Reviewerly/exHarmony huggingface dataset:
Design on Big data Platform-based in Higher Education Institute

According to a continuously increasing amount of information in all aspects whether the sources are retrieved from an internal or external organization, a platform should be provided for the automation of whole processes in the collection, storage, and processing of Big Data. The tool for creating Big Data is a Big Data challenge. Furthermore, the security and privacy of Big Data and Big Data analysis in organizations, government agencies, and educational institutions also have an impact on the aspect of designing a Big Data platform for higher education institute (HEi). It is a digital learning platform that is an online instruction and the use of digital media for educational reform including a module provides information on functions of various modules between computers and humans. 1) Big Data architecture is a framework for an architecture of numerous data which consisting of Big Data Infrastructure (BDI), Data Storage (Cloud-based), processing of a computer system that uses all parts of computer resources for optimal efficiency (High-Performance Computing: HPC), a network system to detect the target device network. Thereafter, according to Hadoop&amp;rsquo;s tools and techniques, when Big Data was introduced with Hadoop&amp;#39;s tools and techniques, the benefits of the Big Data platform would provide desired data analysis by retrieving existing information, to illustrate, student information and teaching information that is large amounts of information to adopt for accurate forecasting.
VR and AR in human performance research―An NUS experience

With the mindset of constant improvement in efficiency and safety in the workspace and training in Singapore, there is a need to explore varying technologies and their capabilities to fulfil this need. The ability of Virtual Reality (VR) and Augmented Reality (AR) to create an immersive experience of tying the virtual and physical environments coupled with information filtering capabilities brings a possibility of introducing this technology into the training process and workspace. This paper surveys current research trends, findings and limitation of VR and AR in its effect on human performance, specifically in Singapore, and our experience in the National University of Singapore (NUS).
Learning Fast Adaptation on Cross-Accented Speech Recognition

Local dialects influence people to pronounce words of the same language differently from each other. The great variability and complex characteristics of accents creates a major challenge for training a robust and accent-agnostic automatic speech recognition (ASR) system. In this paper, we introduce a cross-accented English speech recognition task as a benchmark for measuring the ability of the model to adapt to unseen accents using the existing CommonVoice corpus. We also propose an accent-agnostic approach that extends the model-agnostic meta-learning (MAML) algorithm for fast adaptation to unseen accents. Our approach significantly outperforms joint training in both zero-shot, few-shot, and all-shot in the mixed-region and cross-region settings in terms of word error rate.
The Quantum Ecosystem and Its Future Workforce

Abstract There is no doubt, we are entering the second quantum revolution. Every week some exciting news about quantum technologies appears in the media. Nowadays, several countries worldwide have initiated a quantum program to develop this emerging market. However, this will require having a highly trained and skilled workforce. How can society be ready when the time comes?
Algoritme Spatial Decision Tree untuk Evaluasi Kesesuaian Lahan Padi Sawah Irigasi

Agriculture has a strategic role in a country whereas food self-sufficiency being the main goal to be achieved. Indonesia has set a strategic plan for increasing the productivity of several commodities, including rice, especially irrigated lowland rice. That matter can be done by agricultural land extensification, which requires a land suitability directional map. This study aims to produce irrigated lowland rice land suitability maps which can be obtained by evaluation using spatial decision tree algorithm. The model is made in two different types, where model Y is an optimized version of model X. The dataset consists of two categories, namely eleven explanatory layers which are land and weather characteristics, and a target layer that represents irrigated lowland rice land suitability in study area of Grobogan Regency, Central Java Province. As an addition to planting requirements, two spatial weather datasets were generated using ordinary cokriging interpolation, which was not used in previous research, while actually being important element for determining plant timing an agricultural commodity. Based on accuracy, model Y is the best model with 96.67%, compared to model X with 86%. Both models make relief variable as the root node, but in spatial decision tree result, model X involves all variables, while model Y does not involve an elevation variable. The addition of weather variables in models is appropriate, as evidenced by the involvement in rules.
SOMHunter: Lightweight Video Search System with SOM-Guided Relevance Feedback

