Reviewerly/exHarmony huggingface dataset:
The Extraction of Foreground Regions of the Moving Objects Based on Spatio-Temporal Information under a Static Camera

The rapid development of computer vision technology provides a basic guarantee for public security reliance on video surveillance. In current video surveillance based on static cameras, accurate and quick extractions of foreground regions of moving objects enable quicker analysis of the behavior of meaningful objects and thus improve the intelligent analysis level of video surveillance. However, there would always occur false detection in the extraction of foreground regions, because of the shaking of tree branches and leaves in the scene and the “ghosting” area caused by the delayed updating of the background model. To solve this problem, this paper proposes a method for the extraction of foreground regions by using spatio-temporal information. This method can accurately extract foreground regions of moving objects by utilizing the difference and complementarity between spatial domain methods and temporal domain methods and further in combination with image processing technology. Specifically, the foreground regions of moving objects can be extracted by the morphological processing of the combination of the spatial information and the morphologically processed temporal information in the video. The experimental results show that the proposed method for the extraction of foreground regions of moving objects in view of the spatio-temporal information can reduce false detections caused by the shaking of tree branches and leaves, and thus effectively extract foreground regions of moving objects.
Estimation of 3D anatomically précised hand poses using single shot corrective CNN

A crucial component of human-computer interaction is 3D hand posture assessment. The most recent advancements in computer vision have made estimating 3D hand positions simpler by using deep sensors. The main challenge still stems from unrealistic 3D hand poses because the existing models only use the training dataset to learn the kinematic rules, which is ambiguous, and it is a difficult task to estimate realistic 3D hand poses from datasets because they are not free from anatomical errors. The suggested model in this study is trained using a closed-form expression that encodes the biomechanical rules, thus it does not entirely reliant on the pictures from the annotated dataset. This work also used a Single Shot Detection and Correction convolutional neural network (SSDC-CNN) to handle the issues in imposing anatomically correctness from the architecture level. The ResNetPlus is implemented to improve representation capability with enhanced the efficiency of error back-propagation of the network. The datasets of the Yoga Mudras, like HANDS2017, and MSRA have been used to train and test the future model. As observed from the ground truth the previous hand models have many anatomical errors but, the proposed hand model is anatomically error free hand model compared to previous hand models. By considering the ground truth hand pose, the recommended hand model has shown good accuracy when compared to the state-of-art hand models.
Cattle Target Segmentation Method in Multi-Scenes Using Improved DeepLabV3+ Method

Obtaining animal regions and the relative position relationship of animals in the scene is conducive to further studying animal habits, which is of great significance for smart animal farming. However, the complex breeding environment still makes detection difficult. To address the problems of poor target segmentation effects and the weak generalization ability of existing semantic segmentation models in complex scenes, a semantic segmentation model based on an improved DeepLabV3+ network (Imp-DeepLabV3+) was proposed. Firstly, the backbone network of the DeepLabV3+ model was replaced by MobileNetV2 to enhance the feature extraction capability of the model. Then, the layer-by-layer feature fusion method was adopted in the Decoder stage to integrate high-level semantic feature information with low-level high-resolution feature information at multi-scale to achieve more precise up-sampling operation. Finally, the SENet module was further introduced into the network to enhance information interaction after feature fusion and improve the segmentation precision of the model under complex datasets. The experimental results demonstrate that the Imp-DeepLabV3+ model achieved a high pixel accuracy (PA) of 99.4%, a mean pixel accuracy (MPA) of 98.1%, and a mean intersection over union (MIoU) of 96.8%. Compared to the original DeepLabV3+ model, the segmentation performance of the improved model significantly improved. Moreover, the overall segmentation performance of the Imp-DeepLabV3+ model surpassed that of other commonly used semantic segmentation models, such as Fully Convolutional Networks (FCNs), Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP), and U-Net. Therefore, this study can be applied to the field of scene segmentation and is conducive to further analyzing individual information and promoting the development of intelligent animal farming.
Dynamic Weight and Mapping Mutation Operation-Based Salp Swarm Algorithm for Global Optimization

