Reviewerly/exHarmony huggingface dataset:
Clothing Flexible Retrieval Based on Attention Mechanism

The development of apparel e-commerce has promoted the development of apparel image retrieval technology. The current apparel image retrieval technology has defects such as large data volume, low feature expression capability, and only focusing on the overall features, which leads to problems such as low accuracy of retrieval in the face of complex images and neglect of apparel detail information. This paper proposes a channel and spatial attention network (CSP-Net) based clothing image retrieval algorithm based on the ResNet50. Firstly, ResNet50 is used as the baseline feature extraction network, and the feature extraction process uses the channel and spatial attention mechanisms to strengthen the focus on salient features such as clothing textures and patterns and uses the short connection advantage of the ResNet to fuse the upper and lower layer feature information as a way to strengthen the feature extraction capability. In this paper, a loss function combining Cross Entropy Loss and Triplet Loss is used to enhance the retrieval of apparel within a class based on distinguishing different types of clothing, further improving the accuracy of retrieval. The experimental results show that the method effectively improves the retrieval accuracy of clothing images.
Sparse Versions of Optimized Centroids

Centroid-based methods have an established place in a variety of tasks including object localization in images. A sophisticated method for constructing optimal centroids and corresponding weights has been proposed only recently. In order to reduce the computational demands of applying the optimal centroid, several novel sparse versions of the optimal centroids are proposed here, which are based on trimming away some of their pixels. Suitable novel sparse versions bring improvements compared to available optimal centroids. At the same time, some of the sparse optimal centroids (especially the method with thresholded optimal weights) turn out to be robust to noise in the images.
Particle Swarm Optimized DWT-SVD Watermarking Scheme for Securing EEG Data

A watermark embedding provides a way to secure the audio, video, image, text, and other types of data by which data authentication, ownership, visual quality, etc. can be maintained. In this paper particle swarm optimized watermarking algorithm is proposed for securing the EEG data. This method optimizes the scaling factor to achieve higher imperceptibility and robustness. This proposed watermarking method is based on the singular value decomposition (SVD) in discrete wavelet transform (DWT) domain. Performance of proposed algorithm analyzed against various attacks like rotation, scaling, noise, etc. The experimental result shows that proposed algorithm performs better as compared to already existing techniques in terms of peak signal to noise ratio (PSNR) and Normalized correlation coefficient (NCC). The procured PSNR value is 55.2 dB and NCC value is 0.95.
Deep Learning-based Yoga Pose Recognition System using Hyperparameter Tuning

People have been forgetful of health concerns due to hectic schedules and busy lifestyles. As much as they wish to give priority to healthcare, they aren't able to because it's a choice. The fitness community has been booming in the last few years, with people becoming conscious about their general health. A majority of this community had resorted to Yoga, allowing them to access gateways to earn peace and spirituality. The issue with this would be the accuracy of the Asana being performed. Therefore, an automated deep learning-based yoga pose recognition system is proposed in the present research to accurately recognize the yoga pose performed by the user. The proposed system is divided into three modules: pose detection using MediaPipe, pose recognition using the proposed deep learning model, and alert generation. The intended model is optimized by using the concept of hyperparameter tuning. All the experiments are conducted on a self-created dataset of 5 poses and achieved a 99.6% recognition rate providing competent accuracy with other existing state-of-the-art techniques.
Learning to Stabilize Extreme Neural Machines with Metaplasticity

Training recurrent reservoir networks to solve complex tasks is a difficult problem due to extensive training times and the ambiguity of adjusting synaptic weights located deep within the architecture of the network. Recently, a novel model termed Extreme Neural Machine (ENM) has been proposed to perform one-shot learning in recurrent networks. A drawback of this approach, however, is that synaptic weights may become destabilized after training due to the random activation of recurrent units. In this paper, we propose a solution to this problem by incorporating metaplasticity in the learning rule of ENMs. With this novel approach, networks learned complex functions that can be recalled after a delay period. Using realworld data, networks were trained to produce sound waveforms obtained from spoken English words. Results show that metaplasticity improved task recall in recurrent neural networks.
Emotion Detection in Text Using Convolutional Neural Network

