Reviewerly/exHarmony huggingface dataset:
A Study of fastText Word Embedding Effects in Document Classification in Bangla Language

Natural language processing is the current topic due to many important tasks like document classification, named entity recognition, opinion mining, sentiment analysis, textual entailment, etc. Such types of task in the Bangla language is also important. This research work endeavored to find out the word embedding of the Bengali language. Leveraging the fastText word embedding, it has shown significant performance in Bangla document classification without any prepossessing like lemmatization, stemming, and others. For the extrinsic evaluation of our word vectors, a classification problem-solving strategy has been used which showed an outstanding result. In the classification module, attempts have been made to classify 40 thousand News samples into 12 categories. For this purpose, three deep learning techniques have been used: Convolutional Neural Network (CNN), Bi-Directional LSTM (BLSTM) and Convolutional Bi-Directional LSTM (CBLSTM) alongside fastText. From the analogous study of all the parameters of every classifier implemented here, we found that the BLSTM technique is the most promising technique for this task. This technique achieved 91.49%, 87.87%, and 85.5% accuracies for Training, Testing, and Validation set, respectively.
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This study introduces a privacy-preserving framework for distributed deep fuzzy learning. Assuming training data as private, the problem of learning of local deep fuzzy models is considered in a distributed setting under differential privacy framework. A local deep fuzzy model, formed by a composition of a finite number of Takagi-Sugeno type fuzzy filters, is learned using variational Bayesian inference. This paper suggests an optimal (∊,δ)-differentially private noise adding mechanism that results in multi-fold reduction in noise magnitude over the classical Gaussian mechanism and thus leads to an increased utility for a given level of privacy. Further, the robustness feature, offered by the rule-based fuzzy systems, is leveraged to alleviate the effect of added data noise on the utility. An architecture for distributed form of differentially private learning is suggested where a privacy wall separates the private local training data from the globally shared data, and fuzzy sets and fuzzy rules are used to aggregate robustly the local deep fuzzy models for building the global model. The privacy wall uses noise adding mechanisms to attain differential privacy for each participant’s private training data and thus the adversaries have no direct access to the training data.
Intelligent Hough Transform With Jaya to Detect the Diameter of Red-Hot Circular Workpiece

Diameter detection plays an important role in the production of circular workpieces. It is very difficult to obtain the accurate measurement of diameter when the circular workpiece is red-hot because of the existence of lots of noise. Although Hough transform (HT) has many advantages for diameter detection of circular workpieces, it has still suffered from some serious problems, which include the requirements of a large amount of computation and memory occupancy. To address these problems, we propose an intelligent Hough transform method with Jaya (IHTJ). The key points are as follows. We analyze the problem of diameter detection for red-hot circular workpieces, which is essentially the problem of ellipse fitting. Then the problem of ellipse fitting with HT is converted into searching optimal solution in parameter space, and we construct the fitness function based on the principle of ellipse fitting with HT. In order to fit the ellipse accurately and rapidly, we introduce Jaya to search the optimal ellipse parameters in parameter space at last, and the computation burden and memory occupancy of HT are greatly reduced. Compared with direct least-square (DLS), randomized Hough transform (RHT) and random sample consistency (RANSAC), IHTJ can fit the ellipse rapidly while the advantages of strong robustness and high accuracy of HT are retained. Some simulations and practical experiments are carried out to verify the effectiveness of IHTJ.
A bibliometric analysis of articles on innovation systems in Scopus journals written by authors from Indonesia, Singapore, and Malaysia

Purpose: The aim of this study was to analyze the bibliometric characteristics of articles on innovation systems published in Scopus-indexed journals by authors with Indonesian affiliations from 1998 to 2019, in comparison with those published by authors from Singapore and Malaysia.Methods: We conducted a bibliometric and content analysis of publications in the Scopus database. A total of 138 articles from Indonesia, 209 articles from Singapore, and 309 articles from Malaysia were analyzed. They were classified by publication year, authors, co-authors’ country, affiliation, keywords, and journal title.Results: Authors with affiliations from Malaysia were more productive than authors from Singapore and Indonesia during 1998 to 2019. In terms of the quality of papers, Singapore had more productive authors than Malaysia and Indonesia based on the citation frequency.Conclusion: Although fewer articles on innovation systems were published by authors from Indonesia than by those from Malaysia and Singapore, the recent increase in the number of publications by Indonesian authors suggests that number of articles from Indonesia will soon surpass those from the other two countries. International collaboration will help accelerate the number of publications.
eclingo : A Solver for Epistemic Logic Programs