The salp swarm algorithm imitates the swarm behavior of salps during navigation and hunting that has been proven the superiority of search for best solution. However, although it has sufficient global search ability, it is still worth paying attention to problems of falling into local optima and lower convergence accuracy. This paper proposes some improvements to the salp swarm algorithm that are based on a nonlinear dynamic weight and the mapping mutation operation. Firstly, the nonlinear dynamic weight is helpful for further optimizing the transition from exploration to exploitation and alleviating the local optima stagnation phenomena. Secondly, utilizing a mapping mutation operation can increase the diversity of followers in algorithm, to avoid getting trapped into the local optima during the search and provide a better optimal solution. The proposed algorithm is characterized by a stronger global optimization capability and high convergence accuracy. Finally, to confirm the effectiveness of the proposed algorithm, comparative experiments based on other well-known swarm-based algorithms and each improvement for the original algorithm are conducted. The quantitative results and convergence curves among several algorithms demonstrate that the enhanced algorithm with the nonlinear dynamic weight and mapping mutation operation can outperform the original algorithm.
Design of a Decentralized Identifier-Based Authentication and Access Control Model for Smart Homes

In recent years, smart homes have garnered extensive attention as a prominent application scenario of IoT technology. However, the unique characteristics of smart homes have brought forth serious security threats, emphasizing the paramount importance of identity authentication and access control. The conventional centralized approach is plagued by the issue of having a “single point of failure,” while existing distributed solutions are constrained by limited device resources and the complexities of identity authentication. To tackle these challenges, this paper proposes a smart home authentication and access control model based on decentralized identifiers (DIDs). By leveraging the inherent decentralization of DIDs, which rely on blockchain, a distributed environment is constructed, effectively mitigating the problem of the “single point of failure.” In this model, every participant in the smart home system, including users and smart devices, is uniquely identified by DIDs and through the integration of an improved capability-based access control scheme, which streamlines the user identity authentication process, reduces authentication complexity, and enables convenient cross-household access with a single registration. Our experimental results demonstrate that the application of decentralized identifiers provides the model with various security attributes, including confidentiality, integrity, and traceability. Additionally, the model exhibits low time costs for each module, ensuring timely responses to access service requests and incurring lower gas consumption compared to other Ethereum-based methods. Thus, our research proposes a lightweight authentication and access control solution suitable for smart home environments.
A clustered borderline synthetic minority over-sampling technique for balancing quick access recorder data

Most of the flight accident data have uneven distribution of categories. When the traditional classifier is applied to this data, it will pay less attention to the minority class data. Synthetic Minority Over-sampling Technique (SMOTE), and its improvements are well-known methods to address this imbalance problem at the data level. However, traditional algorithms still have the problems in blurring the boundary of positive and negative classes and changing the distribution of original data. In order to overcome these problems and accurately predict flight accidents, a new Clustered Biased Borderline SMOTE(CBB-SMOTE) is proposed for Quick Access Recorder (QAR) Go-Around data. It generates more obvious positive and negative class boundaries by using K-means for boundary minority class data and safety minority class data respectively, and maintains the original data distribution to the greatest extent through a biased oversampling method. Experiments were carried out on a group of QAR Go-Around data. The data set is balanced by CBB-SMOTE, SMOTE, Cluster-SMOTE algorithm respectively, and the random forest algorithm is used to predict the new data set. The experimental results show that CBB-SMOTE outperforms the SMOTE in terms of G-means value, Recall and AUC.
Multiple Hankel matrix rank minimization for audio inpainting

Sasaki et al. (2018) presented an efficient audio declipping algorithm, based on the properties of Hankel-structure matrices constructed from time-domain signal blocks. We adapt their approach to solving the audio inpainting problem, where samples are missing in the signal. We analyze the algorithm and provide modifications, some of them leading to an improved performance. Overall, it turns out that the new algorithms perform reasonably well for speech signals but they are not competitive in the case of music signals.
Audio-Visual Broadcast Transcription System in the Era of Covid-19

This paper deals with the methods and algorithms for face (mask) detection and recognition in the system for automatic audio-visual TV broadcast transcription. In the era of Covid-19, traditional methods for face detection and recognition were failing as large numbers of people wearing face masks began to appear in television video recordings. Therefore, we focused our work on finding a sufficiently robust detector and classifier for face (masked/unmasked) detection and recognition. As part of this work, a face mask detector and a face recognizer were trained.
Classification of Turkish E-Commerce Product Reviews

Nowadays, e-commerce product reviews play a very important role in online shopping. With the importance of technology in our lives and the intense interest in online shopping, the classification of these comments with text classification is quite important. In addition to supervised and unsupervised machine learning algorithms, which are very useful and effective in text classification, deep learning algorithms are also very popular and have high success rates. The study aims to provide a brief overview of machine learning methods for text classification. In the dataset used, the comments under a product on online shopping sites were collected and a dataset was created by giving class labels according to whether the comments were positive, negative, or neutral. The dataset has 6799 positive, 6978 negatives, and 1393 neutral comments, including 15170 comments. Convolutional Neural Network (CNN) is proposed as a classifier in the classification phase. Also, the proposed method is compared with Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (Bi-LSTM), and Support Vector Machine (SVM).
Using Different Loss Functions with YOLACT++ for Real-Time Instance Segmentation

