Reviewerly/exHarmony huggingface dataset:
Improved Chicken Swarm Optimizer with Vision-based Anomaly Detection on Surveillance Videos for Visually Challenged People

Deep learning technique has been efficiently used for assisting visually impaired people in different tasks and enhancing total accessibility. Designing a vision-based anomaly detection method on surveillance video specially developed for visually challenged people could considerably optimize awareness and safety. While it is a complex process, there is potential to construct a system by leveraging machine learning and computer vision algorithms. Anomaly detection in surveillance video is a tedious process because of the uncertain definition of abnormality. In the complicated surveillance scenario, the types of abnormal events might co-exist and are numerous, like long-term abnormal activities, motion and appearance anomaly of objects, etc. Conventional video anomaly detection techniques could not identify this kind of abnormal action. This study designs an Improved Chicken Swarm Optimizer with Vision-based Anomaly Detection (ICSO-VBAD) on surveillance videos technique for visually challenged people. The purpose of the ICSO-VBAD technique is to identify and classify the occurrence of anomalies for assisting visually challenged people. To obtain this, the ICSO-VBAD technique utilizes the EfficientNet model to produce a collection of feature vectors. In the ICSO-VBAD technique, the ICSO algorithm was exploited for the hyperparameter tuning of the EfficientNet model. For the identification and classification of anomalies, the adaptive neuro fuzzy inference system model was utilized. The simulation outcome of the ICSO-VBAD system was tested on benchmark datasets and the results pointed out the improvements of the ICSO-VBAD technique compared to recent approaches with respect to different measures.
Performing live time-traversal queries on RDF datasets

Guest post by Arcangelo Massari, University of Bologna &lt;em&gt;In this post, Arcangelo Massari, who recently graduated in Digital Humanities and Digital Knowledge under Professor Silvio Peroni at the University of Bologna, shares the results of his master thesis.&lt;/em&gt; A particular problem in information retrieval is that of obtaining data from an evolving dataset, independent of the time at which that item of data was added, changed or removed.
A new type of polytomous surmise system

Doignon and Falmagne (1985) introduced a surmise system, which generalized the precedence relation, allowing multiple possible learning paths for an item. Heller (2021) took into account precedence relations on an extended set of (virtual) items and further generalized quasi-ordinal knowledge spaces to polytomous items. Wang et al. (2022) proposed CD-polytomous knowledge space and provided its corresponding polytomous surmise system. Following these developments and drawing upon the so-called extended polytomous knowledge structure, this paper presents two concepts: weak polytomous structure and extended surmise system. Via setting up a Galois connection, a one-to-one correspondence is established between the collection of all extended surmise functions and the collection of certain weak polytomous structures. This paper also comprehensively discusses the relationships among the precedence relations, the polytomous surmise systems, and the extended surmise systems.
Conscious and unconscious memory and eye movements in context-guided visual search: A computational and experimental reassessment of Ramey, Yonelinas, and Henderson (2019)

Are eye movements unconsciously guided towards target locations in familiar scenes? In a recent eyetracking study, Ramey, Yonelinas, and Henderson (2019) measured eye-movement efficiency (scanpath ratio) and memory judgments when participants searched for targets in repeated and novel scenes. When trials judged new with high confidence were selected, scanpath ratio was lower for old scenes (misses) than for new scenes (correct rejections). In addition, familiarity as measured by recognition confidence did not significantly predict scanpath ratio. Ramey et al. attributed these results to unconscious learning guiding eye movements. In a re-assessment of Ramey et al.’s data, we show that their findings can be accounted for by a single-system computational model in which eye movements and memory judgments are driven by a common latent memory representation. In particular, (a) the scanpath ratio difference between high-confidence misses and correct rejections is a consequence of regression to the mean, while (b) the null correlation between familiarity and scanpath ratio, partly a natural consequence of the low reliability of the scanpath ratio measure, is also reproduced by the model. Two pre-registered experiments confirm a novel prediction of the alternative single-system model. This work offers a parsimonious account of Ramey et al.’s findings without recourse to unconscious guidance of eye movements.
Semantic Contrastive Bootstrapping for Single-Positive Multi-label Recognition

Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets. Existing literature mainly focuses on label completion and co-occurrence learning while facing difficulties with the most common single-positive label manner. To tackle this problem, we present a semantic contrastive bootstrapping (Scob) approach to gradually recover the cross-object relationships by introducing class activation as semantic guidance. With this learning guidance, we then propose a recurrent semantic masked transformer to extract iconic object-level representations and delve into the contrastive learning problems on multi-label classification tasks. We further propose a bootstrapping framework in an Expectation-Maximization fashion that iteratively optimizes the network parameters and refines semantic guidance to alleviate possible disturbance caused by wrong semantic guidance. Extensive experimental results demonstrate that the proposed joint learning framework surpasses the state-of-the-art models by a large margin on four public multi-label image recognition benchmarks. Codes can be found at https://github.com/iCVTEAM/Scob .
Fuzzy Local Information C -Means based clustering and Fractional Dwarf Mongoose optimization enabled deep learning for relevant document retrieval

Document Retrieval (DR) needs an innovative model to rank and retrieve documents based on their relevancy with respect to some questions that requires strong text understanding capability. The prime motive of documents retrieval is to search the relevant documents that satisfy the user's questions. However, it is a complex process because it means the natural language textual content based on the syntax, context and semantics. Conventional techniques for listing files prefer typical word and sentence encrypting to create constant length document abiding. However, the widely used bag-of-words (BoW) method failed to integrate the signify context, which is a crucial problem to compreh end the document-query relevancy. In order to overcome such issues, deep neural networks (DNNs) have been put forward to arrange search outcomes with respect to user's questions. Here, a unified solution is provided to perform relevant document retrieval using Dwarf Mongoose Optimization Fractional-based Deep Convolutional Neural network (DMOF-Deep CNN). Here, the textual content processing is done based on BERT tokenization and feature term extraction. Moreover, the cluster based indexing by elastic search is accomplished using Fuzzy Local Information C-Means (FLICM) clustering and dice coefficient is employed to perform the query matching. Finally, re-ranking based document retrieval is conducted in terms of deep CNN, which is trained using designed DMOF. In addition, the designed DMOF-Deep CNN has outperformed other existing models by delivering maximum precision of 0.854, recall of 0.913, F1-score of 0.882.
Research on Traceability Management Method of Power Grid Engineering Cost Data Based on Blockchain Technology

With the continuous advancement of the “dual carbon goals” and the rapid development of new energy, the construction of new power grid projects is also gradually increasing. Power grid projects have a large geographical span, complex environment and long cycle, and involve civil engineering, electrical, communication and other factors, making cost management very complex. This paper proposes a traceability management method of power grid project cost based on blockchain technology, which can supervise various project cost data and discover data problems in time. The block chain traceability framework of power grid project cost is established, the traceability process is studied, and the three-layer distributed storage structure is constructed. According to the characteristics of power grid engineering cost business, the improved B-M traceability index optimization algorithm and parallel implementation method are proposed. The evaluation of the traceability performance of power grid project cost data is studied, and the traceability time can be greatly reduced from spatio-temporal dimensions. The analysis of engineering examples shows that the traceability method based on block chain can obtain the required reliable cost data system flexibly, and improve the traceability efficiency.
Super Resolution Reconstruction of Infrared Images of Power Equipment Based on Wasserstein Distance and Residual Network

Infrared photoelectric technology plays an increasingly important role in the state detection of power systems. For low resolution infrared image of power system, a super-resolution reconstruction method of infrared images based on residual network and Wasserstein distance is proposed. Based on the super-resolution reconstruction model of generative countermeasure network, long and short connection residual network blocks are added to the generative network to improve the receptive field in the sampling process and reconstruct clearer texture details. The loss function is the weighted sum of the traditional mean square loss, Wasserstein distance and perceived loss. The mean square error loss ensures the stability of the training process, and the Wasserstein distance ensures that the model will not have the problem of gradient disappearance from the training process. Perceptual loss ensures that the reconstructed image details are more complete. The results show that by improving the loss function and the generator network, the overall super-resolution reconstruction effect of the image is not only improved in the subjective visual effect. Compared with the image before reconstruction, the peak signal-to-noise ratio (PSNR) is improved by an average of 11.927 dB, the structural similarity (SSIM) is improved by 0.112 dB on average, which has significant technical application value.
Risk Management of Power Grid Project Cost Data Based on Block Chain

