Reviewerly/exHarmony huggingface dataset:
Approximation of the expectation-maximization algorithm for Gaussian mixture models on big data

Gaussian mixture models are a very useful tool for modeling data distribution. While estimating parameters using the expectation-maximization algorithm, this approach does not scale well with big datasets, especially if it is necessary to prepare many models for the proper selection of metaparameters. In this article we present an approximation of the expectation-maximization algorithm obtained by merging crucial subsets of the dataset, that differ slightly in their effect on the expectation-maximization loss function, into information granules. Furthermore, application examples comparing new method with the classical approach are shown.
Blockchain Interoperability Landscape

Blockchain has become a popular emergent technology in many industries. It is suitable for a broad range of applications, from its base role as an immutable distributed ledger to the deployment of distributed applications. Many organizations are adopting the technology, but choosing a specific blockchain implementation in an emerging field exposes them to significant technology risk. Selecting the wrong implementation could expose an organization to security vulnerabilities, reduce access to its target audience, or cause issues in the future when switching to a more mature protocol. Blockchain interoperability aims to solve this adaptability problem by increasing the extensibility of blockchain, enabling the addition of new use cases and features without sacrificing the performance of the original blockchain. However, most existing blockchain platforms need to be designed for interoperability, and simple operations like sending assets across platforms create problems. Cryptographic protocols that are secure in isolation may become insecure when several different (individually secure) protocols are composed. Similarly, utilizing trusted custodians may undercut most of the benefits of decentralization offered by blockchain-based systems. Even though there is some research and development in the field of blockchain interoperability, a characterization of the interoperability solutions for various infrastructure options is lacking. This paper presents a methodology for characterizing blockchain interoperability solutions that will help focus on new developments and evaluate existing and future solutions in this space.
Role-Oriented Dynamic Network Embedding

Exploring the differences and important patterns of nodes from the perspective of roles has gradually developed into an interesting and important topic in network analysis. However, existing role-oriented network embedding methods focus more on identifying underlying roles for static network, which leads to complex temporal behaviors being overlooked and degraded performance facing dynamic network. The few role analytics methods for dynamic networks either cannot learn general node representations or fail to discovery role transitions of nodes. In this work, we propose a unified framework RDNE (Role-oriented Dynamic Network Embedding) to tackle such challenges, which aim to learn multiple embeddings for individual nodes based on time-varying structural behaviors. Based on regular equivalence, RDNE propagates the structural features over the graph to derive the initial role-oriented representations. Then, it applies capsule network to further model the mapping between nodes and roles, which is the first time capsule network is used for role discovery. For the varying and temporal dependence within dynamic network, we utilize the Gated Recurrent Unit to compute historical information and use historical information to influence the generation of representations at the next snapshot. Comprehensive experiments on both synthetic and real-world networks validate the superiority of the proposed RDNE.
GAN-based Data Augmentation for Credit Card Fraud Detection

Deep generative approaches, such as GANs (generative adversarial networks), can be used to efficiently generate new data points that are similar to existing ones. This can be useful for increasing the size of a dataset or for creating synthetic data points that can be used in place of real ones. In this study, we trained classifiers using our novel K-CGAN approach and compared them to other oversampling approaches. We achieved higher F1 score performance metrics than the other methods. After conducting several experiments, we found that classifiers based on a Random Forest, Nearest Neighbor, Logistic Regression, MLP or Adaboost algorithm trained on the augmented set performed much better than those trained on the original data. This effectively creates a fraud detection mechanism.
Open Set Recognition for Encrypted Traffic using Intra-class Partition and Boundary Sample Generation

Existing deep learning-based encrypted traffic recognition methods can achieve high precision identification performance while protecting user privacy, but almost all of them focus on closed sets, in which training data and test data come from the same label space. However, the real network environment is open, and it is necessary for traffic identification systems to deal with previously unseen categories. To classify known categories at a fine granularity and identify unknown ones, we propose a novel open-set recognition method for encrypted traffic. The decision boundary sample generation method based on constrained Generative Adversarial Networks is presented to generate samples from low-density regions in the known data space. Furthermore, an end-to-end open-set classifier is built using the accurate and diverse simulative samples of unknown classes through intra-class partition and boundary sample generation. Finally, the effectiveness of the proposed method is validated through numerical experiments.
LADS-NET: A Deep Learning Model for Dynamic Sign Language Recognition

