Reviewerly/exHarmony huggingface dataset:
A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence–Supported Platform

The lack of availability of disability data has been identified as a major challenge hindering continuous disability equity monitoring. It is important to develop a platform that enables searching for disability data to expose systemic discrimination and social exclusion, which increase vulnerability to inequitable social conditions.Our project aims to create an accessible and multilingual pilot disability website that structures and integrates data about people with disabilities and provides data for national and international disability advocacy communities. The platform will be endowed with a document upload function with hybrid (automated and manual) paragraph tagging, while the querying function will involve an intelligent natural language search in the supported languages.We have designed and implemented a virtual community platform using Wikibase, Semantic Web, machine learning, and web programming tools to enable disability communities to upload and search for disability documents. The platform data model is based on an ontology we have designed following the United Nations Convention on the Rights of Persons with Disabilities (CRPD). The virtual community facilitates the uploading and sharing of validated information, and supports disability rights advocacy by enabling dissemination of knowledge.Using health informatics and artificial intelligence techniques (namely Semantic Web, machine learning, and natural language processing techniques), we were able to develop a pilot virtual community that supports disability rights advocacy by facilitating uploading, sharing, and accessing disability data. The system consists of a website on top of a Wikibase (a Semantic Web-based datastore). The virtual community accepts 4 types of users: information producers, information consumers, validators, and administrators. The virtual community enables the uploading of documents, semiautomatic tagging of their paragraphs with meaningful keywords, and validation of the process before uploading the data to the disability Wikibase. Once uploaded, public users (information consumers) can perform a semantic search using an intelligent and multilingual search engine (QAnswer). Further enhancements of the platform are planned.The platform ontology is flexible and can accommodate advocacy reports and disability policy and legislation from specific jurisdictions, which can be accessed in relation to the CRPD articles. The platform ontology can be expanded to fit international contexts. The virtual community supports information upload and search. Semiautomatic tagging and intelligent multilingual semantic search using natural language are enabled using artificial intelligence techniques, namely Semantic Web, machine learning, and natural language processing.
A Framework for Accelerating Transformer-Based Language Model on ReRAM-Based Architecture

Transformer-based language models have become the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de-facto</i> standard model for various natural language processing (NLP) applications given the superior algorithmic performances. Processing a transformer-based language model on a conventional accelerator induces the memory wall problem, and the ReRAM-based accelerator is a promising solution to this problem. However, due to the characteristics of the self-attention mechanism and the ReRAM-based accelerator, the pipeline hazard arises when processing the transformer-based language model on the ReRAM-based accelerator. This hazard issue greatly increases the overall execution time. In this article, we propose a framework to resolve the hazard issue. First, we propose the concept of window self-attention to reduce the attention computation scope by analyzing the properties of the self-attention mechanism. After that, we present a window-size search algorithm, which finds an optimal window size set according to the target application/algorithmic performance. We also suggest a hardware design that exploits the advantages of the proposed algorithm optimization on the general ReRAM-based accelerator. The proposed work successfully alleviates the hazard issue while maintaining the algorithmic performance, leading to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.8\times $ </tex-math></inline-formula> speedup over the provisioned baseline. It also delivers up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$39.2\times /643.2\times $ </tex-math></inline-formula> speedup/higher energy efficiency over GPU, respectively.
Optical Character Recognition of 19th Century Classical Commentaries: the Current State of Affairs

Together with critical editions and translations, commentaries are one of the main genres of publication in literary and textual scholarship, and have a century-long tradition. Yet, the exploitation of thousands of digitized historical commentaries was hitherto hindered by the poor quality of Optical Character Recognition (OCR), especially on commentaries to Greek texts. In this paper, we evaluate the performances of two pipelines suitable for the OCR of historical classical commentaries. Our results show that Kraken + Ciaconna reaches a substantially lower character error rate (CER) than Tesseract/OCR-D on commentary sections with high density of polytonic Greek text (average CER 7% vs. 13%), while Tesseract/OCR-D is slightly more accurate than Kraken + Ciaconna on text sections written predominantly in Latin script (average CER 8.2% vs. 8.4%). As part of this paper, we also release GT4HistComment, a small dataset with OCR ground truth for 19th classical commentaries and Pogretra, a large collection of training data and pre-trained models for a wide variety of ancient Greek typefaces.
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation

Just like other few-shot learning problems, few-shot segmentation aims to minimize the need for manual annotation, which is particularly costly in segmentation tasks. Even though the few-shot setting reduces this cost for novel test classes, there is still a need to annotate the training data. To alleviate this need, we propose a self-supervised training approach for learning few-shot segmentation models. We first use unsupervised saliency estimation to obtain pseudo-masks on images. We then train a simple prototype based model over different splits of pseudo masks and augmentations of images. Our extensive experiments show that the proposed approach achieves promising results, highlighting the potential of self-supervised training. To the best of our knowledge this is the first work that addresses unsupervised few-shot segmentation problem on natural images.
A Lightweight Knowledge Graph Embedding Framework for Efficient Inference and Storage

Knowledge graphs, which consist of entities and their relations, have become a popular way to store structured knowledge. Knowledge graph embedding (KGE), which derives a representation for each entity and relation, has been widely used to capture the semantics of the information in the knowledge graphs, and has demonstrated great success in many downstream applications, such as the extraction of similar entities in response to a query entity. However, existing KGE methods cannot work well on emerging knowledge graphs that are large-scale due to the constraints in storage and inference efficiency. In this paper, we propose a lightweight KGE model, LightKG, which significantly reduces storage as well as running time needed for inference. Instead of storing a continuous vector for every entity, LightKG only needs to store a few codebooks, each of which contains some codewords that correspond to the representatives among the embeddings, and the indices that correspond to the codeword selections for entities. Hence LightKG can achieve highly efficient storage. The efficiency of the downstream querying process can be significantly boosted too with the proposed LightKG model as the relevance score between the query and an entity can be efficiently calculated via a quick look-up in a table that contains the scores between the query and codewords. The storage and inference efficiency of LightKG is achieved by its novel design. LightKG is an end-to-end framework that automatically infers codebooks and codewords and generates an approximated embedding for each entity. A residual module is included in LightKG to induce the diversity among codebooks, and a continuous function is adopted to approximate codeword selection, which is non-differential. In addition, to further improve the performance of KGE, we propose a novel dynamic negative sampling method based on quantization, which can be applied to the proposed LightKG or other KGE methods. We conduct extensive experiments on five public datasets. The experiments show that LightKG is search and memory efficient with high approximate search accuracy. Also, the dynamic negative sampling can dramatically improve model performance with over 19% improvement on average.
Deep Self-Adaptive Hashing for Image Retrieval

Hashing technology has been widely used in image retrieval due to its computational and storage efficiency. Recently, deep unsupervised hashing methods have attracted increasing attention due to the high cost of human annotations in the real world and the superiority of deep learning technology. However, most deep unsupervised hashing methods usually pre-compute a similarity matrix to model the pairwise relationship in the pre-trained feature space. Then this similarity matrix would be used to guide hash learning, in which most of the data pairs are treated equivalently. The above process is confronted with the following defects:1) The pre-computed similarity matrix is inalterable and disconnected from the hash learning process, which cannot explore the underlying semantic information. 2) The informative data pairs may be buried by the large number of less-informative data pairs. To solve the aforementioned problems, we propose a Deep Self-Adaptive Hashing(DSAH) model to adaptively capture the semantic information with two special designs: Adaptive Neighbor Discovery(AND) and Pairwise Information Content(PIC). Firstly, we adopt the AND to initially construct a neighborhood-based similarity matrix, and then refine this initial similarity matrix with a novel update strategy to further investigate the semantic structure behind the learned representation. Secondly, we measure the priorities of data pairs with PIC and assign adaptive weights to them, which is relies on the assumption that more dissimilar data pairs contain more discriminative information for hash learning. Extensive experiments on several datasets demonstrate that the above two technologies facilitate the deep hashing model to achieve superior performance.
Review of Malicious URL Detection Using Machine Learning

Web URLs are the base for Internet to locate resources uniquely on the Internet. Recent report from poloalto networks states that more 86 thousand malicious URLs were registered during the Covid period between March and April 2020. Cyber-attacks through malicious URL causes lose more than billion in every year. Attacks through malicious URL are the handy way for the cyber-criminals. Systematic approaches are required to detect the malicious URL to prevent the cyber-attacks. Researchers proposed several techniques to detect the malicious URL. But it requires continuous efforts to block newly generated attacks. This paper presents overview of malicious URL detection techniques and recent research works and the issues. Also highlights the research challenges in malicious URL detection, which can help for the future researchers to bring out new solutions.
An Improved Arithmetic Optimization Algorithm