The construction cost of power grid varies frequently due to the complex geographical location, long span distance and long cycle, and the influence of external environment, market and human stochastic factors. This paper proposes the reliability evaluation and management method of power grid construction cost based on block chain. The characteristics of block chain can effectively overcome the problems of incomplete, authenticity, process control and traceability of cost data, and the data chain of power grid construction cost is built. According to the variable characteristics of cost data, the sensitivity model for reliability analysis of cost data is established, which can determine the influence degree of random factors on cost change. The reliability differential management flowchart of power grid project cost data based on block chain is proposed. The improved differential evolution algorithm can reduce the overall cost fluctuation risk level, perform different data management for different project costs. The analysis of engineering examples shows that the proposed method can improve the reliability of the cost data, proactively respond to the risk of cost changes, and improve the economy of the whole power grid project.
Design and Implementation of Intelligent File Management Product Based on Internet of Things Technology

In the daily file management, there are still manual records to achieve the division and management of employee files. Relying on manual management, the efficiency is low, and the life cycle of paper documents is difficult to control, and the security is extremely poor. Therefore, through the development of an integrated file management system based on the Internet of things technology, take use of RFID file management products (RFID tags of SM7 algorithm, RFID intelligent file racks, RFID intelligent file cabinets, high security access doors for file identification, file entry and exit workstations), sensor products (multi-sensor fusion equipment, temperature and humidity wireless sensor, air quality monitoring sensor, gas sensor and water immersion wireless sensor) to realize highly intelligent and digital management of the files. We will improve the security of file management, the intelligence of file management, and the security level of dynamic environment in an all-round way, build a solid security line, strengthen the security mechanism for information development, strengthen the construction of digital files, and promote the integration of sharing platforms.
Dynamic Context Removal: A General Training Strategy for Robust Models on Video Action Predictive Tasks

Abstract Predicting future actions is an essential feature of intelligent systems and embodied AI. However, compared to the traditional recognition tasks, the uncertainty of the future and the reasoning ability requirement make prediction tasks very challenging and far beyond solved. In this field, previous methods usually care more about the model architecture design but little attention has been put on how to train models with a proper learning policy. To this end, in this work, we propose a simple but effective training strategy, Dynamic Context Removal (DCR), which dynamically schedules the visibility of context in different training stages. It follows the human-like curriculum learning process, i.e., gradually removing the event context to increase the prediction difficulty till satisfying the final prediction target. Besides, we explore how to train robust models that give consistent predictions at different levels of observable context. Our learning scheme is plug-and-play and easy to integrate widely-used reasoning models including Transformer and LSTM, with advantages in both effectiveness and efficiency. We study two action prediction problems, i.e., Video Action Anticipation and Early Action Recognition. In extensive experiments, our method achieves state-of-the-art results on several widely-used benchmarks.
A Unique Approach for Performance Analysis of a Blockchain and Cryptocurrency based Carbon Footprint Reduction System

Blockchain technology is commonly used as a replicated and distributed database in different areas. In this paper, a smart home blockchain network connects smart homes through smart devices for reducing carbon footprint and thereby earning bitcoin value in the network. The network is composed of different smart homes interconnected with smart devices. The user makes a transaction request through the network layer and matches the user’s activity with the reward table located at the incentive layer to estimate the bitcoin value. Furthermore, the miner verifies the transaction and sends the bitcoin value to the user, and adds the respective block to the network structure. The optimal parameter used to estimate the bitcoin value is computed using the proposed Improved Invasive Weed Mayfly Optimization (IIWMO) algorithm. The developed method attained higher performance with the metrics, like coins earned, Annual Carbon Reduction (ACR), and fitness as 0.00357BTC, 23.891, and 0.6618 for 200 users. For 200 users the fitness obtained by the proposed method is 14.41%, 16.68%, and 11.68% higher when compared to existing approaches namely, Without optimization, IIWO, and MA, respectively.
Moving vehicle detection in satellite videos by improved ViBe algorithm

The development of video satellite technology has provided new and reliable data for detecting moving objects. This study proposes a method for detecting moving vehicles in satellite videos based on an improved ViBe algorithm. Based on the spatial distribution characteristics of moving vehicles, the detection area in the video was divided into two parts. Next, the DeeplabV3+ semantic segmentation model was used to automatically extract the road area to remove the interference of various influencing factors outside the road. The detection rate of the ViBe object-detection algorithm for moving regions is improved by setting an adaptive dynamic foreground discrimination threshold. The proposed scale-centre distance constraint method was then used to suppress the interference of false moving targets on the road and solve the problem of large statistical errors caused by the stickiness of the adjacent vehicle targets. Furthermore, the validity of the proposed method is verified using the satellite image data acquired by ‘Jilin-1’ and ‘Skysat-1’.
Factoring textual reviews into user preferences in multi-criteria based content boosted hybrid filtering (MCCBHF) recommendation system