Abstract We describe eclingo, a solver for epistemic logic programs under Gelfond 1991 semantics built upon the Answer Set Programming system clingo. The input language of eclingo uses the syntax extension capabilities of clingo to define subjective literals that, as usual in epistemic logic programs, allow for checking the truth of a regular literal in all or in some of the answer sets of a program. The eclingo solving process follows a guess and check strategy. It first generates potential truth values for subjective literals and, in a second step, it checks the obtained result with respect to the cautious and brave consequences of the program. This process is implemented using the multi-shot functionalities of clingo. We have also implemented some optimisations, aiming at reducing the search space and, therefore, increasing eclingo ’s efficiency in some scenarios. Finally, we compare the efficiency of eclingo with two state-of-the-art solvers for epistemic logic programs on a pair of benchmark scenarios and show that eclingo generally outperforms their obtained results.
A High-Throughput Hardware Implementation of SHA-256 Algorithm

The SHA-256 algorithm is widely used in the field of security. In this paper, we propose a rescheduling method for the SHA-256 round computation. Based on the proposed rescheduling, we propose a design for SHA-256, in which the critical path is reduced. Our design is implemented on the Xilinx Virtex-4 FPGA. It achieves the throughput of 1984 Mbps with the area of 979 slices. Compared with other designs on FPGA, our design shows a better performance in terms of the throughput.
A study of the effects of negative transfer on deep unsupervised domain adaptation methods

Intelligent systems driven by deep learning have become relevant in real-world applications with the increasing availability of technology and data. However, real-world settings require effective and robust deep learning models that are able to deal with unforeseen samples and a variety of data distributions. Recently, Unsupervised Domain Adaptation (UDA) for deep learning models (D-UDA) addresses such limitations by transferring knowledge from a labeled source domain to an unlabeled target domain, reducing the dataset shift between domain distributions. However, despite recent advances in D-UDA, current works have not been focused on studying specific cases in the distribution shifts under which D-UDA methods can ensure that transfer is helpful, avoiding a ‘negative transfer’ risk. In this paper, we present a study about the effect of different cases of negative transfer over the most popular and recent D-UDA methods reported in the literature. For this, we evaluate the accuracy performance of D-UDA methods over different scenarios containing different types of distribution shifts. Experimental results show that specific cases of distribution shifts generate negative transfer over the evaluated D-UDA methods. From this study, we provide some insights to select and design robust D-UDA methods in intelligent systems.
Improved Prosody from Learned F0 Codebook Representations for VQ-VAE Speech Waveform Reconstruction

Vector Quantized Variational AutoEncoders (VQ-VAE) are a powerful representation learning framework that can discover discrete groups of features from a speech signal without supervision. Until now, the VQ-VAE architecture has previously modeled individual types of speech features, such as only phones or only F0. This paper introduces an important extension to VQ-VAE for learning F0-related suprasegmental information simultaneously along with traditional phone features.The proposed framework uses two encoders such that the F0 trajectory and speech waveform are both input to the system, therefore two separate codebooks are learned. We used a WaveRNN vocoder as the decoder component of VQ-VAE. Our speaker-independent VQ-VAE was trained with raw speech waveforms from multi-speaker Japanese speech databases. Experimental results show that the proposed extension reduces F0 distortion of reconstructed speech for all unseen test speakers, and results in significantly higher preference scores from a listening test. We additionally conducted experiments using single-speaker Mandarin speech to demonstrate advantages of our architecture in another language which relies heavily on F0.
CrossASR: Efficient Differential Testing of Automatic Speech Recognition via Text-To-Speech

Automatic speech recognition (ASR) systems are ubiquitous parts of modern life. It can be found in our smartphones, desktops, and smart home systems. To ensure its correctness in recognizing speeches, ASR needs to be tested. Testing ASR requires test cases in the form of audio files and their transcribed texts. Building these test cases manually, however, is tedious and time-consuming. To deal with the aforementioned challenge, in this work, we propose CrossASR, an approach that capitalizes the existing Text-To-Speech (TTS) systems to automatically generate test cases for ASR systems. CrossASR is a differential testing solution that compares outputs of multiple ASR systems to uncover erroneous behaviors among ASRs. CrossASR efficiently generates test cases to uncover failures with as few generated tests as possible; it does so by employing a failure probability predictor to pick the texts with the highest likelihood of leading to failed test cases. As a black-box approach, CrossASR can generate test cases for any ASR, including when the ASR model is not available (e.g., when evaluating the reliability of various third-party ASR services).We evaluated CrossASR using 4 TTSes and 4 ASRs on the Europarl corpus. The experimented ASRs are Deepspeech, Deepspeech2, wav2letter, and wit. Our experiments on a randomly sampled 20,000 English texts showed that within an hour, CrossASR can produce, on average from 3 experiments, 130.34, 123.33, 47.33, and 8.66 failed test cases using Google, Respon-siveVoice, Festival, and Espeak TTSes, respectively. Moreover, when we run CrossASR on the entire 20,000 texts, it can generate 13,572, 13,071, 5,911, and 1,064 failed test cases using Google, ResponsiveVoice, Festival, and Espeak TTSes, respectively. Based on a manual verification carried out on statistically representative sample size, we found that most samples are actual failed test cases (audio understandable to humans but cannot be transcribed properly by an ASR), demonstrating that CrossASR is highly reliable in determining failed test cases. We also make the source code for CrossASR and evaluation data available at https://github.com/soarsmu/CrossASR.
Assessing Action Quality via Attentive Spatio-Temporal Convolutional Networks

