Reviewerly/exHarmony huggingface dataset:
Structure-Aware Positional Transformer for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a cross-modality retrieval problem, which aims at matching the same pedestrian between the visible and infrared cameras. Due to the existence of pose variation, occlusion, and huge visual differences between the two modalities, previous studies mainly focus on learning image-level shared features. Since they usually learn a global representation or extract uniformly divided part features, these methods are sensitive to misalignments. In this paper, we propose a structure-aware positional transformer (SPOT) network to learn semantic-aware sharable modality features by utilizing the structural and positional information. It consists of two main components: attended structure representation (ASR) and transformer-based part interaction (TPI). Specifically, ASR models the modality-invariant structure feature for each modality and dynamically selects the discriminative appearance regions under the guidance of the structure information. TPI mines the part-level appearance and position relations with a transformer to learn discriminative part-level modality features. With a weighted combination of ASR and TPI, the proposed SPOT explores the rich contextual and structural information, effectively reducing cross-modality difference and enhancing the robustness against misalignments. Extensive experiments indicate that SPOT is superior to the state-of-the-art methods on two cross-modal datasets. Notably, the Rank-1/mAP value on the SYSU-MM01 dataset has improved by 8.43%/6.80%.
Communication-efficient federated learning via knowledge distillation

Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw data. However, model updates can be extremely large if they contain numerous parameters, and many rounds of communication are needed for model training. The huge communication cost in federated learning leads to heavy overheads on clients and high environmental burdens. Here, we present a federated learning method named FedKD that is both communication-efficient and effective, based on adaptive mutual knowledge distillation and dynamic gradient compression techniques. FedKD is validated on three different scenarios that need privacy protection, showing that it maximally can reduce 94.89% of communication cost and achieve competitive results with centralized model learning. FedKD provides a potential to efficiently deploy privacy-preserving intelligent systems in many scenarios, such as intelligent healthcare and personalization.
Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers

Abstract Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches, achieving human‐like performance under certain circumstances. However, as studies differ in the datasets and evaluation tasks, it is unclear how the different approaches compare to each other. Furthermore, there are no systematic studies about model performance in cross‐domain scenarios, that is, when applied to data with different characteristics. Here, we address these questions by conducting a large‐scale benchmark. We compare six previously published deep learning models on eight data sets covering local to teleseismic distances and on three tasks: event detection, phase identification and onset time picking. Furthermore, we compare the results to a classical Baer‐Kradolfer picker. Overall, we observe the best performance for PhaseNet, closely followed by EQTransformer and GPD. Furthermore, we conduct a cross‐domain study, analyzing model performance on data sets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but models trained on regional data do not transfer well to teleseismic data. As deep learning for detection and picking is a rapidly evolving field, we ensured extensibility of our benchmark by building our code on standardized frameworks and making it openly accessible. This allows model developers to easily evaluate new models or performance on new data sets. Furthermore, we make all trained models available through the SeisBench framework, giving end‐users an easy way to apply these models.
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised: the ground truth is only known for the slide, not for every single tile. In classical weakly-supervised analysis pipelines, all tiles inherit the slide label while in multiple-instance learning (MIL), only bags of tiles inherit the label. However, it is still unclear how these widely used but markedly different approaches perform relative to each other. We implemented and systematically compared six methods in six clinically relevant end-to-end prediction tasks using data from N=2980 patients for training with rigorous external validation. We tested three classical weakly-supervised approaches with convolutional neural networks and vision transformers (ViT) and three MIL-based approaches with and without an additional attention module. Our results empirically demonstrate that histological tumor subtyping of renal cell carcinoma is an easy task in which all approaches achieve an area under the receiver operating curve (AUROC) of above 0.9. In contrast, we report significant performance differences for clinically relevant tasks of mutation prediction in colorectal, gastric, and bladder cancer. In these mutation prediction tasks, classical weakly-supervised workflows outperformed MIL-based weakly-supervised methods for mutation prediction, which is surprising given their simplicity. This shows that new end-to-end image analysis pipelines in computational pathology should be compared to classical weakly-supervised methods. Also, these findings motivate the development of new methods which combine the elegant assumptions of MIL with the empirically observed higher performance of classical weakly-supervised approaches. We make all source codes publicly available at https://github.com/KatherLab/HIA, allowing easy application of all methods to any similar task.
Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview

Chaotic time series widely exists in nature and society (e.g., meteorology, physics, economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent nonstationary and high complexity. Thankfully, multifarious advanced approaches have been developed to tackle the prediction issues, such as statistical methods, artificial neural networks (ANNs), and support vector machines. Among them, the interval type-2 fuzzy neural network (IT2FNN), which is a synergistic integration of fuzzy logic systems and ANNs, has received wide attention in the field of chaotic time series prediction. This paper begins with the structural features and superiorities of IT2FNN. Moreover, chaotic characters identification and phase-space reconstruction matters for prediction are presented. In addition, we also offer a comprehensive review of state-of-the-art applications of IT2FNN, with an emphasis on chaotic time series prediction and summarize their main contributions as well as some hardware implementations for computation speedup. Finally, this paper trends and extensions of this field, along with an outlook of future challenges are revealed. The primary objective of this paper is to serve as a tutorial or referee for interested researchers to have an overall picture on the current developments and identify their potential research direction to further investigation.
3D Human Pose Estimation With 2D Marginal Heatmaps

Automatically determining three-dimensional human pose from monocular RGB image data is a challenging problem. The two-dimensional nature of the input results in intrinsic ambiguities which make inferring depth particularly difficult. Recently, researchers have demonstrated that the flexible statistical modelling capabilities of deep neural networks are sufficient to make such inferences with reasonable accuracy. However, many of these models use coordinate output techniques which are memory-intensive, not differentiable, and/or do not spatially generalise well. We propose improvements to 3D coordinate prediction which avoid the aforementioned undesirable traits by predicting 2D marginal heatmaps under an augmented soft-argmax scheme. Our resulting model, MargiPose, produces visually coherent heatmaps whilst maintaining differentiability. We are also able to achieve state-of-the-art accuracy on publicly available 3D human pose estimation data.
A literature review on question answering techniques, paradigms and systems

Question Answering (QA) systems enable users to retrieve exact answers for questions posed in natural language. This study aims at identifying QA techniques, tools and systems, as well as the metrics and indicators used to measure these approaches for QA systems and also to determine how the relationship between Question Answering and natural language processing is built. The method adopted was a Systematic Literature Review of studies published from 2000 to 2017. 130 out of 1842 papers have been identified as describing a QA approach developed and evaluated with different techniques. Question Answering researchers have concentrated their efforts in natural language processing, knowledge base and information retrieval paradigms. Most of the researches focused on open domain. Regarding the metrics used to evaluate the approaches, Precision and Recall are the most addressed.
IDMVis: Temporal Event Sequence Visualization for Type 1 Diabetes Treatment Decision Support

Type 1 diabetes is a chronic, incurable autoimmune disease affecting millions of Americans in which the body stops producing insulin and blood glucose levels rise. The goal of intensive diabetes management is to lower average blood glucose through frequent adjustments to insulin protocol, diet, and behavior. Manual logs and medical device data are collected by patients, but these multiple sources are presented in disparate visualization designs to the clinician-making temporal inference difficult. We conducted a design study over 18 months with clinicians performing intensive diabetes management. We present a data abstraction and novel hierarchical task abstraction for this domain. We also contribute IDMVis: a visualization tool for temporal event sequences with multidimensional, interrelated data. IDMVis includes a novel technique for folding and aligning records by dual sentinel events and scaling the intermediate timeline. We validate our design decisions based on our domain abstractions, best practices, and through a qualitative evaluation with six clinicians. The results of this study indicate that IDMVis accurately reflects the workflow of clinicians. Using IDMVis, clinicians are able to identify issues of data quality such as missing or conflicting data, reconstruct patient records when data is missing, differentiate between days with different patterns, and promote educational interventions after identifying discrepancies.
Pedestrian detection with unsupervised multispectral feature learning using deep neural networks