Recommendation systems help customers to find interesting and valuable resources in the internet services. Their priority is to create and examine users’ individual profiles, which contain their preferences, and then update their profile content with additional features to finally increase the users’ satisfaction. Specific characteristics or descriptions and reviews of the items to recommend also play a significant part in identifying the preferences. However, inferring the user’s interest from his activities is a challenging task. Hence it is crucial to identify the interests of the user without the intervention of the user. This work elucidates the effectiveness of textual content together with metadata and explicit ratings in boosting collaborative techniques. In order to infer user’s preferences, metadata content information is boosted with user-features and item-features extracted from the text reviews using sentiment analysis by Vader lexicon-based approach. Before doing sentiment analysis, ironic and sarcastic reviews are removed for better performance since those reviews inverse the polarity of sentiments. Amazon product dataset is used for the analysis. From the text reviews, we identified the reasons that would have led the user to the overall rating given by him, referred to as features of interest (FoI). FoI are formulated as multi-criteria and the ratings for multiple criteria are computed from the single rating given by the user. Multi-Criteria-based Content Boosted Hybrid Filtering techniques (MCCBHF) are devised to analyze the user preferences from their review texts and the ratings. This technique is used to enhance various collaborative filtering methods and the enhanced proposed MCKNN, MCEMF, MCTFM, MCFM techniques provide better personalized product recommendations to users. In the proposed MCCBHF algorithms, MCFM yields better results with the least RMSE value of 1.03 when compared to other algorithms.
A novel deep learning based underwater image de-noising and detecting suspicious object

Exploration of underwater resource play a vital role for nation development. Underwater surveillance systems play a crucial role in security applications, requiring accurate detection of suspicious objects in underwater images. However, the presence of noise, poor visibility, and uneven lighting conditions in underwater environments pose significant challenges for reliable object detection. This work proposes an integrated approach for underwater image de-noising, pre-processing, enhancement, and subsequent suspicious object detection by combining the DnCNN (Deep Convolutional Neural Network), CLAHE (Contrast Limited Adaptive Histogram Equalization), and additional image enhancement techniques. In addition to de-noising and pre-processing, it incorporate various image enhancement techniques to further improve object detection performance. These techniques include color correction, contrast adjustment, and edge enhancement, aiming to enhance the visual characteristics and saliency of suspicious objects in underwater images. To evaluate the effectiveness of proposed approach, this work conducted extensive experiments on an underwater image dataset containing diverse scenes and suspicious objects. The work compares proposed method with existing de-noising, preprocessing, and object detection techniques, analyzing the results using quantitative performance metrics, including precision, recall, and F1 score. The experimental results demonstrate that proposed integrated approach outperforms individual methods and achieves superior detection performance by enhancing the quality of underwater images and improving the visibility of suspicious objects.
Enhancing QoE predictions with complex neural networks and diverse datasets

Quality of Experience (QoE) is a critical aspect of multimedia applications, which directly impacts user satisfaction and adoption. QoE predictions are used to optimize various parameters such as video quality, bitrate, and network bandwidth to enhance the user experience. However, accurate QoE prediction is a challenging task, as it involves various factors such as network conditions, video content, and user preferences. Therefore, there is a need for enhancing QoE predictions with advanced techniques to improve user satisfaction and adoption. This paper proposes incorporating more complex neural network architectures and using more diverse datasets to improve the accuracy and generalization of Quality of Experience (QoE) predictions. The paper suggests experimenting with more advanced architectures such as convolutional neural networks and recurrent neural networks, which have been shown to be effective in various applications. Additionally, the paper highlights the limitation of using a single dataset and proposes using more diverse datasets that capture different types of video content and network conditions. Enhancing QoE predictions with complex neural networks and diverse datasets include improved accuracy, better generalization, more sophisticated models, enhanced user satisfaction and increased adoption. These enhancements are expected to lead to more accurate and reliable QoE predictions, which are crucial for improving user experience in multimedia applications.