Reviewerly/exHarmony huggingface dataset:
Ambient-Aware Sound-Based Production Counter for Manufacturing Machines

Production counters, such as mechanical/photoelectric switch sensors, line sensors, or infrared proximity sensors, are often used as real-time productivity counters for manufacturing machines. These counters can be easily tampered with and are not designed for detecting machine fault. Since manufacturing machines with mechanical operations produce sound, capturing the sound data should provide opportunities for both production counting and detecting anomalies and hence taking proactive action to reduce production wastage. But in the manufacturing environment, often there are more than one machine in operation. These machines generate different background noises at different times, which makes gathering labeled data to train machine learning a challenging task and subsequently affects the production tracking and counting accuracy. The objective of this work is to develop a robust sound-based counter to track and count the productivity of a manufacturing machine in a real environment. Our proposed solution uses the Kolmogorov-Smirnov test to detect changes in the ambient environment. Experimental results show that our approach can achieve an accuracy of up to 92% while other conventional techniques are completely disoriented.
Transient Chaotic Neural Networks Based on Sinusoidal Feedback Terms and Their Applications

There is a novel chaotic neural network model that uses sinusoidal perturbations to add nonlinear elements to enhance the chaotic properties, and then employs maximum Lyapunov exponents and neuronal inverse bifurcation maps to modify the model parameters to ensure that the network has chaotic dynamic properties. Simulation data demonstrate the feasibility of the scheme and how changing the value of the has an impact on the coefficients in the excitation function and the self-feedback term on the output of the model, either by increasing the range of the chaotic traversal or by enhancing the speed of convergence of the chaotic traversal, reflecting the sensitivity of the chaotic neural network model to the initial values of the parameters. The experimental findings show that the transient chaotic neural network model based on sinusoidal perturbations is capable of handling the complexity and diversity of the combinatorial optimization problem and can successfully find the global optimal solution when the new model is applied to a combinatorial optimization problem (TSP problem). providing a new solution to the combinatorial optimization problem.
Image Registration Algorithm Based on Improved SIFT

A method is proposed to combine the Shi-Tomasi algorithm with the SFT (Scale-Invariant Feature Transform) algorithm for image registration, aiming to address the non-real-time issue of the SIFT algorithm. The method utilizes the Shi-Tomasi algorithm to extract feature points, and the more efficient PROSAC algorithm is employed to replace the traditional RANSAC algorithm for removing mismatches. Experimental results demonstrate that the proposed algorithm effectively improves matching accuracy while reducing the image matching time by 66.5%.
Harmony of Agent System with Heterogeneity

This paper focuses on the integration of heterogeneous agents, also known as harmony of them, where heterogeneity emphasized so often.To advance the idea of heterogeneous agents, harmony of the agents, where homogeneity of the agent system turns into heterogeneity one at start, and then the heterogeneity turns back to homogeneity again after.Initiated from hybrid order agents, heterogeneity like social status, encapsulated agents will be introduced step by step.Finally, Chinese philosophy, which always inspires new ideas of thought, can be a good source of research topic.Conclusion that encapsulated agent is the capital Tao of agent systems was drawn from the paper.
A Video Shot Occlusion Detection Algorithm Based on the Abnormal Fluctuation of Depth Information

To make the video more attractive, original video materials usually need postprocessing by video editors, especially to eliminate low-quality abnormal clips, which seriously affect the visual effect. One of the main reasons for the low-quality abnormal clips is that there are occluders that accidentally break into the shot to occlude the protagonist, resulting in the loss of the video protagonist’s information. However, it is time-consuming and laborious to manually find shot occlusion clips, so computer vision technology can be used to assist editors in completing this work. The previous solutions directly utilize neural networks to detect shot occlusion, so their performance is affected by the size and quality of the dataset. In contrast, inspired by the change of depth information in the frame caused by the occluder breaking into the shot, we propose an algorithm for video shot occlusion detection based on the fluctuation of depth information. This algorithm does not need occlusion data training and can detect shot occlusion well only by capturing the abnormal fluctuations of the frame depth information. Additionally, to overcome the defect in that the first video shot occlusion detection (VSOD) dataset released in our conference publication can only verify the sensitivity of detection methods, we expand the VSOD dataset to evaluate the comprehensive performance of detection algorithms. The plentiful experimental results show that, compared with state-of-the-art occlusion detection methods and self-designed baseline methods, our algorithm significantly improves the comprehensive performance of video shot occlusion detection. Furthermore, through verification on datasets with different data types and distributions, our shot occlusion detection algorithm can maintain an occlusion event recall of over 95%, while the false positive rate does not exceed 3%, demonstrating good generalization ability. The code and dataset are available at https://github.com/Junhua-Liao/VSOD.
Temporal Action Proposal Generation With Action Frequency Adaptive Network

