Reviewerly/exHarmony huggingface dataset:
A Novel Object Recognition Algorithm Based on Improved YOLOv5 Model for Patient Care Robots

The rapid development of computer vision raises a new research area involving patient care robots. Such robotic systems require fast target recognition at long ranges, where detecting smaller objects is notoriously challenging due to the cameras’ low resolution and noise. Spurred by these concerns, this paper develops a novel object recognition algorithm that solves these problems. Specifically, we amend YOLOv5 with our proposed sparse detection algorithm aiming to improve detection efficiency by separating the most significant context features and constructing smaller and less computational expensive models. Furthermore, we extend FReLU and suggest a novel activation function to improve recognition accuracy, which presents an extended nonlinearity increasing the expressiveness of the activation function. Finally, we propose sine annealing, which affords a trajectory that tends to cross over barriers and escape from local minima during training phase, addressing the challenging small object detection problem. The experimental results highlight that our algorithm has a lower memory consumption (Mem) value than the traditional YOLOv5 with a 5% boost down. Additionally, our method runs twice as fast as the traditional YOLOv5 while preserving accuracy, achieving more than 14.5 FPS on a medium-capability CPU. Overall, the detection results prove that our method can faster and accurately classifies and localizes most small-scaled objects.
Concept Drift Detection by Tracking Weighted Prediction Confidence of Incremental Learning

Data stream mining is great significant in many real-world scenarios, especially in the big data area. However, conventional machine learning algorithms are incapable to process because of its two characteristics (1) potential unlimited number of data is generated in real-time way, it is impossible to store all the data (2) evolving over time, namely, concept drift, will influence the performance of predictor trained on previous data. Concept drift detection method could detect and locate the concept drift in data stream. However, existing methods only utilize the prediction result as indicator. In this article, we propose a weighted concept drift indicator based on incremental ensemble learning to detect the concept. The indicator not only considers the prediction result, but the change of prediction stability of predictor with occurs of concept drift. Also, an incremental ensemble learning based on vote mechanism is especially used to get constantly updated value of indicator. Based on the experiment result on both benchmark and real-world dataset, our method could effectively detect concept drift and outperform other existing methods.
Research and Application of Clustering Algorithm for Text Big Data

In the era of big data, text as an information reserve database is very important, in all walks of life. From humanities research to government decision-making, from precision medicine to quantitative finance, from customer management to marketing, massive text, as one of the most important information carriers, plays an important role everywhere. The text data generated in these practical problems of humanities research, financial industry, marketing, and other fields often has obvious domain characteristics, often containing the professional vocabulary and unique language patterns in these fields and often accompanied by a variety of "noise." Dealing with such texts is a great challenge for the current technical conditions, especially for Chinese texts. A clustering algorithm provides a better solution for text big data information processing. Clustering algorithm is the main body of cluster analysis, K-means algorithm with its implementation principle is simple, low time complexity is widely used in the field of cluster analysis, but its K value needs to be preset, initial clustering center random selection into local optimal solution, other clustering algorithm, such as mean drift clustering, K-means clustering in mining text big data. In view of the problems of the above algorithm, this paper first extracts and analyzes the text big data and then does experiments with the clustering algorithm. Experimental conclusion: by analyzing large-scale text data limited to large-scale and simple data set, the traditional K-means algorithm has low efficiency and reduced accuracy, and the K-means algorithm is susceptible to the influence of initial center and abnormal data. According to the above problems, the K-means cluster analysis algorithm for data sets with large data volumes is analyzed and improved to improve its execution efficiency and accuracy on data sets with large data volume set. Mean shift clustering can be regarded as making many random centers move towards the direction of maximum density gradually, that is, moving their mean centroid continuously according to the probability density of data and finally obtaining multiple maximum density centers. It can also be said that mean shift clustering is a kernel density estimation algorithm.
Encrypted Network Traffic Classification Using Deep Learning Method

