Reviewerly/exHarmony huggingface dataset:
A New Active Image Captioning Fusion Strategy

Image captioning aims to describe the content of an image through a textual description including attributes and relationships of detected objects. In remote sensing (RS) community IC is becoming an interesting solution for the study of very high spatial resolution (VHR) images that are characterized by high-level information detail. RSIC systems are developed in a supervised way where annotated samples are needed to train the system. However, obtaining a large amount of annotated samples is time-consuming and costly. To address this issue, in this work we propose an active learning solution to select the most important samples to label and include in the training set with the aim of maintaining the system&#x0027;s accuracy as high as possible while using a few amount of training samples. The most important samples are selected based on decision uncertainty and diversity criteria. Experimental results show that the proposed active learning solution represents a good trade-off between the number of training samples and the accuracy of the system.
A New Artificial Intelligence Recognition Technology Based On Convolutional Neural Networks

With the continuous development of social science and technology, artificial intelligence identification has been widely used and plays a very important role in some special fields. Convolutional neural network has a good effect in image processing, so it is widely used in intelligent recognition scenario. Activation functions can help convolutional neural network better understand and fit complex function models, It is necessary to design an efficient activation function. This paper proposes a new convolutional neural network model based on improved activation function usage patterns, and the performances of three common used activation functions, including sigmoid function, tanh function and relu function, in centralized and decentralized training methods are detailed analyzed respectively. The experiment results show that the effect of repeated training with different activation functions is better than that of single linear rectification function in recognition accuracy and recognition of special cases, and the recognition speed is obviously faster than the traditional model. Furthermore, under the same activation function, when the number of training rounds and the training amount are small, the expected accuracy of centralized training is lower compared with that of decentralized training, but the detection accuracy is improved due to the detection mechanism.
The concept of a pulsed neuromorphic optical system based on a 3D model of an optical neuron

A 3D-model of an artificial optical neuron is proposed for scalable implementation of pulsed neuromorphic optical system.
Edge-Cloud Collaboration for Human Activity Recognition on Multiple Subjects

Multi-subject video analysis is one of the most important problems in the field of visual perception for human activity recognition on multiple subjects nowadays. However, multi-subject video analysis is difficult to achieve real-time performance at the edge due to the limited resources of edge devices and the high complexity of the Convolutional Neural Networks (CNN) model used in this task. The common processing method is to upload the video data to the cloud. However, due to the influence of network bandwidth, the transmission time is not fixed, and the latency cannot be guaranteed. Thus, statically deployed model configurations cannot meet some dynamically changing scenarios. To address these challenges, in this paper, we propose an edge-cloud collaboration processing system for multi-subject video stream analysis, which can dynamically configure and optimize the related configurations according to specific scenarios. Specifically, we provide an adaptive configuration optimization solution based on context awareness for edge devices with limited resources such that multi-subject video stream analysis can be processed completely at the edge. For other complex scenarios, we propose an edge-cloud collaboration method to achieve task segmentation and collaboration to meet the performance requirements of the complex scenarios. Experimental results show that our method can achieve an average accuracy of 91.3% and the latency of less than 78ms with arbitrary runtime state.
Performance study of an IoT-integrated solar tracker

The End-of-Degree Project (EDP) aims to study the improvement in photovoltaic production achieved with a two-axis solar tracker with respect to a fixed panel. To this end, a prototype will be designed and built consisting of two small solar panels, one of them fixed and the other on a two-axis tracker controlled by bus-type servos. To study the energy production, each panel will be connected to an active load, designed for this work, and controlled by a microcontroller. The data obtained will be uploaded to an Internet of Things (IoT) platform.
On the Security of an Optical Layer Encryption Using Coherent-based TF-QKD in Classical Optical Fiber Links

Twin-field quantum key distribution (TF-QKD) protocols have remained an enticing solution because it is able to overcome many of the limitations that are inherently found in QKD. However, the performance of this technology is still not adequate to be implemented for secure transmission of data. A variant of the TF-QKD protocol is Coherent-based Two Field QKD (CTF-QKD), which utilizes coherent states for both transmission and detections, is integratable with current infrastructure, and has similar performance to current classical optical communication systems. This proceeding is a continuation of the preliminary security analysis that was previously published in [1–2]. Key vulnerabilities in the current system are identified in simulation and guidelines to prevent an eavesdropper from obtaining data from a tapping attack. Finally, the practical security of the system is discussed and the challenges for an attacker to accomplish real-time decryption of data transmitted.
Fine Grained Feature Extraction Model of Riot-related Images Based on YOLOv5

