Reviewerly/exHarmony huggingface dataset:
Hutch++: Optimal Stochastic Trace Estimation

We study the problem of estimating the trace of a matrix A that can only be accessed through matrix-vector multiplication. We introduce a new randomized algorithm, Hutch++, which computes a (1 ± ε) approximation to tr( A ) for any positive semidefinite (PSD) A using just O(1/ε) matrix-vector products. This improves on the ubiquitous Hutchinson's estimator, which requires O(1/ε2) matrix-vector products. Our approach is based on a simple technique for reducing the variance of Hutchinson's estimator using a low-rank approximation step, and is easy to implement and analyze. Moreover, we prove that, up to a logarithmic factor, the complexity of Hutch++ is optimal amongst all matrix-vector query algorithms, even when queries can be chosen adaptively. We show that it significantly outperforms Hutchinson's method in experiments. While our theory requires A to be positive semidefinite, empirical gains extend to applications involving non-PSD matrices, such as triangle estimation in networks.
Fair Attribute Classification through Latent Space De-biasing

Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world. Models trained from data in which target labels are correlated with protected attributes (e.g., gender, race) are known to learn and exploit those correlations. In this work, we introduce a method for training accurate target classifiers while mitigating biases that stem from these correlations. We use GANs to generate realistic-looking images, and perturb these images in the underlying latent space to generate training data that is balanced for each protected attribute. We augment the original dataset with this generated data, and empirically demonstrate that target classifiers trained on the augmented dataset exhibit a number of both quantitative and qualitative benefits. We conduct a thorough evaluation across multiple target labels and protected attributes in the CelebA dataset, and provide an in-depth analysis and comparison to existing literature in the space. Code can be found at https://github.com/princetonvisualai/gan-debiasing.
HONEST: Measuring Hurtful Sentence Completion in Language Models

Language models have revolutionized the field of NLP.However, language models capture and proliferate hurtful stereotypes, especially in text generation.Our results show that 4.3% of the time, language models complete a sentence with a hurtful word.These cases are not random, but follow language and genderspecific patterns.We propose a score to measure hurtful sentence completions in language models (HONEST).It uses a systematic template-and lexicon-based bias evaluation methodology for six languages.Our findings suggest that these models replicate and amplify deep-seated societal stereotypes about gender roles.Sentence completions refer to sexual promiscuity when the target is female in 9% of the time, and in 4% to homosexuality when the target is male.The results raise questions about the use of these models in production settings.
Mutual CRF-GNN for Few-shot Learning

Graph-neural-networks (GNN) is a rising trend for fewshot learning. A critical component in GNN is the affinity. Typically, affinity in GNN is mainly computed in the feature space, e.g., pairwise features, and does not take fully advantage of semantic labels associated to these features. In this paper, we propose a novel Mutual CRF-GNN (MCGN). In this MCGN, the labels and features of support data are used by the CRF for inferring GNN affinities in a principled and probabilistic way. Specifically, we construct a Conditional Random Field (CRF) conditioned on labels and features of support data to infer a affinity in the label space. Such affinity is fed to the GNN as the node-wise affinity. GNN and CRF mutually contributes to each other in MCGN. For GNN, CRF provides valuable affinity information. For CRF, GNN provides better features for inferring affinity. Experimental results show that our approach outperforms stateof-the-arts on datasets miniImageNet, tieredImageNet, and CIFAR-FS on both 5-way 1-shot and 5-way 5-shot settings.
Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new <scp>CNN</scp> model: MA_ColonNET

Abstract Colon cancer is a common type of carcinoma that occurs in the large intestine. This type of cancer affects millions of people around the world each year. Early and accurate diagnosis is very important in the treatment of colon cancer as in other types of cancer. Thanks to early and accurate diagnosis, many people can get rid of this disease with less damage. Medical imaging techniques are widely used in the early diagnosis, follow‐up, and after the treatment process of colon cancer. Therefore, manually controlling a large number of medical images and their interpretation is a difficult process and consumes more time. In addition, the interpretation of data with traditional methods in this process can cause misdiagnosis due to human errors. For this reason, computer‐aided systems can be used in the diagnosis of colon cancer in order to both help experts and carry out the process more quickly and successfully. In this study, a novel method named by us, CNN‐based, MA_ColonNET is developed for detecting colon cancer image data. A 45‐layer model in MA_ColonNET has been used to classify. A success (accuracy) rate of 99.75% has been achieved by means of the new model. It is shown that the proposed model can detect colon cancer earlier. In this way, the treatment process can be carried out more successfully.
Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