In this paper, we study and analyze the performance of various loss functions on a recently proposed real-time instance segmentation algorithm, YOLACT++. In particular, we study the loss functions, including Huber Loss, Binary Cross Entropy (BCE), Mean Square Error (MSE), Log-Cosh-Dice Loss, and their various combinations within the YOLACT++ architecture. We demonstrate that we can use different loss functions from the default loss function (BCE) of YOLACT++ for improved real-time segmentation results. In our experiments, we show that a certain combination of two loss functions improves the segmentation performance of YOLACT++ in terms of the mean Average Precision (mAP) metric on Cigarettes dataset, when compared to its original loss function.
A Novel Comparison between Different Composite Chaotic Maps Applied on Sound Encryption

This paper presents three new composite chaotic maps used in a ciphering method for audio signals based on wavelet analysis. These new composite chaotic maps: Dyadic-Tent-Fibonacci (DTF), Tent-Fibonacci-Circle (TFC) and Tent-Fibonacci-Logistic (TFL) are compared in terms of encryption metrics (histograms, correlation, entropy, PSNR) with Tent-Logistic-Tent (TLT).
Face Expression Recognition using Recurrent Neural Networks

The development of face expression recognition (FER) technology is a topic of active research, because in the several applications, it is necessary to verify the emotion expression of a given person. As a result, several face expression recognition schemes have been proposed during the last decades. The development of face expression recognition schemes is mainly based on the face image analysis, instead of video sequence analysis. However, considering that a human recognizes an emotion of other person using his/her time video sequence data, it is natural to use video analysis instead of image analysis. This paper proposes a face expression recognition system using video sequences, in which temporal feature vectors are extracted from the face landmarks, which are relevant characteristic points on the face. These feature vectors are then introduced into the different configurations of recurrent neural networks (RNN), such as Long Short Time Memory (LSTM) and Bidirectional Long Short Time Memory (BLSTM) to obtain better results.
Image Authentication via Zero-Watermarking based on a Unidimensional Cellular Automata

This paper presents a zero-watermarking algorithm for authentication of digital images. The main challenge in every zero-watermarking algorithm is to create a scheme with high payload, reliable, secure and that avoid any distortion into the content image. Using a secure hash algorithm, spread spectrum by direct sequence code division multiple access, cellular automata criterion and image processing, the algorithm obtains a zero-watermarking code used to authenticate the ownership and verify the integrity of the digital image. Experimental results show the effectiveness of the proposed method to prove the ownership authentication and integrity content verification, making the proposed scheme suitable in applications where the image manipulations are not allowed, such as evidence proves or medical images.
Transfer Learning for Russian Handwriting Recognition

Handwriting recognition is one of the most important tasks in computer vision. Despite the variety of methods and algorithms used for text recognition in various languages, the recognition of handwritten Russian words is still a completely unsolved problem, which keeps the relevance of this topic. This paper proposes the application of a convolutional neural network (CNN) to the task of recognizing the 100 most frequently used Russian words, trained on a new unique dataset. A unique algorithm for extracting Russian handwritten words from forms was applied to obtain a new dataset. The paper also presents the results of a comparison of such powerful models as VGG-16, VGG-19, ResNet-50, and ResNet-101, which were trained using transfer learning technology. The best results were shown by VGG-16 with 98.7% accuracy, and the worst results were shown by ResNet-50 with 80% accuracy.
Building a Solar Tracker Robot Controlled by the ZedBoard FPGA Board

This work presents how to build a robot who tracks the Sun. Solar tracker robots are used to track the position of the Sun. Rotating solar panels towards the Sun can increase their efficiency considerably. Tracking the Sun can be done with photoresistors or pirheliometric sensors. Rotating solar panels towards the Sun can be done with servomotors. The control of the system can be done with a development board, which in our case is a Digilent ZedBoard with an Artix 7 FPGA. The solar tracker robot is controlled using a tracking algorithm which ca be extended to rotate wind turbines in the direction of the wind just by interchanging the photoresistors or pirheliometric sensors with wind sensors or anemometers.
Artificial Intelligence-based Hidden Communications Detection in Wireless Networks

