Reviewerly/exHarmony huggingface dataset:
UMS-Rep: Unified modality-specific representation for efficient medical image analysis

Medical image analysis typically includes several tasks such as enhancement, segmentation, and classification. Traditionally, these tasks are implemented using separate deep learning models for separate tasks, which is not efficient because it involves unnecessary training repetitions, demands greater computational resources, and requires a relatively large amount of labeled data. In this paper, we propose a multi-task training approach for medical image analysis, where individual tasks are fine-tuned simultaneously through relevant knowledge transfer using a unified modality-specific feature representation (UMS-Rep). We explore different fine-tuning strategies to demonstrate the impact of the strategy on the performance of target medical image tasks. We experiment with different visual tasks (e.g., image denoising, segmentation, and classification) to highlight the advantages offered with our approach for two imaging modalities, chest X-ray and Doppler echocardiography. Our results demonstrate that the proposed approach reduces the overall demand for computational resources and improves target task generalization and performance. Specifically, the proposed approach improves accuracy (up to ∼ 9% ↑) and decreases computational time (up to ∼ 86% ↓) as compared to the baseline approach. Further, our results prove that the performance of target tasks in medical images is highly influenced by the utilized fine-tuning strategy.
An Automated Approach towards Smart Healthcare with Blockchain and Smart Contracts

Healthcare is one of the most crucial components of our lives since the value of our well-being is immeasurable. The current healthcare system, unfortunately, lacks quality service, accessibility and ease of use. Hence, this paper proposes a smart healthcare system to solve the existing issues of this system. Blockchain-based smart contracts are used to secure sensitive information and maintain the privacy aspect. Smart contracts create an environment in which a significant amount of operations become hands-free. This results in resource-friendliness because the system becomes less reliant on manual labor. The information stored is proven to be accurate, immutable and requires the approval of proper authorities in all cases. Along with this healthcare becomes secure, decentralized, automated and accessible to all.
Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning Training

Recent studies have shown that recommender systems are vulnerable, and it is easy for attackers to inject well-designed malicious profiles into the system, leading to biased recommendations. We cannot deny these data's rationality, making it imperative to establish a robust recommender system. Adversarial training has been extensively studied for robust recommendations. However, traditional adversarial training adds small perturbations to the parameters (inputs), which do not comply with the poisoning mechanism in the recommender system. Thus for the practical models that are very good at learning existing data, it does not perform well. To address the above limitations, we propose adversarial poisoning training (APT). It simulates the poisoning process by injecting fake users (ERM users) who are dedicated to minimizing empirical risk to build a robust system. Besides, to generate ERM users, we explore an approximation approach to estimate each fake user's influence on the empirical risk. Although the strategy of "fighting fire with fire" seems counterintuitive, we theoretically prove that the proposed APT can boost the upper bound of poisoning robustness. Also, we deliver the first theoretical proof that adversarial training holds a positive effect on enhancing recommendation robustness. Through extensive experiments with five poisoning attacks on four real-world datasets, the results show that the robustness improvement of APT significantly outperforms baselines. It is worth mentioning that APT also improves model generalization in most cases.
Structured Attention Network for Referring Image Segmentation

Referring image segmentation aims at segmenting out the object or stuff referred to by a natural language expression. The challenge of this task lies in the requirement of understanding both vision and language. The linguistic structure of a referring expression can provide an intuitive and explainable layout for reasoning over visual and linguistic concepts. In this paper, we propose a structured attention network (SANet) to explore the multimodal reasoning over the dependency tree parsed from the referring expression. Specifically, SANet implements the multimodal reasoning using an attentional multimodal tree-structure recurrent module (AMTreeGRU) in a bottom-up manner. In addition, for spatial detail improvement, SANet further incorporates the semantics-guided low-level features into high-level ones using the proposed attentional skip connection module. Extensive experiments on four public benchmark datasets demonstrate the superiority of our proposed SANet with more explainable visualization examples.
Interferometric spectroscopy with quantum light: Revealing out-of-time-ordering correlators