This paper focuses on developing an improved arithmetic optimization algorithm to achieve better convergence during exploration and exploitation phases. The proposed algorithm has been achieved by incorporating other functions like square, cube, sine, and cosine in the algorithms’ stochastic scaling coefficient. Further, a comparison between the proposed method and the conventional technique is made on various benchmark functions. It is observed from the numerical results is that the proposed algorithm with both sin and cos have performed better concerning mean, best, and standard deviation values. Moreover, on achieving the worst global minima, the proposed algorithm showed better performance for most minor functions. The results also highlight that the proposed algorithm has shown the best performance, especially for higher-dimensional systems.
Pseudo-Labeling for Massively Multilingual Speech Recognition

Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual speech recognition systems. In this work, we extend pseudo-labeling to massively multilingual speech recognition with 60 languages. We propose a simple pseudo-labeling recipe that works well even with low-resource languages: train a supervised multilingual model, fine-tune it with semi-supervised learning on a target language, generate pseudo-labels for that language, and train a final model using pseudo-labels for all languages, either from scratch or by fine-tuning. Experiments on the labeled Common Voice and unlabeled VoxPopuli datasets show that our recipe can yield a model with better performance for many languages that also transfers well to LibriSpeech.
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels

Today’s intelligent applications can achieve high performance accuracy using machine learning (ML) techniques, such as deep neural networks (DNNs). Traditionally, in a remote DNN inference problem, an edge device transmits raw data to a remote node that performs the inference task. However, this may incur high transmission energy costs and puts data privacy at risk. In this paper, we propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. The time-varying representation accounts for time-varying channels and can significantly reduce the total energy at the edge device while maintaining high accuracy (low loss). We implement our approach in an image classification task using the MNIST dataset, and the system environment is simulated as a trajectory navigation scenario to emulate different channel conditions. Numerical simulations show that our proposed solution results in minimal energy consumption and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission compared to the considered baselines while exhibiting robust performance across different channel conditions and bandwidth regime choices.
Gaussian Variational State Estimation for Nonlinear State-Space Models

In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the models, the state estimation problem is generally intractable as it involves integrals of general nonlinear functions and the filtered and smoothed state distributions lack closed-form solutions. As such, it is common to approximate the state estimation problem. In this paper, we develop an assumed Gaussian solution based on variational inference, which offers the key advantage of a flexible, but principled, mechanism for approximating the required distributions. Our main contribution lies in a new formulation of the state estimation problem as an optimisation problem, which can then be solved using standard optimisation routines that employ exact first- and second-order derivatives. The resulting state estimation approach involves a minimal number of assumptions and applies directly to nonlinear systems with both Gaussian and non-Gaussian probabilistic models. The performance of our approach is demonstrated on several examples; a challenging scalar system, a model of a simple robotic system, and a target tracking problem using a von Mises-Fisher distribution and outperforms alternative assumed Gaussian approaches to state estimation.
Reactive Collision Avoidance for Nonholonomic Vehicles in Dynamic Environments with Obstacles of Arbitrary Shape

Over the recent years, there has been an increasing interest in autonomous systems. Consequently, the problem of avoiding static and dynamic obstacles without human interference has gained a lot of attention. Avoiding collision, even in completely static environments, is significantly more challenging when the vehicle is subject to nonholonomic constraints. This paper presents a reactive algorithm for collision avoidance of dynamic, arbitrarily shaped obstacles, which is suitable for unicycle-type, nonholonomic vehicles. Unlike most reactive methods, we consider the exact shape of the obstacle, which allows the vehicle to utilize any space that is not occupied by the obstacle. This is an advantage over circle and ellipse approximations, as they can lead to overly conservative maneuvers. We provide explicit conditions under which collision avoidance is mathematically proven and validate the analysis by numerical simulations.
Privacy-Preserving Distributed Multi-Task Learning against Inference Attack in Cloud Computing