In the last decade, the Video Browser Showdown (VBS) became a comparative platform for various interactive video search tools competing in selected video retrieval tasks. However, the participation of new teams with an own, novel tool is prohibitively time-demanding because of the large number and complexity of components required for constructing a video search system from scratch. To partially alleviate this difficulty, we provide an open-source version of the lightweight known-item search system SOMHunter that competed successfully at VBS 2020. The system combines several features for text-based search initialization and browsing of large result sets; in particular a variant of W2VV++ model for text search, temporal queries for targeting sequences of frames, several types of displays including the eponymous self-organizing map view, and a feedback-based approach for maintaining the relevance scores inspired by PICHunter. The minimalistic, easily extensible implementation of SOMHunter should serve as a solid basis for constructing new search systems, thus facilitating easier exploration of new video retrieval ideas.
Exploiting complexity in pen- and touch-based signature biometrics

Biometric signature verification has been traditionally performed in pen-based office-like scenarios using devices specifically designed for acquiring handwriting. However, the high deployment of devices such as smartphones and tablets has given rise to new and thriving scenarios for signature biometrics where handwriting can be performed using not only a pen stylus but also the finger via touch interaction. Some preliminary studies have highlighted the challenge of this new scenario and the necessity of further research on the topic. The main contribution of this work is to propose a new on-line signature verification architecture adapted to the signature complexity in order to tackle this new and challenging scenario. Additionally, an exhaustive comparative analysis of both pen- and touch-based scenarios using our proposed methodology is carried out along with a review of the most relevant and recent studies in the field. Significant improvements of biometric verification performance and practical insights are extracted for the application of signature verification in real scenarios.
Effectiveness of data-driven induction of semantic spaces and traditional classifiers for sarcasm detection

Abstract Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labeled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses their own datasets to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine-learning algorithms to input texts sub-symbolically represented in a Latent Semantic space. The main consequence is that our studies establish both reference datasets and baselines for the sarcasm detection problem that could serve the scientific community to test newly proposed methods.
Undersensitivity in Neural Reading Comprehension

Current reading comprehension methods generalise well to in-distribution test sets, yet perform poorly on adversarially selected data.Prior work on adversarial inputs typically studies model oversensitivity: semantically invariant text perturbations that cause a model's prediction to change.Here we focus on the complementary problem: excessive prediction undersensitivity, where input text is meaningfully changed but the model's prediction does not, even though it should.We formulate an adversarial attack which searches among semantic variations of the question for which a model erroneously predicts the same answer, and with even higher probability.We demonstrate that models trained on both SQuAD2.0 and NewsQA are vulnerable to this attack, and then investigate data augmentation and adversarial training as defences.Both substantially decrease adversarial vulnerability, which generalises to held-out data and held-out attack spaces.Addressing undersensitivity furthermore improves model robustness on the previously introduced ADDSENT and ADDONE-SENT datasets, and models generalise better when facing train/evaluation distribution mismatch: they are less prone to overly rely on shallow predictive cues present only in the training set, and outperform a conventional model by as much as 10.9% F 1 .
CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval

We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia.CLIR-Matrix comprises (1) BI-139, a bilingual dataset of queries in one language matched with relevant documents in another language for 139×138=19,182 language pairs, and (2) MULTI-8, a multilingual dataset of queries and documents jointly aligned in 8 different languages.In total, we mined 49 million unique queries and 34 billion (query, document, label) triplets, making it the largest and most comprehensive CLIR dataset to date.This collection is intended to support research in end-to-end neural information retrieval and is publicly available at https: //github.com/ssun32/CLIRMatrix.We provide baseline neural model results on BI-139, and evaluate MULTI-8 in both singlelanguage retrieval and mix-language retrieval settings.
Towards More Accurate Uncertainty Estimation In Text Classification

The uncertainty measurement of classified results is especially important in areas requiring limited human resources for higher accuracy.For instance, data-driven algorithms diagnosing diseases need accurate uncertainty score to decide whether additional but limited quantity of experts are needed for rectification.However, few uncertainty models focus on improving the performance of text classification where human resources are involved.To achieve this, we aim at generating accurate uncertainty score by improving the confidence of winning scores.Thus, a model called MSD, which includes three independent components as "mix-up", "self-ensembling", "distinctiveness score", is proposed to improve the accuracy of uncertainty score by reducing the effect of overconfidence of winning score and considering the impact of different categories of uncertainty simultaneously.MSD can be applied with different Deep Neural Networks.Extensive experiments with ablation setting are conducted on four real-world datasets, on which, competitive results are obtained.
Optical encryption using uncorrelated characteristics of dynamic scattering media and spatially random sampling of a plaintext