We survey the inclusion of interferometric elements in nonlinear spectroscopy performed with quantum light. Controlled interference of electromagnetic fields coupled to matter can induce constructive or destructive contributions of microscopic coupling sequences (histories) of matter. Since quantum fields do not commute, quantum light signals are sensitive to the order of light-matter coupling sequence. Matter correlation functions are thus imprinted by different field factors, which depend on that order. We identify the associated quantum information obtained by controlling the weights of different contributing pathways, and offer several experimental schemes for recovering it. Nonlinear quantum response functions include out-of-time-ordering matter correlators (OTOC) which reveal how perturbations spread throughout a quantum system (information scrambling). Their effect becomes most notable when using ultrafast pulse sequences with respect to the path difference induced by the interferometer. OTOC appear in quantum-informatics studies in other fields, including black holes, high energy, and condensed matter physics.
Black Boxes or Unflattering Mirrors? Comparative Bias in the Science of Machine Behaviour

The last 5 years have seen a series of remarkable achievements in deep-neural-network-based artificial intelligence research, and some modellers have argued that their performance compares favourably to human cognition. Critics, however, have argued that processing in deep neural networks is unlike human cognition for four reasons: they are (i) data-hungry, (ii) brittle, and (iii) inscrutable black boxes that merely (iv) reward-hack rather than learn real solutions to problems. This article rebuts these criticisms by exposing comparative bias within them, in the process extracting some more general lessons that may also be useful for future debates.
A scalable approach for developing clinical risk prediction applications in different hospitals

Machine learning (ML) algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few efforts have been made in extending the developed solutions to other events or to different hospitals. We provide a scalable solution to extend the process of clinical risk prediction model development of multiple diseases and their deployment in different Electronic Health Records (EHR) systems. We defined a generic process for clinical risk prediction model development. A calibration tool has been created to automate the model generation process. We applied the model calibration process at four hospitals, and generated risk prediction models for delirium, sepsis and acute kidney injury (AKI) respectively at each of these hospitals. The delirium risk prediction models have on average an area under the receiver-operating characteristic curve (AUROC) of 0.82 at admission and 0.95 at discharge on the test datasets of the four hospitals. The sepsis models have on average an AUROC of 0.88 and 0.95, and the AKI models have on average an AUROC of 0.85 and 0.92, at the day of admission and discharge respectively. The scalability discussed in this paper is based on building common data representations (syntactic interoperability) between EHRs stored in different hospitals. Semantic interoperability, a more challenging requirement that different EHRs share the same meaning of data, e.g. a same lab coding system, is not mandated with our approach. Our study describes a method to develop and deploy clinical risk prediction models in a scalable way. We demonstrate its feasibility by developing risk prediction models for three diseases across four hospitals.
Software engineering meets deep learning

Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks. However, since the renaissance of DL techniques is still very recent, we lack works that summarize and condense the most recent and relevant research conducted at the intersection of DL and SE. Therefore, in this paper, we describe the first results of a mapping study covering 81 papers about DL & SE. Our results confirm that DL is gaining momentum among SE researchers over the years and that the top-3 research problems tackled by the analyzed papers are documentation, defect prediction, and testing.
Mobile projective augmented reality for collaborative robots in construction

Augmenting virtual construction information directly in a physical environment is promising to increase onsite productivity and safety. However, it has been found that using off-the-self augmented reality (AR) devices, such as goggles or helmets, could potentially cause more health, safety, and efficiency concerns in complex real-world construction projects, due to the restricted field of view and the non-negligible weight of those devices. To address these issues, we propose a mobile projective AR (MPAR) framework in which the AR device is detached from human workers and carried by one or more mobile collaborative robots (co-robots). MPAR achieves glassless AR that is visible to the naked eye using a camera-projector system to superimpose virtual 3D information onto planar or non-planar physical surfaces. Since co-robots often need to move during the operation, we design algorithms to ensure consistent mobile projection with two major components: projector pose estimation and projection image generation. For planar surfaces, MPAR is achieved by a homography-based pose estimation and image warping. For non-planar surfaces, MPAR uses iterative closest point (ICP) for pose estimation and common graphics pipelines to generate projection images. We conducted both qualitative and quantitative experiments to validate the feasibility of MPAR in a laboratory setting, by projecting 1) as-planned building information onto a planar surface, and 2) as-built 3D information onto a piece-wise planar surface. Our evaluation demonstrated centimeter-level projection accuracy of MPAR from different distances and angles to the two types of surfaces.
Feature Estimations Based Correlation Distillation for Incremental Image Retrieval

