Reviewerly/exHarmony huggingface dataset:
Using Hindsight to Anchor Past Knowledge in Continual Learning

In continual learning, the learner faces a stream of data whose distribution changes over time. Modern neural networks are known to suffer under this setting, as they quickly forget previously acquired knowledge. To address such catastrophic forgetting, many continual learning methods implement different types of experience replay, re-learning on past data stored in a small buffer known as episodic memory. In this work, we complement experience replay with a new objective that we call ``anchoring'', where the learner uses bilevel optimization toupdate its knowledge on the current task, while keeping intact the predictions on some anchor points of past tasks. These anchor points are learned using gradient-based optimization to maximize forgetting, which is approximated by fine-tuning the currently trained model on the episodic memory of past tasks. Experiments on several supervised learning benchmarks for continual learning demonstrate that our approach improves the standard experience replay in terms of both accuracy and forgetting metrics and for various sizes of episodic memory.
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning.

There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet counterfactual explanations have become massively popular in eXplainable AI (XAI) due to their proposed computational psychological, and legal benefits. In contrast however, semifactuals, which are a similar way humans commonly explain their reasoning, have surprisingly received no attention. Most counterfactual methods address tabular rather than image data, partly due to the nondiscrete nature of the latter making good counterfactuals difficult to define. Additionally generating plausible looking explanations which lie on the data manifold is another issue which hampers progress. This paper advances a novel method for generating plausible counterfactuals (and semifactuals) for black box CNN classifiers doing computer vision. The present method, called PlausIble Exceptionality-based Contrastive Explanations (PIECE), modifies all exceptional features in a test image to be normal from the perspective of the counterfactual class (hence concretely defining a counterfactual). Two controlled experiments compare this method to others in the literature, showing that PIECE not only generates the most plausible counterfactuals on several measures, but also the best semifactuals.
Quantile Regression for Uncertainty Estimation in VAEs with Applications to Brain Lesion Detection

The Variational AutoEncoder (VAE) has become one of the most popular models for anomaly detection in applications such as lesion detection in medical images. The VAE is a generative graphical model that is used to learn the data distribution from samples and then generate new samples from this distribution. By training on normal samples, the VAE can be used to detect inputs that deviate from this learned distribution. The VAE models the output as a conditionally independent Gaussian characterized by means and variances for each output dimension. VAEs can therefore use reconstruction probability instead of reconstruction error for anomaly detection. Unfortunately, joint optimization of both mean and variance in the VAE leads to the well-known problem of shrinkage or underestimation of variance. We describe an alternative VAE model, Quantile-Regression VAE (QR-VAE), that avoids this variance shrinkage problem by estimating conditional quantiles for the given input image. Using the estimated quantiles, we compute the conditional mean and variance for input images under the Gaussian model. We then compute reconstruction probability using this model as a principled approach to outlier or anomaly detection. We also show how our approach can be used for heterogeneous thresholding of images for detecting lesions in brain images.
AI Techniques for Software Requirements Prioritization

Aspects such as limited resources, frequently changing market demands, and different technical restrictions regarding the implementation of software requirements (features) often demand for the prioritization of requirements. The task of prioritization is the ranking and selection of requirements that should be included in future software releases. In this context, an intelligent prioritization decision support is extremely important. The prioritization approaches discussed in this paper are based on different Artificial Intelligence (AI) techniques that can help to improve the overall quality of requirements prioritization processes
LIREx: Augmenting Language Inference with Relevant Explanations

Natural language explanations (NLEs) are a special form of data annotation in which annotators identify rationales (most significant text tokens) when assigning labels to data instances, and write out explanations for the labels in natural language based on the rationales. NLEs have been shown to capture human reasoning better, but not as beneficial for natural language inference (NLI). In this paper, we analyze two primary flaws in the way NLEs are currently used to train explanation generators for language inference tasks. We find that the explanation generators do not take into account the variability inherent in human explanation of labels, and that the current explanation generation models generate spurious explanations. To overcome these limitations, we propose a novel framework, LIREx, that incorporates both a rationale-enabled explanation generator and an instance selector to select only relevant, plausible NLEs to augment NLI models. When evaluated on the standardized SNLI data set, LIREx achieved an accuracy of 91.87%, an improvement of 0.32 over the baseline and matching the best-reported performance on the data set. It also achieves significantly better performance than previous studies when transferred to the out-of-domain MultiNLI data set. Qualitative analysis shows that LIREx generates flexible, faithful, and relevant NLEs that allow the model to be more robust to spurious explanations. The code is available at https://github.com/zhaoxy92/LIREx.
FabHandWear