As the cornerstone of human-behavior analysis in video understanding, temporal action proposal generation aims to predict the starting and ending time of human action instances in untrimmed videos. Although large achievements in temporal action proposal generation have been achieved, most previous studies ignore the variability of action frequency in raw videos, leading to unsatisfying performances on high-action-frequency videos. In fact, there exists two main issues which should be well addressed: data imbalance between high and low action-frequency videos, and inferior detection of short actions in high-action-frequency videos. To address the above issues, we propose an effective framework by adapting to the variability of action frequency, namely Action Frequency Adaptive Network (AFAN), which can be flexibly built upon any temporal action proposal generation method. AFAN consists of two modules: Learning From Experts (LFE) and Fine-Grained Processing (FGP). The LFE first trains a series of action proposal generators on different subsets of imbalanced data as experts and then teaches a unified student model via knowledge distillation. To better detect short actions, FGP first finds out high-action-frequency videos and then performs fine-grained detection. Extensive experimental results on four benchmark datasets (ActivityNet-1.3, HACS, THUMOS14 and FineAction) demonstrate the effectiveness and generalizability of the proposed AFAN, especially for high-action-frequency videos.
Computing k-Bisimulations for Large Graphs: A Comparison and Efficiency Analysis

Summarizing graphs w.r.t. structural features is important to reduce the graph’s size and make tasks like indexing, querying, and visualization feasible. Our generic parallel BRS algorithm efficiently summarizes large graphs w.r.t. a custom equivalence relation $$\sim $$ defined on the graph’s vertices V. Moreover, the definition of $$\sim $$ can be chained $$k\ge 1$$ times, so the defined equivalence relation becomes a k-bisimulation. We evaluate the runtime and memory performance of the BRS algorithm for k-bisimulation with $$k=1,\ldots ,10$$ against two algorithms found in the literature (a sequential algorithm due to Kaushik et al. and a parallel algorithm of Schätzle et al.), which we implemented in the same software stack as BRS . We use five real-world and synthetic graph datasets containing 100 million to two billion edges. Our results show that the generic BRS algorithm outperforms the respective native bisimulation algorithms on all datasets for all $$k\ge 5$$ and for smaller k in some cases. The BRS implementations of the two bisimulation algorithms run almost as fast as each other. Thus, the BRS algorithm is an effective parallelization of the sequential Kaushik et al. bisimulation algorithm.
A Survey on Using Evolutionary Approaches-Based High-Utility Itemsets Mining

Frequently item-sets mining, also known as FIM, is a data mining technique used to extract useful knowledge from datasets. FIM is plagued by a number of issues, including high storage charges, a huge time and memory consumption. Classical FIM algorithms suffer with the great number of generated itemsets which contains useless items. And presupposes that all itemsets have the same importance. To deal with these limitations, high-utility itemsets (HUI) is proposed, which are as subset of FIM with the consideration of Utility or profit measure. In the last decade, evolutionary approaches become a trend to solve HUIs mining problem (EA). This paper explores the use of evolutionary techniques in HUIs mining. Moreover, we present an evolutionary techniques-based HUIs mining classification including single objective, multi-objective and Hybrid optimization techniques. This article provides a comparative examination of methodologies and discusses theoretical features of a wide variety of algorithms that are inspired by nature.
DISS: A Discrete Input-Space Sampling Path Planning and Obstacle Avoidance Strategy for Swarm Robotics

In this research, the path planning and obstacle avoidance problem for Swarm Robotics is addressed. Our contribution consists in a Discrete adaptation of the well-known Input-Space Sampling method (DISS). The DISS is integrated into a Swarm Intelligence approach, named Multi-Bee Swarm Optimization (MBSO) that must be embedded into autonomous mobile robots. The MBSO algorithm, in turn, is multi-swarm adaptation of the well-known Bee Swarm Optimization. It aims to resolve the Target Detection Problem by exploring a 2D discrete, complex, and unknown environment. Thus, the search strategy must be provided with an adequate path planning and obstacles avoidance strategy which is a determinant factor to the robots’ efficiency in their search mission. The experimental results show that, by varying the obstacle density in the search environments MBSO’s performances are not much affected. Our path planning and obstacle avoidance strategy offer an efficient and effective navigation capability to the robots of MBSO.
An Enhanced Blood Cell Counting System Using Swin Transformer with Dynamic Head and KNN Model

