Reviewerly/exHarmony huggingface dataset:
Hybrid Quantum Machine Learning Classifier with Classical Neural Network Transfer Learning

The hybrid model uses a minimally trained classical neural network as a pseudo-dimensional pool to reduce the number of features from a data set before using the output for forward and back propagation. This allows the quantum half of the model to train and classify data using a combination of the parameter shift rule and a new “unlearning rate” function. The quantum circuits were run using Penny-Lane simulators. The hybrid model was tested on the wine data set to promising results of up to 97% accuracy. The inclusion of quantum computing in machine learning represents great potential for advancing this area of scientific research because quantum computing offers the potential for vastly greater processing capability and speed. Quantum computing is currently primitive; however, this research takes advantage of the mathematical simulators for its processing that prepares this work to be used on actual quantum computers as soon as they become widely available for machine learning. Our research discusses a benchmark of a Classic Neural Network as made hybrid with the Quantum Machine Learning Classifier. The Quantum Machine Learning Classifier includes the topics of the circuit design principles, the gradient parameter-shift rule and the unlearning rate. The last section is the results obtained, illustrated with visual graphs, with subsections on expectations, weights and metrics.
An Analysis of Current Fall Detection Systems and the Role of Smart Devices and Machine Learning in Future Systems

Fall detection and prevention is a critical area of research especially as senior populations grow around the globe. This paper explores current fall detection systems, and proposes the integration of smart technology into existing fall detection systems via smartphones or smartwatches to provide a broad spectrum of opportunities for data collection and analysis. We created and evaluated three ML classifiers for fall detection, namely, k-NN, SVM, and DNN using an open-source fall dataset. The DNN performed the best with an accuracy of 92.591%. Recommendations are also included that illustrate the limitations of current systems, and suggest how new systems could be designed to improve the accuracy of detecting and preventing falls.
Where and How Did Stone Mine Near St. Petersburg

St. Petersburg is located in a unique geological situation, which determined its unique stone appearance. To the north of the city, rocks of the Baltic Crystal Shield come to the surface. These are igneous and metamorphic rocks (granites, marble, quartzites, gneiss, crystalline shales). To the south of St. Petersburg, you can see a lot of sedimentary rocks (limestone and tuff). All the listed rocks have a variety of colors and could have been delivered by water for the construction of the city.
Supply Chain Management in Blockchain Using Hyperledger

A track of the products is kept from producer to consumer as a test of combining the digital and physical worlds. The final consumer has access to a comprehensive record of information and can be confident that the information is correct and precise. Because it employs a distributed public book of account, one of the emerging technologies, blockchain could be a useful approach in handling supply chain management. As a result, building supply chain on blockchain helps improve overall supply chain management as it eliminates counterfeit products, improves visibility and transparency as well in the supply chain. The following qualities of blockchain technology make it useful in the supply chain sector: it lowers errors, avoids product delays, eliminates fraudulent activities, improves management, promotes consumer/supplier confidence, and so on. As a result, we use blockchain to deploy supply chains to improve overall supply chain management. Blockchain records information on a wide range of products and/or service transactions, which are tracked in real time to address a lack of transparency.
Review on Modern Techniques Behind Virtual Cloth Try-On

The proposed study reviews different techniques such as image segmentation and image augmentation to implement virtual try-on. Generally, parser-free image segmentation leverages more accuracy in the training phase since it works with teacher–student model by considering the parser-based model as teacher. In terms of image augmentation techniques, Augmented Reality (AR)-based approach is easier to some extent and handles system level complexity efficiently compared to the image wrapping approach but this requires large computational power. The main objective of the proposed study is to assist the researchers in utilizing any one of the discussed methods such as image segmentation, image warping, and augmented reality to develop an innovative and reliable technology for enabling virtual try-on in both apparel as well as non-apparel market as per the consumer requirement.
Are CK Metrics Enough to Detect Design Patterns?

Design patterns are used to address common design problems in software systems. Several machine learning models have been proposed for detecting and recommending design patterns for a software system. However, limited research is done on using machine learning models for establishing a correlation between design patterns and code quality using ck-metrics. In this paper, we firstly, present a manually labelled dataset composed of 590 open-source software projects primarily from GitHub and GitLab. Secondly, using ck metrics as input of 9 popular machine learning models we predict design patterns. Lastly we evaluated the nine machine learning models using four standard metrics namely, Precision, Recall, Accuracy and F1-Score. Our proposed approach showed noticeable improvement in precision and accuracy.
Collaborative Design Knowledge Retrieval

Knowledge retrieval in the collaborative design of modern products has become a major factor in improving the efficiency of collaborative design. In this chapter, three different levels of knowledge retrieval approaches are introduced. In the first level, the keyword-based retrieval method for the textual information is described with theories and methods from the IR (Information Retrieval) domain. In the second level, the retrieval of structured design knowledge is presented to improve the retrieval performance, which includes generating summaries for knowledge nodes and filtering retrieval results based on type and status information, finding groups of nodes to achieve better match, and using complex queries to enable users better express knowledge needs. In the third level, the semantic retrieval of design knowledge is illustrated, which aims to enable the system to understand the meanings and contexts of knowledge nodes, the intents behind a user’s queries, and the context in which a request for information is being made. As demonstrated in the evaluations of the prototype system, the retrieval methods developed are effective and achieve good performance in addition to the intelligent assistance offered by the Design Rationale editor tool.
Knowledge Mining from Health Data: Application of Feature Selection Approaches