One of the difficult areas of automatic language understanding is emotion detection. Research related to emotion detection in text is important to be developed so that emotion in text can be detected properly. This research develops emotion detection in text by comparing word embedding such as Word2Vec, BERT and GloVe and using Convolutional Neural Network (CNN). Five kinds of emotions are identified in the text, including happy, angry, sad, scared, and surprised. Three kinds of data are used, including commuter line, transjakarta, and commuter line+transjakarta. BERT+CNN generates the best accuracy compared to Word2Vec+CNN and GloVe+CNN. The accuracy values generated by BERT+CNN on the commuter line, transjakarta and commuter line+transjakarta data are 86.47%, 87.23%, and 86.18%, respectively. Word2Vec+CNN occupies the second best accuracy value and the last is GloVe+CNN. The comparison of Precision, Recall, and F1-Measure results also show that BERT+CNN has the highest score when compared to other methods. This demonstrates that the combination of CNN and BERT embedding methods performs well in text emotion detection.
Very brief on quantum computing with special respect to ion traps technology

Because of the continuing miniaturization of integrated circuits it appears that quantum phenomena will play a more and more dominant role in their design and functioning. Therefore, the work exposes concise quantum mechanical and mathematical background of quantum phenomena based computing through the introduction of concepts such as Hilbert space, qubit, Bloch sphere, quantum gate and measurement. Recent improvements in ion- trap technology based computing suggest ion- trap technology as the most promising one for use in quantum computing. So, the paper describes the ion- trap based technology, its use in quantum computation and its latest applications in quantum computing. It turns out that recent ion- trap technology based computers dramatically improves on all of the Vincenzo’s criterions.
Attention-guided Fine-grained Feature Learning For Robust Face Forgery Detection

With the swift development of deep learning, hyper-realistic images generated by advanced facial manipulation techniques have posed a serious threat to the trustworthiness of digital media information. Most existing approaches formulate face forgery detection as a coarse-grained classification problem. They still significantly focus on low-level semantic cues which are sensitive to common corruptions such as video compression and generalise poorly to unseen forgeries. To address this issue, we propose a novel fine-grained feature learning framework for face forgery detection. To be specific, we adopt fine-grained frequency decomposition via a patch-wise manner to extract more sufficient information hidden in the frequency domain. We also propose the depth-wise separable attention module to select more informative features and captures the fine-grained cues from different input spaces. Moreover, to further explore the essential discrepancies, attention-guided feature augment module is introduced to fully exploit the multi-domain relations and incorporate frequency features into spatial clues. Extensive experiments and visualizations on public datasets fully demonstrate the effectiveness and robustness of our method against the state-of-the-art competitors.
Survey of automation practices in model-driven development and operations

Model-driven methods are gaining momentum in the industry to develop software intensive systems. To be effective in quality and efficient in productivity, they require a strong toolchain with seamless automation. The DevOps approach can help reach this by unifying software development and operations with a strong focus on automation and monitoring. The aim of this short paper is to review automation tasks that are specific to a model-driven context and to classify them according to a typical DevOps lifecycle covering design, code, testing, deployment and runtime activities. Tasks are identified based on different industry use cases experienced in our research centre or reported in the literature. Some challenges are identified and discussed, especially related to the use of bots in a model-driven context.
Coarse-to-fine Face Depth Super-Resolution with Attentive Feature Selection

The application of face depth maps is promising but limited due to the multiple degradations introduced by depth sensors, such as low spatial resolution, noise and blurry edges. In this paper, we propose a novel coarse-to-fine framework that progressively denoises and super-resolves face depth maps with two stages. The coarse stage consists of a denoising sub-network and an edge inference sub-network, and it recovers a denoised coarse depth map and an edge prior to assist the following depth refinement. The refinement stage consists of multiple attentive feature selection and fusion (AFSF) blocks that can enrich the feature diversity and aggregate important features selectively. Moreover, a residual learning scheme is used in the refinement stage to enhance the details. Extensive experiments on both synthetic and real-world face depth datasets demonstrated the superiority of our method over several state-of-the-art methods.
Survey of Intelligent Face Forgery and Detection

Face forgery technology has made considerable progress in recent years, and has several challenges in terms of maintaining social stability and protecting individual rights. Nowadays, an ordinary person can easily generate lifelike fake images and videos, including fake news related to public figures, without any professional knowledge. To eliminate the social security risks caused by fake face images and videos, face forgery detection has become an emerging field that has attracted considerable attention. This survey provides a detailed overview of face forgery and face forgery detection methods. Based on the ratio of manipulations to the original image,this paper first introduces four types of face forgery methods: i) identity swap, ii) expression swap, iii) attribute manipulation, iv) entire face synthesis, and corresponding face forgery detection datasets. We then introduce image- and video-level face forgery detection methods. To improve the manipulation detection results, most face forgery detection methods exploit prior knowledge, such as biological and frequency information, based on deep learning. Subsequently, this paper analyzes the effects of these manipulation detection methods. Finally, we discuss the shortcomings of the current methods in terms of practical applications and discuss the future development trend of face forgery detection.
Synthesizing Counterfactual Samples for Overcoming Moment Biases in Temporal Video Grounding

