Reviewerly/exHarmony huggingface dataset:
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search

In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search. The key idea is to formulate the GAN architecture search problem as a Markov decision process (MDP) for smoother architecture sampling, which enables a more effective RL-based search algorithm by targeting the potential global optimal architecture. To improve efficiency, we exploit an off-policy GAN architecture search algorithm that makes efficient use of the samples generated by previous policies. Evaluation on two standard benchmark datasets (i.e., CIFAR-10 and STL-10) demonstrates that the proposed method is able to discover highly competitive architectures for generally better image generation results with a considerably reduced computational burden: 7 GPU hours. Our code is available at https://github.com/Yuantian013/E2GAN .
The Best Way to Select Features? Comparing MDA, LIME, and SHAP

Feature selection in machine learning is subject to the intrinsic randomness of the feature selection algorithms (e.g., random permutations during MDA). Stability of selected features with respect to such randomness is essential to the human interpretability of a machine learning algorithm. The authors propose a rank-based stability metric called the <i>instability index</i> to compare the stabilities of three feature selection algorithms—MDA, LIME, and SHAP—as applied to random forests. Typically, features are selected by averaging many random iterations of a selection algorithm. Although the variability of the selected features does decrease as the number of iterations increases, it does not go to zero, and the features selected by the three algorithms do not necessarily converge to the same set. LIME and SHAP are found to be more stable than MDA, and LIME is at least as stable as SHAP for the top-ranked features. Hence, overall, LIME is best suited for human interpretability. However, the selected set of features from all three algorithms significantly improves various predictive metrics out of sample, and their predictive performance does not differ significantly. Experiments were conducted on synthetic datasets, two public benchmark datasets, an S&amp;P 500 dataset, and on proprietary data from an active investment strategy. <b>TOPICS:</b>Big data/machine learning, simulations, statistical methods <b>Key Findings</b> ▪ The authors propose a novel ranked-based instability index to measure the stability of feature selection algorithms MDA, LIME, and SHAP. ▪ LIME is more stable than MDA and SHAP on the features with high importance scores. ▪ LIME improves the exemplified trading strategy in both Sharpe ratio and cumulative return.
A Multicomponent Model of Working Memory

The multicomponent model aims to provide a broad theoretical framework enabling both more detailed fractionation and analysis of its components, and a capacity for it be used fruitfully beyond the laboratory. In its current form it comprises four interacting components. Two of these are modality-specific memory storage systems, one verbal-acoustic, the phonological loop, and one visuospatial, the sketchpad. Information in both these stores can be temporarily maintained via focused attention termed ‘refreshing’, while the phonological loop can also maintain familiar verbalizable material by subvocal or overt rehearsal. Both subsystems are controlled by a third component, the central executive, a supervisory system with limited resources. The central executive is principally concerned with internally directed attentional control processes but also has a role in the attentional selection of perceptual information. Information from these three components is coordinated with information from perception and long-term memory through the fourth component, a multidimensional, multimodal episodic buffer. This component is capable of holding up to around four episodic chunks, and is a valuable but essentially passive storage system, controlled by the central executive and accessible to conscious awareness. The multicomponent model has been systematically developed using a number of experimental tools. These include, principally, similarity effects to identify the type of coding involved, concurrent task methods to assess the contributions of the various subsystems to complex tasks, and neuropsychological evidence, in particular from the study of single cases with very specific deficits. The model continues to evolve and has proved successful both in accounting for a broad range of data on memory and related cognitive areas and in its application to the understanding of a wide range of cognitive activities and populations.
Graph Moving Object Segmentation

Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to undesirable variations in the background scene, MOS becomes very challenging for static and moving camera sequences. Several deep learning methods have been proposed for MOS with impressive performance. However, these methods show performance degradation in the presence of unseen videos; and usually, deep learning models require large amounts of data to avoid overfitting. Recently, graph learning has attracted significant attention in many computer vision applications since they provide tools to exploit the geometrical structure of data. In this work, concepts of graph signal processing are introduced for MOS. First, we propose a new algorithm that is composed of segmentation, background initialization, graph construction, unseen sampling, and a semi-supervised learning method inspired by the theory of recovery of graph signals. Second, theoretical developments are introduced, showing one bound for the sample complexity in semi-supervised learning, and two bounds for the condition number of the Sobolev norm. Our algorithm has the advantage of requiring less labeled data than deep learning methods while having competitive results on both static and moving camera videos. Our algorithm is also adapted for Video Object Segmentation (VOS) tasks and is evaluated on six publicly available datasets outperforming several state-of-the-art methods in challenging conditions.
A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement

Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains on paired or unpaired low-/normal-images. However, existing methods suffer color deviation and fail to generalize to various lighting conditions. This paper presents a novel self-regularized method based on Retinex, which, inspired by HSV, preserves all colors (Hue, Saturation) and only integrates Retinex theory into brightness (Value). We build a reflectance estimation network by restricting the consistency of reflectances embedded in both the original and a novel random disturbed form of the brightness of the same scene. The generated reflectance, which is assumed to be irrelevant of illumination by Retinex, is treated as enhanced brightness. Our method is efficient as a low-light image is decoupled into two subspaces, color and brightness, for better preservation and enhancement. Extensive experiments demonstrate that our method outperforms multiple state-of-the-art algorithms qualitatively and quantitatively and adapts to more lighting conditions.
Digitalization and the Challenges for the Accounting Profession

Accounting profession is traditional profession and accounting rules and principles have been established and are the same for many years. However, globalization of business, stronger regulations and numerous technological solutions and innovations are not bypassing the accounting profession either. Challenges for the accounting profession are reflected in the need for rapid adaptation and transformation of business practice and business processes without abandoning away from basic accounting rules and principles. The aim of this paper is to analyze and systematize the key challenges that digitalization brings for the accounting profession. Research is based on the review of relevant and available professional and academic literature. The results are showing that accounting profession is faced with numerous challenges in the era of digitalization. Key challenges could be systematized in following: the use of big data in accounting and reporting, cloud computing and continuous accounting, artificial intelligence and blockchain technology. The conclusion of this research is that changes in technology and digitalization will have a significant impact on the accounting profession in the coming period. Changes are moving toward reporting on a daily basis, difference on the way of preparing business and strategic plans, implementation of digital wallet and on-line accounting as well as outsourcing of accounting in distant countries. Education system is going to change in circumstances of growing digitalization of business. Also, users of accounting information in digital era are changing; they want accounting information right away when a business event occurs, not with time lag. Those changes will influence on the way accountants carry out their job, it will be necessary the knowledge of new skills especially in engineering, and, finally, that will lead to new types of the accounting professionals.
Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering

Understanding narratives requires reasoning about implicit world knowledge related to the causes, effects, and states of situations described in text. At the core of this challenge is how to access contextually relevant knowledge on demand and reason over it. In this paper, we present initial studies toward zero-shot commonsense question answering by formulating the task as inference over dynamically generated commonsense knowledge graphs. In contrast to previous studies for knowledge integration that rely on retrieval of existing knowledge from static knowledge graphs, our study requires commonsense knowledge integration where contextually relevant knowledge is often not present in existing knowledge bases. Therefore, we present a novel approach that generates contextually-relevant symbolic knowledge structures on demand using generative neural commonsense knowledge models. Empirical results on two datasets demonstrate the efficacy of our neuro-symbolic approach for dynamically constructing knowledge graphs for reasoning. Our approach achieves significant performance boosts over pretrained language models and vanilla knowledge models, all while providing interpretable reasoning paths for its predictions.
External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature

There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated.A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data.We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0-94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th-75th percentile [interquartile range (IQR)], 0.66-0.79), representing a median percent decrease in discrimination of -11.1% (IQR, -32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was -3.7% (IQR, -13.2 to 3.1) for closely related validations (n=123), -9.0 (IQR, -27.6 to 3.9) for related validations (n=862), and -17.2% (IQR, -42.3 to 0) for distantly related validations (n=717; P<0.001).Many published cardiovascular CPMs have never been externally validated, and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings.
Security of medical images for telemedicine: a systematic review

Abstract Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data security approaches and the different challenges associated with them. An in-depth overview of security techniques, such as cryptography, steganography, and watermarking are introduced with a full survey of recent research. The objective of the paper is to summarize and assess the different algorithms of each approach based on different parameters such as PSNR, MSE, BER, and NC.
CAPSO: Chaos Adaptive Particle Swarm Optimization Algorithm

As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life.However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is still a key dispute to be resolved.In this paper, we propose a novel PSO algorithm called Chaos Adaptive Particle Swarm Optimization (CAPSO), which adaptively adjust the inertia weight parameter w and acceleration coefficients c 1 , c 2 , and introduces a controlling factor γ based on chaos theory to adaptively adjust the range of chaotic search.This makes the algorithm have favorable adaptability, and then the particles cannot only effectively prevent missing the global optimal solution, but also have a high probability of jumping out of the local optimal solution.To verify the stability, convergence speed, and accuracy of CAPSO, we conduct ample experiments on 6 test functions.In addition, to further verify the effectiveness and scalability of CAPSO, comparative experiments are carried out on the CEC2013 test suite.Finally, the results prove that CAPSO outperforms other peer algorithms to achieve satisfactory performance.
BDEdge: Blockchain and Deep-Learning for Secure Edge-Envisioned Green CAVs