The blood cell count is a valuable feature that is usually measured with specialized tools for diagnosis and monitoring purposes. However, the conventional counting methods are limited by their performance. Hence arise the need for a rapid and effective detection and counting system using the blood cell images. In this article, we propose an automatic approach for the blood cell enumeration using swin transformer with dynamic head detector and KNN model. The experimental results show that our proposed approach, “SwinT-Dyhead+KNN” can significantly improve the blood cell detection and counting tasks using the blood cell count dataset (BCCD).
Semantic representation and attention alignment for Graph Information Bottleneck in video summarization

End-to-end Long Short-Term Memory (LSTM) has been successfully applied to video summarization. However, the weakness of the LSTM model, poor generalization with inefficient representation learning for inputted nodes, limits its capability to efficiently carry out node classification within user-created videos. Given the power of Graph Neural Networks (GNNs) in representation learning, we adopted the Graph Information Bottle (GIB) to develop a Contextual Feature Transformation (CFT) mechanism that refines the temporal dual-feature, yielding a semantic representation with attention alignment. Furthermore, a novel Salient-Area-Size-based spatial attention model is presented to extract frame-wise visual features based on the observation that humans tend to focus on sizable and moving objects. Lastly, semantic representation is embedded within attention alignment under the end-to-end LSTM framework to differentiate indistinguishable images. Extensive experiments demonstrate that the proposed method outperforms State-Of-The-Art (SOTA) methods.
Multi-Scale Visual Perception based Progressive Feature Interaction Network for Stereo Image Super-Resolution

In recent years, stereo image super-resolution based on convolutional neural network has been extensively researched and achieved impressive performance by introducing complementary information from another view. However, most existing methods still cannot fully capture both intra- and cross-view information due to the neglect of multi-scale information perception, multi-scale binocular alignment and the excitation of large scale to small scale in human vision system. And they generated blurry results due to the consideration of irrelevant information in search for cross-view information. To address these issues, we propose a multi-scale visual perception based progressive feature interaction network (MS-PFINet) for stereo image super-resolution. Specifically, to exploit comprehensive intra- and cross-view information for image reconstruction, we design a two-stream network with multi-branch structure to extract multi-scale features and progressively use cross-view interaction at larger scales to guide that at smaller scales. Moreover, to explore more proper and accurate cross-view information, we propose a feature transformer module (FTM) to search and transfer the most relevant features from another view by hard attention maps and soft attention maps, which are calculated by patch-wise similarity rather than pixel-wise. In addition, in order to encourage a more effective way to transfer texture features for the target view, we propose a perceptual texture matching loss to supervise the accuracy of feature transformer modules. Experimental results show that our proposed method is superior to the state-of-the-art methods in most cases.
Chaotic medical image encryption method using attention mechanism fusion ResNet model

With the rapid advancement of artificial intelligence (AI) technology, the protection of patient medical image privacy and security has become a critical concern in current research on image privacy protection. However, traditional methods for encrypting medical images have faced criticism due to their limited flexibility and inadequate security. To overcome these limitations, this study proposes a novel chaotic medical image encryption method, called AT-ResNet-CM, which incorporates the attention mechanism fused with the ResNet model.The proposed method utilizes the ResNet model as the underlying network for constructing the encryption and decryption framework. The ResNet's residual structure and jump connections are employed to effectively extract profound information from medical images and expedite the model's convergence. To enhance security, the output of the ResNet model is encrypted using a logistic chaotic system, introducing randomness and complexity to the encryption process. Additionally, an attention mechanism is introduced to enhance the model's response to the region of interest within the medical image, thereby strengthening the security of the encrypted network.Experimental simulations and analyses were conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method outperforms alternative models in terms of encryption effectiveness, as indicated by a horizontal correlation coefficient of 0.0021 and information entropy of 0.9887. Furthermore, the incorporation of the attention mechanism significantly improves the encryption performance, reducing the horizontal correlation coefficient to 0.0010 and increasing the information entropy to 0.9965. These findings validate the efficacy of the proposed method for medical image encryption tasks, as it offers enhanced security and flexibility compared to existing approaches.In conclusion, the AT-ResNet-CM method presents a promising solution to address the limitations of traditional encryption techniques in protecting patient medical images. By leveraging the attention mechanism fused with the ResNet model, the method achieves improved security and flexibility. The experimental results substantiate the superiority of the proposed method in terms of encryption effectiveness, horizontal correlation coefficient, and information entropy. The proposed method not only addresses the shortcomings of traditional methods but also provides a more robust and reliable approach for safeguarding patient medical image privacy and security.
Dynamic Bayesian Contrastive Predictive Coding Model for Personalized Product Search