With growing use of internet and online applications, network traffic classification could be much more useful nowadays, because managing network services and quality assurance, two key points in network structure, could be done easily using this kind of classification. Different methods are used for this task, including port-based classification, machine learning and some other algorithms that each of them had its own advantages and disadvantages. For eliminating such disadvantages, deep learning methods are new ways for doing this task due to the power and excellent performance they showed. Furthermore, most of the work done in this field are using non-encrypted traffic or encrypted traffic in mobile networks, but as we know, privacy of data is very important these days. In this article, with the use of deep learning neural network, encrypted traffic of non-mobile data is being classified. For this purpose, we use the UNB ISCX VPN-non-VPN dataset that includes encrypted and unencrypted traffic of different applications. Then we design an algorithm based on DNN that could classify these traffics effectively. Performance of the model was evaluated and 0.86 accuracy and 0.78 fl-score showed that model works well compared to other algorithms used in this area.
Information seeking within academic digital libraries

When searching within an academic digital library, a variety of information seeking strategies may be employed. The purpose of this study is to determine whether graduate students choose appropriate information seeking strategies for the complexity of a given search scenario, and to explore among other factors that could influence their decisions. We used a survey method in which participants (n=33) were asked to recall their most recent instance of an academic digital library search session that matched two given scenarios (randomly chosen from four alternatives), and for each scenario identify whether they employed search strategies associated with four different information seeking models. Although we expected that the information seeking strategies used would be influenced by the search scenario, this was not the case. The factors that affected whether a participant would use an advanced information seeking strategy were based on their graduate-level academic search training and their primary research methodology. These findings highlight that while it is important to train graduate students on how to conduct academic digital library searches, more work is needed to train them on matching the information seeking strategies to the complexity of their search tasks and developing interfaces that guide their search process.
N-fold Bernoulli probability based adaptive fast-tracking algorithm and its application to autonomous aerial refuelling

Recently, deep learning has been widely utilized for object tracking tasks. However, deep learning encounters limits in tasks such as Autonomous Aerial Refueling (AAR), where the target object can vary substantially in size, requiring high-precision real-time performance in embedded systems. This paper presents a novel embedded adaptiveness single-object tracking framework based on an improved YOLOv4 detection approach and an n-fold Bernoulli probability theorem. First, an Asymmetric Convolutional Network (ACNet) and dense blocks are combined with the YOLOv4 architecture to detect small objects with high precision when similar objects are in the background. The prior object information, such as its location in the previous frame and its speed, is utilized to adaptively track objects of various sizes. Moreover, based on the n-fold Bernoulli probability theorem, we develop a filter that uses statistical laws to reduce the false positive rate of object tracking. To evaluate the efficiency of our algorithm, a new AAR dataset is collected, and extensive AAR detection and tracking experiments are performed. The results demonstrate that our improved detection algorithm is better than the original YOLOv4 algorithm on small and similar object detection tasks; the object tracking algorithm is better than state-of-the-art object tracking algorithms on refueling drogue tracking tasks.
Time evolution of quantum correlations in presence of state dependent bath

Abstract The emerging quantum technologies heavily rely on the understanding of dynamics in open quantum systems. In the Born approximation, the initial system-bath correlations are often neglected which can be violated in the strong coupling regimes and quantum state preparation. In order to understand the influence of initial system-bath correlations, we study the extent to which these initial correlations and the distance of separation between the qubits influence the dynamics of quantum entanglement and coherence. It is shown that at low temperatures, the initial correlations have no role to play while at high temperatures, these correlations strongly influence the dynamics. Furthermore, we have shown that the distance of separation between the qubits in presence of a collective bath helps to maintain entanglement and coherence at long times.
A Scientific Research Information System via Intelligent Blockchain Technology for the Applications in University Management

The scientific research information system plays an essential role in improving management efficiency and promoting technological innovation in universities. With the increasing computational demand for human-centric research management, blockchain technology, with distributed storage, consensus sharing, and security traceability, has efficiently assisted the research information system in dealing with various issues such as big-data scale, information security, interconnection, rapid response, and private security. A novel scientific information system framework based on intelligent blockchain technology is proposed to promote university scientific research’s information level and management efficiency. Moreover, four smart data contracts, including data collection, verification, sharing, and supervision, are custom-designed under an efficient scientific research information system. Those intelligent contracts provide reliable data security and traceability algorithms to guarantee the practical application of the scientific research information system. The results show that the constructed system can relieve the centralized storage pressure of scientific research information and solve the cross-subject sharing obstacle of massive safety data among different systems. Thereby, the system increases the transparency of scientific evaluation and realizes the credible supervision of scientific research information, which provides a way to promote the innovative application of blockchain technology in scientific research management in colleges and universities.
Brain Tumor Segmentation on MRI using a GVF Snake Model

