Reviewerly/exHarmony huggingface dataset:
Chronus

Modern GPU clusters support Deep Learning training (DLT) jobs in a distributed manner. Job scheduling is the key to improve the training performance, resource utilization and fairness across users. Different training jobs may require various objectives and demands in terms of completion time. How to efficiently satisfy all these requirements is not extensively studied.
Intelligent Ensemble Learning Approach for Phishing Website Detection Based on Weighted Soft Voting

The continuous development of network technologies plays a major role in increasing the utilization of these technologies in many aspects of our lives, including e-commerce, electronic banking, social media, e-health, and e-learning. In recent times, phishing websites have emerged as a major cybersecurity threat. Phishing websites are fake web pages that are created by hackers to mimic the web pages of real websites to deceive people and steal their private information, such as account usernames and passwords. Accurate detection of phishing websites is a challenging problem because it depends on several dynamic factors. Ensemble methods are considered the state-of-the-art solution for many classification tasks. Ensemble learning combines the predictions of several separate classifiers to obtain a higher performance than a single classifier. This paper proposes an intelligent ensemble learning approach for phishing website detection based on weighted soft voting to enhance the detection of phishing websites. First, a base classifier consisting of four heterogeneous machine-learning algorithms was utilized to classify the websites as phishing or legitimate websites. Second, a novel weighted soft voting method based on Kappa statistics was employed to assign greater weights of influence to stronger base learners and lower weights of influence to weaker base learners, and then integrate the results of each classifier based on the soft weighted voting to differentiate between phishing websites and legitimate websites. The experiments were conducted using the publicly available phishing website dataset from the UCI Machine Learning Repository, which consists of 4898 phishing websites and 6157 legitimate websites. The experimental results showed that the suggested intelligent approach for phishing website detection outperformed the base classifiers and soft voting method and achieved the highest accuracy of 95% and an Area Under the Curve (AUC) of 98.8%.
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery

People often use physical intuition when manipulating articulated objects, irrespective of object semantics. Motivated by this observation, we identify an important embodied task where an agent must play with objects to recover their parts. To this end, we introduce Act the Part (AtP) to learn how to interact with articulated objects to discover and segment their pieces. By coupling action selection and motion segmentation, AtP is able to isolate structures to make perceptual part recovery possible without semantic labels. Our experiments show AtP learns efficient strategies for part discovery, can generalize to unseen categories, and is capable of conditional reasoning for the task. Although trained in simulation, we show convincing transfer to real world data with no fine-tuning. A summery video, interactive demo, and code will be available at https://atp.cs.columbia.edu.
Anomaly detection in blockchain using network representation and machine learning

Abstract The vast majority of digital currency transactions rely on a blockchain framework to ensure quick and accurate execution. As such, understanding how a blockchain works is vital to understanding the dynamics of cryptocurrency operations. One of the key benefits of this type of system is the exhaustive records captured in a given marketplace. The interwoven movement between agents can effectively be expressed as a graph via the extraction of historical data from the blockchain. By looking at a specific blockchain as an interaction of its agents, network representation learning can be leveraged to examine these relationships. Furthermore, the analysis of a graph structure can be enhanced through the application of modern and sophisticated machine learning techniques. Leveraging the automated nature of these methods can create meaningful observations of the input network. In this paper, we utilize several machine learning models to detect anomalous transactions in various digital currency markets. We find that supervised learning techniques yield encouraging results, whereas unsupervised learning techniques struggle more with the classification.
Building product ontology: Core ontology for Linked Building Product Data