Green Connected and Autonomous Vehicles (CAVs) are the future of next-generation Intelligent Transportation Systems (ITS) that will help humans to improve road safety and reduce pollution, energy consumption, and travel delays. To increase the performance of green CAV, the data generated by Autonomous Vehicles (AVs) and associated infrastructure needs to be processed in real-time. Mobile Edge Computing (MEC) is a promising paradigm that can be integrated with green CAV to save energy and to improve the network performance in terms of low latency for data processing. However, MEC servers and other communication entities in green CAV environment cannot be fully trusted and may bring vulnerabilities related to data privacy and security. Motivated by the above challenges, we design a blockchain and Deep-Learning (DL)-enabled secure data processing framework for an edge-envisioned green CAV environment (hereafter referred to as BDEdge). In blockchain-based scheme, all communication entities are registered, verified and thereafter validated using smart contract-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The authenticated data is forwarded to DL scheme. In DL-based scheme, an Intrusion Detection System (IDS) based on a hybrid model of Sparse Auto-Encoder-enabled Attention Bidirectional Gated Recurrent Unit (SAE-ABIGRU) is designed by analyzing the link load behaviors of the MEC-enabled Road Side Unit (RSU) server. The security analysis and comparative simulation findings shows that the proposed BDEdge framework can significantly reduce false alarm rate and increase accuracy close to 99%.
NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer

Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists.This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes.This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.
Modeling and estimation of the optimal tilt angle, maximum incident solar radiation, and global radiation index of the photovoltaic system

This research was designed to apply measured and theoretically derived models to estimate the optimal tilt angle (β), maximum incident solar radiation (HT), and global radiation index (GRI) in Lagos and 37 metropolitan cities in Nigeria. Six modules were mounted at different tilt angles with two modules north-facing, three south-facing, and one positioned horizontally to determine the orientation and tilt angle performance. Overall, the 16.8° module 4 south-facing tilted emerged as the best performing module for HT, with maximum output power, and much more than 6.174 annual earned energy reported on module 6 mounted horizontally. The GRI obtained from the six solar modules revealed a significant coefficient of 1.0269 for module 1 north-facing, 0.9923 for module 2 north-facing, 1.0217 for module 3 south-facing, 0.9609 for module 4 south-facing, and 1.0232 for module 5 south-facing for the values obtained from the horizontally positioned module 6. In-depth statistical analysis of the effectiveness of values observed against the predicted values to estimate the optimal and maximum inclination angle of incident solar radiation using error metrics and GPI revealed that model 5, model 12, model 14, model 17, and model 14 outperform the other estimation models in their respective categories. A similar statistical analysis of model 14 with the best performance was performed to estimate the best inclination angle in 37 metropolitan cities in Nigeria compared to two models consolidated in the literature; model 14 performed admirably in terms of accuracy compared to the two models obtained from the literature.
Attribute-Based Progressive Fusion Network for RGBT Tracking

RGBT tracking usually suffers from various challenge factors, such as fast motion, scale variation, illumination variation, thermal crossover and occlusion, to name a few. Existing works often study fusion models to solve all challenges simultaneously, and it requires fusion models complex enough and training data large enough, which are usually difficult to be constructed in real-world scenarios. In this work, we disentangle the fusion process via the challenge attributes, and thus propose a novel Attribute-based Progressive Fusion Network (APFNet) to increase the fusion capacity with a small number of parameters while reducing the dependence on large-scale training data. In particular, we design five attribute-specific fusion branches to integrate RGB and thermal features under the challenges of thermal crossover, illumination variation, scale variation, occlusion and fast motion respectively. By disentangling the fusion process, we can use a small number of parameters for each branch to achieve robust fusion of different modalities and train each branch using the small training subset with the corresponding attribute annotation. Then, to adaptive fuse features of all branches, we design an aggregation fusion module based on SKNet. Finally, we also design an enhancement fusion transformer to strengthen the aggregated feature and modality-specific features. Experimental results on benchmark datasets demonstrate the effectiveness of our APFNet against other state-of-the-art methods.
Multiscale Low-Light Image Enhancement Network With Illumination Constraint

Images captured under low-light environments typically have poor visibility, affecting many advanced computer vision tasks. In recent years, there have been some low-light image enhancement models based on deep learning, but they have not been able to effectively mine the deep multiscale features in the image, resulting in poor generalization performance and instability of the model. The disadvantages are mainly reflected in the color distortion, color unsaturation and artifacts. Current methods unable to adjust the exposure effectively, resulting in uneven exposure or partial overexposure. To address these issues, we propose an end-to-end low-light image enhancement model, which is called multiscale low-light image enhancement network with illumination constraint (MLLEN-IC), to achieve preferable generalization ability and stable performance. On the one hand, we use the squeeze-and-excitation-Res2Net block (SE-Res2block) as a base unit to enhance the model’s ability by extracting deep multiscale features. On the other hand, to make the model more adaptable in low-light image enhancement tasks, we calculate the illumination constraint by the low-light itself to prevent overexposure, uneven exposure, and unsaturated colors. Extensive experiments are conducted to demonstrate MLLEN-IC not only adjusts light levels, but also has a more natural visual effect, and avoids problems such as color distortion, artifacts, and uneven exposure. In particular, MLLEN-IC has pretty generalization and stability performance. The source code and supplementary are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CCECfgd/MLLEN-IC</uri> .