Reviewerly/exHarmony huggingface dataset:
Time-Varying Graph Learning Under Structured Temporal Priors

This paper endeavors to learn time-varying graphs by using structured temporal priors that assume underlying relations between arbitrary two graphs in the graph sequence. Different from many existing methods that only describe vari-ations between two consecutive graphs, we propose a structure named temporal graph to characterize the underlying real temporal relations. Under this framework, classic priors like temporal homogeneity are actually special cases of our temporal graph. To address computational issue, we further develop a distributed algorithm based on Alternating Direction Method of Multipliers (ADMM) to solve the induced optimization problem. Numerical experiments on synthetic and real data demonstrate the superiorities of our method.
A Multiobjective Crystal Optimization-based association rule mining enhanced with TOPSIS for predictive maintenance analysis

To obtain computer procedures that intelligently guide the search process by efficiently exploring the search space corresponds to an optimization problem that is solved using meta-heuristics. However, there are many optimization problems whose objective is to extract a single efficient solution that reflects the quality of the system performance. Hence, we also encounter multi-objective problems. This paper studies a multi-objective optimization problem that addresses the complexity of problems with synchronous indices. In this paper, association rule mining (ARM) is treated as an optimization problem. In recent literature, most of the proposed methods of ARM generate a large number of redundant and irrelevant rules. Therefore, we propose an improved multi-objective crystal structure algorithm using the TOPSIS approach. The experimental study is performed in two steps. First, we use a classical benchmark suite, namely WFG, to analyze the effectiveness of the proposed approach in generating solutions close to the Pareto fronts. In a second step, to confirm the performance of our algorithm, we apply it on the database of the Bosch Production line performance, and we conduct a comparative evaluation towards recent algorithms. The obtained results demonstrate the efficiency of the proposed algorithm in terms of number of rules, average support, average confidence, average conviction, average certain factor.
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization

Sample selection is an effective strategy to mitigate the effect of label noise in robust learning. Typical strategies commonly apply the small-loss criterion to identify clean samples. However, those samples lying around the decision boundary with large losses usually entangle with noisy examples, which would be discarded with this criterion, leading to the heavy degeneration of the generalization performance. In this paper, we propose a novel selection strategy, Self-Filtering (SFT), that utilizes the fluctuation of noisy examples in historical predictions to filter them, which can avoid the selection bias of the small-loss criterion for the boundary examples. Specifically, we introduce a memory bank module that stores the historical predictions of each example and dynamically updates to support the selection for the subsequent learning iteration. Besides, to reduce the accumulated error of the sample selection bias of SFT, we devise a regularization term to penalize the confident output distribution. By increasing the weight of the misclassified categories with this term, the loss function is robust to label noise in mild conditions. We conduct extensive experiments on three benchmarks with variant noise types and achieve the new state-of-the-art. Ablation studies and further analysis verify the virtue of SFT for sample selection in robust learning.
Central Bank Digital Currencies

Today’s societal digitization continues to advance at exponential speeds driven by technology trends. Billions of Internet of Things devices have made their way into our daily lives but also into healthcare, manufacturing, and supply chains. In contrast, the financial sector still largely operates on legacy infrastructures, where merchants receive their payments long after they released the digital/physical good to the consumer. In addition, the emergence of Decentralized Finance through blockchain technology, and the accumulation of data in private silos, has demonstrated a capacity to impact national sovereignty and monetary transmission channels. Against this backdrop, many central banks have recently started to research and test the issuance of digitally native fiat money—or Central Bank Digital Currencies (CBDCs)—in an effort to redesign the essence and use of physical cash. CBDCs present a broad variety of designs, which translate into manifold techno-legal and standardization policy questions. In this context, this chapter surveys the state-of-the art with specific focus on “retail” CBDCs. In doing so, it provides an overview of candidate architectures, heeds legal impacts and regulatory compliance issues, presents a set of case studies and touches upon cross-border CBDC challenges.
A Data-Centric Approach for Improving Ambiguous Labels with Combined Semi-supervised Classification and Clustering

