Reviewerly/exHarmony huggingface dataset:
Multimode Fabry-Perot laser as a reservoir computing and extreme learning machine photonic accelerator

Abstract In this work, we introduce Fabry–Perot lasers as neuromoprhic nodes in the context of time-delayed reservoir computing and extreme learning machine (ELM) for the processing of temporal signals and the high-speed classification of images. By exploiting the multi-wavelength emission capabilities of the Fabry–Perot lasers, additional processing nodes can be introduced, thus raising the computational power without sacrificing processing speed. An experimental validation of this concept using a Fabry–Perot ELM is presented targeting a time depedent task such as channel equalization for a 50 km 28 Gbaud ‘PAM-4’ transmission, offering hard-decision forward error correction compatible performance. Additionally, the Fabry–Perot neuromorphic concept has been further strengthened by modifying the data entry technique by parallelelly assigning different samples of the input signal to different modes so as to significantly reduce speed penalty. Numerical simulations revealed that this alternative data insertion technique can offer a reduction of the processing delay and physical footprint by 75% compared to the conventional approach assigning the same symbols to all Fairy–Perot modes. Moreover, by using a similar data processing scheme in ‘MNIST’ image classification task we were able to numerically achieve a processing speed of 255.1 Mimages s −1 and a classification accuracy up to 95.95%.
Parallel encoder–decoder framework for image captioning

Recent progress in deep learning has led to successful utilization of encoder–decoder frameworks inspired by machine translation in image captioning models. The stacking of layers in encoders and decoders has made it possible to use several modules in encoders and decoders. However, just one type of module in encoder or decoder has been used in stacked models. In this research, we propose a parallel encoder–decoder framework that aims to take advantage of multiple of types modules in encoders and decoders, simultaneously. This framework contains augmented parallel blocks, which include stacking modules or non-stacked ones. Then, the results of the blocks are integrated to extract higher-level semantic concepts. This general idea is not limited to image captioning and can be customized for many applications that utilize encoder–decoder frameworks. We evaluated our proposed method on the MS-COCO dataset and achieved state-of-the-art results. We got 149.92 for CIDEr-D metric outperforming state-of-the-art image captioning models.
Climate uncertainty effects on bitcoin ecological footprint through cryptocurrency environmental attention

In this study, we investigate the dynamic relationship between physical climate risks and Bitcoin's energy consumption and carbon footprint, focusing on cryptocurrency environmental performance. Our research represents the first endeavor to examine the predictive role of natural disasters and global warming in shaping cryptocurrency environmental attention and Bitcoin's ecological footprint, utilizing the Bayesian TVP-SVAR-SV model. The results reveal a significant increase in Bitcoin's energy usage before 2018 and emphasize the crucial impact of cryptocurrency environmental attention in mitigating Bitcoin's carbon and energy footprint. These findings carry substantial policy implications for both investors and policymakers.
Emotion quantification and classification using the neutrosophic approach to deep learning

Advancements in the rapidly evolving specialization of deep learning have aided in improving several natural language understanding tasks. Sentiment and emotion classification models have improved, but when it comes to fine-grained sentiment analysis, these models can perform better. Human sentiment in natural language is generally an intricate combination of emotions, which can sometimes be indeterminate, neutral, or ambiguous. In the case of fine-grained sentiment analysis, the sentiments can be very similar to each other and interconnected, e.g., anger and fear. Most deep learning systems try to solve the problem of fine-grained sentiment analysis as a classification problem. However, fine-grained sentiments might combine similar emotions with one primary emotion. Trying to solve the problem as a classification task can result in better performance on benchmarks but does not ensure a better understanding and representation of language. The proposed work explores applying neutrosophy for fine-grained sentiment analysis using large language models. Neutrosophy identifies neutralities and employs membership functions (neutral, positive, negative) to quantify an instance into Single Valued Neutrosophic Sets (SVNS). This paper introduces Refined Emotion Neutrosophic Sets (RENS) for emotions (with four emotions) and Refined Ekman’s Emotion Neutrosophic Sets (REENS) with seven emotions. In this paper, refined neutrosophic sets with membership functions are employed for each sentiment across a given taxonomy and assigned their values using the Neutrosophic Iterative Neural Clustering (NINC) algorithm proposed in this paper. It facilitates not only classifying sentiments but also quantifying the presence of each sentiment present in a given sample. It aids in better understanding and representation of samples across multiple sentiments, as in fine-grained sentiment analysis, experiments are performed on the GoEmotions dataset. The proposed approach performs on par with cross-entropy deep learning classifiers and is reproducible across different pre-trained language models.
Research and implementation of a personnel management system for private universities in China based on Java

