Reviewerly/exHarmony huggingface dataset:
RO-TextCNN Based MUL-MOVE-Net for Camera Motion Classification

Video auto-editing is a new application of artificial intelligence technology in the media industry. Camera motion is one of the critical characteristics of videos and is vital for shot arrangement. Rules based camera motion classification algorithms generalize poorly from one dataset to another. Machine learning or deep learning based algorithms have better cross-task performance. However, in order to remove the motion information in the foreground area, researchers have to use semantic segmentation neural networks that are computationally expensive. Existing foreground segmentation algorithms are only effective for samples with clear foreground areas. The salient areas generated by the saliency segmentation algorithms are not the same as the foreground areas in many cases. This paper proposes a novel deep learning based camera motion classification framework MUL-MOVE-Net, which is composed of multiple instantaneous motion networks MOVE-Net. In MOVE-Net, a lightweight RO- TextCNN module is proposed to learn multi-scale templates in the 1D angle histogram of optical flow information. Without using semantic segmentation network, the algorithm is capable of foreground fault tolerance while ensuring efficiency. For the experiments, a dataset MOVE-SET is constructed with more than 100, 000 pairs of instantaneous camera motion samples. On the testing set, our algorithm achieves an accuracy of 95.3% and a Macro-F1 value of 0.9385. In the shot-level motion classification task, the accuracy of MUL-MOVE-Net gets 4% higher than that of SGNet, and Macro-F1 0.3 higher. As a result, MUL-MOVE-Net could efficiently classify the camera motion in real time and is helpful for video auto-editing.
Physical modelling techniques for the dynamical characterization and sound synthesis of historical bells

Abstract Capable of maintaining characteristics practically intact over the centuries, bells are musical instruments able to provide important and unique data for the study of musicology and archaeology essential to understand past manufacturing and tuning techniques. In this research we present a multidisciplinary approach based on both direct and reverse engineering processes for the dynamical characterization and sound synthesis of historical bells which proven particularly useful to extract and preserve important information for Cultural Heritage. It allows the assessment of the bell’s 3D morphology, sound properties and casting and tuning techniques over time. The accuracy and usefulness of the developed techniques are illustrated for three historical bells, including the oldest recognized bell in Portugal, dated 1287, and two eighteenth century bells from the Mafra National Palace carillons (Portugal). The proposed approach combines non-invasive up-to-date imaging technology with modelling and computational techniques from vibration analysis, and can be summarized in the following steps: (1) For the diagnosis of existing bells, a precise assessment of the bell geometry is achieved through 3D scanning technologies, used for the field measurement and reconstruction of a 3D geometry model of each bell; (2) To access the modal properties of the bells, for any given (at the design stage) or measured geometry, a finite element model is built to compute the significant frequencies of the bell partials, and the corresponding modal masses and modeshapes. In the case of existing bells, comparison of the computed modes with those obtained from vibrational data, through experimental modal identification, enables the validation (or otherwise correction) of the finite element model; (3) Using the computed or experimentally identified modes, time-domain dynamical responses can be synthesized for any conceivable bell, providing realistic sounds for any given clapper and impact location. Although this study primarily aimed to better understand the morphology and sounds of historical bells to inform their conservation/preservation, this technique can be also applied to modern instruments, either existing or at design stages. To a larger extent, it presents strong potential for applications in the bell industry, namely for restoration and re-tuning, as well as in virtual museology.
A feed‐forward framework integrating saliency and geometry discrimination for shadow detection in SAR images

Shadow has been increasingly a kind of significant aid information for object extraction and scene interpretation in synthetic aperture radar images, which makes SAR shadow detection an important issue. In this paper, we propose a feed-forward framework integrating saliency and geometry discrimination for shadow detection in SAR images. We firstly develop a global contrast based shadow saliency model to extract suspected shadows. Considering that such suspected regions mostly contain some non-shadow areas, a discrimination strategy based on geometric relationships between objects and shadows is designed to remove falsely detected areas. Then the remaining regions become the final shadow detection results. Several experiments are carried out on images from two real datasets, Moving and Stationary Target Acquisition Recognition and MiniSAR, to evaluate the performance of our method. From the perspectives of three commonly used metrics, the proposed algorithm comprehensively outperforms two other classic methods, presenting reliable shadow detection ability. Moreover, the detection results of the two classic methods are significantly enhanced in controlling false alarms after the discrimination module is introduced. The results demonstrate that our algorithm is practically applicable to shadow detection in SAR images, and the discrimination strategy can be flexibly extended for related tasks.
Research on Color Feature Extraction Method of Digital Camouflage Based on Visual Perception