Because of the powerful computing and storage capability in cloud computing, machine learning as a service (MLaaS) has recently been valued by the organizations for machine learning training over some related representative datasets. When these datasets are collected from different organizations and have different distributions, multi-task learning (MTL) is usually used to improve the generalization performance by scheduling the related training tasks into the virtual machines in MLaaS and transferring the related knowledge between those tasks. However, because of concerns about privacy breaches (e.g., property inference attack and model inverse attack), organizations cannot directly outsource their training data to MLaaS or share their extracted knowledge in plaintext, especially the organizations in sensitive domains. In this article, we propose a novel privacy-preserving mechanism for distributed MTL, namely NOInfer, to allow several task nodes to train the model locally and transfer their shared knowledge privately. Specifically, we construct a single-server architecture to achieve the private MTL, which protects task nodes’ local data even if n-1 out of n nodes colluded. Then, a new protocol for the Alternating Direction Method of Multipliers (ADMM) is designed to perform the privacy-preserving model training, which resists the inference attack through the intermediate results and ensures that the training efficiency is independent of the number of training samples. When releasing the trained model, we also design a differentially private model releasing mechanism to resist the membership inference attack. Furthermore, we analyze the privacy preservation and efficiency of NOInfer in theory. Finally, we evaluate our NOInfer over two testing datasets and evaluation results demonstrate that NOInfer efficiently and effectively achieves the distributed MTL.
Reducing the Human Factor in Virtual Reality Research to Increase Reproducibility and Replicability

The replication crisis is real, and awareness of its existence is growing across disciplines. We argue that research in human-computer interaction (HCI), and especially virtual reality (VR), is vulnerable to similar challenges due to many shared methodologies, theories, and incentive structures. For this reason, in this work, we transfer established solutions from other fields to address the lack of replicability and reproducibility in HCI and VR. We focus on reducing errors resulting from the so-called human factor and adapt established solutions to the specific needs of VR research. In addition, we present a toolkit to support the setup, execution, and evaluation of VR research. Some of the features aim to reduce human errors and thus improve replicability and reproducibility. Finally, the identified chances are applied to a typical scientific process in VR.
Towards Anomaly-resistant Graph Neural Networks via Reinforcement Learning

In general, graph neural networks (GNNs) adopt the message-passing scheme to capture the information of a node (i.e., nodal attributes, and local graph structure) by iteratively transforming, aggregating the features of its neighbors. Nonetheless, recent studies show that the performance of GNNs can be easily hampered by the existence of abnormal or malicious nodes due to the vulnerability of neighborhood aggregation. Thus it is necessary to learn anomaly-resistant GNNs without the prior knowledge of ground-truth anomalies, given the fact that labeling anomalies is costly and requires intensive domain knowledge. Though removing anomalies through unsupervised anomaly detection methods could be a possible solution, it may render unreasonable GNN model performance on target tasks due to the non-differentiable gap between the two learning procedures. In order to keep the effectiveness of GNNs on anomaly-contaminated graphs, in this paper, we propose a new framework named RARE-GNN (Reinforced Anomaly-REsistant Graph Neural Networks) which can detect anomalies from the input graph and learn anomaly-resistant GNNs simultaneously. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed framework.
Active learning via adaptive weighted uncertainty sampling applied to additive manufacturing

The acquisition of large amounts of high-dimensional data is becoming prevalent in additive manufacturing, arising from sensor integration in such processes. Although supervised machine learning has gained popularity to predict process quality from such data, annotation can be time-consuming and labor intensive. Active learning is a sub-field within machine learning concerned with maximizing model performance using the least amount of annotated data. The focus of this study is twofold. Firstly, a novel active learning method is introduced, called adaptive weighted uncertainty sampling (AWUS) which balances uncertainty with random sampling using the model change between active learning iterations. This way, AWUS is able to adapt from exploration of the instance space to exploitation of model knowledge throughout the active learning process. Secondly, a novel feature extraction and classification method is proposed for directed-energy-deposition additive manufacturing processes which predicts image and process quality based upon the visibility of the melt-pool using the visual signature of occluding features in the image. AWUS is compared against 6 state-of-the-art query strategies on 28 datasets from the OpenML database and 8 in-situ machine vision recordings of directed-energy-deposition processes using 4 different classifiers. The results show that AWUS outperforms the state-of-the-art across the board of the datasets, classifiers and active learning batch sizes. Furthermore, the application of AWUS reduces the amount of necessary annotations by 20–70% in 90% of our experiments compared to random sampling.