Reviewerly/exHarmony huggingface dataset:
Towards Online End-to-end Transformer Automatic Speech Recognition

The Transformer self-attention network has recently shown promising performance as an alternative to recurrent neural networks in end-to-end (E2E) automatic speech recognition (ASR) systems. However, Transformer has a drawback in that the entire input sequence is required to compute self-attention. We have proposed a block processing method for the Transformer encoder by introducing a context-aware inheritance mechanism. An additional context embedding vector handed over from the previously processed block helps to encode not only local acoustic information but also global linguistic, channel, and speaker attributes. In this paper, we extend it towards an entire online E2E ASR system by introducing an online decoding process inspired by monotonic chunkwise attention (MoChA) into the Transformer decoder. Our novel MoChA training and inference algorithms exploit the unique properties of Transformer, whose attentions are not always monotonic or peaky, and have multiple heads and residual connections of the decoder layers. Evaluations of the Wall Street Journal (WSJ) and AISHELL-1 show that our proposed online Transformer decoder outperforms conventional chunkwise approaches.
Competing (Semi-)Selfish Miners in Bitcoin

The Bitcoin protocol prescribes certain behavior by the miners who are responsible for maintaining and extending the underlying blockchain; in particular, miners who successfully solve a puzzle, and hence can extend the chain by a block, are supposed to release that block immediately. Eyal and Sirer showed, however, that a selfish miner is incentivized to deviate from the protocol and withhold its blocks under certain conditions.
How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods.

As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous. Using extensive evaluation with multiple real-world datasets (including COMPAS), we demonstrate how extremely biased (racist) classifiers crafted by our framework can easily fool popular explanation techniques such as LIME and SHAP into generating innocuous explanations which do not reflect the underlying biases.
Integrating augmented reality into worksheets: Unveil learning to support higher-order thinking skills

Learning in the 21st century has encouraged the presence of technology in education. Physics is one of the subjects that require a practicum to deliver abstract material. However, in the current physics practicum, student worksheet is still being used, which does not support high-level thinking skills and technology is not yet integrated. This study aims to produce practical worksheet equipped with augmented reality (AR) in physics material class XII odd semester. This study uses Dick and Carey model of research and development method. The data of this study were obtained from the results of validation by experts in the material, media, and learning, as well as the results of trials by teachers and students conducted with a Likert scale questionnaire. A pretest and posttest are also conducted to test the improvement of students. The product produced is worksheet equipped with AR technology that can display videos at stimulus stages, procedures, and conceptual applications by scanning images bearing the AR logo with an android smartphone. The results of the worksheet feasibility test conducted by material experts received an average percentage of 93%, the feasibility test conducted by media experts obtained an average percentage of 90%, and the feasibility test conducted by learning experts received an average percentage of 81%. The results of user trials by teachers get an average percentage of 98%, user trials by students get an average of 90.1%, and the results of pretest and posttest get N-gain of 0.72. This study indicates that the Worksheet for Students to practice higher-order thinking skills has met the criteria very well based on the assessment in terms of material, media and learning and declared fit as Physics teaching materials in class material XII odd semester.
Random Subspace Ensemble With Enhanced Feature for Hyperspectral Image Classification

In this letter, we propose a new hyperspectral image (HSI) classification approach, called the random subspace ensemble with enhanced feature (RSE-EF), which trains several individual classifiers with enhanced spatial information. The proposed approach aims to address two common issues: the curses of the imbalanced training samples and high feature-to-instance ratio. Specifically, we first propose a similar-neighboring-sample-search (SNSS) method to address the issue of imbalanced training samples. Afterward, we generate the enhanced random subspaces (ERSs) that possess relatively lower dimensionality and more distinctive information compared with the original random subspaces (RSs) so as to alleviate the curse of high feature-to-instance ratio more effectively. Furthermore, a shallow neural network kernel-based extreme learning machine (KELM) is applied to the RSE-EF to classify image pixels. Experimental results on two public hyperspectral data sets illustrate that the proposed RSE-EF approach outperforms the state-of-the-art HSI classification counterparts.
Dice Loss for Data-imbalanced NLP Tasks

Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples (or easy-negative examples) overwhelms the training. The most commonly used cross entropy (CE) criteria is actually an accuracy-oriented objective, and thus creates a discrepancy between training and test: at training time, each training instance contributes equally to the objective function, while at test time F1 score concerns more about positive examples. In this paper, we propose to use dice loss in replacement of the standard cross-entropy objective for data-imbalanced NLP tasks. Dice loss is based on the Sorensen-Dice coefficient or Tversky index, which attaches similar importance to false positives and false negatives, and is more immune to the data-imbalance issue. To further alleviate the dominating influence from easy-negative examples in training, we propose to associate training examples with dynamically adjusted weights to deemphasize easy-negative examples.Theoretical analysis shows that this strategy narrows down the gap between the F1 score in evaluation and the dice loss in training. With the proposed training objective, we observe significant performance boost on a wide range of data imbalanced NLP tasks. Notably, we are able to achieve SOTA results on CTB5, CTB6 and UD1.4 for the part of speech tagging task; SOTA results on CoNLL03, OntoNotes5.0, MSRA and OntoNotes4.0 for the named entity recognition task; along with competitive results on the tasks of machine reading comprehension and paraphrase identification.
What Can History Tell Us?

Recommendation systems have been widely applied to many E-commerce and online social media platforms. Recently, sequential item recommendation, especially session-based recommendation, has aroused wide research interests. However, existing sequential recommendation approaches either ignore the historical sessions or consider all historical sessions without any distinction that whether the historical sessions are relevant or not to the current session, which motivates us to distinguish the effect of each historical session and identify relevant historical sessions for recommendation. In light of this, we propose a novel deep learning based sequential recommender framework for session-based recommendation, which takes Nonlocal Neural Network and Recurrent Neural Network as the main building blocks. Specifically, we design a two-layer nonlocal architecture to identify historical sessions that are relevant to the current session and learn the long-term user preferences mostly from these relevant sessions. Besides, we also design a gated recurrent unit (GRU) enhanced by the nonlocal structure to learn the short-term user preferences from the current session. Finally, we propose a novel approach to integrate both long-term and short-term user preferences in a unified way to facilitate training the whole recommender model in an end-to-end manner. We conduct extensive experiments on two widely used real-world datasets, and the experimental results show that our model achieves significant improvements over the state-of-the-art methods.
Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework

Inspired by the human visual perception system, hexagonal image processing in the context of machine learning deals with the development of image processing systems that combine the advantages of evolutionary motivated structures based on biological models. While conventional state of the art image processing systems of recording and output devices almost exclusively utilize square arranged methods, their hexagonal counterparts offer a number of key advantages that can benefit both researchers and users. This contribution serves as a general application-oriented approach with the synthesis of the therefor designed hexagonal image processing framework, called Hexnet, the processing steps of hexagonal image transformation, and dependent methods. The results of our created test environment show that the realized framework surpasses current approaches of hexagonal image processing systems, while hexagonal artificial neural networks can benefit from the implemented hexagonal architecture. As hexagonal lattice format based deep neural networks, also called H-DNN, can be compared to their square counterpart by transforming classical square lattice based data sets into their hexagonal representation, they can also result in a reduction of trainable parameters as well as result in increased training and test rates.
Comparative Study on Histogram Equalization Techniques for Medical Image Enhancement

Magnetic Resonance Imaging (MRI) is a medical imaging technique used for analyzing and diagnosing diseases such as cancer or tumor in a brain. In order to analyze these diseases, physicians require good contrast scanned images obtained from MRI for better treatment purpose as it contains maximum information of the disease. MRI images are low-contrast images which lead to difficulty in diagnoses, hence better localization of image pixels is required. Histogram equalization techniques help to enhance the image so that it gives an improved visual quality and a well-defined problem. The contrast and brightness are enhanced in such a way that it does not lose its original information and the brightness is preserved. In this paper, we compared the different equalization techniques which are critically studied and elaborated. Various parameters are calculated and tabulated, finally concluded the best among them.
Improving the indistinguishability of single photons from an ion-cavity system