Deep learning for fine-grained image retrieval in an incremental context is less investigated. In this paper, we explore this task to realize the model&#x2019;s continuous retrieval ability. That means, the model enables to perform well on new incoming data and reduce forgetting of the knowledge learned on preceding old tasks. For this purpose, we distill semantic correlations knowledge among the representations extracted from the new data only so as to regularize the parameters updates using the teacher-student framework. In particular, for the case of learning multiple tasks sequentially, aside from the correlations distilled from the penultimate model, we estimate the representations for all prior models and further their semantic correlations by using the representations extracted from the new data. To this end, the estimated correlations are used as an additional regularization and further prevent catastrophic forgetting over all previous tasks, and it is unnecessary to save the stream of models trained on these tasks. Extensive experiments demonstrate that the proposed method performs favorably for retaining performance on the already-trained old tasks and achieving good accuracy on the current task when new data are added at once or sequentially.
A study on using data clustering for feature extraction to improve the quality of classification

Abstract There is a certain belief among data science researchers and enthusiasts alike that clustering can be used to improve classification quality. Insofar as this belief is fairly uncontroversial, it is also very general and therefore produces a lot of confusion around the subject. There are many ways of using clustering in classification and it obviously cannot always improve the quality of predictions, so a question arises, in which scenarios exactly does it help? Since we were unable to find a rigorous study addressing this question, in this paper, we try to shed some light on the concept of using clustering for classification. To do so, we first put forward a framework for incorporating clustering as a method of feature extraction for classification. The framework is generic w.r.t. similarity measures, clustering algorithms, classifiers, and datasets and serves as a platform to answer ten essential questions regarding the studied subject. Each answer is formulated based on a separate experiment on 16 publicly available datasets, followed by an appropriate statistical analysis. After performing the experiments and analyzing the results separately, we discuss them from a global perspective and form general conclusions regarding using clustering as feature extraction for classification.
Discover the Unknown Biased Attribute of an Image Classifier

Recent works find that AI algorithms learn biases from data. Therefore, it is urgent and vital to identify biases in AI algorithms. However, the previous bias identification pipeline overly relies on human experts to conjecture potential biases (e.g., gender), which may neglect other underlying biases not realized by humans. To help human experts better find the AI algorithms’ biases, we study a new problem in this work – for a classifier that predicts a target attribute of the input image, discover its unknown biased attribute.To solve this challenging problem, we use a hyperplane in the generative model’s latent space to represent an image attribute; thus, the original problem is transformed to optimizing the hyperplane’s normal vector and offset. We propose a novel total-variation loss within this framework as the objective function and a new orthogonalization penalty as a constraint. The latter prevents trivial solutions in which the discovered biased attribute is identical with the target or one of the known-biased attributes. Extensive experiments on both disentanglement datasets and real-world datasets show that our method can discover biased attributes and achieve better disentanglement w.r.t. target attributes. Furthermore, the qualitative results show that our method can discover unnoticeable biased attributes for various object and scene classifiers, proving our method’s generalizability for detecting biased attributes in diverse domains of images.
The design of dynamic ensemble selection strategy for the error-correcting output codes family