The personnel management system in universities is playing an increasingly important role in the management of university teachers. However, traditional personnel management systems only focus on the collection of basic information and simple statistics of tables, there is a lack of research and practice on the personalized needs by universities, especially teacher performance management. Based on the personnel management system of a university, this paper summarizes the needs of the personnel management system and the needs of teachers' performance management, carries out theoretical analysis and system design, and finally implements the system based on Java technology, Spring Framework, Mybatis and other technologies. The practical results show that the system functions well to meet the needs of the university for personnel system management.
Research on sentiment analysis method of opinion mining based on multi-model fusion transfer learning

Abstract With the popularity of social media, opinion mining has gradually become a popular research field. Among these fields, sentiment analysis mining is an important research direction in the field of opinion mining. It aims to reveal the public's sentiment tendency, and attitude towards specific topics or events by analyzing text data generated by users on online platforms and digital media. However, the large amount of opinion data usually lacks effective annotation, which limits the learning and construction of opinion models. Therefore, focusing on the problem of the scarcity of labeled data in opinion analysis, this paper proposes a mining method for public opinion sentiment analysis based on multi-model fusion transfer learning, that can make full use of the limited labeled data to improve the learning efficiency of sentiment features by integrating the advantages of different models. Additionally, it introduces a transfer learning strategy to enable the models of the target domains to perform better in the absence of labeled data. Furthermore, the attention mechanism is combined to strengthen the acquisition of key features of the emotional colors and improve the accuracy of sentiment analysis. Specifically, the paper uses the ERNIE model to generate dynamic representations of the text word vectors in the dataset. It also uses TextCNN and BiGRU to construct a joint model for extracting local and overall features of the text word vectors. The parameters of the feature layer of the trained model are migrated to the target domain through transfer learning. The attention mechanism is combined with the model to identify the extreme elements of the sentiment. Finally, the local and overall features are fused to achieve comprehensive mining of public opinion and emotional information. This method can effectively improve the accuracy and generalization of public opinion analysis in cases of data scarcity. In the experimental part, the paper conducts comparisons and analyses in eight aspects: word embedding model, model combination, attention mechanism, transfer learning, source domain dataset, target domain dataset, model training, and baseline model. The four indicators, namely accuracy, precision, recall, and F1-measure are used to evaluate the performance of the method. The experiments are thorough and detailed, demonstrating the effective improvement of opinion mining performance.
Simultaneous three-dimensional deformation measuring based on a simple phase-shift device combination with tri-wavelength and three-CCD camera

A simple and effective phase-shift device is designed for the simultaneous and high-accuracy measurement of three-dimensional (3D) deformation using tri-wavelength electronic speckle pattern interferometry and a three-sensor charge-coupled device camera. The designed phase-shifter can provide tri-wavelength lasers quasi-π / 2 phase-step simultaneously. Multi-component phases related to 3D deformation information are extracted by a 90 deg step 4-frame technique from the separated red, green, and blue channels of color images. Six derivative components are further determined based on the retrieval phase maps. Experimental results demonstrate the validity and feasibility of the proposed method.
Accelerating relaxation through Liouvillian exceptional point

We investigate speeding up of relaxation of Markovian open quantum systems with the Liouvillian exceptional point (LEP), where the slowest decay mode degenerates with a faster decay mode. The degeneracy significantly increases the gap of the Liouvillian operator, which determines the time scale of such systems in converging to stationarity and thus accelerates the relaxation process. We explore an experimentally relevant three-level atomic system whose eigenmatrices and eigenspectra are obtained completely analytically. This allows us to gain insights into the LEP and examine the respective dynamics with details. We illustrate that the gap can be further widened by Floquet engineering, which further accelerates the relaxation process. Finally, we extend this approach to analyze laser cooling of trapped ions, where vibrations (phonons) couple to the electronic states. An optimal cooling condition is obtained analytically, which agrees with both existing experiments and numerical simulations. In this paper, we provide analytical insights into understanding LEP as well as controlling and optimizing the dissipative dynamics of atoms and trapped ions.
SC-YOLOv8: A Security Check Model for the Inspection of Prohibited Items in X-ray Images