Action quality assessment, which aims at evaluating the performance of specific actions, has drawn more and more attention due to its extensive demand in sports, health care, etc. Unlike action recognition, in which a few typical frames are sufficient for classification, action quality assessment requires analysis at a fine temporal granularity to discover the subtle motion difference. In this paper, we propose a novel spatio-temporal framework for action quality assessment at full-frame-rate (25fps), which consists of two steps: i.e. spatio-temporal feature extraction and temporal feature fusion, respectively. In the first step, to generate representative spatio-temporal dynamics, we utilize a spatial convolutional network (SCN) together with specially designed temporal convolutional networks (TCNs) and train them by a two-stage strategy. In the second step, we introduce an attention mechanism to fuse features in the temporal dimension according to their impact on the overall performance. Compared with existing three dimensional convolutional neural networks (3D-CNN) based methods, our model is capable of capturing more action quality relevant details. As a by-product, our model can also attend to the highlight moments in sports videos, which gives a better interpretation of the score. Extensive experiments on three public benchmarks demonstrate that the proposed method has distinct advantage in action quality assessment and achieves improvement over the state-of-the-art.
The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures

Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand: e.g., previous work has suggested there is a connection between the expected success of bilingual lexicon induction (BLI) and the assumption of (approximate) isomorphism between monolingual embedding spaces.In this work we present a large-scale study focused on the correlations between monolingual embedding space similarity and task performance, covering thousands of language pairs and four different tasks: BLI, parsing, POS tagging and MT.We hypothesize that statistics of the spectrum of each monolingual embedding space indicate how well they can be aligned.We then introduce several isomorphism measures between two embedding spaces, based on the relevant statistics of their individual spectra.We empirically show that 1) language similarity scores derived from such spectral isomorphism measures are strongly associated with performance observed in different crosslingual tasks, and 2) our spectral-based measures consistently outperform previous standard isomorphism measures, while being computationally more tractable and easier to interpret.Finally, our measures capture complementary information to typologically driven language distance measures, and the combination of measures from the two families yields even higher task performance correlations.
Analysis of artificial neural network performance based on influencing factors for temperature forecasting applications

Artificial neural network (ANN)-based methods belong to one of the most growing research fields within the artificial intelligence ecosystem, and many novel contributions have been developed over the last years. They are applied in many contexts, although some “influencing factors” such as the number of neurons, the number of hidden layers, and the learning rate can impact the performance of the resulting artificial neural network-based applications. This paper provides a deep analysis about artificial neural network performance based on such factors for real-world temperature forecasting applications. An improved back propagation algorithm for such applications is also presented. By using the results of this paper, researchers and practitioners can analyse the encountered issues when applying ANN-based models for their own specific applications with the aim of achieving better performance indexes.
Be Different to Be Better! A Benchmark to Leverage the Complementarity of Language and Vision

This paper introduces BD2BB, a novel language and vision benchmark that requires multimodal models combine complementary information from the two modalities.Recently, impressive progress has been made to develop universal multimodal encoders suitable for virtually any language and vision tasks.However, current approaches often require them to combine redundant information provided by language and vision.Inspired by real-life communicative contexts, we propose a novel task where either modality is necessary but not sufficient to make a correct prediction.To do so, we first build a dataset of images and corresponding sentences provided by human participants.Second, we evaluate state-of-the-art models and compare their performance against human speakers.We show that, while the task is relatively easy for humans, best-performing models struggle to achieve similar results.
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability

We consider the blackbox transfer-based targeted adversarial attack threat model in the realm of deep neural network (DNN) image classifiers. Rather than focusing on crossing decision boundaries at the output layer of the source model, our method perturbs representations throughout the extracted feature hierarchy to resemble other classes. We design a flexible attack framework that allows for multi-layer perturbations and demonstrates state-of-the-art targeted transfer performance between ImageNet DNNs. We also show the superiority of our feature space methods under a relaxation of the common assumption that the source and target models are trained on the same dataset and label space, in some instances achieving a $10\times$ increase in targeted success rate relative to other blackbox transfer methods. Finally, we analyze why the proposed methods outperform existing attack strategies and show an extension of the method in the case when limited queries to the blackbox model are allowed.
Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators

We introduce a new multilevel ensemble Kalman filter method (MLEnKF) which consists of a hierarchy of independent samples of ensemble Kalman filters (EnKF). This new MLEnKF method is fundamentally different from the preexisting method introduced by Hoel, Law and Tempone in 2016, and it is suitable for extensions towards multi-index Monte Carlo based filtering methods. Robust theoretical analysis and supporting numerical examples show that under appropriate regularity assumptions, the MLEnKF method has better complexity than plain vanilla EnKF in the large-ensemble and fine-resolution limits, for weak approximations of quantities of interest. The method is developed for discrete-time filtering problems with finite-dimensional state space and linear observations polluted by additive Gaussian noise.
Projection-based augmented reality system for assembly guidance and monitoring

Purpose This study aims to monitor and guide the assembly process. The operators need to change the assembly process according to the products’ specifications during manual assembly of mass customized production. Traditional information inquiry and display methods, such as manual lookup of assembly drawings or electronic manuals, are inefficient and error-prone. Design/methodology/approach This paper proposes a projection-based augmented reality system (PBARS) for assembly guidance and monitoring. The system includes a projection method based on viewpoint tracking, in which the position of the operator’s head is tracked and the projection images are changed correspondingly. The assembly monitoring phase applies a method for parts recognition. First, the pixel local binary pattern (PX-LBP) operator is achieved by merging the classical LBP operator with the pixel classification process. Afterward, the PX-LBP features of the depth images are extracted and the randomized decision forests classifier is used to get the pixel classification prediction image (PCPI). Parts recognition and assembly monitoring is performed by PCPI analysis. Findings The projection image changes with the viewpoint of the human body, hence the operators always perceive the three-dimensional guiding scene from different viewpoints, improving the human-computer interaction. Part recognition and assembly monitoring were achieved by comparing the PCPIs, in which missing and erroneous assembly can be detected online. Originality/value This paper designed the PBARS to monitor and guide the assembly process simultaneously, with potential applications in mass customized production. The parts recognition and assembly monitoring based on pixels classification provides a novel method for assembly monitoring.
Blockchain Technology for Hospitality Industry

Blockchain technology and its economic, social, and technological implications, have seen significant upsurge among researchers across the globe. Blockchain has revolutionized the concept of transactions by enhancing their security and efficiency. The blockchain technology is primarily associated with Bitcoin but however, the technology has the potential to go far beyond cryptocurrencies across various verticals. In a recent survey performed by Deloitte, more than 53% of the responds across various industries see blockchain technology as a critical requirement for their respective organizations [4]. The hospitality industry is one such domain where blockchain can prove enormously beneficial. The paper explores major application areas that involve the applicability of blockchain technology in the hospitality industry. The work investigates the implications of blockchain technology in enhancing operational efficiency, increased revenue and improved security and privacy for the hospitality industry. The paper establishes a link between blockchain technology and the hospitality sector and subsequently analyses recent works and case studies. A two-step research study has been introduced to present a systematic review of some of the main contributions in the literature that focuses on the integration of the blockchain technology and hospitality industry. The article adds to an interesting concept of blockchain technology, and its current research trends with respect to the hospitality industry and their various areas of application.
A tucker decomposition based knowledge distillation for intelligent edge applications

Knowledge distillation(KD) has been proven an effective method in intelligent edge computing and have achieved extensive study in recent deep learning research. However, when the teacher network is too stronger compared to the student network, the effect of knowledge distillation is not ideal. Aiming at resolving this problem, an improved method of knowledge distillation (TDKD) is proposed, which enables to transfer the complex mapping functions learned by cumbersome models to relatively simpler models. Firstly, the tucker-2 decomposition was performed on the convolutional layers of the original teacher model to reduce the capacity variance between the teacher network and student network. Then, the decomposed model will be used as a new teacher to participate in knowledge distillation for the student model. The experimental results show that the TDKD method can effectively solve the problem of poor distillation performance, which not only get better results if the KD method is effective, but also can reactivate the invalid KD method to some extents.