We investigate two schemes for generating indistinguishable single photons, a key feature of quantum networks, from a trapped ion coupled to an optical cavity. Through selection of the initial state in a cavity-assisted Raman transition, we suppress the detrimental effects of spontaneous emission on the photon's coherence length, measuring a visibility of $81(2)%$ without subtraction of background counts in a Hong-Ou-Mandel interference measurement, the highest reported for an ion-cavity system. In comparison, a visibility of 50(2)% was measured using a more conventional single-photon scheme. We demonstrate through numerical analysis of the single-photon generation process that our scheme produces photons of a given indistinguishability with a greater efficiency than the conventional one. Single-photon schemes such as the one demonstrated here have applications in distributed quantum computing and communications, which rely on high-fidelity entanglement swapping and state transfer through indistinguishable single photons.
Function boundary detection in stripped binaries

Automated cyber defense tools require the ability to analyze binary applications, detect vulnerabilities and automatically patch those vulnerabilities. The insertion of security mechanisms that operate at function boundaries (e.g, control flow mitigation, stack guards) require automated detection of those boundaries. This paper introduces a publicly available function boundary detection tool for 32 and 64-bit Intel binaries running under Linux, that is more accurate than other reported approaches.
A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms

Although mammography is the gold standard for breast cancer screening, the high rates of false-positive mammograms remain a concern. Thus, there is an unmet clinical need for a non-invasive and reliable test to differentiate between malignant and benign breast lesions in order to avoid subjecting patients with abnormal mammograms to unnecessary follow-up diagnostic procedures. Serum samples from 116 malignant breast lesions and 64 benign breast lesions were comprehensively profiled for 2,083 microRNAs (miRNAs) using next-generation sequencing. Of the 180 samples profiled, three outliers were removed based on the principal component analysis (PCA), and the remaining samples were divided into training (n = 125) and test (n = 52) sets at a 70:30 ratio for further analysis. In the training set, significantly differentially expressed miRNAs (adjusted p < 0.01) were identified after correcting for multiple testing using a false discovery rate. Subsequently, a predictive classification model using an eight-miRNA signature and a Bayesian logistic regression algorithm was developed. Based on the receiver operating characteristic (ROC) curve analysis in the test set, the model could achieve an area under the curve (AUC) of 0.9542. Together, this study demonstrates the potential use of circulating miRNAs as an adjunct test to stratify breast lesions in patients with abnormal screening mammograms.
A Breakup Machine Learning Approach for Breast Cancer Prediction

Breast cancer is one of the most common cancers and is the major cause of cancer-related deaths in women worldwide. Breast cancer is a disease in which cells in the breast grow in a rapid state and out of control. Breast cancer can grow in different parts of the breast and may or may not spread outside the breast. If caught on early stage, the breast cancer can be cured before they spread but if the cancer cells have spread to other parts of the body, usually it is hard to cure. Early diagnosis of breast cancer might help increasing the life-span of cancer affected women. In this paper, a novel method for prediction of breast cancer, that enhances the accuracy using machine learning is proposed using six machine learning algorithms. The unbiased estimates of the algorithms are measured using k-fold cross-validation method. This proposed approach proves to increase the accuracy of traditional machine learning algorithms.
Blockchain outlook for deployment of IoT in distribution networks and smart homes

Nowadays, unlike depleting fossil fuel resources, the integration of different types of renewable energy, as distributed generation sources, into power systems is accelerated and the technological development in this area is evolving at a frantic pace. Thus, inappropriate use of them will be irrecoverably detrimental. The power industry will reach a turning point in the pervasiveness of these infinite energy sources by three factors. Climate changes due to greenhouse gas accumulation in the atmosphere; increased demand for energy consumption all over the world, especially after the genesis of Bitcoin and base cryptocurrencies; and establishing a comprehensive perspective for the future of renewable energy. The increase in the pervasiveness of renewable energy sources in small-scale brings up new challenges for the power system operators to manage an abundant number of small-scale generation sources, called microsources. The current structure of banking systems is unable to handle such massive and high-frequency transactions. Thus the incorporation of cryptocurrencies is inevitable. In addition, by utilization of IoT-enabled devices, a large body of data will be produced must be securely transferred, stored, processed, and managed in order to boost the observability, controllability, and the level of autonomy of the smart power systems. Then the appropriate controlling measures must be performed through control signals in order to serve the loads in a stable, uninterruptible, reliable, and secure way. The data acquires from IoT devices must be analyzed using artificial intelligence methods such as big data techniques, data mining, machine learning, etc. with a scant delay or almost real-time. These measures are the controversial issues of modern power systems, which are yet a matter of debate. This study delves into the aforementioned challenges and opportunities, and the corresponding solutions for the incorporation of IoT and blockchain in power systems, particularly in the distribution level and residential section, are addressed. In the last section, the role of IoT in smart buildings and smart homes, especially for energy hubs schemes and the management of residential electric vehicle supply equipment is concisely discussed.
Benchmarking Adversarial Robustness

Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning. While a lot of efforts have been made in recent years, it is of great significance to perform correct and complete evaluations of the adversarial attack and defense algorithms. In this paper, we establish a comprehensive, rigorous, and coherent benchmark to evaluate adversarial robustness on image classification tasks. After briefly reviewing plenty of representative attack and defense methods, we perform large-scale experiments with two robustness curves as the fair-minded evaluation criteria to fully understand the performance of these methods. Based on the evaluation results, we draw several important findings and provide insights for future research.
Leveraging Title-Abstract Attentive Semantics for Paper Recommendation

Paper recommendation is a research topic to provide users with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring their semantic relationship. In this paper, we regard the abstract as a sequence of sentences, and propose a two-level attentive neural network to capture: (1) the ability of each word within a sentence to reflect if it is semantically close to the words within the title. (2) the extent of each sentence in the abstract relative to the title, which is often a good summarization of the abstract document. Specifically, we propose a Long-Short Term Memory (LSTM) network with attention to learn the representation of sentences, and integrate a Gated Recurrent Unit (GRU) network with a memory network to learn the long-term sequential sentence patterns of interacted papers for both user and item (paper) modeling. We conduct extensive experiments on two real datasets, and show that our approach outperforms other state-of-the-art approaches in terms of accuracy.
An improved algorithm of white matter hyperintensity detection in elderly adults

Automated segmentation of the aging brain raises significant challenges because of the prevalence, extent, and heterogeneity of white matter hyperintensities. White matter hyperintensities can be frequently identified in magnetic resonance imaging (MRI) scans of older individuals and among those who have Alzheimer’s disease. We propose OASIS-AD, a method for automatic segmentation of white matter hyperintensities in older adults using structural brain MRIs. OASIS-AD is an approach evolved from OASIS, which was developed for automatic lesion segmentation in multiple sclerosis. OASIS-AD is a major refinement of OASIS that takes into account the specific challenges raised by white matter hyperintensities in Alzheimer’s disease. In particular, OASIS-AD combines three processing steps: 1) using an eroding procedure on the skull stripped mask; 2) adding a nearest neighbor feature construction approach; and 3) applying a Gaussian filter to refine segmentation results, creating a novel process for WMH detection in aging population. We show that OASIS-AD performs better than existing automatic white matter hyperintensity segmentation approaches.
Recognizing Instagram Filtered Images with Feature De-Stylization

Deep neural networks have been shown to suffer from poor generalization when small perturbations are added (like Gaussian noise), yet little work has been done to evaluate their robustness to more natural image transformations like photo filters. This paper presents a study on how popular pretrained models are affected by commonly used Instagram filters. To this end, we introduce ImageNet-Instagram, a filtered version of ImageNet, where 20 popular Instagram filters are applied to each image in ImageNet. Our analysis suggests that simple structure preserving filters which only alter the global appearance of an image can lead to large differences in the convolutional feature space. To improve generalization, we introduce a lightweight de-stylization module that predicts parameters used for scaling and shifting feature maps to “undo” the changes incurred by filters, inverting the process of style transfer tasks. We further demonstrate the module can be readily plugged into modern CNN architectures together with skip connections. We conduct extensive studies on ImageNet-Instagram, and show quantitatively and qualitatively, that the proposed module, among other things, can effectively improve generalization by simply learning normalization parameters without retraining the entire network, thus recovering the alterations in the feature space caused by the filters.