Reviewerly/exHarmony huggingface dataset:
Time series analysis and anomaly detection for trustworthy smart homes

• To establish trust in the smart home IoT environment using machine learning methods . • We rely on the Change Finder algorithm to observe change points in our time series plots. The algorithm deploys a log-likelihood function on Sequentially Discounting Autoregressive (SDAR) algorithm for evaluating scores. • The study incorporates several machine learning algorithms such as ARIMA, SARIMA, LSTM , Prophet, Light GBM, and VAR for the analysis. • Performed and the analysis has been supported and validated using data visualization techniques as well as evaluation parameters like MSE , RMSE , and MAE . The IoT network is expected to harbor several zettabytes of information in the future. Since trust and integrity are critical to IoT, it is essential to imbibe trust into the IoT environment for ensuring dependability and reliability. We propose a machine learning-based trustworthy system for the IoT-based smart home environment. Multiple appliances connected through the internet are susceptible to privacy issues, hence utmost care must be taken to ensure trust in the network. We consider the energy data and weather information with respect to smart homes, for comprehending the relationship between energy consumption by appliances and time period for detecting anomalous usage of appliances using the SDAR-based Change Finder algorithm. Time series analysis is performed using ARIMA, SARIMA, LSTM, Prophet, Light GBM, and VAR. The evaluation has been performed using RMSE, MSE, and MAE, and the study establishes that the ARIMA model outperforms the other models.
DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

The daily practice of sharing images on social media raises a severe issue about privacy leakage. To address the issue, privacy-leaking image detection is studied recently, with the goal to automatically identify images that may leak privacy. Recent advance on this task benefits from focusing on crucial objects via pretrained object detectors and modeling their correlation. However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors. 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images. To overcome the limitations, we propose the Dynamic Region-Aware Graph Convolutional Network (DRAG) that dynamically finds out crucial regions including objects and other important elements, and model their correlation adaptively for each input image. To find out crucial regions, we cluster spatially-correlated feature channels into several region-aware feature maps. Furthermore, we dynamically model the correlation with the self-attention mechanism and explore the interaction among the regions with a graph convolutional network. The DRAG achieved an accuracy of 87% on the largest dataset for privacy-leaking image detection, which is 10 percentage points higher than the state of the art. The further case study demonstrates that it found out crucial regions containing not only objects but other important elements like textures. The code and more details are in https://github.com/guang-yanng/DRAG.
MAPF-LNS2: Fast Repairing for Multi-Agent Path Finding via Large Neighborhood Search

Multi-Agent Path Finding (MAPF) is the problem of planning collision-free paths for multiple agents in a shared environment. In this paper, we propose a novel algorithm MAPF-LNS2 based on large neighborhood search for solving MAPF efficiently. Starting from a set of paths that contain collisions, MAPF-LNS2 repeatedly selects a subset of colliding agents and replans their paths to reduce the number of collisions until the paths become collision-free. We compare MAPF-LNS2 against a variety of state-of-the-art MAPF algorithms, including Prioritized Planning with random restarts, EECBS, and PPS, and show that MAPF-LNS2 runs significantly faster than them while still providing near-optimal solutions in most cases. MAPF-LNS2 solves 80% of the random-scenario instances with the largest number of agents from the MAPF benchmark suite with a runtime limit of just 5 minutes, which, to our knowledge, has not been achieved by any existing algorithms.
TDv2: A Novel Tree-Structured Decoder for Offline Mathematical Expression Recognition

In recent years, tree decoders become more popular than LaTeX string decoders in the field of handwritten mathematical expression recognition (HMER) as they can capture the hierarchical tree structure of mathematical expressions. However previous tree decoders converted the tree structure labels into a fixed and ordered sequence, which could not make full use of the diversified expression of tree labels. In this study, we propose a novel tree decoder (TDv2) to fully utilize the tree structure labels. Compared with previous tree decoders, this new model does not require a fixed priority for different branches of a node during training and inference, which can effectively improve the model generalization capability. The input and output of the model make full use of the tree structure label, so that there is no need to find the parent node in the decoding process, which simplifies the decoding process and adds a prior information to help predict the node. We verified the effectiveness of each part of the model through comprehensive ablation experiments and attention visualization analysis. On the authoritative CROHME 14/16/19 datasets, our method achieves the state-of-the-art results.
Adaptive Orthogonal Projection for Batch and Online Continual Learning