Current hand wearables have limited customizability, they are loose-fit to an individual's hand and lack comfort. The main barrier in customizing hand wearables is the geometric complexity and size variation in hands. Moreover, there are different functions that the users can be looking for; some may only want to detect hand's motion or orientation; others may be interested in tracking their vital signs. Current wearables usually fit multiple functions and are designed for a universal user with none or limited customization. There are no specialized tools that facilitate the creation of customized hand wearables for varying hand sizes and provide different functionalities. We envision an emerging generation of customizable hand wearables that supports hand differences and promotes hand exploration with additional functionality. We introduce FabHandWear, a novel system that allows end-to-end design and fabrication of customized functional self-contained hand wearables. FabHandWear is designed to work with off-the-shelf electronics, with the ability to connect them automatically and generate a printable pattern for fabrication. We validate our system by using illustrative applications, a durability test, and an empirical user evaluation. Overall, FabHandWear offers the freedom to create customized, functional, and manufacturable hand wearables.
Handling Extreme Class Imbalance in Technical Logbook Datasets

Technical logbooks are a challenging and under-explored text type in automated event identification.These texts are typically short and written in non-standard yet technical language, posing challenges to off-the-shelf NLP pipelines.The granularity of issue types described in these datasets additionally leads to class imbalance, making it challenging for models to accurately predict which issue each logbook entry describes.In this paper we focus on the problem of technical issue classification by considering logbook datasets from the automotive, aviation, and facilities maintenance domains.We adapt a feedback strategy from computer vision for handling extreme class imbalance, which resamples the training data based on its error in the prediction process.Our experiments show that with statistical significance this feedback strategy provides the best results for four different neural network models trained across a suite of seven different technical logbook datasets from distinct technical domains.The feedback strategy is also generic and could be applied to any learning problem with substantial class imbalances.
Unsupervised Continual Learning via Self-adaptive Deep Clustering Approach

Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground truth incurring expensive labelling cost. Another issue lies in the problem of task boundaries and task IDs which must be known for model’s updates or model’s predictions hindering feasibility for real-time deployment. Knowledge Retention in Self-Adaptive Deep Continual Learner, (KIERA), is proposed in this paper. KIERA is developed from the notion of flexible deep clustering approach possessing an elastic network structure to cope with changing environments in the timely manner. The centroid-based experience replay is put forward to overcome the catastrophic forgetting problem. KIERA does not exploit any labelled samples for model updates while featuring a task-agnostic merit. The advantage of KIERA has been numerically validated in popular continual learning problems where it shows highly competitive performance compared to state-of-the art approaches. Our implementation is available in https://researchdata.ntu.edu.sg/dataset.xhtml?persistentId=doi:10.21979/N9/P9DFJH .
Introduction to Conversational AI

A chatbot is a user interface designed to simulate conversation with human users online. The word is a combination of the word chat (a conversation) and robot.
Introduction to Artificial Intelligence

The term Artificial Intelligence (AI) is a branch of computer science to make computers perform human-like tasks, and thus, computers can appropriately sense and learn inputs for perception, knowledge representation, reasoning, problem-solving, and planning. Various types of innovative AI technologies are designed to imitate the cognitive abilities of human beings, which can, therefore, deal with more complicated and ill-defined problems in an intentional, intelligent, and adaptive manner. Typically, AI can be regarded as a conjunction of machine learning and data analytics.
An End-to-End Solution for Named Entity Recognition in eCommerce Search

Named entity recognition (NER) is a critical step in modern search query understanding. In the domain of eCommerce, identifying the key entities, such as brand and product type, can help a search engine retrieve relevant products and therefore offer an engaging shopping experience. Recent research shows promising results on shared benchmark NER tasks using deep learning methods, but there are still unique challenges in the industry regarding domain knowledge, training data, and model production. This paper demonstrates an end-to-end solution to address these challenges. The core of our solution is a novel model training framework ”TripleLearn” which iteratively learns from three separate training datasets, instead of one training set as is traditionally done. Using this approach, the best model lifts the F1 score from 69.5 to 93.3 on the holdout test data. In our offline experiments, TripleLearn improved the model performance compared to traditional training approaches which use a single set of training data. Moreover, in the online A/B test, we see significant improvements in user engagement and revenue conversion. The model has been live on homedepot.com for more than 9 months, boosting search conversions and revenue. Beyond our application, this TripleLearn framework, as well as the end-to-end process, is model-independent and problem-independent, so it can be generalized to more industrial applications, especially to the eCommerce industry which has similar data foundations and problems.
Environmental Uncertainty and End-User Security Behaviour: A Study During the COVID-19 Pandemic

