Reviewerly/exHarmony huggingface dataset:
Joint Inductive and Transductive Learning for Video Object Segmentation

Semi-supervised video object segmentation is a task of segmenting the target object in a video sequence given only a mask annotation in the first frame. The limited information available makes it an extremely challenging task. Most previous best-performing methods adopt matching-based transductive reasoning or online inductive learning. Nevertheless, they are either less discriminative for similar instances or insufficient in the utilization of spatio-temporal information. In this work, we propose to integrate transductive and inductive learning into a unified framework to exploit the complementarity between them for accurate and robust video object segmentation. The proposed approach consists of two functional branches. The transduction branch adopts a lightweight transformer architecture to aggregate rich spatio-temporal cues while the induction branch performs online inductive learning to obtain discriminative target information. To bridge these two diverse branches, a two-head label encoder is introduced to learn the suitable target prior for each of them. The generated mask encodings are further forced to be disentangled to better retain their complementarity. Extensive experiments on several prevalent benchmarks show that, without the need of synthetic training data, the proposed approach sets a series of new state-of-the-art records. Code is available at https://github.com/maoyunyao/JOINT.
The boundary for quantum advantage in Gaussian boson sampling

Identifying the boundary beyond which quantum machines provide a computational advantage over their classical counterparts is a crucial step in charting their usefulness. Gaussian boson sampling (GBS), in which photons are measured from a highly entangled Gaussian state, is a leading approach in pursuing quantum advantage. State-of-the-art GBS experiments that run in minutes would require 600 million years to simulate using the best preexisting classical algorithms. Here, we present faster classical GBS simulation methods, including speed and accuracy improvements to the calculation of loop hafnians. We test these on a ∼100,000-core supercomputer to emulate GBS experiments with up to 100 modes and up to 92 photons. This reduces the simulation time for state-of-the-art GBS experiments to several months, a nine-orders of magnitude improvement over previous estimates. Last, we introduce a distribution that is efficient to sample from classically and that passes a variety of GBS validation methods.
Towards Real-world X-ray Security Inspection: A High-Quality Benchmark And Lateral Inhibition Module For Prohibited Items Detection

Prohibited items detection in X-ray images often plays an important role in protecting public safety, which often deals with color-monotonous and luster-insufficient objects, resulting in unsatisfactory performance. Till now, there have been rare studies touching this topic due to the lack of specialized high-quality datasets. In this work, we first present a High-quality X-ray (HiXray) security inspection image dataset, which contains 102,928 common prohibited items of 8 categories. It is the largest dataset of high quality for prohibited items detection, gathered from the real-world airport security inspection and annotated by professional security inspectors. Besides, for accurate prohibited item detection, we further propose the Lateral Inhibition Module (LIM) inspired by the fact that humans recognize these items by ignoring irrelevant information and focusing on identifiable characteristics, especially when objects are overlapped with each other. Specifically, LIM, the elaborately designed flexible additional module, suppresses the noisy information flowing maximumly by the Bidirectional Propagation (BP) module and activates the most identifiable charismatic, boundary, from four directions by Boundary Activation (BA) module. We evaluate our method extensively on HiXray and OPIXray and the results demonstrate that it outperforms SOTA detection methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Mining Contextual Information Beyond Image for Semantic Segmentation

This paper studies the context aggregation problem in semantic image segmentation. The existing researches focus on improving the pixel representations by aggregating the contextual information within individual images. Though impressive, these methods neglect the significance of the representations of the pixels of the corresponding class beyond the input image. To address this, this paper proposes to mine the contextual information beyond individual images to further augment the pixel representations. We first set up a feature memory module, which is updated dynamically during training, to store the dataset-level representations of various categories. Then, we learn class probability distribution of each pixel representation under the supervision of the ground-truth segmentation. At last, the representation of each pixel is augmented by aggregating the dataset-level representations based on the corresponding class probability distribution. Furthermore, by utilizing the stored dataset-level representations, we also propose a representation consistent learning strategy to make the classification head better address intra-class compactness and inter-class dispersion. The proposed method could be effortlessly incorporated into existing segmentation frameworks (e.g., FCN, PSPNet, OCRNet and DeepLabV3) and brings consistent performance improvements. Mining contextual information beyond image allows us to report state-of-the-art performance on various benchmarks: ADE20K, LIP, Cityscapes and COCO-Stuff <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Learning common and label-specific features for multi-Label classification with correlation information