Multispectral pedestrian detection is an important functionality in various computer vision applications such as robot sensing, security surveillance, and autonomous driving. In this paper, our motivation is to automatically adapt a generic pedestrian detector trained in a visible source domain to a new multispectral target domain without any manual annotation efforts. For this purpose, we present an auto-annotation framework to iteratively label pedestrian instances in visible and thermal channels by leveraging the complementary information of multispectral data. A distinct target is temporally tracked through image sequences to generate more confident labels. The predicted pedestrians in two individual channels are merged through a label fusion scheme to generate multispectral pedestrian annotations. The obtained annotations are then fed to a two-stream region proposal network (TS-RPN) to learn the multispectral features on both visible and thermal images for robust pedestrian detection. Experimental results on KAIST multispectral dataset show that our proposed unsupervised approach using auto-annotated training data can achieve performance comparable to state-of-the-art deep neural networks (DNNs) based pedestrian detectors trained using manual labels.
Software defect number prediction: Unsupervised vs supervised methods

Context: Software defect number prediction (SDNP) can rank the program modules according to the prediction results and is helpful for the optimization of testing resource allocation. Objective: In previous studies, supervised methods vs unsupervised methods is an active issue for just-in-time defect prediction and file-level defect prediction based on effort-aware performance measures. However, this issue has not been investigated for SDNP. To the best of our knowledge, we are the first to make a thorough comparison for these two different types of methods. Method: In our empirical studies, we consider 7 real open-source projects with 24 versions in total, use FPA and Kendall as our effort-aware performance measures, and consider three different performance evaluation scenarios (i.e., within-version scenario, cross-version scenario, and cross-project scenario). Result: We first identify two unsupervised methods with best performance. These two methods simply rank modules according to the value of metric LOC and metric RFC from large to small respectively. Then we compare 9 state-of-the-art supervised methods incorporating SMOTEND, which is used for handling class imbalance problem, with the unsupervised method based on LOC metric (i.e., LOC_D method). Final results show that LOC_D method can perform significantly better than or the same as these supervised methods. Later motivated by a recent study conducted by Agrawla and Menzies, we apply differential evolutionary (DE) to optimize parameter value of SMOTEND used by these supervised methods and find that using DE can effectively improve the performance of these supervised methods for SDNP too. Finally, we continue to compare LOC_D with these optimized supervised methods using DE, and LOC_D method still has advantages in the performance, especially in the cross-version and cross-project scenarios. Conclusion: Based on these results, we suggest that researchers need to use the unsupervised method LOC_D as the baseline method, which is used for comparing their proposed novel methods for SDNP problem in the future.
Deep Network Embedding for Graph Representation Learning in Signed Networks

Network embedding has attracted an increasing attention over the past few years. As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network. The vast majority of existing network embedding algorithms, however, are only designed for unsigned networks, and the signed networks containing both positive and negative links, have pretty distinct properties from the unsigned counterpart. In this paper, we propose a deep network embedding model to learn the low-dimensional node vector representations with structural balance preservation for the signed networks. The model employs a semisupervised stacked auto-encoder to reconstruct the adjacency connections of a given signed network. As the adjacency connections are overwhelmingly positive in the real-world signed networks, we impose a larger penalty to make the auto-encoder focus more on reconstructing the scarce negative links than the abundant positive links. In addition, to preserve the structural balance property of signed networks, we design the pairwise constraints to make the positively connected nodes much closer than the negatively connected nodes in the embedding space. Based on the network representations learned by the proposed model, we conduct link sign prediction and community detection in signed networks. Extensive experimental results in real-world datasets demonstrate the superiority of the proposed model over the state-of-the-art network embedding algorithms for graph representation learning in signed networks.
PV system design with the effect of soiling on the optimum tilt angle

Local particle deposition and solar irradiance data, from Photovoltaic Geographical Information System (PVGIS) in the Alentejo region (southern Portugal), are used for the development of a modified effective irradiance model featuring soiling. The model allows to calculate the optimum tilt angle for soiled PV systems, as the inclusion of soiling gives realistic results for different tilt angles to be used in the absence of cleaning; instead of using an optimum tilt angle solely based on irradiance. Consequently, the proposed model enables an increase of maximum energy production and the design of multiple tilt angle configurations to further maximize the annual energy yield, regarding a common fixed structure. The presented calculations include the seasonal soiling effect for the region, showing the applicability that this type of approach has for multiple tilt angle configurations. An economic analysis is also carried out to choose the best design, taking into account the trade-off between the increase in energy production and the costs of changing the tilt angle. The effective irradiance model for clean and soiled scenarios is also explored with the development of a method to infer when a PV installation should be cleaned for the desired efficiency.
A Primer for Information Technology General Control Considerations on a Private and Permissioned Blockchain Audit