With the rapid development of Internet technology, the type of information in the Internet is extremely complex, and a large number of riot contents containing bloody, violent and riotous components have appeared.These contents pose a great threat to the network ecology and national security.As a result, the importance of monitoring riotous Internet activity cannot be overstated.Convolutional Neural Network (CNN-based) target detection algorithm has great potential in identifying rioters, so this paper focused on the use of improved backbone and optimization function of You Only Look Once v5 (YOLOv5), and further optimization of hyperparameters using genetic algorithm to achieve fine-grained recognition of riot image content.First, the fine-grained features of riot-related images were identified, and then the dataset was constructed by manual annotation.Second, the training and testing work was carried out on the constructed dedicated dataset by supervised deep learning training.The research results have shown that the improved YOLOv5 network significantly improved the fine-grained feature extraction capability of riot-related images compared with the original YOLOv5 network structure, and the mean average precision (mAP) value was improved to 0.6128.Thus, it provided strong support for combating riot-related organizations and maintaining the online ecological environment.
The Financial Digital Assets Frontier

The COVID-19 pandemic has disrupted the value chains for all major business sectors, with a great impact on the way we interact, socialize, transact, and trust the systems that we use on a daily basis. The magnitude of this major health crisis imposed a new level of digitalization, to which everyone needed to adapt, with great costs in terms of social, psychological, political, and economic transformation. In order to adapt in a new digital environment, imposed by the pandemic, we witnessed the adoption of decentralized ecosystems and technologies. One of the technologies that stood as foundation for this decentralization movement is blockchain. The first adaptations for blockchain technology happened within the financial services, with the introduction of the financial digital assets or crypto assets. This created the base for concepts like decentralized finance (DeFi), Web 3.0, and the metaverse. This chapter will assess the frontier of financial digital assets, which has emerged and developed the perfect infrastructure for scalability, efficiency, and transparency.
Deep side group sparse coding network for image denoising

Recently, deep learning has made significant progress in image denoising. However, most of existing deep learning based methods are purely data-driven, without considering the knowledge of image denoising. Moreover, the parameters of deep denoising network are not explainable. According to these issues, this paper proposes a deep side group sparse coding network for image denoising, named a side group sparse coding (SGSC)-Net. First, SGSC model for image denoising by exploiting prior information regarding the group sparse coefficients consistency is developed. Specifically, the side information is constructed as the weighted combination of intermediate estimations, and updated iteratively. Then, the optimisation solution of SGSC model is turned into a deep neural network using deep unfolding, that is, SGSC-Net. The computational path of SGSC-Net fully follows the iterations of optimisation solution, and consequently the network parameters are interpretable. Furthermore, the design of SGSC-Net employs the insight of SGSC denoising model. The experimental results on well-known datasets quantitatively and qualitatively demonstrate that SGSC-Net is competitive to existing deep unfolding-based and typical deep neural network-based methods.
A Smart Visual Sensing Concept Involving Deep Learning for a Robust Optical Character Recognition under Hard Real-World Conditions

In this study, we propose a new model for optical character recognition (OCR) based on both CNNs (convolutional neural networks) and RNNs (recurrent neural networks). The distortions affecting the document image can take different forms, such as blur (focus blur, motion blur, etc.), shadow, bad contrast, etc. Document-image distortions significantly decrease the performance of OCR systems, to the extent that they reach a performance close to zero. Therefore, a robust OCR model that performs robustly even under hard (distortion) conditions is still sorely needed. However, our comprehensive study in this paper shows that various related works can somewhat improve their respective OCR recognition performance of degraded document images (e.g., captured by smartphone cameras under different conditions and, thus, distorted by shadows, contrast, blur, etc.), but it is worth underscoring, that improved recognition is neither sufficient nor always satisfactory-especially in very harsh conditions. Therefore, in this paper, we suggest and develop a much better and fully different approach and model architecture, which significantly outperforms the aforementioned previous related works. Furthermore, a new dataset was gathered to show a series of different and well-representative real-world scenarios of hard distortion conditions. The new OCR model suggested performs in such a way that even document images (even from the hardest conditions) that were previously not recognizable by other OCR systems can be fully recognized with up to 97.5% accuracy/precision by our new deep-learning-based OCR model.
Clarence "skip" Ellis