One objective of neuroimaging is to study the brain structures of healthy and pathological subjects. The considerable variation of structures requires implementing specific study methods, often addressed through Magnetic Resonance Imaging (MRI). Currently, segmentation constitutes a big step in the treatment and interpretation of medical images. Many approaches have been proposed for segmentation tasks. Accordingly, several methods have resulted. Among these methods, we find the Gradient Vector Flow GVF Snake Model method, which presents the subject of our work. The basic idea of GVF Snake Model is to evolve an initial contour according to specific equations to reach the desired object boundaries; this method helps us get a closed and skinny contour (one pixel of thickness). We opted for a contour segmentation method to refine the initial segmentation; parametric deformable models have been successfully applied to tumour segmentation, so we use the active contours guided by GVF with constraints from spatial relations. The results show that our method significantly improves brain tumour extraction and segmentation. Through this humble search, we can finally look forward to the generalization of this method on 3D space to have the global information of the pathology to help the practitioner in the diagnosis.
Boundary assembling method for joint entity and relation extraction

In recent years, a paradigm shift has occurred in the field of joint entity and relation extraction from token tagging to span classification, because the latter can handle nested named entities in a sentence and better utilize the global features of a possible named entity. Because relation extraction should verify every entity pair in a sentence, the performance of joint entity and relation extraction significantly depends on the quality of the entity span proposals. Most models enumerate numerous inaccurate entity spans, causing a severe data imbalance problem and high computational complexity. To address these problems, we propose a boundary assembling model for joint entity and relation extraction, in which entity boundaries are assembled to enumerate entity spans. Entity boundaries have small granularity and are less ambiguous, hence, the proposed model can benefit from accurate entity spans. Furthermore, boundary detection, span classification, and relation extraction are integrated into an end-to-end framework; thus, our model can share model parameters for multi-objective learning, which enhances the discriminability of a neural network. Experiments show that our boundary assembling model outperforms existing state-of-the-art models on four evaluation datasets: SciERC, ADE, ACE05, and CoNLL04.
GAAF: Searching Activation Functions for Binary Neural Networks Through Genetic Algorithm

Binary neural networks (BNNs) show promising utilization in cost and power-restricted domains such as edge devices and mobile systems. This is due to its significantly less computation and storage demand, but at the cost of degraded performance. To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs. These AFs can help extract extra information from the input data in the forward pass, while allowing improved gradient approximation in the backward pass. Fifteen novel AFs are identified through our GA-based search, while most of them show improved performance (up to 2.54% on ImageNet) when testing on different datasets and network models. Interestingly, periodic functions are identified as a key component for most of the discovered AFs, which rarely exist in human designed AFs. Our method offers a novel approach for designing general and application-specific BNN architecture. GAAF will be released on GitHub.
Sentiment Classification of Tourism Reviews Based on Visual and Textual Multifeature Fusion

Most of the traditional tourism review sentiment analysis methods ignore the complementary role between review text and images and do not pay attention to the differences in the sentimental expression of different users and the characteristics of the text content of tourism reviews other than the plain text such as emojis. For this reason, a sentiment analysis method for tourism online reviews based on multifeature fusion of images and text is proposed. Firstly, a text sentiment classification model is constructed, and a variety of sentiment features are combined to form a multi-input matrix, and then, it is input into a multichannel CNN (convolutional neural network) to extract sentiment features in order to complete the text sentiment classification. In addition, an image sentiment classification model is constructed by merging the global image and the face image. On the basis of the CNN, the supervision module with weighted loss is added to extract the facial sentiment features, and the facial target sentiment is fused with the sentiment directly recognized by the whole image, and the sentiment polarity of the image in the posted tourism review is determined. Finally, a decision fusion method is designed to fuse the output of the text and image sentiment classification models. The experimental results show that the proposed image-text fusion sentiment classification model effectively enhances the ability of the model to capture sentimental semantics of tourism reviews through the combination of text content features and image sentiment features and achieves excellent results in multiple performance metrics, with better sentiment classification performance than other state-of-the-art models.
Sequential reasoning for optimizing compilers under weak memory concurrency