In multi-label classification, many existing works only pay attention to the label-specific features and label correlation while they ignore the common features and instance correlation, which are also essential for building a competitive classifier. Besides, existing works usually depend on the assumption that they tend to have the similar label-specific features if two labels are correlated. However, this assumption cannot always hold in some cases. Therefore, in this paper, we propose a new approach of learning common and label-specific features for multi-label classification using the correlation information from labels and instances. First, we introduce l2,1-norm and l1-norm regularizers to learn common and label-specific features simultaneously. Second, we use a regularizer to constrain label correlations on label outputs instead of coefficient matrix. Finally, instance correlations are also considered through the k-nearest neighbor mechanism. Comprehensive experiments manifest the superiority of our proposed approach against other well-established multi-label learning algorithms for label-specific features.
Cloud Mining Pool Aided Blockchain-Enabled Internet of Things: An Evolutionary Game Approach

The past few years have witnessed an exponential growth of diverse Internet of Things (IoT) devices as well as compelling applications ranging from industrial production to medical care. Dramatic advances in IoT technology not only brought enormous economic opportunities but also challenges (e.g., privacy and security vulnerabilities). Recently, with the appearance of blockchain technology, the integration of IoT and blockchain (BCoT) is considered a promising solution to address these issues. Blockchain provides a secure and scalable data management framework for IoT devices. However, the huge computation and energy cost of the consensus process in blockchain prevents it from being directly applied as a generic platform. To overcome this challenge, in this article, we propose a cloud mining pool-aided BCoT architecture, where the IoT devices can rent the computing resources from the cloud mining pools to offload the mining process. Based on this architecture, we study the mining pool selection problem and analyze the colony behaviors of IoT devices with different pooling strategies. We propose a centralized evolutionary game-based pool selection algorithm for the sake of maximizing the system utility. Considering the non-cooperative relationship among multiple miners, we also propose a lightweight distributed reinforcement learning algorithm, named the ‘WoLF-PHC’ algorithm.
openGauss

Although learning-based database optimization techniques have been studied from academia in recent years, they have not been widely deployed in commercial database systems. In this work, we build an autonomous database framework and integrate our proposed learning-based database techniques into an open-source database system openGauss. We propose effective learning-based models to build learned optimizers (including learned query rewrite, learned cost/cardinality estimation, learned join order selection and physical operator selection) and learned database advisors (including self-monitoring, self-diagnosis, self-configuration, and self-optimization). We devise an effective validation model to validate the effectiveness of learned models. We build effective training data management and model management platforms to easily deploy learned models. We have evaluated our techniques on real-world datasets and the experimental results validated the effectiveness of our techniques. We also provide our learnings of deploying learning-based techniques.
Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting loaded virtual machines. Due to the minimum solution space of proposed algorithm, convergence and response time is significantly decreased. It finds a feasible scheduling solution to minimize the running time of workflows and tasks. The proposed algorithm achieved 64% decrease in execution time and 11% decrease in overall data center costs.
Evolution of a hydrothermal ore-forming system recorded by sulfide mineral chemistry: a case study from the Plaka Pb–Zn–Ag Deposit, Lavrion, Greece