• Linked Building Product Data applied Linked Data to define building products. • Modular and flexible approach for individual configurations. • Building Product Ontology serves as a core structure of all configurations. • Uniform querying and reasoning possible. The digitalisation of the Architecture, Engineering and Construction domain introduced new methods for digital collaboration, i.e. Building Information Modelling (BIM). While this method focuses on building data, the distribution of digital product models is still problematic, complicating uniform product searches and automated product data processing. Existing schemas, such as a subpart of the Industry Foundation Classes or the German VDI 3805, rely on rigid or template-driven schemas, that do not support the description of innovative or multi-functional products or impose a large schema overhead and complexity on manufacturers. Therefore, this article combines flexible and modular product descriptions with Semantic Web technologies and Linked Data. By applying Web-based technologies, the searchability of product data and the applicability of distributed data are expected to be enhanced. More precisely, this article proposes a concept for Linked Building Product Data and introduces the generic Building Product Ontology as a potential core schema of the concept. To demonstrate the feasibility of Linked Building Product Data and the Building Product Ontology, the authors apply both the concept and the data schema to innovative and multi-functional example products that cannot be described with the existing approaches for product descriptions. The evaluation demonstrates the flexibility, modularity and overall suitability of the presented concepts, meeting all collected requirements for digital product descriptions. Hence, Linked Building Product Data may solve existing issues with rigid product description schemas. At the same time, this approach complements the current research trend of Linked Building Data.
Blockchain and Business Process Management in Health Care, Especially for COVID-19 Cases

From this global health disaster, the health profession is searching for a new technology to monitor and mitigate COVID-19 infections. Accurate and reliable data are needed to surveil and prevent the diffusion of the coronavirus. However, there is a lack of consistent data on existing technologies. Various entities—for example, medical labs and public hospitals—could provide data on patients with coronavirus infection. Yet, this information might not be precise since it is not supervised and recorded correctly. This paper proposes a Blockchain and Business Process Management (BBPM) system in healthcare to solve these problems. This system could be crucial in tracing coronavirus diffusion and discovering more dangerous patients and is extremely proficient at controlling the disease. BBPM can be utilized like a digital database that includes information concurrently utilized and distributed inside a wide, decentralized, and openly accessible network. Usage of blockchains in the medical sector is anticipated to revolutionize the industry in many regions, primarily because centralization in current health technologies inhibits information sharing and causes a lack of confidentiality. Furthermore, BBPM could help supervise the diffusion of coronavirus infection worldwide by utilizing blockchain networks on the mobile devices of individuals. Protecting patient information is one of the critical strengths of BBPM. Participating BBPM nodes can be governments, hospitals, testing labs, or patients. In addition, the digital ledger has a few documents, including patient reports, consequences, the condition of treatment, and a summary of discharge. The BBPM system is categorized into four processes. In the first process, the patient is analyzed and diagnosed by a testing lab to detect any early signs of COVID-19 infection. A sample of the patients was taken. If it is positive, the second process begins. In the second process, the patient is isolated and begins treatment for a minimum of 14 days. If the patient’s health condition is improving, the third process begins. In the third process, the patients were retested for COVID-19. If the patient sample is negative, the patient is discharged, and an outline is created. During discharge, the patient pays the hospital for treatment. However, if the patient sample is positive, the isolation period is continued for another 14 days. In the last process, the details of total COVID-19 are confirmed, recovered, and death cases are conveyed to the government by the testing labs and hospitals. Patient records are stored for upcoming usage; their confidentiality is maintained when distributed on a larger scale. BBPM can guarantee the security and accuracy of patients’ recorded data.
Rethinking the U-Shape Structure for Salient Object Detection

The U-shape structure has shown its advantage in salient object detection for efficiently combining multi-scale features. However, most existing U-shape-based methods focused on improving the bottom-up and top-down pathways while ignoring the connections between them. This paper shows that we can achieve the cross-scale information interaction by centralizing these connections, hence obtaining semantically stronger and positionally more precise features. To inspire the newly proposed strategy's potential, we further design a relative global calibration module that can simultaneously process multi-scale inputs without spatial interpolation. Our approach can aggregate features more effectively while introducing only a few additional parameters. Our approach can cooperate with various existing U-shape-based salient object detection methods by substituting the connections between the bottom-up and top-down pathways. Experimental results demonstrate that our proposed approach performs favorably against the previous state-of-the-arts on five widely used benchmarks with less computational complexity. The source code will be publicly available.
Comparing deep learning-based automatic segmentation of breast masses to expert interobserver variability in ultrasound imaging