We formally show that sequential reasoning is adequate and sufficient for establishing soundness of various compiler optimizations under weakly consistent shared-memory concurrency. Concretely, we introduce a sequential model and show that behavioral refinement in that model entails contextual refinement in the Promising Semantics model, extended with non-atomic accesses for non-racy code. This is the first work to achieve such result for a full-fledged model with a variety of C11-style concurrency features. Central to our model is the lifting of the common data-race-freedom assumption, which allows us to validate irrelevant load introduction, a transformation that is commonly performed by compilers. As a proof of concept, we develop an optimizer for a toy concurrent language, and certify it (in Coq) while relying solely on the sequential model. We believe that the proposed approach provides useful means for compiler developers and validators, as well as a solid foundation for the development of certified optimizing compilers for weakly consistent shared-memory concurrency.
Sentiment Analysis of IMDb Movie Reviews : A comparative study on Performance of Hyperparameter-tuned Classification Algorithms

Sentiment Analysis (SA) is a sub-domain of Natural Language Processing where useful insights on sentiment and opinion of people can be obtained and analyzed from various textual data in structured, unstructured and semi-structured format. In this work, I have tried to analyze the sentiment of viewers from the IMDb Movie Reviews dataset. For this, I have taken three different Supervised learning methods, namely, Linear Support Vector Machine, Logistic Regression and Multinomial Naive Bayes Classifier, each with different settings of hyperparameters. Moreover, to capture the notion of informal jargon, approach of N-grams has been followed. Furthermore, a comparative study has been performed to find the one best model from each of the different types of above mentioned Supervised learning techniques based on their Accuracy Score, F1-Score and AUC Score. In this approach, I have obtained the best accuracy score of around 0.910 and mean F1-score after 10-fold Cross validation of around 0.894.
A Reinforcement Learning Approach Based on Automatic Policy Amendment for Multi-AUV Task Allocation in Ocean Current

In this paper, the multiple autonomous underwater vehicles (AUVs) task allocation (TA) problem in ocean current environment based on a novel reinforcement learning approach is studied. First, the ocean current environment including direction and intensity is established and a reward function is designed, in which the AUVs are required to consider the ocean current, the task emergency and the energy constraints to find the optimal TA strategy. Then, an automatic policy amendment algorithm (APAA) is proposed to solve the drawback of slow convergence in reinforcement learning (RL). In APAA, the task sequences with higher team cumulative reward (TCR) are recorded to construct task sequence matrix (TSM). After that, the TCR, the subtask reward (SR) and the entropy are used to evaluate TSM to generate amendment probability, which adjusts the action distribution to increase the chances of choosing those more valuable actions. Finally, the simulation results are provided to verify the effectiveness of the proposed approach. The convergence performance of APAA is also better than DDQN, PER and PPO-Clip.
SCPA‐Net: Self‐calibrated pyramid aggregation for image dehazing

Abstract Dehazing as an important image processing field has developed for many years, there exist many excellent methods for exploring more complex networks to solve this problem. In this paper, instead of designing a complex network structure, we propose a novel dehazing network based on the consideration of enhancing feature aggregation and feature representation abilities of dehazing architecture. Specifically, we propose a self‐calibrated pyramid aggregation network (SCPA‐Net) for image dehazing, which is based on an encoder‐decoder architecture. In the encoder, we build a self‐attention block as a unit to aggregate information from a neighborhood to adapt to its content. In the decoder, we introduce a self‐calibration block to capture long‐range spatial and channel dependencies to produce more discriminative representations. Finally, to learn the scale information, the pyramid upsampling structure is applied to aggregate the multiscale self‐calibrated attentive features. Experimental results show our SCPA‐Net can achieve impressive dehazing performance.
The role of digital interactive technology in cultural heritage learning: Evaluating a mid‐air gesture‐based interactive media of Ruihetu

Abstract In this study, we investigate the role of mid‐air gesture‐based interaction technologies in cultural heritage learning. In an experiment, a mid‐air gesture‐based interactive media for Chinese Song Dynasty traditional paintingRuihetu was developed and validated. Participants tested three experimental conditions: video learning only; interactive experience first, then video learning; and video learning first, then interactive experience. According to the research results, the outcomes of participants' learning of this cultural heritage differed significantly across all three experimental conditions. This study's findings offer insights into cultural learning of Chinese traditional painting in museums using mid‐air gesture‐based technology, specifically that video learning exhibits should be combined with and preceded by multimedia interactive exhibits for improved memory and understanding.