increasing due to the increasing development of mobile technologies. However, it is often the case that web applications are not tested on mobile devices which has led to, among others, usability errors. Therefore, the importance of usability testing in mobile devices has been increasing, but it is costly and time-consuming; the use of these devices in many cases cannot be replicated solely in a laboratory, but it should be replicated in the field which is more complex to implement. In addition to these usability testing and to reduce the time and complexity of testing in these devices, we proposed an automatic approach to identify indicators of usability problems (Usability Smells). To do so, we implemented a process of pattern matching in the tool UseSkill and proposed Usability Smells and their corresponding patterns for this context. This tool uses the data from the interactions of the user with a Mobile Web Application, discovering Usability Smells. This new process was experimented in two study cases with two applications where it collected data of participants who used it on a mobile device (smartphone). The results obtained were promising in identifying usability smells in relation to usability problems.
Social Media and Library Services: A Case of Covid-19 Pandemic Era

Before the advent of the Covid 19 pandemic, many libraries have traditionally provided library services to users in manual and print format while few others opted for digital collection. However, with the lockdown imposed on every sector including the library, the inefficiency of most libraries in continuing her provision of useful and up to date library service to her users have been exposed. This unfortunate situation has accelerated their need to innovate new systems that will aid them to be more visible and effective in the delivery of their mandate notwithstanding the Covid 19 lockdown. This has given rise to the adoption of social media tools such as Facebook, Whatsapp, Twitter, Instagram, YouTube, Pinterest, Podcasts, Flickr and blogs, Instant Messaging (IM), and Rich Site Summary (RSS) in libraries. This paper discusses social media for library services; adoption of social media for library services in the Covid 19 era; and issues of social media for library services in the Covid 19 era. The paper concluded that social media by libraries have increased the effectiveness of most libraries in their provision of library services to their users who are isolated and restricted to use the regular physical library collection. Recommendations were that library staff should be provided with regular training in the use of new tools and social media platforms to make them IT compliant and in turn, they can utilize these tools for library service delivery
Deep learning based static hand gesture recognition

Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and image segmentation algorithms computer vision methods from the OpenCV and Intel Real Sense libraries are used. The subsystem for features extracting and gestures classification is based on the modified VGG-16 by using the TensorFlow&amp;amp;Keras deep learning framework. Performance of the static gestures recognition system is evaluated using maching learning metrics. Experimental results show that the proposed model, trained on a database of 2000 images, provides high recognition accuracy both at the training and testing stages.
Web-Based Flood Warning System Using Decision Tree Method

Flooding is a disaster that often occurs in Indonesia, especially in the Dayahkolout Region from the beginning until now, and causes very significant damage in life. Based on this problem, we need a system that is able to handle the problem. This system is designed using the Decision Tree C4.5 Algorithm method to predict potentially flooded areas based on parameters that will later be connected to the Internet of Things. The parameters used are water level, rainfall, and water discharge. Where each parameter will be connected to IoT and the results of the predictions will later be shown on the web application. From the results of testing that has been done, the C4.5 algorithm has the best performance on the 70%: 30% data partition which has an accuracy of 100%.