Dr Clarence "Skip" Ellis was the first African-American to receive a Ph.D. in computer science (University of Illinois at Urbana-Champaign, 1969). He is also the first African-American to be elected as a fellow of the ACM for his contributions to computer science, which include 1 :
AI leads in the fight against fraud

As home working and digital commerce escalate, companies are struggling to safeguard their remote customers and staff from cyber-fraud. Nuance's Simon Marchand outlines the solution that voice and behavioural biometrics can offer.
Assessing the Fidelity of Seismic Records from Microfilm and Paper Media

Abstract During the analog era of seismology, seismograms were typically written to paper media. The legacy records from this era make up an irreplaceable resource that covers over a century, and many of them remain extant in both their original paper form and as microformat copies. While microformat copies of seismograms drove decades of seismological research, new capabilities have been developed that allow us to revisit the analog data sets and extend application of contemporary analyses to a considerably longer seismological record. Though microformat seismogram copies have been extensively used, we seek to understand possible differences that could arise from using data recovered from such copies compared to using the original seismograms. Identification of these differences provides understanding of the fidelity of information that is recoverable from these different media. We present a case study here, applying a number of quantitative measures to a pair of records (original paper and its 35 mm microfilm copy), and revealing that microfilm-derived time series differ from the original paper in timing and amplitude. Based on a simple analysis of this pair of short-period vertical records, we estimate uncertainties of ±0.1–0.3 for earthquake magnitude (hence, a factor of about 1.3–2.0 in energy) and ±10 km for hypocentral location.
Deep Learning-Based Named Entity Recognition System Using Hybrid Embedding

Retrieval of meaningful information out of voluminous data available on the internet is a big challenge these days. Named entity recognition system deals with this challenge efficiently and achieves promising results in accessing information of interest in several NLP applications. The present study proposes a deep learning-based named entity recognition system using hybrid embedding which is the combination of fasttext and bidirectional LSTM based character embedding. These embeddings capture the contextual, syntactic and semantic properties of the text which improves the cognitive power of deep learning methods. We have performed different experiments with important variants of recurrent neural network (RNN) namely long short-term memory network (LSTM) and bidirectional LSTM as well as gated recurrent unit (GRU) and bidirectional GRU on manually annotated Punjabi dataset and annotated Hindi dataset collected from international joint conference on natural language processing (IJCNLP-08) website. We have also explored different word embeddings and character embeddings for named entity recognition task. Out of all the experiments, the bidirectional GRU model using hybrid embedding has outperformed with precision, recall, and f-score values of 84%, 83%, and 83.50% respectively for Punjabi named entity recognition and 75%, 77%, 75.99% respectively for Hindi named entity recognition.
Saddlepoint approximation for weighted log-rank tests based on block truncated binomial design

Clustered data frequently occur in biomedical research fields and clinical trials. The log-rank tests are widely used for two-independent samples of clustered data tests. The randomized block design and truncated binomial design are used for forcing balance in clinical trials and reducing selection bias. In this paper, survival clustered data are randomized by generalized randomized block, and subsequently clustered data in each block are randomized by truncated binomial design. Consequently, the p-values of the null permutation distribution of log-rank tests for clustered data are approximated via the double saddlepoint approximation method. Comprehensive numerical studies are carried out to assess the accuracy of the saddlepoint approximation. This approximation has a great accuracy over the asymptotic normal approximation.
Analysis of Machine Translation and Post-Translation Editing Ability Using Semantic Information Entropy Technology

Large-scale corpus application has presented MT with new opportunities as well as challenges in recent years. This study investigates MT and post-translation editing capability using AI technology. The grammar rules of the target language are first examined. Then, a significant amount of data on semantic information entropy are projected, and the semantic Gaussian marginal rectangular window function is obtained. The semantic correlation factors of words are added to the text information entropy and information gain, and the nonlinear spectral properties of adaptive matching semantics are obtained. In this way, it corrects the significant flaw in the way semantic features are extracted using conventional techniques. In order to speed up MT and enhance translation quality, this study proposes automatic post-translation editing to filter those common MT errors that occur frequently and regularly. According to the experimental findings, word translation and segmentation accuracy can both reach 95.27 and 93.12 percent, respectively. In terms of language translation, this approach is accurate and trustworthy. I hope it will serve as a useful source for subsequent research.
Large-scale complex ontology matching through anchor-based semantic partitioning technique and confidence matrix based evolutionary algorithm