X-ray package security check systems are widely used in public places, but they face difficulties in accurately detecting prohibited items due to the stacking and diversity of shapes of the objects inside the luggage, posing a threat to personal safety in public places. The existing methods for X-ray image object detection suffer from low accuracy and poor generalization, mainly due to the lack of large-scale and high-quality datasets. To address this gap, a novel large-scale X-ray image dataset for object detection, LSIray, is provided, consisting of high-quality X-ray images of luggage and objects of 21 types and sizes. LSIray covers some common categories that were neglected in previous research. The dataset provides more realistic and rich data resources for X-ray image object detection. To address the problem of poor security inspection, an improved model based on YOLOv8 is proposed, named SC- YOLOv8, consisting of two new modules: CSPnet Deformable Convolution Network Module (C2F_DCN) and Spatial Pyramid Multi-Head Attention Module (SPMA). C2F_DCN uses deformable convolution, which can adaptively adjust the position and shape of the receptive field to accommodate the diversity of targets. SPMA adopts the spatial pyramid head attention mechanism, which can utilize feature information from different scales and perspectives to enhance the representation ability of targets. The proposed method is evaluated through extensive experiments using the LSIray dataset and comparisons with the existing methods. The results show that the method surpasses the state-of-the-art methods on various indicators. Experimenting using the LSIray dataset and the OPIXray dataset, our SC-YOlOv8 model achieves 82.7% and 89.2% detection accuracies, compared to the YOLOv8 model, which is an improvement of 1.4% and 1.2%, respectively. The work not only provides valuable data resources, but also offers a novel and effective solution for the X-ray image security check problem.
Interactive teaching methods as a means of successful student learning activity

Interactive teaching methods are the alternative of educational activity in modern changes of socio-economic life of the country during a global pandemic. There is a gradual departure from traditional teaching methods, associated with the rapid development of various technologies that can be used in the educational process. The aim of the article is to study the efficiency of using interactive teaching methods of schoolchildren at the lessons of geography in 6-7 classes. Main material. Interactive methods are aimed at developing skills of independent processing of new information, abilities and skills that help in adapting to complex, fleeting and unexpected situations. Today, there are many diverse accessible sources of knowledge, which provide new opportunities for learning. Thus, these teaching methods allow us to work out conditions for successful learning, assimilation of information, positive emotions, motivation for educational activities, formation of group, team- work skills, directions of schoolchildren’s activities. It is established that mastering of geography knowledge by students is more effective during the solution to problem searching situations. Thus, such approaches to learning can be the basis of interactive technologies that make learning accessible, successful and interesting. The student community acquires the most valuable skills: the ability to overcome difficulties, analyze them and look for ways to further implementation. The analysis of the pedagogical experiment allowed us to establish the advantages and disadvantages of using different interactive teaching methods. The advantages include active learning activities of students, encouraging them to understand and use information, developing creative opportunities, further practical activities, the disadvantages - the technical feasibility of implementing these methods into the educational process, low computer and information literacy of the participants. Conclusions. Interactive teaching methods are aimed at the formation of a high level of knowledge, self-study and selforganization of educational activities. The most convenient and used are multimedia teaching methods, the use of project technologies, debate, peer review and discussion.
Operating with Quantum Integers: An Efficient ‘Multiples of’ Oracle

Quantum algorithms are a very promising field. However, creating and manipulating these kind of algorithms is a very complex task, specially for software engineers used to work at higher abstraction levels. The work presented here is part of a broader research focused on providing operations of a higher abstraction level to manipulate integers codified as a superposition. These operations are designed to be composable and efficient, so quantum software developers can reuse them to create more complex solutions. Specifically, in this paper we present a 'multiples of' operation. To validate this operation, we show several examples of quantum circuits and their simulations, including its composition possibilities. A theoretical analysis proves that both the complexity of the required classical calculations and the depth of the circuit scale linearly with the number of qubits. Hence, the 'multiples of' oracle is efficient in terms of complexity and depth. Finally, an empirical study of the circuit depth is conducted to further reinforce the theoretical analysis.
Does a Recurrent Neural Network Form Recognizable Representations of a Fixed Event Series?