Catastrophic forgetting is a key obstacle to continual learning. One of the state-of-the-art approaches is orthogonal projection. The idea of this approach is to learn each task by updating the network parameters or weights only in the direction orthogonal to the subspace spanned by all previous task inputs. This ensures no interference with tasks that have been learned. The system OWM that uses the idea performs very well against other state-of-the-art systems. In this paper, we first discuss an issue that we discovered in the mathematical derivation of this approach and then propose a novel method, called AOP (Adaptive Orthogonal Projection), to resolve it, which results in significant accuracy gains in empirical evaluations in both the batch and online continual learning settings without saving any previous training data as in replay-based methods.
Safe Distillation Box

Knowledge distillation (KD) has recently emerged as a powerful strategy to transfer knowledge from a pre-trained teacher model to a lightweight student, and has demonstrated its unprecedented success over a wide spectrum of applications. In spite of the encouraging results, the KD process \emph{per se} poses a potential threat to network ownership protection, since the knowledge contained in network can be effortlessly distilled and hence exposed to a malicious user. In this paper, we propose a novel framework, termed as Safe Distillation Box~(SDB), that allows us to wrap a pre-trained model in a virtual box for intellectual property protection. Specifically, SDB preserves the inference capability of the wrapped model to all users, but precludes KD from unauthorized users. For authorized users, on the other hand, SDB carries out a knowledge augmentation scheme to strengthen the KD performances and the results of the student model. In other words, all users may employ a model in SDB for inference, but only authorized users get access to KD from the model. The proposed SDB imposes no constraints over the model architecture, and may readily serve as a plug-and-play solution to protect the ownership of a pre-trained network. Experiments across various datasets and architectures demonstrate that, with SDB, the performance of an unauthorized KD drops significantly while that of an authorized gets enhanced, demonstrating the effectiveness of SDB.
Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey

While commonsense knowledge acquisition and reasoning has traditionally been a core research topic in the knowledge representation and reasoning community, recent years have seen a surge of interest in the natural language processing community in developing pre-trained models and testing their ability to address a variety of newly designed commonsense knowledge reasoning and generation tasks. This paper presents a survey of these tasks, discusses the strengths and weaknesses of state-of-the-art pre-trained models for commonsense reasoning and generation as revealed by these tasks, and reflects on future research directions.
Learning Disentangled Attribute Representations for Robust Pedestrian Attribute Recognition

Although various methods have been proposed for pedestrian attribute recognition, most studies follow the same feature learning mechanism, \ie, learning a shared pedestrian image feature to classify multiple attributes. However, this mechanism leads to low-confidence predictions and non-robustness of the model in the inference stage. In this paper, we investigate why this is the case. We mathematically discover that the central cause is that the optimal shared feature cannot maintain high similarities with multiple classifiers simultaneously in the context of minimizing classification loss. In addition, this feature learning mechanism ignores the spatial and semantic distinctions between different attributes. To address these limitations, we propose a novel disentangled attribute feature learning (DAFL) framework to learn a disentangled feature for each attribute, which exploits the semantic and spatial characteristics of attributes. The framework mainly consists of learnable semantic queries, a cascaded semantic-spatial cross-attention (SSCA) module, and a group attention merging (GAM) module. Specifically, based on learnable semantic queries, the cascaded SSCA module iteratively enhances the spatial localization of attribute-related regions and aggregates region features into multiple disentangled attribute features, used for classification and updating learnable semantic queries. The GAM module splits attributes into groups based on spatial distribution and utilizes reliable group attention to supervise query attention maps. Experiments on PETA, RAPv1, PA100k, and RAPv2 show that the proposed method performs favorably against state-of-the-art methods.
DLME: Deep Local-Flatness Manifold Embedding

Manifold learning (ML) aims to seek low-dimensional embedding from high-dimensional data. The problem is challenging on real-world datasets, especially with under-sampling data, and we find that previous methods perform poorly in this case. Generally, ML methods first transform input data into a low-dimensional embedding space to maintain the data’s geometric structure and subsequently perform downstream tasks therein. The poor local connectivity of under-sampling data in the former step and inappropriate optimization objectives in the latter step leads to two problems: structural distortion and underconstrained embedding. This paper proposes a novel ML framework named Deep Local-flatness Manifold Embedding (DLME) to solve these problems. The proposed DLME constructs semantic manifolds by data augmentation and overcomes the structural distortion problem using a smoothness constrained based on a local flatness assumption about the manifold. To overcome the underconstrained embedding problem, we design a loss and theoretically demonstrate that it leads to a more suitable embedding based on the local flatness. Experiments on three types of datasets (toy, biological, and image) for various downstream tasks (classification, clustering, and visualization) show that our proposed DLME outperforms state-of-the-art ML and contrastive learning methods.
Data Contamination: From Memorization to Exploitation

