Reviewerly/exHarmony huggingface dataset:
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?

Even though Generative Adversarial Networks (GANs) have shown a remarkable ability to generate high-quality images, GANs do not always guarantee the generation of photorealistic images. Occasionally, they generate images that have defective or unnatural objects, which are referred to as `artifacts'. Research to investigate why these artifacts emerge and how they can be detected and removed has yet to be sufficiently carried out. To analyze this, we first hypothesize that rarely activated neurons and frequently activated neurons have different purposes and responsibilities for the progress of generating images. In this study, by analyzing the statistics and the roles for those neurons, we empirically show that rarely activated neurons are related to the failure results of making diverse objects and inducing artifacts. In addition, we suggest a correction method, called `Sequential Ablation’, to repair the defective part of the generated images without high computational cost and manual efforts.
Online Matching with Controllable Rewards and Arrival Probabilities

Online bipartite matching has attracted much attention due to its importance in various applications such as advertising, ride-sharing, and crowdsourcing. In most online matching problems, the rewards and node arrival probabilities are given in advance and are not controllable. However, many real-world matching services require them to be controllable and the decision-maker faces a non-trivial problem of optimizing them. In this study, we formulate a new optimization problem, Online Matching with Controllable Rewards and Arrival probabilities (OM-CRA), to simultaneously determine not only the matching strategy but also the rewards and arrival probabilities. Even though our problem is more complex than the existing ones, we propose a fast 1/2-approximation algorithm for OM-CRA. The proposed approach transforms OM-CRA to a saddle-point problem by approximating the objective function, and then solves it by the Primal-Dual Hybrid Gradient (PDHG) method with acceleration through the use of the problem structure. In simulations on real data from crowdsourcing and ride-sharing platforms, we show that the proposed algorithm can find solutions with high total rewards in practical times.
Annotated Sequent Calculi for Paraconsistent Reasoning and Their Relations to Logical Argumentation

We introduce annotated sequent calculi, which are extensions of standard sequent calculi, where sequents are combined with annotations that represent their derivation statuses. Unlike in ordinary calculi, sequents that are derived in annotated calculi may still be retracted in the presence of conflicting sequents, thus inferences are made under stricter conditions. Conflicts in the resulting systems are handled like in adaptive logics and argumentation theory. The outcome is a robust family of proof systems for non-monotonic reasoning with inconsistent information, where revision considerations are fully integrated into the object level of the proofs. These systems are shown to be strongly connected to logical argumentation.
The effect of Habruta learning method on self-directed learning ability, academic self-efficacy, learning commitment and critical thinking disposition of nursing students in online class

Objectives This study applied the Habruta learning method in an online class to enhance the understanding of basic major subjects, which are essential for effective nursing practice. It was carried out to confirm the effect. Methods The subjects of this study were 57 second-year nursing students enrolled in a university, and the Habruta learning method was applied for 10 weeks. This is a single-group pre- and post-primitive experimental study that investigated the self-directed learning ability, academic self-efficacy, learning immersion, and critical thinking tendency of subjects before and after the application of the Habruta learning method. IBM SPSS WIN 26.0 program was used for data analysis, and the general characteristics of subjects were analyzed by descriptive statistics, and the effects of dependent variables were analyzed by paired t-test. Results As a result of the study, Self-directed learning ability (t=3.73, p&lt;.001) increased after the application of the Habruta learning method, and the learning plan of the sub-domain (t=2.10, p=.040), learning evaluation (t=29.72, p&lt;.001) was significantly increased. Academic self-efficacy (t=2.39, p=.020) was sub-domain self-regulation efficacy (t=2.47, p&lt;.017), confidence (t=2.28, p&lt;.026). was significantly increased. In the case of learning immersion (t=3.30, p=.002), the immersion condition (t=2.70, p=.009) and immersion experience (t=3.27, p=.002) of the sub-domains were statistically significantly increased, critical As for the thinking propensity (t=2.13, p=.038), the lower-order memory systemicity (t=2.48, p=.016) was statistically significantly increased. Conclusions By applying the Habruta learning method to the nursing major basic subject in the online class, the self-directed learning ability, academic self-efficacy, learning immersion, and critical thinking tendency of nursing students were improved. In the future, it is necessary to apply the Havruta learning method to various online nursing online classes to confirm the effect.&#x0D;
A new Ant colony optimization metaheuristic based on pheromone guided local search instead of constructive approach