Consistently high data quality is essential for the development of novel loss functions and architectures in the field of deep learning. The existence of such data and labels is usually presumed, while acquiring high-quality datasets is still a major issue in many cases. Subjective annotations by annotators often lead to ambiguous labels in real-world datasets. We propose a data-centric approach to relabel such ambiguous labels instead of implementing the handling of this issue in a neural network. A hard classification is by definition not enough to capture the real-world ambiguity of the data. Therefore, we propose our method "Data-Centric Classification & Clustering (DC3)" which combines semi-supervised classification and clustering. It automatically estimates the ambiguity of an image and performs a classification or clustering depending on that ambiguity. DC3 is general in nature so that it can be used in addition to many Semi-Supervised Learning (SSL) algorithms. On average, our approach yields a 7.6% better F1-Score for classifications and a 7.9% lower inner distance of clusters across multiple evaluated SSL algorithms and datasets. Most importantly, we give a proof-of-concept that the classifications and clusterings from DC3 are beneficial as proposals for the manual refinement of such ambiguous labels. Overall, a combination of SSL with our method DC3 can lead to better handling of ambiguous labels during the annotation process. (Source code is available at https://github.com/Emprime/dc3 ).
Dynamic Event-Triggered Feedback Fusion Estimation for Nonlinear Multi-Sensor Systems With Auto/Cross-Correlated Noises

This paper aims to solve the distributed fusion estimation problem for a nonlinear system with auto/cross-correlated noises. An equivalent nonlinear system with uncorrelated noises is obtained by means of a de-correlation method. Due to the nonlinear characteristics, the order of de-correlation affects whether the noises are completely uncorrelated or not. In order to improve accuracy of fusion estimation while avoiding the increase of communication burden, fusion predictions are fed back to local filters according to a dynamic event-triggered scheduling (DETS). The feedback frequency is reduced by introducing real-time adjusted offset variables into the DETS, which makes the event-triggered scheduling more strict. Subsequently, a local filter in the form of unscented Kalman filter (UKF) is designed using the measurement and received feedback information. Based on the Kalman-like fusion strategy, a distributed fusion estimation algorithm subject to auto/cross-correlated noises is developed, and boundedness of the fusion error covariance as well as complexity of the fusion algorithm are analyzed. Finally, performance of the proposed fusion estimation algorithm is verified by a numerical simulation.
Recommending Online Course Resources Based on Knowledge Graph

Nowadays, it is challenging for college students or lifelong education learners to choose the courses they need under the constant growth of massive online course resources. Therefore the recommendation systems are used to meet their personalized interests. In the scenario of course recommendation, traditional collaborative filtering (CF) is not applicable because of the sparsity of user-item interactions and the cold start problem. Learned from MKR, MKCR is proposed to enhance online courses sources recommendation when the interaction between students and courses is extremely sparse. MKCR is an end-to-end framework that utilizes a knowledge graph embedding task to assist recommendation tasks. The experiment data partially come from the MOOC platform of Chinese universities. The results show MKCR is better performance than other methods in the experiments.
Bounded Adversarial Attack on Deep Content Features

We propose a novel adversarial attack targeting content features in some deep layer, that is, individual neurons in the layer. A naive method that enforces a fixed value/percentage bound for neuron activation values can hardly work and generates very noisy samples. The reason is that the level of perceptual variation entailed by a fixed value bound is non-uniform across neurons and even for the same neuron. We hence propose a novel distribution quantile bound for activation values and a polynomial barrier loss function. Given a benign input, a fixed quantile bound is translated to many value bounds, one for each neuron, based on the distributions of the neuron's activations and the current activation value on the given input. These individualized bounds enable fine-grained regulation, allowing content feature mutations with bounded perceptional variations. Our evaluation on ImageNet and five different model architectures demonstrates that our attack is effective. Compared to seven other latest adversarial attacks in both the pixel space and the feature space, our attack can achieve the state-of-the-art trade-off between attack success rate and imperceptibility. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code and Samples are available on Github [37].
Takagi–Sugeno Fuzzy Regression Trees With Application to Complex Industrial Modeling

Fuzzy decision trees (FDTs) is one of the considerably excellent methods. Most of the existing FDTs’ methods are oriented to classification tasks. Applying FDTs to regression tasks may solve complex industrial modeling problems. In this article, we propose the Takagi–Sugeno (T–S) fuzzy regression tree (TSFRT), which uses the hypothesis of “feature screening followed by T–S fuzzy reasoning.” In the TSFRT, the growth process (crisp set theory) can be deemed as feature screening, and each leaf node (fuzzy set theory) is viewed as a T–S inference reasoning system. Thus, the TSFRT becomes a top-down structure. We develop multiple strategies to identify the parameters of the T–S system in the leaf node using sample-by-sample and batch samples. To improve the method's generalization performance, we also generalize an ensemble method with pseudoinverse and ridge regression. The proposed methods are evaluated by several high- and low-dimensional complex industrial processes. The experimental results show that the proposed method remarkably outperforms other popular regression methods.
On the structure of the Common Digital Space of Scientific knowledge ontology