Ontology presents a field knowledge by defining the meaning of domain concepts and relations among them. To conduct the inter-operability between two heterogeneous ontologies, we need to determine a number of entity correspondences, the so-called Ontology Matching (OM). Traditional OM techniques dedicate to find the simple correspondences which cannot sufficiently express the diverse kinds of heterogeneity, and therefore, complex correspondences are required, which maps one or multiple entities from one ontology to those from the other. Due to the semantic complexity, automatically discovering large-scale simple and complex correspondences has become a challenge. To face this challenge, in this work, an Anchor-based Semantic Partitioning Technique (ASPT) is first presented to divide the large-scale ontologies into several semantic segments, whose computational complexity is lower than the existing ontology partitioning algorithms. Then, a Pattern-based Similarity Measure (PSM) is used to distinguish both simple and complex correspondences. Finally, a Confidence Matrix based Evolutionary Algorithm (CM-EA) is proposed to efficiently match the ontology segments. We compare our proposal with EA-based matching technique and state-of-the-art ontology matching systems on OAEI's Complex track, and the experimental results show that our approach is able to efficiently match large-scale complex ontologies.
Fusing visual and textual content for knowledge graph embedding via dual-track model

Large-scale knowledge graphs are usually incomplete. Knowledge graph embedding has achieved encouraging performance in alleviating the incompleteness of knowledge graphs. There are approaches to leverage the multi-modal content, such as text description and images, to improve the performance of knowledge graph embedding. However,due to the heterogeneity across different modalities, current methods are not effective to fuse the multi-modal content and network structure information to learn the embedding. In this work, a dual-track model DuMF for knowledge graph embedding enhancement is proposed. The model includes two tracks to fuse multi-modal content and network structure respectively. In each track, the expressiveness of joint features is improved by the bilinear method, and meanwhile the task-specific important features are learned by deliberate attention. Finally, the fused features are generated by the gating network. To extensively evaluate the model, two challenging datasets are enriched with additional multi-modal data. Experimental results show that DuMF is superior to baselines in link prediction. The flexibility of the model is promising for the further improvement of model performance.
Big Data Logistics Service Supply Chain Innovation Model Based on Artificial Intelligence and Blockchain

Blockchain technology is widely used in various fields according to its programmable advantages. Such technology has several typical characteristics, such as openness, automation, independence, security, sharing, and modularity. The logistics system is greatly beneficial from such characteristics. Therefore, blockchain technology can provide core technical support for the construction of logistics supply chain system. In this paper, we focus on the current demands of “blockchain + logistics” business system and reduce half risks in the supply chain through decentralization and detrusting of blockchain technology. Finally, experimental results show that it has a certain practical significance for the adjustment of the strategic layout and sound development of supply chain logistics.
Palladiferous placer gold from the Paraíba do Sul River in northern Rio de Janeiro (Brazil) and its geological implications

Placer gold recovered from the Paraíba do Sul River, which flows along a high-grade shear zone in northern Rio de Janeiro, Brazil, has a distinctive component of palladiferous gold. Its finding, reported for the first time in the state of Rio de Janeiro, implies a hydrothermal system of high salinity at relatively low temperatures, in which metal transport is facilitated by hematite-rich rocks and generally precipitated at redox fronts. Such a hydrothermal system is not geologically manifested in the currently exposed high-grade shear zone. The finding of palladiferous gold suggests that its source was eroded during periods of increased denudation rates since the Lower Cretaceous, as indicated by apatite fission-track (AFT) data that have been reported for the region.
Reducing the complexity of candidate selection using Natural Language Processing

The recruitment industry is facing major challenges in terms of selecting candidates who best fit a vacancy. Artificial Intelligence, Machine Learning, and Natural Language Processing, although they have greatly increased the processing of mass data for candidates, are not without limitations. This paper presents a hybrid solution to candidate selection with a job title that is fitting for an open vacancy. This hybrid approach is a combination of two Machine Learning techniques, XGBoost and BERTopic. This solution reduces processing time and memory usage. The definition of job title similarity in the context of similar industries provides higher accuracy to the complex and delicate process such as candidate selection fitting for an open vacancy.
Satellite Maneuver Detection Using Machine Learning and Neural Network MethodsBehaviors

This study explores methods to improve the accuracy of satellite maneuver detection by comparing traditional statistical methods with machine learning and neural network-based methods. The two alternative methods explored are a Random Forest algorithm and a Deep Neural Network. Simulation data is generated and appropriate features are selected and computed. Data is split into training and validation. Next, alternative methods are trained and their performance is validated. Optimization techniques are then applied to achieve better results. Finally, the three methods are compared to each other. Conclusions and future work are described.