Deep learning is a powerful tool that became practical in 2008, harnessing the power of Graphic Processing Unites, and has developed rapidly in image, video, and natural language processing. There are ongoing developments in the application of deep learning to medical data for a variety of tasks across multiple imaging modalities. The reliability and repeatability of deep learning techniques are of utmost importance if deep learning can be considered a tool for assisting experts, including physicians, radiologists, and sonographers. Owing to the high costs of labeling data, deep learning models are often evaluated against one expert, and it is unknown if any errors fall within a clinically acceptable range. Ultrasound is a commonly used imaging modality for breast cancer screening processes and for visually estimating risk using the Breast Imaging Reporting and Data System score. This process is highly dependent on the skills and experience of the sonographers and radiologists, thereby leading to interobserver variability and interpretation. For these reasons, we propose an interobserver reliability study comparing the performance of a current top-performing deep learning segmentation model against three experts who manually segmented suspicious breast lesions in clinical ultrasound (US) images. We pretrained the model using a US thyroid segmentation dataset with 455 patients and 50,993 images, and trained the model using a US breast segmentation dataset with 733 patients and 29,884 images. We found a mean Fleiss kappa value of 0.78 for the performance of three experts in breast mass segmentation compared to a mean Fleiss kappa value of 0.79 for the performance of experts and the optimized deep learning model.
Adversarial Separation Network for Cross-Network Node Classification

Node classification is an important yet challenging task in various network applications, and many effective methods have been developed for a single network. While for cross-network scenarios, neither single network embedding nor traditional domain adaptation can directly solve the task. Existing approaches have been proposed to combine network embedding and domain adaptation for cross-network node classification. However, they only focus on domain-invariant features, ignoring the individual features of each network, and they only utilize 1-hop neighborhood information (local consistency), ignoring the global consistency information. To tackle the above problems, in this paper, we propose a novel model, Adversarial Separation Network(ASN), to learn effective node representations between source and target networks. We explicitly separate domain-private and domain-shared information. Two domain-private encoders are employed to extract the domain-specific features in each network and a shared encoder is employed to extract the domain-invariant shared features across networks. Moreover, in each encoder, we combine local and global consistency to capture network topology information more comprehensively. ASN integrates deep network embedding with adversarial domain adaptation to reduce the distribution discrepancy across domains. Extensive experiments on real-world datasets show that our proposed model achieves state-of-the-art performance in cross-network node classification tasks compared with existing algorithms.
Characterizing Phase Noise in a Gain-Switched Laser Diode for Quantum Random-Number Generation

While operating a quantum random-number generator (QRNG), it is extremely useful to have a model of the physical entropy source to guarantee that the device is delivering randomness of genuine quantum origin. In this work we consider a QRNG based on a gain-switched laser diode and we develop a model to quantify its phase noise. This model is based on the laser rate equations and the state-of-the-art techniques for the characterization of laser diodes used in lightwave systems. These tools let us achieve a faithful modeling of the phase noise and we verify its accuracy through comparisons with experimental measurements. Furthermore, the model can be used to select optimal parameters to maximize the QRNG performance and monitor the device behavior to detect malfunctioning or malicious tampering of the device.
Anomaly Detection in Dynamic Graphs via Transformer

Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity. Recent deep learning-based approaches have shown promising results over shallow methods. However, they fail to address two core challenges of anomaly detection in dynamic graphs: the lack of informative encoding for unattributed nodes and the difficulty of learning discriminate knowledge from coupled spatial-temporal dynamic graphs. To overcome these challenges, in this paper, we present a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>T</b></u> ransformer-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>A</b></u> nomaly <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>D</b></u> etection framework for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>DY</b></u> namic graphs ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TADDY</b> ). Our framework constructs a comprehensive node encoding strategy to better represent each node’s structural and temporal roles in an evolving graphs stream. Meanwhile, TADDY captures informative representation from dynamic graphs with coupled spatial-temporal patterns via a dynamic graph transformer model. The extensive experimental results demonstrate that our proposed TADDY framework outperforms the state-of-the-art methods by a large margin on six real-world datasets.
Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder–decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively.
Compositional data analysis of regional geochemical data in the Lhasa area of Tibet, China