Ubiquitous wireless networks are becoming a reality in our everyday lives. In this scenario, vulnerabilities and cyber-attacks represent issues that must be efficiently fulfilled, to avoid corruption of the system ‘s security policies as well as theft of sensible data. This paper investigates about the use of artificial intelligence (AI) techniques to overcome security threats. We develop a hidden wireless communication by modulating the inter-arrival packet delays (i.e., a covert timing channel) and propose 4 deep learning (DL) frameworks to discriminate between legitimate and illegitimate traffic. Our results, after comparisons with existing conventional machine learning (ML) methods, demonstrates the flexibility and robustness of such approaches for detecting hidden communications in wireless networks.
Chatbot for Trading Cryptocurrency

Many chatbots have been created, offering a variety of services via various channels. A brand-new conversational agent in the quickly evolving realm of technology is a chatbot. Chatbots are getting more and more common because of their intelligence and machine learning. A chatbot is an extension of human interaction technologies like phone calls and social media. Similar to digital or virtual currency, cryptocurrency is a fresh extension created to function as a means of exchange. Investors and other interested parties are keen to learn more about this new form of currency's capabilities in the current world of digital exchange. A chatbot is one method that might be used to swiftly and automatically retrieve information. A chatbot assists the users by providing services to themselves, and they always favour text-based support. Cryptocurrency is a decentralized, blockchain-based, encrypted form of digital money. Blockchain refers to a digital ledger that is accessible only to authorized users in the context of cryptocurrencies. This ledger records transactions involving a range of resources, including money, property, and even intangible assets. The chatbot is deployed for the purpose that it serves the customers at any time. Humans can work only for a limited time whereas, the chatbot is available 24x7 for customer support and assistance. The chatbot helps users in trading cryptocurrencies without any human involvement. It can handle all the queries related to cryptocurrencies that are raised by the customer. And the chatbot can also converse in multiple languages. This helps a lot of customers to use the chatbot in an easy and effective manner. Customers feel so easy to work with the chatbot that it responds to them in the language that they choose to converse. The chatbot stores all the information related to the customer like details of the customer, time stayed on the chatbot, the way customer interacts with the chatbot, etc. The chatbot can predict how likely the user of the chatbot can become a customer to the company of the chatbot. The agents of the company can view the live visitors using the chatbot and their conversation that they have with the chatbot.
Analysing the Emotions of Food Products Reviewsusing NLP and Adaboost Algorithm

In the modern world, communication takes place over a large geographic region, and the Internet is expanding. Social media platforms enable text-based communication and human contact using either Emojis or text. The ability to express one’s emotions through emotion is crucial to human existence. Speaking, making gestures, showing facial expression, and writing in comments are all ways to express emotions. A fascinating field of study has just arisen, and researchers are delving deeper into a variety of sorts of analysis, with emotional analysis being the most popular. Emotional analysis comes in three flavours: positive, negative, and neutral. Giving computers the ability to perceive, comprehend, and elicit certain emotional traits in a manner similar to those of humans is the aim of emotional computingso that they can communicate naturally, amicably, vividly, and with the same emotional intelligence as people. The development goal of the next-generation computer is to figure out how to humanise it and adaptively create the cosiest discussion atmosphere for the dialogue objects. In today’s fiercely competitive business environment, gaining knowledge about consumer habits, wants, preferences, purchasing trends, and decision-making processes is crucial for brands and businesses. When determining the breadth and type of emotional analysis based on food reviews and feedback in e-commerce, our research will take a close look at the most often held points of view. A study on emotional analysis is required for social platforms because the information may become popular and have a big impact on social life. Emoji-based text-based emotion recognition is related to the NLP area (natural language processing). Additionally, we suggested the Adaboost algorithm as an approach to identifying emotion using only emojis. These techniques address the issue of identifying emotions both within and outside of sentences.
Interactive Learning using Mobile based Augmented Reality Application

Augmented Reality has become a state-of-the-art technology that enhances the real-world environment with interactive and immersive virtual objects. This technology mixes the real and virtual worlds, enabling realtime interaction and accurate 3D registration of real and virtual objects. One of the most significant uses of Augmented Reality is in the educational system, especially for primary classes, where it brings imagination to life and increases children’s interest and excitement in their studies. The technology can also be incorporated into mobile apps that are accessible and user-friendly. Software such as Unity and Vuforia are leading the development of Augmented Reality. In education, this technology has made a significant impact and made learning more engaging and interesting. The augmented reality application can be used to teach geometric figures, the solar system, wild animals, automobiles, and more. It uses target images and software that can detect special markers. 3D objects, animations, and other interactive elements come to life when the target image is placed in front of a mobile device’s camera, making learning more enjoyable. The application has received positive feedback from both teachers and students in initial tests.