Pretrained language models are typically trained on massive web-based datasets, which are often "contaminated" with downstream test sets.It is not clear to what extent models exploit the contaminated data for downstream tasks.We present a principled method to study this question.We pretrain BERT models on joint corpora of Wikipedia and labeled downstream datasets, and fine-tune them on the relevant task.Comparing performance between samples seen and unseen during pretraining enables us to define and quantify levels of memorization and exploitation.Experiments with two models and three downstream tasks show that exploitation exists in some cases, but in others the models memorize the contaminated data, but do not exploit it.We show that these two measures are affected by different factors such as the number of duplications of the contaminated data and the model size.Our results highlight the importance of analyzing massive web-scale datasets to verify that progress in NLP is obtained by better language understanding and not better data exploitation.
HLDC: Hindi Legal Documents Corpus

Many populous countries including India are burdened with a considerable backlog of legal cases.Development of automated systems that could process legal documents and augment legal practitioners can mitigate this.However, there is a dearth of high-quality corpora that is needed to develop such data-driven systems.The problem gets even more pronounced in the case of low resource languages such as Hindi.In this resource paper, we introduce the Hindi Legal Documents Corpus (HLDC), a corpus of more than 900K legal documents in Hindi.Documents are cleaned and structured to enable the development of downstream applications.Further, as a use-case for the corpus, we introduce the task of bail prediction.We experiment with a battery of models and propose a Multi-Task Learning (MTL) based model for the same.MTL models use summarization as an auxiliary task along with bail prediction as the main task.Experiments with different models are indicative of the need for further research in this area.
Symptom Based Explainable Artificial Intelligence Model for Leukemia Detection

Leukemia is not only fatal in nature, it is also extremely expensive to treat. However, leukemia detection at early stage can save lives and money of the affected people, specially children among whom leukemia as a cancer type is very common. In this paper, we propose an explainable supervised machine learning model that accurately predicts the likelihood of early-stage leukemia based on symptoms only. The proposed model is developed based on primary data collected from two major hospitals in Bangladesh. Sixteen features of the datasets are collected through a survey on leukemia and non-leukemia patients in consultation with a specialist physician. Our explainable supervised model is based on a decision tree classifier which provides significantly better results compared to other algorithms and generates explainable rules that are ready to use. We have employed Apriori algorithm for generating explainable rules for leukemia prediction. In addition, feature analysis and feature selection are performed on the dataset to show the strength of individual features and enhance the performance of the classification models. Several classifiers are experimented on the dataset to show how the proposed model that is simple yet explainable, performs significantly better compared to most other models that we have used. The decision tree model proposed in our experiments has achieved 97.45% of accuracy, 0.63 of Mathew’s Correlation Coefficient (MCC) and 0.783 of area under Receiver Operating Characteristic (ROC) curve on the test set. We have also made the dataset and the source code of the methods used in this work available for future use by the researchers, which can be found in the following link: https://github.com/AkterHossain312/BDLeukemiaWithML.
Local Languages, Third Spaces, and other High-Resource Scenarios

How can language technology address the diverse situations of the world's languages?In one view, languages exist on a resource continuum and the challenge is to scale existing solutions, bringing under-resourced languages into the high-resource world.In another view, presented here, the world's language ecology includes standardised languages, local languages, and contact languages.These are often subsumed under the label of 'under-resourced languages' even though they have distinct functions and prospects.I explore this position and propose some ecologically-aware language technology agendas.
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning

Recent Active Learning (AL) approaches in Natural Language Processing (NLP) proposed using off-the-shelf pretrained language models (LMs).In this paper, we argue that these LMs are not adapted effectively to the downstream task during AL and we explore ways to address this issue.We suggest to first adapt the pretrained LM to the target task by continuing training with all the available unlabeled data and then use it for AL.We also propose a simple yet effective fine-tuning method to ensure that the adapted LM is properly trained in both low and high resource scenarios during AL.Our experiments demonstrate that our approach provides substantial data efficiency improvements compared to the standard finetuning approach, suggesting that a poor training strategy can be catastrophic for AL. 1
Changes to Visual Parameters Following Virtual Reality Gameplay