SUMMARY Blockchain is a disruptive technology that offers advantages to the audit profession such as transparency into all transactions, an immutable ledger, and the potential for real-time auditing. However, to realize these benefits, the profession must be prepared to gain comfort over blockchains as a component of organizations' information technology infrastructure. This paper considers risks to private and permissioned blockchains through the lens of information technology general controls (ITGCs) as part of an audit of internal control over financial reporting. I discuss new ITGC areas of focus for auditors to consider, along with areas of risk that blockchain could eliminate. To help readers better understand this emerging topic, I provide illustrations, a summary table of key points, and a glossary of blockchain-related terms used throughout the paper. This paper should be viewed as a primer of ITGC considerations on blockchain audits, as more nuanced concerns will emerge as the technology evolves.
Integrating TOPSIS with interval-valued intuitionistic fuzzy cognitive maps for effective group decision making

Many real-life situations require ranking alternative decisions with respect to multiple criteria. The problem becomes more complicated when the knowledge of the considered criteria is vague and unreliable. In order to cope with the vagueness, the values of criteria have to be represented in an approximated way. To overcome the lack of reliability, many experts can be involved in the decision process and thus cooperatively elaborate more credible decisions. However, as it turns out, experts are usually unable to provide plausible information on interactions between vague decision criteria. Since those interactions substantially affect the ranking of alternative decisions, they should be taken into account in the decision process. To effectively cope with that issue, we propose a novel multi-criteria group decision-making method that integrates TOPSIS (technique for order of preference by similarity to ideal solution) with IVIFCMs (interval-valued intuitionistic fuzzy cognitive maps), a tool that is able to model interactions among highly imprecise criteria. We illustrate the application of the proposed IVIFCM-TOPSIS to the supplier selection task. Finally, we present the advantages derived from the use of our method compared to competitive approaches known from the literature. In particular, we show that our method is more consistent than the existing state-of-the-art methods used to solve the addressed decision problem.
Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs

Autonomous driving is one of the most important AI applications and has attracted extensive interest in recent years. A large number of studies have successfully applied reinforcement learning techniques in various aspects of autonomous driving, ranging from low-level control of driving maneuvers to higher level of strategic decision-making. However, comparatively less progress has been made in investigating how co-existing autonomous vehicles would interact with each other in a common environment and how reinforcement learning can be helpful in such situations by applying multiagent reinforcement learning techniques in the high-level strategic decision-making of the following or overtaking for a group of autonomous vehicles in highway scenarios. Learning to achieve coordination among vehicles in such situations is challenging due to the unique feature of vehicular mobility, which renders it infeasible to directly apply the existing coordinated learning approaches. To solve this problem, we propose using dynamic coordination graph to model the continuously changing topology during vehicles' interactions and come up with two basic learning approaches to coordinate the driving maneuvers for a group of vehicles. Several extension mechanisms are then presented to make these approaches workable in a more complex and realistic setting with any number of vehicles. The experimental evaluation has verified the benefits of the proposed coordinated learning approaches, compared with other approaches that learn without coordination or rely on some traditional mobility models based on some expert driving rules.
Left-Ventricle Quantification Using Residual U-Net

Estimating dimensional measurements of the left ventricle provides diagnostic values which can be used to assess cardiac health and identify certain pathologies. In this paper we describe our methodology of calculating measurements from left ventricle segmentations automatically generated using deep learning. We use a U-net convolutional neural network architecture built from residual units to segment the left ventricle and then process these segmentations to estimate the area of the cavity and myocardium, the dimensions of the cavity, and the thickness of the myocardium. Determining if an image is part of the diastolic or systolic portion of the cardiac cycle is done by analysing the cavity volume. The quality of our results are dependent on our training regime where we have generated a large derivative dataset by augmenting the original images with free-form deformations. Our expanded training set, in conjunction with simple affine image transforms, creates a sufficiently large training population to prevent over-fitting of the network while still creating an accurate and robust segmentation network. Assessing our method on the STACOM18 LVQuan challenge dataset we find that it significantly outperforms the previously published state-of-the-art on a 5-fold validation all tasks considered.