It is now widely recognized that geochemical survey data are compositional in nature, with each component having relative importance as part of a whole. Compositional data analysis (CoDA) based on log-ratio transformations can be used to deal with the ‘sum to one’ data constraint. This study explored the use of two data-driven CoDA approaches, centered log-ratio (clr)-biplot analysis and a compositional-balance approach, to investigate associations between elements for potential mineral exploration in the Lhasa area of Tibet, China. Results revealed that (1) the compositional-balance approach, based on a hierarchical cluster and sequential binary partition (SBP) technique, well reflects the range of rocks and metal deposits in the area; (2) the clr-biplot indicates associated relationships between elements, and a consistency is found between principal component (PC1 and PC2) and key compositional balances (Balance 1 and 4); and (3) comparison with traditional integrated geochemical mapping, a knowledge-driven method, proves the validity of the compositional-balance and clr-biplot methods. These results provided metallogenic and petrogenetic information and crucial evidence supporting further geological and geochemical exploration. The improved knowledge demonstrates the importance of using a CoDA approach for geochemical data before performing further statistical analysis.
An Improved Model for Analyzing Textual Sentiment Based on a Deep Neural Network Using Multi-Head Attention Mechanism

Due to the increasing growth of social media content on websites such as Twitter and Facebook, analyzing textual sentiment has become a challenging task. Therefore, many studies have focused on textual sentiment analysis. Recently, deep learning models, such as convolutional neural networks and long short-term memory, have achieved promising performance in sentiment analysis. These models have proven their ability to cope with the arbitrary length of sequences. However, when they are used in the feature extraction layer, the feature distance is highly dimensional, the text data are sparse, and they assign equal importance to various features. To address these issues, we propose a hybrid model that combines a deep neural network with a multi-head attention mechanism (DNN–MHAT). In the DNN–MHAT model, we first design an improved deep neural network to capture the text’s actual context and extract the local features of position invariants by combining recurrent bidirectional long short-term memory units (Bi-LSTM) with a convolutional neural network (CNN). Second, we present a multi-head attention mechanism to capture the words in the text that are significantly related to long space and encoding dependencies, which adds a different focus to the information outputted from the hidden layers of BiLSTM. Finally, a global average pooling is applied for transforming the vector into a high-level sentiment representation to avoid model overfitting, and a sigmoid classifier is applied to carry out the sentiment polarity classification of texts. The DNN–MHAT model is tested on four reviews and two Twitter datasets. The results of the experiments illustrate the effectiveness of the DNN–MHAT model, which achieved excellent performance compared to the state-of-the-art baseline methods based on short tweets and long reviews.
ADAST: Attentive Cross-Domain EEG-Based Sleep Staging Framework With Iterative Self-Training

Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep staging. They mainly train the model on a large public labeled sleep dataset and test it on a smaller one with subjects of interest. However, they usually assume that the train and test data are drawn from the same distribution, which may not hold in real-world scenarios. Unsupervised domain adaption (UDA) has been recently developed to handle this domain shift problem. However, previous UDA methods applied for sleep staging have two main limitations. First, they rely on a totally shared model for the domain alignment, which may lose the domain-specific information during feature extraction. Second, they only align the source and target distributions globally without considering the class information in the target domain, which hinders the classification performance of the model while testing. In this work, we propose a novel adversarial learning framework called ADAST to tackle the domain shift problem in the unlabeled target domain. First, we develop an unshared attention mechanism to preserve the domain-specific features in both domains. Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels. We also propose dual distinct classifiers to increase the robustness and quality of the pseudo labels. The experimental results on six cross-domain scenarios validate the efficacy of our proposed framework and its advantage over state-of-the-art UDA methods.