Early diagnosis of breast cancer, the most common disease among women around the world, increases the chance of treatment and is highly important. Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading of breast cancer. Grading images by pathologists is a time consuming and subjective task. Therefore, the existence of a computer-aided system for nuclear atypia grading is very useful and necessary. In this study, two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed. A patch-based approach is introduced due to the large size of the histopathological images and restriction of the training data. In the proposed system I, the most important patches in the image are detected first and then a three-hidden-layer convolutional neural network (CNN) is designed and trained for feature extraction and to classify the patches individually. The proposed system II is based on a combination of the CNN for feature extraction and a two-layer Long short-term memory (LSTM) network for classification. The LSTM network is utilised to consider all patches of an image simultaneously for image grading. The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.
Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation

Pose estimation plays a critical role in human-centered vision applications. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-constrained edge devices due to the high computational cost (more than 150 GMACs per frame). In this paper, we study efficient architecture design for real-time multi-person pose estimation on edge. We reveal that HRNet's high-resolution branches are redundant for models at the low-computation region via our gradual shrinking experiments. Removing them improves both efficiency and performance. Inspired by this finding, we design LitePose, an efficient single-branch architecture for pose estimation, and introduce two simple approaches to enhance the capacity of LitePose, including fusion deconv head and large kernel conv. On mobile platforms, LitePose reduces the latency by up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5.0\times$</tex> without sacrificing performance, compared with prior state-of-the-art efficient pose estimation models, pushing the frontier of real-time multi-person pose estimation on edge. Our code and pretrained models are released at https://github.com/mit-han-lab/litepose.
Blockchain Technology: Security Issues, Healthcare Applications, Challenges and Future Trends

Blockchain technology provides a data structure with inherent security properties that include cryptography, decentralization, and consensus, which ensure trust in transactions. It covers widely applicable usages, such as in intelligent manufacturing, finance, the Internet of things (IoT), medicine and health, and many different areas, especially in medical health data security and privacy protection areas. Its natural attributes, such as contracts and consensus mechanisms, have leading-edge advantages in protecting data confidentiality, integrity, and availability. The security issues are gradually revealed with in-depth research and vigorous development. Unlike traditional paper storage methods, modern medical records are stored electronically. Blockchain technology provided a decentralized solution to the trust-less issues between distrusting parties without third-party guarantees, but the “trust-less” security through technology was easily misunderstood and hindered the security differences between public and private blockchains appropriately. The mentioned advantages and disadvantages motivated us to provide an advancement and comprehensive study regarding the applicability of blockchain technology. This paper focuses on the healthcare security issues in blockchain and sorts out the security risks in six layers of blockchain technology by comparing and analyzing existing security measures. It also explores and defines the different security attacks and challenges when applying blockchain technology, which promotes theoretical research and robust security protocol development in the current and future distributed work environment.
Approaches for preserving content integrity of sensitive online Arabic content: A survey and research challenges

Trends in Internet usage and accessing online content in different languages and formats are proliferating at a considerable speed. There is a vast amount of digital online content available in different formats that are sensitive in nature with respect to writing styles and arrangement of diacritics. However, research done in the area aimed at identifying the necessary techniques suitable for preserving content integrity of sensitive digital online content is limited. So, it is a challenge to determine the techniques most suitable for different formats such as image or binary. Hence, preserving and verifying sensitive content constitutes an emerging problem and calls for timely solutions. The digital Holy Qur'an in Arabic, constitutes, one case of such sensitive content. Due to the different characteristics of the Arabic letters like diacritics (punctuation symbols), kashidas (extended letters) and other symbols, it is very easy to alter the original meaning of the text by simply changing the arrangement of diacritics. This article surveys the different approaches that are presently employed in the process of preserving and verifying the content integrity of sensitive online content. We present the state-of-the-art in content integrity verification and address the existing challenges in preserving the integrity of sensitive texts using the Digital Qur'an as a case study. The proposed taxonomy provides an effective classification and analysis of existing related schemes and their limitations. The paper discusses the recommendations of the expected efficiency of such approaches when applied for use in digital content integrity. Some of the main findings suggest unified approaches of watermarking and string matching approaches can be used to preserve content integrity of any sensitive digital content.
Fiber bundle image restoration using deep learning