Moment bias is a critical issue in temporal video grounding (TVG), where models often exploit superficial correlations between language queries and moment locations as shortcuts to predict temporal boundaries. In this paper, we propose a model-agnostic counterfactual samples synthesizing method to overcome moment biases by endowing TVG models with sensitivity to linguistic and visual variations. The models with sensitivity sufficiently utilize linguistic information and focus on important video clips rather than fixed patterns, therefore are not dominated by moment biases. Specifically, we synthesize counterfactual samples by masking important words in queries or deleting important frames in videos for training TVG models. During training, we penalize the model if it makes similar predictions on counterfactual samples and original samples to encourage the model to perceive linguistic and visual variations. Experiment results on two datasets (i.e., Charades-CD and ActivityNet-CD) demonstrate the effectiveness of our method.
Variational Graph Autoencoders Method Based on Attentional Mechanisms for Overlapping Community Detection

Detecting overlapping communities of an attribute network is an important and difficult issue in network science. Traditional methods for overlapping community detection generally considered only its topological structure and neglected node attribute so that the detected communities could be inaccurate. Graph neural networks (GNN) have recently gained attention because of their learning representation ability for node attributes and the topology of a graph and demonstrating superior performance in natural language processing, data mining and pattern recognition, etc. However, GNN methods are still less for overlapping community detection. In this paper, we present an overlapping community detection method based on GNN for attribute networks. It applies a variational graph autoencoder based on attentional mechanisms to learn the representation of nodes in the graph and enhances the representation learning capability of GNN through semi-supervised learning. Various real-world networks of publicly available datasets are used to verify the effectiveness of the proposed approach. The experimental results show that the proposed method outperforms many other methods.
Digital Image Analysis for Early Diagnosis of Cancer

Cancer diagnoses so far are based on pathologists' criteria. Hence, they are based on qualitative assessment. Histopathological images of cancer biopsy samples are now available in digital format. Such digital images are now gaining importance. To avoid individual pathologists' qualitative assessment, digital images are processed further through use of computational algorithm. To extract characteristic features from the digital images in quantitative terms, different techniques of mathematical morphology are in use. Recently several other statistical and machine learning techniques have developed to classify histopathological images with the pathologists' criteria. Here, the authors discuss some characteristic features of image processing techniques along with the different advanced analytical methods used in oncology. Relevant background information of these techniques are also elaborated and the recent applications of different image processing techniques for the early detection of cancer are also discussed.
A Collaborative Filtering Recommendation Method with Integrated User Profiles

In the article recommendation, text information as the main body of the recommendation is rich in semantic content. Especially for content-based recommendation methods, whether an accurate and concise feature representation can be extracted from existing text information is the key to the effective recommendation. Since the long-term use of content-based recommendation methods to generate personalized result sets can make the recommendation variety too homogeneous, the collaborative filtering recommendation method compensates for the above problem by finding other preferred articles of similar users for the recommendation. In this paper, we propose a collaborative filtering recommendation method that incorporates user profiles. This method designs a user portrait labeling system for the article recommendation scenario. Moreover, it uses relevant text processing techniques to extract multi-dimensional user features, which can alleviate the cold start and matrix sparsity problems when performing collaborative filtering recommendations. Finally, we tested our scheme with the MIND Data Set and analyzed the advantages of our scheme.
Multi-view Geometry Consistency Network for Facial Micro-Expression Recognition From Various Perspectives

Gaze estimation plays an essential role in human attention recognition, human behavior analysis and augmented reality applications. Most of the deep neural network-based gaze estimation techniques apply supervised learning to extract features and regress 3D gaze vectors directly, leading to a vulnerability of high labor cost and limited generalization. In this work, we proposed a weakly-supervised method to jointly optimize the depth values of eye landmarks and relative poses with a multi-view geometric constraint to determine the final gaze vectors of observers. Specifically, we feed in sequential eye region images, and design a depth regression network to estimate the depth of the eye region landmarks, which are further utilized by the pose estimation network to estimate the relative changes of gaze vectors with multi-view geometric constraints in the iris regions. Experiments on both synthetic and real data show that the proposed method is feasible and promising to learn gaze estimation without strong pose supervision.
Driver-Centric Velocity Prediction With Multidimensional Fuzzy Granulation