Deaf-mutes cannot communicate with the outside world normally due to their own reasons, while the sign language is an important medium for them to participate in social life. Intelligent sign language recognition (SLR) can not only solve the problem of communication between deaf-mutes and healthy people but also contribute to the development of human-computer interaction. The SLR method based on data gloves is not limited by the experimental site, which can fully capture various hand movements of users and extract action information more accurately. Therefore, a dynamic SLR model was constructed by integrating the Long Short-Term Memory network based on Attention Mechanism (LSTM-Attention) and the 2D Dense Convolution Network (2D-Densenet), which uses a data glove as an action information acquisition device. The spatiotemporal feature data of finger posture and palm position were collected by the data glove, and then both sets of data were fed into the LSTM-Attention and 2D-DenseNet networks for training respectively. Finally, the two kinds of features were fused by series fusion for classification and recognition, and a recognition rate of 99.13% was obtained. The experiment results show that the LADS-NET, proposed in this paper, has a higher recognition rate and better results compared with other models.
Methods for location and recognition of chess pieces based on machine vision

The positioning and recognition of chess pieces is the key to the chess robot's recognition of games. Aiming at the positioning and identification of chess pieces a method of centroid positioning of the maximum connected components and a method of convolutional neural network identification were proposed. First, the pieces were pre-segmented based on the HSV color space, and then the segmentation results were eroded and dilated to obtain the maximum connected components of the pieces, and the centroid coordinates of the connected components were calculated as the positioning results of the pieces. Finally the chess pieces were identified using the trained convolutional neural network. The network used a variety of techniques to reduce the number of parameters under the premise of ensuring a high recognition rate so that it could be deployed on resource-constrained devices. The results show that the average positioning error of the method on a chessboard with a size of 28cm and 28cm is 0.48mm, the average positioning time is 11ms and the recognition accuracy of chess pieces is 98.7%•
Research on Gesture Recognition and Classification Based on Attention Mechanism

Since the 20th century, the computer field has developed rapidly and human-computer interaction has become more and more frequent. As an important development area of human-computer interaction, gesture recognition research is in line with the development needs of today's intelligent society. In this paper, the static gesture data set is used as the research object, and the ResNet in convolutional neural network is used to train it, and the CBAM attention mechanism is introduced to conduct comparison experiments and ablation experiments. The final experimental results show that the ResNet model based on the attention mechanism improves the accuracy of gesture recognition and classification by about 2.5% compared to the ResNet model without the attention mechanism, and both models achieve high accuracy on the dataset, confirming the necessity and effectiveness of the method of introducing the attention mechanism.
Multi-granularity Partial and Identity-aware Global Feature Learning for RGB-Infrared Person Re-identification

RGB-Infrared person Re-identification (RGB-IR ReID) aims to match pedestrians with the same identity and different modalities in the 24-hour intelligent surveillance system, facing the challenge of how to mitigate intra-class variations and large cross-modality discrepancy. Although achieving promising progress, existing methods still suffer from two main drawbacks. On the one hand, conventional methods struggle with aligning the RGB and infrared modalities into the same space by simply learning global or partial modality-shared features, in which the rich fine-grained partial detail information is underutilized. On the other hand, these methods mainly focus on mining the discriminative representations in the feature-space or pixel-space while ignoring the inherent identity information, eventually leading to performance decline. To solve the aforementioned two problems, we first propose a novel method, termed Multi-granularity Partial and Identity-aware Global feature learning network (MPIG). In brief, MPIG integrates three kinds of fine-grained partial and coarse-grained global features to exploit the image and identity information in a more precise fashion. Based on this design, we further introduce a novel triple center loss function that exploits one identity center and two different modality centers to reduce the intra-class cross-modality discrepancy while enlarging the homo-modality inter-class distance. Experimental results show that our proposed method outperforms state-of-the-arts by large margins on SYSU-MM01 and RegDB datasets, respectively.
Quantum Machine Learning: An Effective Approach to High-dimensional Learning

Machine learning (ML) algorithms have gained great success during the last 20 years. However, this development is encountering growing difficulties. With dataset sizes continually increasing and Moore's Law coming to an end, it is predicted that soon a stage will arrive where present computing tools will no longer suffice. With quantum computing technology approaching the point of commercialization enabling quantum supremacy, they can efficiently tackle certain challenges in machine learning that are thought to be difficult for conventional machines. This paper details how quantum machine learning works, with special attention to the quantum support vector classifier. This work also brings out the comparison between the Quantum support vector classifier and its classical equivalent by working on the Cleveland heart disease dataset.
Designing a Wearable Jacket for the Visually Impaired People