Abstract Laser ablation-inductively coupled plasma-mass spectrometry and electron-probe microanalysis were used to investigate the trace-element contents of sphalerite, chalcopyrite and pyrite from the Plaka Pb–Zn–Ag deposit. Using petrographic observations, the analytical results could be linked to the temporal evolution of the Plaka ore-forming system. Sphalerite chemistry reliably records the temperature and f S 2 evolution of the system, with estimated formation temperatures reproducing the microthermometric results from previous fluid-inclusion studies. Chalcopyrite chemistry also shows systematic variations over time, particularly for Cd, Co, Ge, In, Sn and Zn concentrations. Measurable pyrite was only found in association with early high-temperature mineralisation, and no clear trends could therefore be identified. We note, however, that As and Se contents in pyrite are consistent with formation temperatures estimated from co-existing sphalerite. Statistical analysis of the sphalerite data allowed us to identify the dominant geological controls on its trace-element content. The three investigated factors temperature, f S 2 , and sample location account for &gt; 80% of the observed variance in Mn, Fe, Co, Ga, Ge, In, Sb and Hg concentrations, and &gt; 60% of the observed variance in Cd and Sn concentrations. Only for Cu and Ag concentrations is the explained variance &lt; 50%. A similarly detailed analysis was not possible for chalcopyrite and pyrite. Nevertheless, comparison of the results for all three investigated minerals indicates that there are some systematic variations across the deposit which may be explained by local differences in fluid composition.
Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms

Speech signals are being used as a primary input source in human-computer interaction (HCI) to develop several applications, such as automatic speech recognition (ASR), speech emotion recognition (SER), gender, and age recognition. Classifying speakers according to their age and gender is a challenging task in speech processing owing to the disability of the current methods of extracting salient high-level speech features and classification models. To address these problems, we introduce a novel end-to-end age and gender recognition convolutional neural network (CNN) with a specially designed multi-attention module (MAM) from speech signals. Our proposed model uses MAM to extract spatial and temporal salient features from the input data effectively. The MAM mechanism uses a rectangular shape filter as a kernel in convolution layers and comprises two separate time and frequency attention mechanisms. The time attention branch learns to detect temporal cues, whereas the frequency attention module extracts the most relevant features to the target by focusing on the spatial frequency features. The combination of the two extracted spatial and temporal features complements one another and provide high performance in terms of age and gender classification. The proposed age and gender classification system was tested using the Common Voice and locally developed Korean speech recognition datasets. Our suggested model achieved 96%, 73%, and 76% accuracy scores for gender, age, and age-gender classification, respectively, using the Common Voice dataset. The Korean speech recognition dataset results were 97%, 97%, and 90% for gender, age, and age-gender recognition, respectively. The prediction performance of our proposed model, which was obtained in the experiments, demonstrated the superiority and robustness of the tasks regarding age, gender, and age-gender recognition from speech signals.
Blockchain-Based Model to Improve the Performance of the Next-Generation Digital Supply Chain

In the era of the fourth industrial revolution, all aspects of the industrial domain are being affected by emerging technologies. Digitalization of every process is taking place or under process. One of the most important components common to every domain is the supply chain process. Organizations employ a digital supply chain to track the delivery of their products or materials. The digital supply chain is still suffering from a few issues such as no provenance, less transparency, and a trust issue. Blockchain technology, one of the emerging technologies, can be integrated with the supply chain to deal with the existing issues and to improve its performance. In this paper, a model is proposed to integrate blockchain technology with the supply chain to improve performance. The proposed model uses the combination of the Ethereum blockchain and the interplanetary file system to maintain the traceability, transparency, and trustworthiness of the supply chain.
Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph

Instant analysis of cybersecurity reports is a fundamental challenge for security experts as an immeasurable amount of cyber information is generated on a daily basis, which necessitates automated information extraction tools to facilitate querying and retrieval of data. Hence, we present Open-CyKG: an Open Cyber Threat Intelligence (CTI) Knowledge Graph (KG) framework that is constructed using an attention-based neural Open Information Extraction (OIE) model to extract valuable cyber threat information from unstructured Advanced Persistent Threat (APT) reports. More specifically, we first identify relevant entities by developing a neural cybersecurity Named Entity Recognizer (NER) that aids in labeling relation triples generated by the OIE model. Afterwards, the extracted structured data is canonicalized to build the KG by employing fusion techniques using word embeddings. As a result, security professionals can execute queries to retrieve valuable information from the Open-CyKG framework. Experimental results demonstrate that our proposed components that build up Open-CyKG outperform state-of-the-art models.1
Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach

The early detection of breast cancer (BC) has a significant impact on reducing the disease’s mortality rate. As an effective cost- and time-saving tool, computer-aided diagnosis (CAD) systems have been developed in this research field, aiding clinicians and radiologists’ decision-making process by offering highly accurate information. In this study, a shallow artificial neural network (ANN) model with one hidden layer is used to diagnose and predict BC using the Wisconsin breast cancer dataset (WBCD) and the Wisconsin diagnostic breast cancer (WDBC) dataset without employing feature optimization or selection algorithms. The datasets are divided into 80% for training and 20% for testing using five-fold cross-validation. The model’s effectiveness and efficiency are evaluated based on sensitivity, specificity, precision, accuracy, and F1 score, along with the area under the receiver-operating characteristic curve (AUC). In the task of classifying benign and malignant tumours using the WBCD, the shallow ANN model showed promising performance with an average accuracy of 99.85%, specificity of 99.72%, sensitivity of 100%, precision of 99.69%, and F1 score of 99.84%. For BC detection using WDBC, it achieved an average accuracy of 99.47%, specificity of 99.53%, sensitivity of 99.59%, precision of 98.71%, and F1 score of 99.13%. The AUC of the proposed model was 99.86% and 99.56% for the WBCD and the WDBC dataset, respectively, illustrating the model’s discrimination capacity. Moreover, the ANN model outperformed state-of-the-art models that integrate feature optimization and selection algorithms to classify BC tumours using WBCD and WDBC. Hence, the shallow ANN model presented here demonstrates significant potential for diagnosing BC using WBCD and WDBC without the need for feature optimization or selection algorithms.
A comparison of the effects of haptic and visual feedback on presence in virtual reality

In the current consumer market, Virtual reality experiences are predominantly generated through visual and auditory feedback. Haptics are not yet well established, but are increasingly introduced to enhance the user’s sense of ‘reality’. With haptic (vibrotactile) feedback now part of the built-in mechanism of VR consumer devices, there is an urgent need to understand how different modalities work together to improve the user experience. This paper reports an experiment that explores the contributions made to participants’ sense of presence by haptic and visual feedback in a virtual environment. Participants experienced a virtual ball bouncing on a virtual stick resting across their avatar hands. We found that presence was enhanced when they could both see and feel the ball’s action; with a strong suggestion that haptic feedback alone gave rise to a greater sense of presence than visual alone. Similarly, whilst visual or bimodal feedback enhanced participants’ ability to locate where the ball bounced on the stick, our results suggest that the action itself was more readily discerned haptically than visually.
Multinomial random forest

Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze its consistency and privacy-preservation. Instead of deterministic greedy split rule or with simple randomness, the MRF adopts two impurity-based multinomial distributions to randomly select a splitting feature and a splitting value, respectively. Theoretically, we prove the consistency of MRF and analyze its privacy-preservation within the framework of differential privacy. We also demonstrate with multiple datasets that its performance is on par with the standard RF. To the best of our knowledge, MRF is the first consistent RF variant that has comparable performance to the standard RF. The code is available at https://github.com/jiawangbai/Multinomial-Random-Forest.
A shared ontology suite for digital construction workflow

With ongoing advancements in information and communication technologies (ICTs) in all stages of the construction lifecycle, information from entities related to construction workflow (CW) can now be automatically collected. These implementations are point solutions, which require systematic integration to combine their information to enable a holistic picture of CW. The major barrier to such integration is information heterogeneity, where the information is collected from different systems under multiple contexts. Scholars in the construction domain have explored the use of ontology to solve the information-integration problem, although an ontology that both adequately represents the CW and integrates the digitalized information of CW via various systems and multiple contexts is currently missing from the existing literature. This research thus presents an ontology set for formalizing and integrating CW information within the digital construction context. The proposed digital construction ontologies (DiCon) are shared representations of construction domain knowledge that specify the terms and relations of CWs and their related information. We developed the DiCon based on a hybrid ontology development approach. The DiCon includes six modules: Entities, Processes, Information, Agents, Variables, and Contexts. The developed DiCon was further evaluated by approaches including automatic consistency checking, criteria-based evaluation, expert workshops, and task-based evaluation and involved two use cases by answering relevant competency questions via SPARQL queries. The results of the evaluation demonstrate that the DiCon ontologies are sufficient to represent domain knowledge and can formalize and integrate CW information within the digital construction context.