The COVID-19 pandemic has forced individuals to adopt online applications and technologies, as well as remote working patterns. However, with changes in technology and working patterns, new vulnerabilities are likely to arise. Cybersecurity threats have rapidly evolved to exploit uncertainty during the pandemic, and users need to apply careful judgment and vigilance to avoid becoming the victim of a cyber-attack. This paper explores the factors that motivate security behaviour, considering the current environmental uncertainty. An adapted model, primarily based on the Protection Motivation Theory (PMT), is proposed and evaluated using data collected from an online survey of 222 respondents from a Higher Education institution. Data analysis was performed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results confirm the applicability of PMT in the security context. Respondents’ behavioural intention, perceived threat vulnerability, response cost, response efficacy, security habits, and subjective norm predicted self-reported security behaviour. In contrast, environmental uncertainty, attitude towards policy compliance, self-efficacy and perceived threat severity did not significantly impact behavioural intention. The results show that respondents were able to cope with environmental uncertainty and maintain security behaviour.
Review on Deep Learning Based Fault Diagnosis

The massive high-dimensional measurements accumulated by distributed control systems bring great computational and modeling complexity to the traditional fault diagnosis algorithms, which fail to take advantage of the higher-order for online estimation. In view of its powerful ability of representation learning, deep learning based fault diagnosis is extensively studied, both in academia and in industry, making intelligent process control more automated and effective. In this paper, deep learning based fault diagnosis is reviewed and summarized as four parts, i.e., stacked auto-encoder based fault diagnosis, deep belief network based fault diagnosis, convolutional neural network based fault diagnosis, and recurrent neural network based fault diagnosis. Furthermore, some necessity and potential trends, integrated innovation, + knowledge and information fusion, are discussed from the view of data preprocessing, network design and decision.
FFNet: A simple image dedusting network with feature fusion

Summary Dust is a common air pollution source. The color of images captured under dusty weather is usually yellow even brown, which reduces scene visibility and causes the loss of image details. To remove the dust and make image scene clear, this article presents a simple and effective image dedusting network called FFNet. In the process of image feature extraction, the FFNet uses several residual blocks with smoothed dilated convolution, that is, common dilated convolution followed by separable and shared (SS) blockwise fully connected operation to extend the receptive field and reduce gridding artifacts caused by common dilated convolution. Furthermore, the FFNet fuses image features from different layers via an adaptive weighting scheme. Due to the difficulty of collecting real dusty images, we used our proposed dusty image synthesis scheme to achieve data augmentation for better network training. Experiments on a series of synthetic and real dusty images demonstrate that the FFNet obtains better image dedusting performance than several state‐of‐the‐art image restoration methods.
Augmented Reality based Adaptive and Collaborative Learning Methods for Improved Primary Education Towards Fourth Industrial Revolution (IR 4.0)

Existing primary education is not organized properly to fulfil the demand of fourth industrial revolution (IR 4.0) which causes lack of engagement in learning materials, lack of spatial ability and motivation towards learning by young age students.Students especially from initial education level tend to learn from seeing rather than reading or memorizing.In this context, technology like augmented reality and virtual reality bears such visual power in lieu with interactivity and engaging characteristics by bringing virtual and real world together.In addition, flexible presentation mechanisms in lieu with tagging and tracking technology with various degree of freedom provide the effective ground of augmented reality and virtual reality to integrate with current primary education or initial level of education.In this context, this research presents comprehensive and critical reviews in the previous research in terms with several aspects such as methods, research design and experimental validation.Each of the aspects is demonstrated with adequate advantages and disadvantages to design the integration of augmented reality and virtual reality with primary education methodology.In addition, this research illustrates extensive critical explanation for various challenges to integrate augmented reality and virtual reality with primary education to make students motivated towards education, effective activity and memorization with visualization.The overall investigation proposed in this research is expected to fulfil the future demand for Fourth Industrial Revolution (IR 4.0).
Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent

We show how multiple data-owning parties can collaboratively train several machine learning algorithms without jeopardizing the privacy of their sensitive data. In particular, we assume that every party knows specific features of an overlapping set of people. Using a secure implementation of an advanced hidden set intersection protocol and a privacy-preserving Gradient Descent algorithm, we are able to train a Ridge, LASSO or SVM model over the intersection of people in their data sets. Both the hidden set intersection protocol and privacy-preserving LASSO implementation are unprecedented in literature.
AES-LBBB: AES Mode for Lightweight and BBB-Secure Authenticated Encryption