Color is one of its major features of image recognition, also a key factor in affecting camouflage effect. At present, the methods of color feature extraction used take no account of human visual perception, which may result in inaccurate color feature extraction and lead to bad effect of the digital camouflage in the practical application. In this paper, a new method of color feature extraction of digital camouflage based on color histograms combined with human visual perception is proposed. By introducing visual attention function into the traditional color histogram, several dominant colors are obtained as the color features of digital camouflage. The experiment results shows that the color features extracted by our method are more closed to the background images, in order to reduce the detection probability of target.
Cluster analysis with regression of non‐Gaussian functional data on covariates

Cluster analysis with functional data often imposes normality assumptions on outcomes and is typically carried out without covariates or supervision. However, nonnormal functional data are frequently encountered in practice, and unsupervised learning, without directly tying covariates to clusters, often makes the resulting clusters less interpretable. To address these issues, we propose a new semiparametric transformation functional regression model, which enables us to cluster nonnormal functional data in the presence of covariates. Our model incorporates several unique features. First, it omits the normality assumptions on the functional response, which adds more flexibility to the modelling. Second, our model allows clusters to have distinct relationships between functional responses and covariates, and thus makes the clusters formed more interpretable. Third, unlike various competing methods, we allow the number of clusters to be unspecified and data‐driven. We develop a new method, which combines penalized likelihood and estimating equations, to estimate the number of clusters, regression parameters, and transformation functions simultaneously; we also establish the large‐sample properties such as consistency and asymptotic normality. Simulations confirm the utility of our proposed approach. We use our proposed method to analyze Chinese housing market data and garner some interesting findings.
Interpretable cognitive learning with spatial attention for high-volatility time series prediction

In reality, some kinds of time series have the characteristics of high volatility, where fluctuation patterns of sequential data contain rich semantic knowledge and represent the spatial features from two-dimensional perspective. Especially, some non-trivial fluctuations may provide key information for the forecasting of time series. However, how to appropriately represent the fluctuation patterns and achieve the causal reasoning among them remains open. In this work, by learning the causal knowledge of fluctuation patterns, a novel spatial attention fuzzy cognitive map with high-order structure is proposed for the interpretable prediction of time series with high volatility. Firstly, a kind of extended polar fuzzy information granules is utilized to convert time series into granule sequences with interpretable fluctuation features, based on which fuzzy cognitive maps can be constructed by using full data-driven way. Secondly, in order to capture the key fluctuation patterns, the attention mechanism is first introduced into fuzzy cognitive map, where the spatial features of the focused patterns can be taken fully utilized. Thirdly, the high-order structure is involved into the proposed model for the learning of the temporal knowledge existing in the pattern sequences. Finally, real-world financial time series with strong noises and high volatility are empirically utilized to verify the promising performance of the proposed method.
GeneSis: A Generative Approach to Substitutes in Context

The lexical substitution task aims at generating a list of suitable replacements for a target word in context, ideally keeping the meaning of the modified text unchanged.While its usage has increased in recent years, the paucity of annotated data prevents the finetuning of neural models on the task, hindering the full fruition of recently introduced powerful architectures such as language models.Furthermore, lexical substitution is usually evaluated in a framework that is strictly bound to a limited vocabulary, making it impossible to credit appropriate, but out-of-vocabulary, substitutes.To assess these issues, we propose GENESIS (Generating Substitutes in contexts), the first generative approach to lexical substitution.Thanks to a seq2seq model, we generate substitutes for a word according to the context it appears in, attaining state-of-theart results on different benchmarks.Moreover, our approach allows silver data to be produced for further improving the performances of lexical substitution systems.Along with an extensive analysis of GENESIS results, we also present a human evaluation of the generated substitutes in order to assess their quality.We release the fine-tuned models, the generated datasets and the code to reproduce the experiments at https://github.com/SapienzaNLP/genesis.
Corrected CBOW Performs as well as Skip-gram

Mikolov et al. (2013a) observed that continuous bag-of-words (CBOW) word embeddings tend to underperform Skip-gram (SG) embeddings, and this finding has been reported in subsequent works.We find that these observations are driven not by fundamental differences in their training objectives, but more likely on faulty negative sampling CBOW implementations in popular libraries such as the official implementation, word2vec.c,and Gensim.We show that after correcting a bug in the CBOW gradient update, one can learn CBOW word embeddings that are fully competitive with SG on various intrinsic and extrinsic tasks, while being many times faster to train.
NLP Based Chatbot for Multiple Restaurants