Scattering media are generally regarded as an obstacle in optical imaging. However, the scattering of a diffuser can be exactly taken as an advantage to act as random phase masks in the field of optical encryption to enhance information security. Here, we propose and demonstrate a dynamic diffuser based optical encryption method, which increases the ciphering strength by exploiting the uncorrelated characteristics of the dynamic diffuser as well as randomly sampling the plaintext multiple times. The light emitted from a randomly sampled plaintext passing through the dynamic diffuser generates noise-like speckles, and then SNR of the recorded speckles is further reduced for obtaining the ciphertexts, which makes COA using PRA almost impossible. The specific uncorrelated characteristics of the dynamic diffuser make the ciphertexts and the PSF keys of the optical encryption unique. Therefore, only authorized users who mastered the keys can decrypt the plaintext. The proposed method is very simple and flexible since it can also achieve the encryption offline by performing convolutions on partial-plaintexts with pre-recorded uncorrelated PSFs to generate speckle patterns and then reducing their SNR to obtain the ciphertexts. This type of encryption technique has a promising prospect in applications involving images and/or videos information encryption owing to its simplicity and flexibility.
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks

We study multi-turn response generation for open-domain dialogues.The existing state-ofthe-art addresses the problem with deep neural architectures.While these models improved response quality, their complexity also hinders the application of the models in real systems.In this work, we pursue a model that has a simple structure yet can effectively leverage conversation contexts for response generation.To this end, we propose four auxiliary tasks including word order recovery, utterance order recovery, masked word recovery, and masked utterance recovery, and optimize the objectives of these tasks together with maximizing the likelihood of generation.By this means, the auxiliary tasks that relate to context understanding can guide the learning of the generation model to achieve a better local optimum.Empirical studies with three benchmarks indicate that our model can significantly outperform state-of-the-art generation models in terms of response quality on both automatic evaluation and human judgment, and at the same time enjoys a much faster decoding process.
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training

Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment classes.These artefacts are exploited by neural networks even when only considering the hypothesis and ignoring the premise, leading to unwanted biases.Belinkov et al. (2019b) proposed tackling this problem via adversarial training, but this can lead to learned sentence representations that still suffer from the same biases.We show that the bias can be reduced in the sentence representations by using an ensemble of adversaries, encouraging the model to jointly decrease the accuracy of these different adversaries while fitting the data.This approach produces more robust NLI models, outperforming previous de-biasing efforts when generalised to 12 other NLI datasets (Belinkov et al., 2019a;Mahabadi et al., 2020).In addition, we find that the optimal number of adversarial classifiers depends on the dimensionality of the sentence representations, with larger sentence representations being more difficult to de-bias while benefiting from using a greater number of adversaries.
Investigating the Effects of Interactive E-Book towards Academic Achievement

Universities are trending towards electronic books (e-books) as instructional materials, displacing traditional printed books. The rapid acquisition of e-books has changed the way information is presented and one of the improvements is to make e-books interactive. However, there is an incomplete body of knowledge on how interactive e-books affect students, particularly in the learning of statistics. This paper aims to examine the effects of interactive e-books on academic achievement. This paper adopted an experimental approach to test the causal effect of two types of e-books, namely Traditional E-book (TE) and Interactive E-book (IE) on a sample of undergraduates enrolled in an introductory statistics unit. The experimental results indicated that students who learn statistics through IE produced higher scores in academic achievement than students who learn through TE. The findings of the study first extend the existing theory by showing that TE and IE can account for the variations in academic achievement. The study implied that e-books should not be static and e-book publishers and educators can choose to design their e-books using interactive formats with animation components depending on available resources. The study offers new insights on how academic achievement of students can be better managed through the design of e-book types.
Data and Representation for Turkish Natural Language Inference

Large annotated datasets in NLP are overwhelmingly in English.This is an obstacle to progress in other languages.Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive.At the same time, commercial machine translation systems are now robust.Can we leverage these systems to translate Englishlanguage datasets automatically?In this paper, we offer a positive response for natural language inference (NLI) in Turkish.We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels.Using these datasets, we address core issues of representation for Turkish NLI.We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large.Finally, we show that models trained on our machinetranslated datasets are successful on humantranslated evaluation sets.We share all code, models, and data publicly.
Deep learning based question answering system in Bengali