The work is a development of the research conducted by the authors in the field of creating a Common Digital Space of Scientific Knowledge (CDSSK). The CDSSK is a digital information structure aggregating heterogeneous polythematic information related to scientific knowledge and including a set of subspaces related to various thematic scientific areas. The sources of the CDSSK content are existing information systems, such as the Great Russian Encyclopedia, the Unified Catalog of Geographical Names, the National Electronic Library, etc. A distinctive features of the CDSSK are (a) data support in a structure that complies with the rules of the semantic WEB, and (b) the ability to process a wide range of polythematic queries and provide the ability to navigate through heterogeneous resources using semantic links between them. The real design of the CDSSK should begin with the formation of the ontology of space as a whole and its individual subspaces. In this paper, a hierarchical structuring of the CDSSK ontology is proposed. Such elements as "subspace", "class of objects", "object", "attributes of an object", three types of relations of objects and attributes (universal, quasi-universal and specific) are distinguished and defined. the concepts "reference books" and "dictionaries" of the CDSSK are introduced. Reference books contain information about attributes and their relationships and are used in the processes of content formation and implementation of search and navigation logic; dictionaries contain specific values of attributes and relationships. The formalization of these elements representations is proposed, which makes it quite simple to add, as necessary, new attributes and relationships between objects.
NoSneaky: A Blockchain-Based Execution Integrity Protection Scheme in Industry 4.0

The advancement of information technology allows the creation of smart devices that not only are programable, but also can perform machine-to-machine communication in order to reach a flexible large-scale manufacturing strategy in Industry 4.0.However, as more components are connected to the Internet, cyber-criminals can perform malicious actions remotely.As one lasting threat, sabotaging smart devices' execution integrity can cause a large financial loss, i.e., causing malfunctioning.Hence, it is important to secure the execution integrity of smart devices in Industry 4.0.Motivated by the emerging blockchain technology, in this paper, we focus on how blockchain can help Industry 4.0 application protect execution integrity and propose a blockchainbased execution protection scheme named NoSneaky, which is low-cost and can be easily integrated into the current production systems.In the evaluation, we demonstrate its performance and effectiveness in securing the execution integrity.
Pooling is Not Favorable: Decentralize Mining Power of PoW Blockchain Using Age-of-Work

As the underlying consensus protocol of Bitcoin and Ethereum blockchains, Proof-of-Work (PoW) features a cryptographic mathematical puzzle whose solution is easy to verify but extremely hard to solve. Under PoW, miners maintain the security of blockchain by devoting computing powers to solve the puzzle; the miner who has solved the puzzle successfully generates a block, along with a reward (e.g., a set of cryptocurrency). The average waiting time to generate a block is inversely proportional to the computing power of the miner. To reduce the average block generation time, a group of individual miners can form a centralized mining pool to aggregate their computing power to solve the puzzle together and share the reward contained in the block. However, if the aggregated computing power of the pool forms a substantial portion of the total computing power in the network, the pooled mining undermines the core spirit of blockchain, i.e., the decentralization, and harms its security. To discourage the pooled mining, we develop a new consensus protocol called Proof-of-Age (PoA) that builds upon the native PoW protocol. The core idea of PoA lies in using Age-of-Work (AoW) to measure the effective mining periods that the miners have devoted to maintaining the security of blockchain. Unlike in the native PoW protocol, in our PoA protocol, miners benefit from its effective mining periods even if they have not successfully mined a block. We first employ a continuous time Markov chain (CTMC) to model the block generation process of the PoA based blockchain. Based on this CTMC model, we then analyze the block generation rates of the mining pool and solo miners respectively. Our analytical results verify that under PoA, the block generation rates of miners in the mining pool are reduced compared to that of solo miners, thereby disincentivizing the pooled mining. Finally, we simulate the mining process in the PoA blockchain to demonstrate the consistency of the analytical results.
Learning to Assess Danger from Movies for Cooperative Escape Planning in Hazardous Environments

There has been a plethora of work towards im-proving robot perception and navigation, yet their application in hazardous environments, like during a fire or an earthquake, is still at a nascent stage. We hypothesize two key challenges here: first, it is difficult to replicate such scenarios in the real world, which is necessary for training and testing purposes. Second, current systems are not fully able to take advantage of the rich multi-modal data available in such hazardous environments. To address the first challenge, we propose to harness the enormous amount of visual content available in the form of movies and TV shows, and develop a dataset that can represent hazardous environments encountered in the real world. The data is annotated with high-level danger ratings for realistic disaster images, and corresponding keywords are provided that summarize the content of the scene. In response to the second challenge, we propose a multi-modal danger estimation pipeline for collaborative human-robot escape scenarios. Our Bayesian framework improves danger estimation by fusing information from robot's camera sensor and language inputs from the human. Furthermore, we augment the estimation module with a risk-aware planner that helps in identifying safer paths out of the dangerous environment. Through extensive simulations, we exhibit the advantages of our multi-modal perception framework that gets translated into tangible benefits such as higher success rate in a collaborative human-robot mission.
Self-Training of Handwritten Word Recognition for Synthetic-to-Real Adaptation