In this article, we study the problem of dynamic personalized product search. Due to the data-sparsity problem in the real world, existing methods suffer from the challenge of data inefficiency. We address the challenge by proposing a Dynamic Bayesian Contrastive Predictive Coding model (DBCPC), which aims to capture the rich structured information behind search records to improve data efficiency. Our proposed DBCPC utilizes contrastive predictive learning to jointly learn dynamic embeddings with structure information of entities (i.e., users, products, and words). Specifically, our DBCPC employs structured prediction to tackle the intractability caused by non-linear output space and utilizes the time embedding technique to avoid designing different encoders each time in the Dynamic Bayesian models. In this way, our model jointly learns the underlying embeddings of entities (i.e., users, products, and words) via prediction tasks, which enables the embeddings to focus more on their general attributes and capture the general information during the preference evolution with time. For inferring the dynamic embeddings, we propose an inference algorithm combining the variational objective and the contrastive objectives. Experiments were conducted on an Amazon dataset and the experimental results show that our proposed DBCPC can learn the higher-quality embeddings and outperforms the state-of-the-art non-dynamic and dynamic models for product search.
Topology Abstraction-Based Routing Scheme for Secret-Key Provisioning in Hybrid GEO/LEO Quantum Satellite Networks

Quantum key distribution (QKD) is a promising technique to resist the threat against quantum computers. However, the high loss of quantum signals over a long-distance optical fiber is an obstacle for QKD in the intercontinental domain. In this context, the quantum satellite network is preferred over the terrestrial quantum optical network. Due to the mobility of satellites, the satellite topology is dynamic in the quantum satellite network, which remains a challenge for routing. In hybrid geostationary-earth-orbit (GEO)/low-earth-orbit (LEO) quantum satellite networks, the lack of an efficient scheduling scheme for GEO/LEO satellites also limits the construction of quantum satellite networks. Therefore, this paper provides a topology abstraction-based routing scheme for secret-key provisioning, where the dynamic physical topology is translated into a quasi-static abstracted topology. This scheme contributes to saving the precious secret key resources. In order to improve the success probability of long-distance QKD requests, three novel resource-scheduling heuristic algorithms are proposed in hybrid GEO/LEO quantum satellite networks. Simulation results indicate that the proposed algorithms can improve the success probability of QKD requests by 47% compared to the benchmark.
Selective Query Processing: A Risk-Sensitive Selection of Search Configurations

In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches, and these optimized parameters are then used as the search configuration for all subsequent queries. A better approach, however, would be to adapt the parameters to fit the query at hand. Selective query expansion is one such an approach, in which the system decides automatically whether or not to expand the query, resulting in two possible search configurations. This approach was extended recently to include many other parameters, leading to many possible search configurations where the system automatically selects the best configuration on a per-query basis. One problem with this approach is the system training, which requires evaluation of each training query with every possible configuration. In real-world systems, so many parameters and possible values must be evaluated that this approach is impractical, especially when the system must be updated frequently, as is the case for commercial search engines. In general, the more configurations, the greater the effectiveness when configuration selection is appropriate but also the greater the risk of decreasing effectiveness in the case of an inappropriate configuration selection. To determine the ideal configurations to be used for each query in real-world systems, we have developed a method in which a limited number of possible configurations are pre-selected, then used in a meta-search engine that decides the best search configuration for each query. We define a risk-sensitive approach for configuration pre-selection that considers the risk-reward tradeoff between the number of configurations kept and system effectiveness. We define two alternative risk functions to apply to different goals. For final configuration selection, the decision is based on query feature similarities. We compare two alternative risk functions on two query types (ad hoc and diversity) and compare these to more sophisticated machine learning based methods. We find that a relatively small number of configurations (20) selected by our risk-sensitive model is sufficient to obtain results close to the best achievable results for each query. Effectiveness is increased by about 15% according to the P@10 and nDCG@10 evaluation metrics when compared to traditional grid search using a single configuration and by about 20% when compared to learning to rank documents. Our risk-sensitive approach works for both diversity- and ad hoc oriented searches. Moreover, the similarity-based selection method outperforms the more sophisticated approaches. Thus, we demonstrate the feasibility of developing per-query information retrieval systems, which will guide future research in this direction.
LAVA: Log Authentication and Verification Algorithm

Log files provide essential information regarding the actions of processes in critical computer systems. If an attacker modifies log entries, then critical digital evidence is lost. Therefore, many algorithms for secure logging have been devised, each achieving different security goals under different assumptions. We analyze these algorithms and identify their essential security features. Within a common system and attacker model, we integrate these algorithms into a single (parameterizable) “meta” algorithm called LAVA that possesses the union of the security features and can be parameterized to yield the security features of former algorithms. We present a security and efficiency analysis and provide a Python module that can be used to provide secure logging for forensics and incident response.