One of the most crucial components of each living thing is sight. It enables one to see what is in their environment, including vital items like paths, barriers, and other things. But consider a scenario in which someone is blind. It seems like life is challenging. According to the World Health Organization (WHO),around 273 million individuals worldwide have vision impairments, of which 39 million are totally blind. To assist the sight challenged, a number of strategies were used. A stick is more commonly used in conventional situations. Things would be comparatively simple if sensors and other pertinent electrical devices were added to the system. The more intelligent approach might involve creating a system that can recognize impediments and alert the user. As a result, these technologies can be used to develop and implement a smarter system in this living environment, which could be a huge benefit to mankind. [2]
Multiclass Objects Localization Using Deep Learning Technique in Autonomous Vehicle

In a conventional automobile, the vehicle is controlled by a human brain. The eyes, which gather visual data from surrounding traffic, are the most important human sense. This visual input is then sent to the brain through the eyes. The eyes then communicate this visual information to the brain, which is in charge of operating the car by applying brakes, accelerating, or turning left or right. Autonomous vehicles or self-driving cars, on the other hand, rely on sensors, LiDAR, RADAR, and cameras to gather traffic data. This data from multiple sensors and cameras must be processed for the car to travel smoothly and securely. Intelligent software that can simulate a human brain, classify, and localize numerous sorts of traffic objects in real- time is required for autonomous vehicles. The image is the major input of an autonomous vehicle, and the program must detect the object from the input image. Object localization, or predicting the true position of a recognized object in an image, is a critical problem. The software can only predict the next step or decide what distance to keep between the car and other traffic objects, as well as whether to brake and accelerate, after exact object localization. We are proposing an object localization method using YOLOv5 as an object detector and LiDAR to calculate the distance. We created a custom dataset of 6000 images of five different classes and achieved 89.2% mAP value and at a .95 IoU threshold, our model achieved 52.1 mAP. We have also compared several conceivable computer vision challenges and real-time deep learning models in object localization in this work.
The Construction and Experimental Approach of Anonymous Network Analysis and Control Platform

According to the traffic features of anonymous networks such as TOR and I2P, this paper proposes an anonymous network traffic filtering approach based on payload size entropy. It exploits the filtered traffic as the training set to build a machine learning model for traffic classification, by adopting decision tree learning algorithm. In order to extract target attributes from multiple characteristic attributes of anonymous network traffics, the Bayesian classification algorithm is utilized to analyze the impact of each attribute on the information entropy gain, so as to select target attributes in the traffic classification model. To be specific, this paper focuses on constructing an anonymous network analysis and control platform, and carries out experimental verification in an anonymous network environment. The experimental results show that the identification accuracy of the proposed approach for anonymous network traffic is as high as 96.8%, and the packet loss rate is less than 0.1% in the Gigabit network environment within the allowable error range.
Quantum Cryptography: A Pathway to Secure Communication

Cryptography was introduced to prevent a third party from accessing and learning the contents of private messages sent during a communication process. Quantum Cryptography looks promising to provide a new level of secure communication by applying quantum mechanics concepts to cryptography. The research in the domain emphasizes that such systems can detect eavesdropping and ensure that it does not occur at all. This paper reviews the existing state of quantum cryptography, which includes an introduction to quantum computing and quantum key distribution algorithm, special attention is given to the implementation and working of the BB84 Protocol. It also provides a glimpse of post-quantum cryptography.
Lightened Context Extraction Network for Object Detection

Multi-scale feature maps have long been used for object detection,especially for small objects with weak appearance.Feature Pyramid Network(FPN)accomplishes multi-scale feature fusion in an end-to-end framework,which attracts significant attention. In order to take full advantages of feature maps at each level,we follow part of AC-FPN(Attention-guided Context Feature Pyramid Network) to stack feature maps after every dilation convolution.The number of parameters keeps increasing during feature fusion,we propose to lighten our model by Channel Group Average without bringing in new parameters.Experiments on PASCAL VOC2007,we observe mAP is increased by 9.45% comparing to SSD+MobileNet+FPN and 2.04% comparing to Faster R-CNN.Furthermore,the mAP can be increased by another 1.08% when the DIoU loss being replaced with our new LSIoU(Location and Size-Based IoU)loss function.
SWOT Analysis and Strategies of High-Quality Career Guidance Education in Universities in the Context of Big Data