Errand arranged chatbots can be utilized to robotize a specific task, for example, finding a restaurant and reserving a spot. Carrying out such a conversational framework can be difficult, requiring space information and handmade principles.Restaurant chatbot is an automated system for carrying out day to day work in a restaurant such as ordering, reservations, FAQs at reception, and many more tasks which are not that much important but take much time of the receptionist while working. Chatbot is a system backed by an Artificial Intelligence technology to learn, adapt and work according to the surrounding environment. The main focus of this proposal was to assess the chance of utilizing a neural organization based model to make a start to finish teachable chatbot that can mechanize an eatery reservation administration. The reason for this review was to investigate clients’ insights and practices when utilizing chatbots in cafe takeout orders. Based on the social presence hypothesis, this review led a lab examination to look at and think about three requesting techniques in speedy help and full-administration restaurant.
Improved DASH Video Streaming Performance by MEC-Enabled Optical Access

We discuss how different service placement can improve DASH performances on an edge-enabled architecture in a MEC infrastructure. Network experiments emulate a real optical network behaviour allowing to measure improvements with different service placement.
Deep Learning-Based Facial Landmarks Localization Using Compound Scaling

Facial landmarks are crucial information needed in numerous facial analysis applications which can help to resolve difficult computer vision-related problems.The localization of landmarks, which involves facial keypoints such as eye centers, eyebrows, nose center, etc, offers necessary information for face analysis like expressions, emotions, health conditions, etc.The applications with requirement constraints such as the model size and computational load are often scaled up with better accuracy and efficiency.In this paper, we propose a deep learning-based approach for facial landmarks localization with compound dimension scaling.We modify the baseline network called EfficientNet with multi-scale fully connected layers to predict the facial landmarks on human faces which are mapped on the detected face in real-time.The proposed model with the compound scaling method gives a scalable model by uniformly scaling the width, depth and resolution dimensions.The model is evaluated with an adaptive wing loss function for both larger and smaller models.We also assessed the robustness of the model with various head poses and occlusion conditions.The proposed model which is trained with a large dataset can achieve 90% of accuracy for a larger model with a model size of 24.6 MB and approximately 88%∼89% of accuracy for smaller models.Hence, the smaller models can still achieve acceptable accuracy compared to the larger model with fewer parameters.
Hybrid Deep Neural Network for Facial Expressions Recognition

Facial expressions are critical indicators of human emotions where recognizing facial expressions has captured the attention of many academics, and recognition of expressions in natural situations remains a challenge due to differences in head position, occlusion, and illumination. Several studies have focused on recognizing emotions from frontal images only, while in this paper wild images from the FER2013 dataset have been used to make a more generalizing model with the existence of its challenges, it is among the most difficult datasets that only got 65.5 % accuracy human-level. This paper proposed a model for recognizing facial expressions using pre-trained deep convolutional neural networks and the technique of transfer learning. this hybrid model used a combination of two pre-trained deep convolutional neural networks, training the model in multiple cases for more efficiency to categorize the facial expressions into seven classes. The results show that the best accuracy of the suggested models is 74.39% for the hybrid model, and 73.33% for Fine-tuned the single EfficientNetB0 model, while the highest accuracy for previous methods was 73.28%. Thus, the hybrid and single models outperform other state of art classification methods without using any additional, the hybrid and single models ranked in the first and second position among these methods. Also, The hybrid model has even outperformed the second-highest in accuracy method which used extra data. The incorrectly labeled images in the dataset unfairly reduce accuracy but our best model recognized their actual classes correctly.
Research Progress and Prospect of Blockchain + Agricultural Science Data Management

Agricultural data management plays an important role in the development of agriculture and even the national economy.Data has been written into the national documents as a factor of production, and its importance is not strict.As the technical support of agricultural data management, blockchain, big data and Internet of Things technology drive the rapid development of agricultural data management.In this paper, the application of blockchain in agricultural data management in the past five years is described in a panoramic manner by using data visualization method.At the same time, content analysis is carried out based on literature content, and the research status at home and abroad is summarized.92/5000.Through analysis, it is concluded that the application of blockchain technology in agricultural data management mainly includes blockchain + agricultural Internet of Things mode, blockchain + agricultural supply chain mode, blockchain + agricultural data sharing mode, etc.In the future, we need to further expand the application mode of blockchain technology in agricultural data management.
A cloud service recommendation method based on extended multi‐source information fusion