We propose a deep learning-based restoration method to remove honeycomb patterns and improve resolution for fiber bundle (FB) images. By building and calibrating a dual-sensor imaging system, we capture FB images and corresponding ground truth data to train the network. Images without fiber bundle fixed patterns are restored from raw FB images as direct inputs, and spatial resolution is significantly enhanced for the trained sample type. We also construct the brightness mapping between the two image types for the effective use of all data, providing the ability to output images of the expected brightness. We evaluate our framework with data obtained from lens tissues and human histological specimens using both objective and subjective measures.
Do financial inclusion, stock market development attract foreign capital flows in developing economy: a panel data investigation

Do financial inclusion and stock market development play the deterministic role of attracting foreign capital flows in developing countries. With this study, we tried to figure out the answer by investigating the magnitude of financial inclusion and stock market development towards capital flows in the economy considering a panel of 58 developing countries for the span of 1993Ƀ2017. Dynamic panel System-GMM estimation was performed by decomposing the development and degradation namely, positive and negative shocks in financial inclusion and stock market development. Study findings ascertain the asymmetric relationship between financial inclusion, stock market development, and cross-broader capital flows in developing countries. We also observed positive shock in financial inclusion and stock market development positive linked with cross-broader capital flows. In particular, development in financial inclusion in the financial system encourages foreign capital flows in the form of FDI, on the other hand well developed and efficient financial market particularly stock market development encourage the positive trend in foreign portfolio investment. The study also revealed that past behavior of foreign capital flows could consider as the future predictor for foreign capital flows in recipients countries. Dumitrescu and Hurlin (2012) Panel Causality Test was performed and estimation unveiled bidirectional causality between stock market development and capital flows and unidirectional causality from financial inclusion to foreign capital flows in the short-run.
Prototype-guided Attribute-wise Interpretable Scheme for Clothing Matching

Recently, as an essential part of people's daily life, clothing matching has gained increasing research attention. Most existing efforts focus on the numerical compatibility modeling between fashion items with advanced neural networks, and hence suffer from the poor interpretation, which makes them less applicable in real world applications. In fact, people prefer to know not only whether the given fashion items are compatible, but also the reasonable interpretations as well as suggestions regarding how to make the incompatible outfit harmonious. Considering that the research line of the comprehensively interpretable clothing matching is largely untapped, in this work, we propose a prototype-guided attribute-wise interpretable compatibility modeling (PAICM) scheme, which seamlessly integrates the latent compatible/incompatible prototype learning and compatibility modeling with the Bayesian personalized ranking (BPR) framework. In particular, the latent attribute interaction prototypes, learned by the non-negative matrix factorization (NMF), are treated as templates to interpret the discordant attribute and suggest the alternative item for each fashion item pair. Extensive experiments on the real-world dataset have demonstrated the effectiveness of our scheme.
Follow The Clicks: Learning and Anticipating Mouse Interactions During Exploratory Data Analysis

Abstract The goal of visual analytics is to create a symbiosis between human and computer by leveraging their unique strengths. While this model has demonstrated immense success, we are yet to realize the full potential of such a human‐computer partnership. In a perfect collaborative mixed‐initiative system, the computer must possess skills for learning and anticipating the users' needs. Addressing this gap, we propose a framework for inferring attention from passive observations of the user's click, thereby allowing accurate predictions of future events. We demonstrate this technique with a crime map and found that users' clicks can appear in our prediction set 92% ‐ 97% of the time. Further analysis shows that we can achieve high prediction accuracy typically after three clicks. Altogether, we show that passive observations of interaction data can reveal valuable information that will allow the system to learn and anticipate future events.
An Improved Bat Algorithm Based on Lévy Flights and Adjustment Factors

This paper proposed an improved bat algorithm based on Lévy flights and adjustment factors (LAFBA). Dynamically decreasing inertia weight is added to the velocity update, which effectively balances the global and local search of the algorithm; the search strategy of Lévy flight is added to the position update, so that the algorithm maintains a good population diversity and the global search ability is improved; and the speed adjustment factor is added, which effectively improves the speed and accuracy of the algorithm. The proposed algorithm was then tested using 10 benchmark functions and 2 classical engineering design optimizations. The simulation results show that the LAFBA has stronger optimization performance and higher optimization efficiency than basic bat algorithm and other bio-inspired algorithms. Furthermore, the results of the real-world engineering problems demonstrate the superiority of LAFBA in solving challenging problems with constrained and unknown search spaces.
Restricted Boltzmann Machines With Gaussian Visible Units Guided by Pairwise Constraints