Talent is the first resource for economic and social development, and the improvement of graduates' employment quality is an important feedback of the effect of talent cultivation in colleges and universities. In the background of big data, employment guidance has advantages of diversified data sources and digitalized problem presentation, but it also has shortcomings of weak awareness, imperfect working mechanism and unsound professional talent system to promote employment guidance in high quality. At the same time, it also faces challenges such as the "information cocoon" effect caused by the "explosion" of employment guidance information and the unlimited application of big data. To carry out career guidance education for college graduates, we need to coordinate advantages and disadvantages, opportunities and challenges, and study and formulate four-dimensional development strategies.
gr-tempest: an open-source GNU Radio implementation of TEMPEST

Like all time-varying voltage and current, a video interface connecting a PC to its monitor emits electromagnetic waves. The attack commonly known as TEMPEST (or Van Eck Phreaking) consists in receiving this signal and inferring the image being displayed on the monitor; that is to say, pointing an antenna to a PC and spying the monitor. This is a particularly interesting application for Software Defined Radio (SDR), as it requires modeling the signal and implementing a custom receiver. However, and although the first public demonstrations date back to the mid-80s by Wim Van Eck, no open-source implementation was available until Martin Marinov's TempestSDR was published in 2014. TempestSDR consists of a module written in C that takes care of the signal processing, plug-ins for various models of SDR hardware, and a Java-based GUI. This results in a multi-platform software that, although functional, it is difficult to extend or tweak. For instance, new plug-ins have to be written for new SDR hardware, or including filters or other DSP blocks in the signal's flow is not straightforward at all. To remedy this we developed gr-tempest, an open-source GNU Radio-based implementation of TEMPEST (available at https://github.com/git-artes/gr-tempest). This is an on-going project whose objective is to enable simpler experimentation by taking advantage of GNU Radio's functionalities and support. We describe the mathematical principles behind the TEMPEST attack and present how grtempest works. Furthermore, we show several real-world examples including both VGA and HDMI, and the fundamental differences between both types of signals. Finally, some of the advantages of using GNU Radio's framework are showcased by introducing modifications to the DSP chain that allows significant improvements of the resulting image with respect to the original method used in TempestSDR.
Image-dev: An Advance Text to Image AI model

In the recent years, with the rapid growth of Artificial Intelligence, there is increasing interest in Text-to-Image models. High-quality images can be generated with state-of-art text-to-image AI models such as Imagen, DALL.E-2, Draw-Bench. However, these models struggle with generating well aligned images for conflict category and low database. Therefore, Image-dev is a Text-To-Image model that blends TF-IDF(Term Frequency - Inverse Document Frequency) model along with preposition model, to evaluate the relation between the data object. Proposed model output images have an unparalleled level of artistic finish and an added level of language understanding and interpretation further enhance model to produce conflict category images. Image-dev help user's to generate a high-quality, photorealistic images without any pre-context based on GANs, VAEs and diffusion model. Image-dev is based on diffusion model. Diffusion model is more relevant because of its high quality and realistic output generation capacity.
Human Emotion Recognition Using Gabor Variance Features with Back Propagation Neural Network Classifier

It is natural as stimuli-response humans always express their feelings & reaction to any certain event automatically appears on the 'face,. Facial expression is an important medium for human communication as it express human thinking, feelings and his or her current mental situation, thus it is being used in many application areas. This paper aims to introduce a novel method for human emotion recognition using average variance as the feature vectors obtained from the Gabor filter convolved 'n, images which helps in classifying those emotions. Based on the Gabor variance features, a three layer back propagation neural network (BPNN) has been used as a classifier. The BPNN architecture used in the experimentation work contains 210 input units in the input layer, which corresponds to the displacement information of the Gabor variance feature vectors. There are 6 units in the output layer and one hidden layer of 256 units. According to the JAFFE database, the average accuracy of the proposed emotion recognition algorithm was the highest with 94.66%. Timing analysis using the same database shows that the template response time is lower because the BPNN is only 3-tier architecture which requires a single training as emphasis is on recall time. Because network training is only needed once, recall time is more important than training time.
An Insulator Detection Model Using Bidirectional Feature Fusion Structure Based On YOLO X

Transmission line detection is the key to ensure the safe operation of the power system. How to identify the power equipment and further detect the faults is an important topic. Firstly, based on the You Only Look Once X(YOLO X), the Bidirectional Feature Fusion(BFF) structure is proposed to replace the Feature Pyramid Networks(FPN) and Path Aggregation Network(PAN) of the model, making multi-scale feature fusion more effective. Then, a transmission line insulator fault dataset is used to verify the effectiveness and accuracy of the proposed model. The results show that the mean average precision(mAP) of this model reaches 96.56% (compared to YOLO X increased by 0.54%), and the detection speed reaches 17 FPS on NVIDIA GeForce RTX 3080 Laptop.