Abstract With the rapid development of information technology, the problem of “information overload” emerges when users choose cloud services. How to integrate multi‐source information to achieve accurate service recommendation is an urgent problem to be solved by current recommendation systems. This article proposes a cloud service recommendation method based on extended multi‐source information fusion. First, we propose a score prediction based on matrix decomposition and topic matrix, and we fully mine existing explicit data and feedback data, such as user ratings, social trust information, reviews, and user personalized preferences and so on. Second, in order to solve the problems of data sparseness and cold start of the system, we integrate the score, social trust information and review into a comprehensive model through collaborative filtering (CF), and propose a multi‐source information fusion recommendation method. The CF fusion method mainly combines two parts: social matrix decomposition and topic matrix decomposition. Finally, in order to further improve the accuracy and scalability, the implicit feature matrix is integrated into the user rating matrix, and the original CF enhancement based on scoring matrix decomposition is a matrix decomposition method that can learn implicit features. Experimental results show that compared with other recommendation algorithms, the cloud service recommendation method proposed in this article can improve the recommendation accuracy and allow users to choose satisfactory cloud services.
Deformable Enhancement and Adaptive Fusion for Depth Map Super-Resolution

Depth map super-resolution (DMSR) is an effective solution to improve the quality of depth maps captured by low-cost depth sensors. Most existing methods introduce guidance from the RGB images of the same scene and achieve significant improvements. However, how to utilize the RGB information is still an open challenge because of the structure inconsistencies between RGB images and depth maps. In this letter, we present a novel convolutional neural network with deformable enhancement and adaptive fusion, termed DEAF-Net, to further improve the performance of DMSR. Specifically, we design a deformable convolution enhancement module, in which sufficient color features are used for enhancing depth features. An adaptively feature fusion module is exploited to improve the efficiency of fully connected feature fusion. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed method.
Indoor Positioning Algorithm Based on Reconstructed Observation Model and Particle Filter

In a complex indoor environment, wireless signals are affected by multiple factors such as reflection, scattering or diffuse reflection of electromagnetic waves from indoor walls and other objects, and the signal strength will fluctuate significantly. For the signal strength and the distance between the unknown nodes and the known nodes are a typical nonlinear estimation problem, and the unknown nodes cannot receive all Access Points (APs) signal strength data, this paper proposes a Particle Filter (PF) indoor position algorithm based on the Kernel Extreme Learning Machine (KELM) reconstruction observation model. Firstly, on the basis of establishing a fingerprint database of wireless signal strength and unknown node position, we use KELM to convert the fingerprint location problem into a machine learning problem and establish the mapping relationship between the location of the unknown node and the wireless signal strength, thereby refocusing construct an observation model of the indoor positioning system. Secondly, according to the measured values obtained by KELM, PF algorithm is adopted to obtain the predicted value of the unknown nodes. Thirdly, the predicted value is fused with the measured value obtained by KELM to locate the position of the unknown nodes. Moreover, a novel control strategy is proposed by introducing a reception factor to deal with the situation that unknown nodes in the system cannot receive all of the AP data, i.e., data loss occurs. This indoor positioning experimental results show that the accuracy of the method is significantly improved contrasted with commonly used PF, GP-PF and other positioning algorithms.
Jespipe: A Plugin-Based, Open MPI Framework for Adversarial Machine Learning Analysis

Research is increasingly showing the tremendous vulnerability in machine learning models to seemingly undetectable adversarial inputs. One of the current limitations in adversarial machine learning research is the incredibly time-consuming testing of novel defenses against various attacks and across multiple datasets, even with high computing power. To address this limitation, we have developed Jespipe as a new plugin-based, parallel-by-design Open MPI framework that aids in evaluating the robustness of machine learning models. The plugin-based nature of this framework enables researchers to specify any pre-training data manipulations, machine learning models, adversarial models, and analysis or visualization metrics with their input Python files. Because this framework is plugin-based, a researcher can easily incorporate model implementations using popular deep learning libraries such as PyTorch, Keras, TensorFlow, Theano, or MXNet, or adversarial robustness tools such as IBM’s Adversarial Robustness Toolbox or Foolbox. The parallelized nature of this framework also enables researchers to evaluate various learning or attack models with multiple datasets simultaneously by specifying all the models and datasets they would like to test with our XML control file template. Overall, Jespipe shows promising results by reducing latency in adversarial machine learning algorithm development and testing compared to traditional Jupyter notebook workflows.