This paper proposes a new algorithm based on a new idea inspired by the Ant Colony Optimization (ACO) metaheuristic. The goal of the proposed algorithm is to increase the performance of the traditional ACO algorithms by changing the classic role played by ants. In fact, ants are not anymore used to construct solutions but to improve the quality of a population of solutions using a specific local search strategy. Thus the new algorithm, called Ant-PLS, is a population based local search algorithm which uses the stored knowledge in pheromone trails to guide the ant when choosing its neighboring solution from a dynamic candidate list. Ant-PLS is applied on the symmetric traveling salesman problem. The experimental results show the effectiveness of the Ant-PLS when applied on large benchmark instances of TSP. In fact Ant-PLS results are significantly better than the compared ACO algorithms.
Evaluation of Mixed Reality Support for Bridge Inspectors Using BIM Data

Abstract Bridge inspectors work for the safety of our infrastructure and mobility. In regular intervals, they conduct structural inspections – a manual task with a long-lasting and firmly normed analogue tradition. In our collaborative research project, we developed Mixed Reality (MR) and Virtual Reality (VR) prototypes to support that work digitally. We propose a mixed analogue and digital workflow using Building Information Modeling (BIM) data that can be ready-to-hand for bridge inspectors during their work on-site at a bridge. In this paper, we describe the system and the evaluation results of our final MR demonstrator at a autobahn-bridge in Germany. We identified a need for a digital MR tool to support the bridge inspection in-situ. In general, this matches with the trend to bring the computer-supported office-work out into the real world. However, there are also challenges to consider, like lacking BIM data for existing bridges and structures, appropriate user-interfaces in this new application domain, or the need to adopt norms and guidelines for public tender. We argue to consider a user-centered design approach for future developments to best profit from the bridge inspectors’, as well as the MR- and CSCW-researchers expertise, and ultimately increase the acceptance of the developed information systems.
FewJoint: few-shot learning for joint dialogue understanding

Few-shot learning (FSL) is one of the key future steps in machine learning and raises a lot of attention. In this paper, we focus on the FSL problem of dialogue understanding, which contains two closely related tasks: intent detection and slot filling. Dialogue understanding has been proven to benefit a lot from jointly learning the two sub-tasks. However, such joint learning becomes challenging in the few-shot scenarios: on the one hand, the sparsity of samples greatly magnifies the difficulty of modeling the connection between the two tasks; on the other hand, how to jointly learn multiple tasks in the few-shot setting is still less investigated. In response to this, we introduce FewJoint, the first FSL benchmark for joint dialogue understanding. FewJoint provides a new corpus with 59 different dialogue domains from real industrial API and a code platform to ease FSL experiment set-up, which are expected to advance the research of this field. Further, we find that insufficient performance of the few-shot setting often leads to noisy sharing between two sub-task and disturbs joint learning. To tackle this, we guide slot with explicit intent information and propose a novel trust gating mechanism that blocks low-confidence intent information to ensure high quality sharing. Besides, we introduce a Reptile-based meta-learning strategy to achieve better generalization in unseen few-shot domains. In the experiments, the proposed method brings significant improvements on two datasets and achieve new state-of-the-art performance.
Distributed evolution strategies for large-scale optimization

As their underlying models are becoming larger and data-driven, an increasing number of modern real-world applications can be mathematically formulated as large-scale continuous optimization. In this paper, we propose a distributed evolution strategy (DES) for large-scale black-box optimization (specifically with memory-costly function evaluations), running on the mainstream clustering computing platform. In order to amortize the memory cost, DES utilizes the distributed shared memory to support parallelism of function evaluations. For better fitting into the scalable computing architecture of interest, DES adopts the well-known island model to distribute one low-rank version of covariance matrix adaptation (CMA), because the quadratic complexity of the standard CMA is not well scalable. For a proper trade-off between exploration and exploitation, DES needs to, on-the-fly, adjust strategy parameters at two time-scale levels. Experiments show its efficiency on most of test functions chosen.
Late feature supplement network for early action prediction

Early action prediction is a new hotspot in the field of computer vision. To improve the accuracy of early action prediction, a new end-to-end late feature supplement-based early action prediction network is proposed in this work. Different from the existing methods that use the model transfer strategy, a feature transfer strategy is defined in this work. Specifically, the features of the late clip are regarded as labels, and a feature transfer model is built to achieve mapping from the features of the early clip to the late features. After feature transfer, the generated late feature is fused into the early feature to form the final video feature. Finally, the final video feature is applied to action classification. The proposed method is evaluated on the action classification task of the early clip. The experimental results show that compared with the existing methods, the proposed method has better performance at different observation ratios. The ablation study verifies that the proposed feature transfer strategy can significantly improve the accuracy of early action prediction.
ColorUAV: A genetic algorithm for dynamic drone path planning under multiple constraints