Virtual reality (VR) gameplay is popular with a range of games and educational resources available. However, it puts high demands on the visual system. Current evidence shows conflicting impacts on visual parameters. Therefore, this study explores the changes to vision following VR gameplay.The study was conducted at the School of Health Sciences, University of Liverpool. All participants had binocular vision with good visual acuity and no manifest strabismus. Participants were assessed before and after playing 15 minutes of the VR game Beat Saber, which incorporated convergence and divergence movements. Clinical assessments including near point of convergence (NPC) and near point of accommodation (NPA) using the RAF rule; accommodative convergence to accommodation (AC/A) ratio; motor fusion using the prism fusion range (at 33cm), accommodation facility using +2.00/-2.00DS flipper lenses, and stereoacuity using the Frisby stereo test were assessed before and after playing.Seventy-eight participants (19-25 years old) were included in the study, with 16 males and 41 females respectively. The breakpoint of convergence reduced by 0.5 cm (p = 0.001). The binocular accommodative facility improved by 2 cycles per minute (cpm); p = 0.004. The mean, near horizontal prism fusion range (PFR) base break and recovery points both worsened by of 5.0 dioptres (p = 0.003), whereas the mean near horizontal PFR base in recovery point improved by of 4.0 dioptres (p = 0.003).The study validated previous findings as VR gameplay over-exercised and fatigued convergence muscles, but to a small degree. The VR experience improved the participants' ability to change focus quickly and improve accommodation, as well as the divergence function of the eye. However, as the participants were retested directly after the VR gameplay, the findings were limited to short term effects on vision.
Sequence-to-sequence AMR Parsing with Ancestor Information

AMR parsing is the task of mapping a sentence to an AMR semantic graph automatically.The difficulty comes from generating the complex graph structure.The previous state-of-the-art method translates the AMR graph into a sequence, then directly fine-tunes a pretrained sequence-to-sequence Transformer model (BART).However, purely treating the graph as a sequence does not take advantage of structural information about the graph.In this paper, we design several strategies to add the important ancestor information into the Transformer Decoder.Our experiments 1 show that we can improve the performance for both the AMR 2.0 and AMR 3.0 dataset and achieve new state-of-the-art results.
Mental Health Diagnosis: A Case for Explainable Artificial Intelligence

Mental illnesses are becoming increasingly prevalent, in turn leading to an increased interest in exploring artificial intelligence (AI) solutions to facilitate and enhance healthcare processes ranging from diagnosis to monitoring and treatment. In contrast to application areas where black box systems may be acceptable, explainability in healthcare applications is essential, especially in the case of diagnosing complex and sensitive mental health issues. In this paper, we first summarize recent developments in AI research for mental health, followed by an overview of approaches to explainable AI and their potential benefits in healthcare settings. We then present a recent case study of applying explainable AI for ADHD diagnosis which is used as a basis to identify challenges in realizing explainable AI solutions for mental health diagnosis and potential future research directions to address these challenges.
Cybersecurity Awareness in Online Education: A Case Study Analysis

This study presents to what extent Kyrgyz-Turkish Manas University students are knowledgeable about cybersecurity in the distance education process. The survey was conducted with a sample of 517 students from all faculties of the university at the undergraduate, graduate, and PhD levels. Our research study shows that although huge numbers of cyberattacks are occurring around the world, the students did not have any knowledge about cybersecurity and the effects of cyberattacks overall. An analysis of cybersecurity awareness was undertaken by asking questions focused on malicious software, password security, and social media security. Although we live in an age of technology where our entire lives are indexed to the internet through the distance education process, it has been determined that students have a weak cybersecurity awareness. It has been further concluded that cybersecurity education should be given to prevent the students from becoming a victim of cyberattacks, helping them to use the internet more effectively.
An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models

We propose a framework for training nonautoregressive sequence-to-sequence models for editing tasks, where the original input sequence is iteratively edited to produce the output.We show that the imitation learning algorithms designed to train such models for machine translation introduces mismatches between training and inference that lead to undertraining and poor generalization in editing scenarios.We address this issue with two complementary strategies: 1) a roll-in policy that exposes the model to intermediate training sequences that it is more likely to encounter during inference, 2) a curriculum that presents easy-to-learn edit operations first, gradually increasing the difficulty of training samples as the model becomes competent.We show the efficacy of these strategies on two challenging English editing tasks: controllable text simplification and abstractive summarization.Our approach significantly improves output quality on both tasks and controls output complexity better on the simplification task.