Restricted Boltzmann machines (RBMs) and their variants are usually trained by contrastive divergence (CD) learning, but the training procedure is an unsupervised learning approach, without any guidances of the background knowledge. To enhance the expression ability of traditional RBMs, in this paper, we propose pairwise constraints (PCs) RBM with Gaussian visible units (pcGRBM) model, in which the learning procedure is guided by PCs and the process of encoding is conducted under these guidances. The PCs are encoded in hidden layer features of pcGRBM. Then, some pairwise hidden features of pcGRBM flock together and another part of them are separated by the guidances. In order to deal with real-valued data, the binary visible units are replaced by linear units with Gaussian noise in the pcGRBM model. In the learning process of pcGRBM, the PCs are iterated transitions between visible and hidden units during CD learning procedure. Then, the proposed model is inferred by approximative gradient descent method and the corresponding learning algorithm is designed. In order to compare the availability of pcGRBM and traditional RBMs with Gaussian visible units, the features of the pcGRBM and RBMs hidden layer are used as input “data” for K-means, spectral clustering (SP) and affinity propagation (AP) algorithms, respectively. We also use tenfold cross-validation strategy to train and test pcGRBM model to obtain more meaningful results with PCs which are derived from incremental sampling procedures. A thorough experimental evaluation is performed with 12 image datasets of Microsoft Research Asia Multimedia. The experimental results show that the clustering performance of K-means, SP, and AP algorithms based on pcGRBM model are significantly better than traditional RBMs. In addition, the pcGRBM model for clustering tasks shows better performance than some semi-supervised clustering algorithms.
Self-Supervised Speaker Embeddings

Contrary to i-vectors, speaker embeddings such as x-vectors are incapable of leveraging unlabelled utterances, due to the classification loss over training speakers. In this paper, we explore an alternative training strategy to enable the use of unlabelled utterances in training. We propose to train speaker embedding extractors via reconstructing the frames of a target speech segment, given the inferred embedding of another speech segment of the same utterance. We do this by attaching to the standard speaker embedding extractor a decoder network, which we feed not merely with the speaker embedding, but also with the estimated phone sequence of the target frame sequence. The reconstruction loss can be used either as a single objective, or be combined with the standard speaker classification loss. In the latter case, it acts as a regularizer, encouraging generalizability to speakers unseen during training. In all cases, the proposed architectures are trained from scratch and in an end-to-end fashion. We demonstrate the benefits from the proposed approach on VoxCeleb and Speakers in the wild, and we report notable improvements over the baseline.
Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multihop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop reasoning is still challenging because the reasoning process usually experiences multiple semantic issue that a relation or an entity has multiple meanings. In order to deal with the situation, we propose a novel Hierarchical Reinforcement Learning framework to learn chains of reasoning from a Knowledge Graph automatically. Our framework is inspired by the hierarchical structure through which human handle cognitionally ambiguous cases. The whole reasoning process is decomposed into a hierarchy of two-level Reinforcement Learning policies for encoding historical information and learning structured action space. As a consequence, it is more feasible and natural for dealing with the multiple semantic issue. Experimental results show that our proposed model achieves substantial improvements in ambiguous relation tasks.
COMETA: A Corpus for Medical Entity Linking in the Social Media

Whilst there has been growing progress in Entity Linking (EL) for general language, existing datasets fail to address the complex nature of health terminology in layman's language.Meanwhile, there is a growing need for applications that can understand the public's voice in the health domain.To address this we introduce a new corpus called COMETA, consisting of 20k English biomedical entity mentions from Reddit expert-annotated with links to SNOMED CT, a widely-used medical knowledge graph.Our corpus satisfies a combination of desirable properties, from scale and coverage to diversity and quality, that to the best of our knowledge has not been met by any of the existing resources in the field.Through benchmark experiments on 20 EL baselines from string-to neural-based models we shed light on the ability of these systems to perform complex inference on entities and concepts under 2 challenging evaluation scenarios.Our experimental results on COMETA illustrate that no golden bullet exists and even the best mainstream techniques still have a significant performance gap to fill, while the best solution relies on combining different views of data.