We propose ColorUAV, a genetic algorithm for UAV path planning under multiple constraints. Our solution enables drones to travel towards a destination point while avoiding obstacles and navigating as close as possible to base stations in order to maximize communication opportunities. ColorUAV learning phase creates UAVs able to interact with their environment and compute paths whose weight is within 17% of the optimal. More interestingly, our trained UAVs are capable of adapting to moderate changes in the environment without re-training and while remaining efficient in terms of the path quality.
Light field structured light projection data generation with Blender

Light field structured light 3D measurement has gained popularity by merging the advantages of light field and structured light methods. Generating light field structured light dataset is necessary for studying light field 3D reconstruction algorithms, but it is time-consuming and expensive in a real sense, especially for ground truth data. This paper proposes a method to generate light field structured light projection data with Blender simulation. The proposed method allows for the modification of camera and projector settings and parameters, as well as rotating objects. The dataset generated by this method contains 107730 light field structured light images. The label data (ground truth data) including depth and disparity by the 9×9 light field camera array are provided for the performance evaluation of 3D reconstruction algorithms. To the best of our knowledge, it is the first public dataset in the light field structured light projection environment. Diverse 3D reconstruction methods, including deep learning methods, are used to evaluate the proposed data generation method and dataset. The dataset is available at https://github.com/sabaizzz/Light-field-structured-light-dataset.
Network Situation Features Extraction Method of Computer Network Based on Knowledge Graph

A feature extraction method of network situation based on knowledge graph is proposed for network situation assessment. The knowledge graph of network flow is constructed by knowledge graph technology. Combined with graph algorithm and complex network feature analysis method, different types of node features and overall network features in network situation are extracted, so as to obtain the importance ranking of nodes and the overall evaluation value of network situation. The experimental results show that the algorithm can accurately locate the key node information in the computer network and scientifically describe its network connection relationship, which provides an effective means for the network to achieve more efficient management and maintenance.
Research on Consensus Algorithm of Hierarchical Partition Blockchain for Virtual Power Plant Transactions

With the construction of new power systems, the proportion of new energy power generation will gradually increase, and the energy transaction of virtual power plants will become more complex and challenging. With the increase in the number of energy entities involved in virtual power plants, the traditional centralized dispatching method can no longer meet the needs of the development of virtual power plants. The decentralization, tamper resistance and smart contracts of the blockchain can improve the transaction efficiency of virtual power plants. However, with the increasing number of distributed users participating in transactions, the consensus delay of the blockchain in completing transactions hinders the expansion of the blockchain network..This paper first proposes a blockchain-based virtual power plant system architecture, and expounds the relationship and role of each level. Later, a blockchain partition consensus algorithm for virtual power plant transactions was proposed, namely Partition Practical Byzantine Fault Tolerance (PPBFT). Finally, using OMNET++ to simulate the algorithm in this paper, it is concluded that when the number of nodes increases, the algorithm can effectively reduce the consensus time and improve the fairness and timeliness of transactions.
An Efficient Ensemble Based Model for Data Classification

Classification techniques are very useful in prediction, decision making of data. Classification technique is being used in IT industry, government organization for classifying the data and to infer the knowledge for decision making or knowledge discovery. There are various classification algorithms like Logistic regression, Na&#x00EF;ve Bayes, Decision tree, Support vector machine, and K-Nearest Neighbors etc. these algorithms have limitations like high time requirement to train the data and data sensitivity, performance. In this paper we propose a hybrid model i.e. ensemble and stacking technique to overcome the limitations of existing classification techniques. Hybridization enhances the accuracy and precision value and also over comes the limitations of existing classification algorithms. Most of the times when classifying textual data using individual learners such as Naive Bayes, Support vector machine, Logistic Regression they have a high bias or high variance because of which the performance is reduced. Ensemble methods can be used to increase the performance by combining several weak learners together in order to create a strong learner. The main aim is to implement a hybrid ensemble method for classification on the given dataset and perform comparative study analysis with some of the existing algorithms by considering performance indicators such as precision, recall, F1-score and support.
Analysis of Optimization Method of Laboratory Management System Based on Data Processing and Intelligent Sensor Technology of the Internet of Things