Performances of Handwritten Text Recognition (HTR) models are largely determined by the availability of labeled and representative training samples. However, in many application scenarios, labeled samples are scarce or costly to obtain. In this work, we propose a self-training approach to train a HTR model solely on synthetic samples and unlabeled data. The proposed training scheme uses an initial model trained on synthetic data to make predictions for the unlabeled target dataset. Starting from this initial model with rather poor performance, we show that a considerable adaptation is possible by training against the iteratively predicted pseudo-labels. Therefore, the investigated self-training method does not require any manually annotated training samples. We evaluate the proposed method on four benchmark datasets and show its effectiveness on reducing the gap to a model trained in a fully-supervised manner.
DevOps Challenges in Organizations: Through Professional Lens

DevOps is a set of organizational practices as well as a culture which tries to eliminate the barriers between the Devs and Ops teams, improve the collaboration and communication among teammates. DevOps is used in different organizations because it supports quicker production, stability and reliability for software development. While the success factors of DevOps adoption have been studied in the extant literature, also the perceived challenges that a company faces during the adoption are crucial to discover. This paper explores and highlights these challenges through an open ended survey (N = 15) and in-depth interviews with DevOps professionals (N = 16). According to the findings, there are various challenges while implementing DevOps in the organizations. The research suggests that (i) lack of team coordination, (ii) risky change and development, (iii) team members expertise level, (iv) lack of focus or differences in development, (v) test and production environment, (vi) poorly defined functional and technical requirements, (vii) poor integration and test process, (viii) pipeline execution problems, (ix) tools integration challenges, (x) people challenges and silo-ed thinking, (xi) debugging challenges due to remote execution, (xii) feature release challenges, (xiii) integrating new standards, (xiv) challenges with clients, (xv) knowledge sharing, (xvi) responsibility distribution issues are the challenges while using DevOps. The founded list of perceived challenges will help future research to suggest mitigation and risk management strategies for successful use of DevOps.
Where in the World Is This Image? Transformer-Based Geo-localization in the Wild

Predicting the geographic location (geo-localization) from a single ground-level RGB image taken anywhere in the world is a very challenging problem. The challenges include huge diversity of images due to different environmental scenarios, drastic variation in the appearance of the same location depending on the time of the day, weather, season, and more importantly, the prediction is made from a single image possibly having only a few geo-locating cues. For these reasons, most existing works are restricted to specific cities, imagery, or worldwide landmarks. In this work, we focus on developing an efficient solution to planet-scale single-image geo-localization. To this end, we propose TransLocator, a unified dual-branch transformer network that attends to tiny details over the entire image and produces robust feature representation under extreme appearance variations. TransLocator takes an RGB image and its semantic segmentation map as inputs, interacts between its two parallel branches after each transformer layer and simultaneously performs geo-localization and scene recognition in a multi-task fashion. We evaluate TransLocator on four benchmark datasets - Im2GPS, Im2GPS3k, YFCC4k, YFCC26k and obtain $$5.5\%$$ , $$14.1\%$$ , $$4.9\%$$ , $$9.9\%$$ continent-level accuracy improvement over the state-of-the-art. TransLocator is also validated on real-world test images and found to be more effective than previous methods.
Deployment of Breast Cancer Hybrid Net using Deep Learning

Breast cancer (BC) occurs when healthy breast cells grow out of control and become tumors. According to American Cancer Society, breast cancer occurs in one out of eight women and in one out of thousand men. Early breast cancer detection is thus important to give the maximum chance of survival for the patient. Breast biopsy is used to analyze the breast cells and diagnose whether the sample of cells contain breast cancer. It is an invasive method and manually analyzed by a pathologist under a microscope. There is a chance of human error in such a method, and it is time consuming. Oncologist can diagnose breast cancer at a faster and accurate and less painful way if they use machine learning and image classification algorithms. One of the best machine learning techniques is Support Vector Machine. If it is combined with the computational power of Convolutional Neural Network, it becomes an immensely powerful classification algorithm. Support Vector Machine and Convolutional Neural Network model gives better accuracy than other image classifiers such as VGG16, RESNET 50, and INCEPTIONV3 models. This research is to design Deployment of Breast Cancer Hybrid Net using Deep Learning For research dataset of 3538 images was deployed. During research experiments SVM-CNN, VGG16, RESNET 50, and INCEPTIONV3 models accuracy reported were 93.35%, 89.54%, 92.45% and 88.6% respectively.