In this paper, a new lightweight authenticated encryption scheme AESLBBB is proposed, which was designed to provide backward compatibility with advanced encryption standard (AES) as well as high security and low memory. The primary design goal, backward compatibility, is motivated by the fact that AES accelerators are now very common for devices in the field; we are interested in designing an efficient and highly secure mode of operation that exploits the best of those AES accelerators. The backward compatibility receives little attention in the NIST lightweight cryptography standardization process, in which only 3 out of 32 round-2 candidates are based on AES. Our mode, LBBB, is inspired by the design of ALE in the sense that the internal state size is a minimum 2n bits when using a block cipher of length n bits for the key and data. Unfortunately, there is no security proof of ALE, and forgery attacks have been found on ALE. In LBBB, we introduce an additional feed from block cipher’s output to the key state via a certain permutation λ, which enables us to prove beyond-birthday-bound (BBB) security. We then specify its AES instance, AES-LBBB, and evaluate its performance for (i) software implementation on a microcontroller with an AES coprocessor and (ii) hardware implementation for an application-specific integrated circuit (ASIC) to show that AES-LBBB performs better than the current state-of-the-art Remus-N2 with AES-128.
Sentiment Analysis for Telugu Text Using Cuckoo Search Algorithm

In recent times, most of the microblogging applications have started allowing people to express their opinions and feeling toward entities in regional languages. This resulted in good demand for sentiment analysis in regional languages. Telugu is a morphologically rich agglutinative south Indian language with nearly 100 million native speakers. In this paper, various unsupervised machine learning algorithms are explored for the classification of Telugu text into negative or positive classes.
Streaming and Small Space Approximation Algorithms for Edit Distance and Longest Common Subsequence

The edit distance (ED) and longest common subsequence (LCS) are two fundamental problems which quantify how similar two strings are to one another. In this paper, we first consider these problems in the asymmetric streaming model introduced by Andoni, Krauthgamer and Onak [Andoni et al., 2010] (FOCS'10) and Saks and Seshadhri [Saks and Seshadhri, 2013] (SODA'13). In this model we have random access to one string and streaming access the other one. Our main contribution is a constant factor approximation algorithm for ED with memory O(n^δ) for any constant δ > 0. In addition to this, we present an upper bound of O _e(√n) on the memory needed to approximate ED or LCS within a factor 1±e. All our algorithms are deterministic and run in polynomial time in a single pass.We further study small-space approximation algorithms for ED, LCS, and longest increasing sequence (LIS) in the non-streaming setting. Here, we design algorithms that achieve 1 ± e approximation for all three problems, where e > 0 can be any constant and even slightly sub-constant. Our algorithms only use poly-logarithmic space while maintaining a polynomial running time. This significantly improves previous results in terms of space complexity, where all known results need to use space at least Ω(√n). Our algorithms make novel use of triangle inequality and carefully designed recursions to save space, which can be of independent interest.
Handbook of Research on Blockchain Technology

Handbook of Research on Blockchain Technology presents the latest information on the adaptation and implementation of Blockchain technologies in real world business, scientific, healthcare and biomedical applications. The book's editors present the rapid advancements in existing business models by applying Blockchain techniques. Novel architectural solutions in the deployment of Blockchain comprise the core aspects of this book. Several use cases with IoT, biomedical engineering, and smart cities are also incorporated. As Blockchain is a relatively new technology that exploits decentralized networks and is used in many sectors for reliable, cost-effective and rapid business transactions, this book is a welcomed addition on existing knowledge.Financial services, retail, insurance, logistics, supply chain, public sectors and biomedical industries are now investing in Blockchain research and technologies for their business growth. Blockchain prevents double spending in financial transactions without the need of a trusted authority or central server. It is a decentralized ledger platform that facilitates verifiable transactions between parties in a secure and smart way.Presents the evolution of blockchain, from fundamental theories, to present formsExplains the concepts of blockchain related to cloud/edge computing, smart healthcare, smart cities and Internet of Things (IoT)Provides complete coverage of the various tools, platforms and techniques used in blockchainExplores smart contract tools and consensus algorithmsCovers a variety of applications with real world case studies in areas such as biomedical engineering, supply chain management, and tracking of goods and delivery