At present, colleges and universities have done much research on the management of laboratories and equipment on campus. The focus of the work is on the optimization of manual laboratory management. The work content management of the laboratory is still concentrated in the manual management stage. The workload is very large, and the content is very complicated. Therefore, it is necessary to optimize the above problems. In the process of optimization, artificial intelligence and the Internet of Things, as new information age technologies, have received extensive attention. This paper firstly introduces the data processing and intelligent sensor technology of the Internet of Things from the perspective of the traditional laboratory management system. On this basis, it gradually combines artificial intelligence and cloud computing to improve laboratory management&#x0027;s core content. Based on the above technical core, this paper designs and optimizes the laboratory management system of artificial intelligence algorithms.
Implementing Large-Scale ABox Materialization Using Subgraph Reasoning

AbstractThe ontology knowledge base can be divided into two parts: TBox and ABox, where the former models schema-level knowledge within the domain, and the latter is a statement of assertions or facts about a set of instances. ABox materialization is the process of discovering implicit assertions in ABox by reasoning based on existing knowledge, which is important in knowledge base applications. Ontology reasoning is a common method for ABox materialization. However, it is considered to be a computationally intensive operation and does not scale well for large-scale ABox. To solve this problem, this paper proposes an approximate reasoning hypothesis: materialization on the overall ABox is approximately equivalent to the collection of subgraph reasoning on ABox. Based on this hypothesis, a subgraph reasoning method for large-scale ABox materialization is proposed. Subgraph reasoning first divides ABox into instance-centered multi-hops subgraphs, then performs ontology reasoning on each subgraph, and finally takes the collection of all subgraph reasoning results as the result of ABox materialization. We conduct experiments on multiple open-source ontologies, and analyze the rationality of the approximate reasoning hypothesis. The experimental results show that subgraph reasoning can effectively improve the reasoning efficiency and achieve superior scalability for large-scale ABox materialization reasoning.KeywordsABox materializationSubgraph reasoningLarge-scale ABoxOWL DLSWRL
Channel Recombination and Projection Network for Blind Image Quality Measurement

In this paper, a novel channel recombination and projection network-based no-reference image quality assessment (NR-IQA) is proposed, which is termed as CRPNet. The proposed CRPNet is composed of four parts, a feature extractor, a channel recombination strategy, a saliency-guided selective projection, and a channel score weighting. The feature extractor is first utilized to learn patch-level features from patches to address the lack of training samples. The channel recombination strategy is proposed to obtain image-level features by recombining patch-level features in channel dimension, which can solve the mismatch problem between the image score and the patch-level quality. To further increase the image quality prediction accuracy, the saliency-guided selective projection is designed, in which the mapping ratios of fully connected layers are calculated by the saliency priority of patches. Moreover, a channel score weighting is introduced to enhance the image visual quality representation. Experimental results on six image quality assessment datasets, e.g., LIVE, TID2013, CSIQ, LIVE MD, LIVE CH and Waterloo Exploration Database, show that the proposed CRPNet outperforms the state-of-the-art NR-IQA methods, and achieves the superior performance. Code is available at https: //github.com/zhaob10/CRPNet.
Comparing ML-Based Predictions and Static Analyzer Tools for Vulnerability Detection

Finding and eliminating security issues early in the development process is critical as software systems are shaping many aspects of our daily lives. There are numerous approaches for automatically detecting security vulnerabilities in the source code from which static analysis and machine learning based methods are the most popular. However, we lack comprehensive benchmarking of vulnerability detection methods across these two popular categories. In one of our earlier works, we proposed an ML-based line-level vulnerability prediction method with the goal of finding vulnerabilities in JavaScript systems. In this paper, we report results on a systematic comparison of this ML-based vulnerability detection technique with three widely used static checker tools NodeJSScan ( https://github.com/ajinabraham/nodejsscan ), ESLint ( https://eslint.org ), and CodeQL ( https://codeql.github.com ) using the OSSF CVE Benchmark ( https://github.com/ossf-cve-benchmark/ossf-cve-benchmark ). We found that our method was more than capable of finding vulnerable lines, managing to find 60% of all vulnerabilities present in the examined dataset, which corresponds to the best recall of all tools. Nonetheless, our method had higher false-positive rate and running time than that of the static checkers.