Error-Correcting Output Codes (ECOC) is widely deployed to tackle the multiclass classification problem by reducing the original multi-class problem to several binary sub-problems. This study attempts to design a dynamic ensemble selection strategy to promote the performance of ECOC algorithms. Concretely, each column in a coding matrix is matched with a set of feature subsets generated by various feature selection methods. In the decoding process, a novel criterion based on the data complexity theory is proposed to pick up an optimal feature subset from the candidate subsets, so as to better distinguish unknown samples. As this strategy can be embedded in all types of ECOC algorithms, seven classical ECOC algorithms are deployed to verify the effectiveness of our strategy. Experiments are carried out on a set of UCI data sets, and the results confirm that despite different working principle, the proposed strategy can further improve the performance of various ECOC algorithms in most cases. Our python source code is available at: https://github.com/MLDMXM2017/ECOC_DES .
Bilateral Weighted Regression Ranking Model With Spatial-Temporal Correlation Filter for Visual Tracking

Many discriminative correlation filter (DCF)-based methods have successfully leveraged the guidance for solving two problems (i.e., the boundary effect and temporal filtering degradation) as a model prior to visual tracking. The intuitive motivation of these methods is to control the degeneration of the updating loss of the objective function with a structural framework. While these methods rely mostly on various regularization items, they always ignore the loss from data fidelity term. Therefore, we propose a bilateral weighted regression ranking model termed as BWRR. Here, we resort to two procedures for solving the above problems. First, BWRR introduces a bilateral constraint into the data fidelity term to control the loss of rows and columns of the filter learning data term. The weighted matrices could impose an adaptive penalty for large data loss during the learning process to avoid the model degradation problem. Second, the data of the updated weighted matrices is not directly applied to the calculation of the filter during each iteration. Instead, a new weighted product matrix is obtained by ranking and numerical transformation for updating the filter. We show that the proposed model converts the original correlation filter regression problem into a regression-with-ranking problem, thus avoiding the problem of positive and negative sample imbalance. Overall, the BWRR model is iteratively solved by the alternating direction method of multipliers(ADMM). Qualitative and quantitative evaluations demonstrate the effectiveness and superiority of our proposed method by extensive and quantitative experiments on the OTB, VOT, and UAV datasets.
Efficient n-gram construction for text categorization using feature selection techniques

In this paper, we present a novel approach for n-gram generation in text classification. The a-priori algorithm is adapted to prune word sequences by combining three feature selection techniques. Unlike the traditional two-step approach for text classification in which feature selection is performed after the n-gram construction process, our proposal performs an embedded feature elimination during the application of the a-priori algorithm. The proposed strategy reduces the number of branches to be explored, speeding up the process and making the construction of all the word sequences tractable. Our proposal has the additional advantage of constructing a low-dimensional dataset with only the features that are relevant for classification, that can be used directly without the need for a feature selection step. Experiments on text classification datasets for sentiment analysis demonstrate that our approach yields the best predictive performance when compared with other feature selection approaches, while also facilitating a better understanding of the words and phrases that explain a given task; in our case online reviews and ratings in various domains.
Objectivity and Subjectivity in Games: Understanding Engagement and Addiction Mechanism

The gaming industry had rapidly been expanding globally, where it encompasses more than the purpose of recreational and increasingly becomes more immersive and engaging, and potentially leads to pathological gaming behaviors that lead to addiction.Such experience of engagement and addiction involves understanding the fundamental functions of the human mind's dynamic state.This study uncovers the mind's underlying physics via the analogy of motion (i.e., mass, velocity, etc.) using games as the source of information.This study also conjectures that the law of conservation in mind occurred in games where momentum and energy were conserved over time, where game-playing experience relative to the gambling psychology and perceptive force were identified from the objective and subjective perspectives.It was found that momentum conservation provides new engagement measures while energy conservation, considering several other factors, provides the necessary components of understanding the addiction mechanism in the game-playing context.This measurement is examined in various domains, such as popular boards and sports games, and public gambling, where its effectiveness is determined.
Fractal dimension analysis as an easy computational approach to improve breast cancer histopathological diagnosis

Abstract Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different ( p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promissing statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p &lt; 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.
Phishing Attack Detection using Feature Selection Techniques

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