Dear Editor, This letter deals with a real-world problem regarding chaotic time series prediction, where a driver-centric velocity prediction model is presented for vehicle intelligent control and advanced driver assistance, i.e., multi-dimension fuzzy predictor. Inspired by fuzzy granulation technology, a finite-state Markov chain (MC) is reinforced to capture probabilities of the transitions between velocity and acceleration and present signals that vary in a continuous range. The predictability of the multi-dimensional fuzzy predictor is examined by comparing two existing MC-based predictors over the two laboratory cycles and one virtual driving cycle, both of which have high accuracy.
The substantiation of the necessity to develop faculty’s digital competence on the example of higher educational institution of technical and natural-scientific profile

The COVID-19 pandemic, which led to lockdown, self-isolation, and the transition to distance learning, has profoundly affected the higher education system, revealing quite a large number of problems that need to be solved. Unlike representatives of other professions, university faculty members continued the educational process remotely and this situation acutely and instantly revealed the problem of incongruence in the level of digital competence of all participants in the educational process during the pandemic. We can definitely say that, since April 2020, the pace of digitalization, informatization and technologization has accelerated many times in all spheres of society, and especially in the higher education system. The online educational space has significantly transformed, and is no longer just a collection of learning resources, platforms, or information sources. The author’s task is to substantiate the necessity for developing digital competence and improving the existing skills of faculty members at the Krasnoyarsk State Agrarian University, in order to improve the education quality, to meet the challenges of modern digital society, and increase the competitiveness of both the university and its graduates. This will help to attract foreign students and develop programs in English. In particular, the Krasnoyarsk State Agrarian University has already started developing such a program within the framework of the Erasmus+ Improve Agro grant “Internationalization of Master’s programs in agriculture through teaching in English” to strengthen regional cooperation in the field of training specialists for agriculture and forestry and the development of inter-institutional partnerships.
On Environmental Education in 4.0 Industrial Development Era

Environmental education is an important way to realize the strategy of sustainable development. With the advent of the “ecological era” of building a community with a shared future for mankind, China's environmental education has entered a new historical stage. The article introduces the concept of environmental education and commonly adopted environmental education models, and briefs on the environmental education in Europe, the United States and Japan. Faced the advent of the “ecological era” era and the challenges of Education 4.0, the concept, challenge modeling and creative teaching methods are explored in order to enhance citizens' awareness of ecological civilization, establish a correct development concept and put those into practices, which in result enable to lay the foundation for building a beautiful China.
A software impact analysis tool based on change history learning and its evaluation

Software change impact analysis plays an important role in controlling software evolution in the maintenance of continuous software development. We developed a tool for change impact analysis, which machine-learns change histories and directly outputs candidates of the components to be modified for a change request. We applied the tool to real project data to evaluate it with two metrics: coverage range ratio and accuracy in the coverage range. The results show that it works well for software projects having many change histories for one source code base.
Feature Relevance Analysis of Product Reviews to Support Online Shopping

The number of online shoppers has been increasing in recent years. Online shopping involves the risk that the purchased product may not be what was expected. Recently, the number of product review videos has also been increasing, and more users are using them as a reference because they provide a more accurate understanding of how the product is used than conventional reviews. With this development in mind, we have been developing a review video recommendation system to support online shopping. Our system helps users to know which product review videos they should watch. In this paper, we propose a review video feature analysis method, which is a necessary technology to realize the proposed system, and conduct two evaluation experiments to confirm the effectiveness of the proposed method. The results of the evaluation revealed that the proposed system received good ratings from the users, which confirmed the effectiveness of the proposed method.
IoT Database Forensics—A Case Study with Video Door Bell Analysis

The use of Internet of Things (IoT) devices in and around the houses has grown enormously in recent years. One of the IoT devices that is being widely used is the smart doorbell also named a video doorbell. Lots of people buy a video doorbell to increase security and or for the prevention. In the context of digital forensics, more video doorbells, which the owners are consciously or unconsciously making (video) recordings of people or situations in front of their houses or company buildings, can lead to important evidence or a clue to solve a case. Hence, this chapter presents a forensic approach for acquiring and analyzing the artifacts from video doorbells taking into account the smart device, network and mobile phone forensics.