Recent advances in the field of natural language processing has improved state-of-the-art performances on many tasks including question answering for languages like English. Bengali language is ranked seventh and is spoken by about 300 million people all over the world. But due to lack of data and active research on QA similar progress has not been achieved for Bengali. Unlike English, there is no benchmark large scale QA dataset collected for Bengali, no pretrained language model that can be modified for Bengali question answering and no human baseline score for QA has been established either. In this work we use state-of-the-art transformer models to train QA system on a synthetic reading comprehension dataset translated from one of the most popular benchmark datasets in English called SQuAD 2.0. We collect a smaller human annotated QA dataset from Bengali Wikipedia with popular topics from Bangladeshi culture for evaluating our models. Finally, we compare our models with human children to set up a benchmark score using survey experiments.
Image Retrieval Using the Fused Perceptual Color Histogram

Extracting visual features for image retrieval by mimicking human cognition remains a challenge. Opponent color and HSV color spaces can mimic human visual perception well. In this paper, we improve and extend the CDH method using a multi-stage model to extract and represent an image in a way that mimics human perception. Our main contributions are as follows: (1) a visual feature descriptor is proposed to represent an image. It has the advantages of a histogram-based method and is consistent with visual perception factors such as spatial layout, intensity, edge orientation, and the opponent colors. (2) We improve the distance formula of CDHs; it can effectively adjust the similarity between images according to two parameters. The proposed method provides efficient performance in similar image retrieval rather than instance retrieval. Experiments with four benchmark datasets demonstrate that the proposed method can describe color, texture, and spatial features and performs significantly better than the color volume histogram, color difference histogram, local binary pattern histogram, and multi-texton histogram, and some SURF-based approaches.
The Integration of Sustainable Development Goals in Educational Robotics: A Teacher Education Experience

Education is of key importance to achieve the Sustainable Development Goals (SGDs), particularly now that the COVID-19 pandemic threatens the progress made toward a more inclusive, peaceful, fair, and prosperous society. In this research study, students of the bachelor’s degree in Pedagogy receive a specialized training that addresses the SDGs through Information and Communication Technologies (ICT) referring to computers and digital technologies. In particular, the training addresses the SDGs through Educational Robotics (ER). The aim of the training course is twofold: first, to reinforce the students’ Teacher Digital Competence (TDC), defined as teachers’ ICT competence and didactic and methodological skills; and second, to train them in the integration of SDGs in ER educational projects. This paper presents a case study in which qualitative and quantitative methods are implemented. The objectives of the study are to investigate the students’ level of TDC and examine their ability to integrate SDGs in ER projects. The research instruments employed are the COMDID-A questionnaire, a lesson plan template, and a lesson plan evaluation rubric. Among the most prominent findings, it is shown that the training enabled the students to work on sustainability in a cross-curricular manner and successfully develop ER projects centered on different SDGs. These conclusions will encourage teacher education institutions to integrate SDGs in their curriculum.
The Octave Allegro Method in Risk Management Assessment of Educational Institutions

Risk management is useful in overcoming various problems such as not optimal business processes, the company’s reputation down, financial loss, or bankruptcy of a company. In the application of information systems, most organizations or companies have not noticed the importance of information systems security as well as the assets and impacts that arise. For that, the risk management assessment is used in reducing the errors that occur in the information system of the company's business processes. The risk management assessment is applied to the information system along with its assets in evaluating the possibilities of menaces and vulnerabilities. The Risk management assessment analysis is applied to the academic information system in universities. The result of the risk assessment is the results of recommendations on the stages that need to be done in protecting the assets of information systems and information systems themselves.
Parallelized Radix-4 Scalable Montgomery Multipliers

This paper describes a parallelized radix-4 scalable Montgomery multiplier implementation. The design does not require hardware multipliers, and uses parallelized multiplication to shorten the critical path. By left-shifting the sources rather than right-shifting the result, the latency between processing elements is shortened from two cycles to nearly one. Multiplexers are used to select precomputed products. Carry-save adders propagate carry bits before words are discarded. The new design can perform 1024-bit modular exponentiation in 9.4 ms and 256-bit exponentiation in 0.38 ms using 4997 Virtex2 4-input lookup tables, while consuming 30% fewer LUTs than a previous parallelized radix-4 design. This is comparable to radix-2 for long multiplies and nearly twice as fast for short ones.