Functioning in the flow of events is possible since the subject recognizes current events as familiar and acts in accordance with the representation of ​​them. The presence of internal representations is called reflection in a broad sense and can be regarded as a requirement for effective solving of some tasks, for example, winning in a reflexive game. We consider tasks that imitate Even-odd and Rock-scissors-paper games by replacing a playmate with fixed sequences of moves. In this paper, we investigate the question whether it is possible to identify which of the available fixed sequences of moves is currently receiving by model object – a simple recurrent neural network, by decoding signals on its neurons. We show that neural-network based decoding method allows recognizing the current sequence of moves by the neural activity of the playing network, separating data that does not correspond to any of the known sequences. Therefore, simple recurrent neural networks can form stable recognizable representations associated with fixed sequences of game events in the imitation of reflexive games. This result indicates that these model objects implement reflexive processing of information and can be used for studying reflection phenomenon.
Research on automatic pilot repetition generation method based on deep reinforcement learning

Using computers to replace pilot seats in air traffic control (ATC) simulators is an effective way to improve controller training efficiency and reduce training costs. To achieve this, we propose a deep reinforcement learning model, RoBERTa-RL (RoBERTa with Reinforcement Learning), for generating pilot repetitions. RoBERTa-RL is based on the pre-trained language model RoBERTa and is optimized through transfer learning and reinforcement learning. Transfer learning is used to address the issue of scarce data in the ATC domain, while reinforcement learning algorithms are employed to optimize the RoBERTa model and overcome the limitations in model generalization caused by transfer learning. We selected a real-world area control dataset as the target task training and testing dataset, and a tower control dataset generated based on civil aviation radio land-air communication rules as the test dataset for evaluating model generalization. In terms of the ROUGE evaluation metrics, RoBERTa-RL achieved significant results on the area control dataset with ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.9962, 0.992, and 0.996, respectively. On the tower control dataset, the scores were 0.982, 0.954, and 0.982, respectively. To overcome the limitations of ROUGE in this field, we conducted a detailed evaluation of the proposed model architecture using keyword-based evaluation criteria for the generated repetition instructions. This evaluation criterion calculates various keyword-based metrics based on the segmented results of the repetition instruction text. In the keyword-based evaluation criteria, the constructed model achieved an overall accuracy of 98.8% on the area control dataset and 81.8% on the tower control dataset. In terms of generalization, RoBERTa-RL improved accuracy by 56% compared to the model before improvement and achieved a 47.5% improvement compared to various comparative models. These results indicate that employing reinforcement learning strategies to enhance deep learning algorithms can effectively mitigate the issue of poor generalization in text generation tasks, and this approach holds promise for future application in other related domains.
Change point detection in text data

Abstract The analysis of text data using artificial intelligence and statistical methods has become increasingly important in recent years. One application is the automatic assignment of documents. For this purpose, a classification model is trained on the basis of historical data. If the structure of the texts to be classified changes over time, the quality of the classification will decrease. Change point detection algorithms can counteract this. Such algorithms automatically detect changes in the structure of the texts and indicate that the trained classification model has to be adapted. However, the undesired influence of the length of the document needs to be handled when modeling the text data. We present a multinomial change-point model detecting changes in text structures. The results are supported by simulation studies.
Assisting Multimodal Named Entity Recognition by cross-modal auxiliary tasks

Although the existing Multimodal Named Entity Recognition (MNER) methods have achieved promising performance, they suffer from the following drawbacks in social media scenarios. Firstly, most existing methods are based on a strong assumption that the textual content and the associated images are matched, which is not always valid in real scenarios; Secondly, current methods fail to filter out modality-specific random noise, which impedes models from exploiting modality-shared features. In this paper, a novel multi-task multimodal learning architecture is put forward, which aims to improve Multimodal Named Entity Recognition (MNER) performance by cross-modal auxiliary tasks (CMAT). Specifically, we first separate the shared and task-specific features for the main task and auxiliary tasks respectively, which is accomplished by cross-modal gate-control mechanism. Subsequently, without extra pre-processing or annotations, we utilize the cross-modal matching to address the issue of mismatched image-text pairs, and the cross-modal mutual information maximization to optimize the most relevant cross-modal features. Moreover, experimental results on the two widely used datasets confirm the superiority of our proposed approach.