This paper aims to measure the performance of feature selection approaches for mining knowledge from health datasets. We compare seven popular knowledge mining approaches, namely relaxed Lasso, random forest, ReliefF, OneR, information gain, T-test, and Chi-squared test. The support vector machine (SVM) classifier applies to determine the accuracy and area under the curve (AUC) values of the knowledge miners. We use six popular Affymetrix and cDNA datasets. The results reveal that the relaxed lasso works well with Affymetrix, and the relaxed Lasso with random forest approaches perform well with the cDNA datasets. This paper will enrich the existing literature and assist to identify the best feature for knowledge mining in the health informatics domain.
Real-Time Hand Gesture Recognition for Humanoid Robot Control Using Python CVZone

Humanoid robot is able to mimic repetitive activities done by human. By having them, humans can focus on tasks that require extra focus, while leaving the simple one to be done by humanoid robot. To extend the functionality of the 3D-printed upper-body humanoid robot, a camera along with a machine learning algorithm is implemented. In this case, their responses can be categorized based on the gesture it sees. In this paper, the authors evaluate the performance of an upper-body humanoid robot that implements CVZone. The method of evaluation is conducted under three different distances, and each distance has three different brightness levels. It is found that the success rate is highest at the shortest distance which is 50 cm, and under medium-lighted environment. As the distance increases, the hand gesture gets harder to differentiate. At the same time, low or high brightness environment makes the image that contain hand gesture hard to interpret, thus lowering the success rate. Finally, hand gesture that involves third and little finger has the lowest success rate of 75%, while the movement that involves three fingers simultaneously has the second lowest success rate of 80.9%. Meanwhile, simple hand gesture such as clenched-fist or lifting a thumb has 100% success rate under all condition.
A Proof of P ! = NP: New Symmetric Encryption Algorithm Against Any Linear Attacks and Differential Attacks

P vs NP problem is the most important unresolved problem in the field of computational complexity. Its impact has penetrated into all aspects of algorithm design, especially in the field of cryptography. The security of cryptographic algorithms based on short keys depends on whether P is equal to NP. Cryptography algorithms used in practice are all based on short key, and the security of the short key mechanism is ultimately based on “one-way” assumption, that is, it is assumed that a one-way function exists. In fact, the existence of one-way function can directly lead to the important conclusion P ! = NP. In this paper, we originally constructed a short-key block cipher algorithm. The core feature of this algorithm is that for any block, when a plaintext-ciphertext pair is known, any key in the key space can satisfy the plaintext-ciphertext pair, that is, for each block, the plaintext-ciphertext pair and the key are independent, and the independence between blocks is also easy to construct. This feature is completely different from all existing short-key cipher algorithms. Based on the above feature, we construct a problem and theoretically prove that the problem satisfies the properties of one-way functions, thereby solving the problem of the existence of one-way functions, that is, directly proving that P ! = NP.
The Study of the Unsupervised Classification Method Using the K-means Algorithm by a Proposition of a Simple Initialization Technique

Image segmentation is a fundamental step in image processing, The existing segmentation techniques are numerous, but they are generally grouped into three main approaches which are the contour approach, the region approach and the cooperative approach. The segmentation by region approach aims to group the pixels with the same characteristics into homogeneous regions. It is characterized by the measure of uniformity of the regions constructed into the image. Generally, we distinguish three families of algorithms for the region approach: classification methods, region growth methods, and methods that divide or merge regions according to the chosen criterion. Our study is based on the unsupervised classification method using the K-means algorithm. This is the most known and used clustering algorithm, because of its simplicity of implementation. We know that this algorithm still suffers from several drawbacks such as The number of classes must be fixed at the beginning, The result depends on initial draw or starting values of the centers of the classes, so a main problem in the initialization phase. For this we propose to improve this behind by a simple initialization technique based on the optimization of the distance between the objects inside each class to obtain a set of compact and clearly separated clusters.
UHD Underwater Image Enhancement via Frequency-Spatial Domain Aware Network

Currently, carrying ultra high definition (UHD) imaging equ- ipment to record rich environmental conditions in deep water has become a hot issue in underwater exploration. However, due to the poor light transmission in deep water spaces and the large number of impurity particles, UHD underwater imaging is often plagued by low contrast and blur. To overcome these challenges, we propose an efficient two-path model (UHD-SFNet) that recovers the color and the texture of an underwater blurred image in the frequency and the spatial domains. Specifically, the method consists of two branches: in the first branch, we use a bilateral enhancement pipeline that extracts the frequency domain information of a degraded image to reconstruct clear textures. In the pipeline, we embed 1D convolutional layers in the MLP-based framework to capture the local characteristics of the token sequence. In the second branch, we develop U-RSGNet to capture the color features of the image after Gaussian blurring to generate a feature map rich in color information. Finally, the extracted texture features are fused with the color features to produce a clear underwater image. In addition, to construct paired high-quality underwater image enhancement dataset, we propose UHD-CycleGAN with the help of domain adaptation to produce more realistic UHD synthetic images. Experimental results show that our algorithm outperforms existing methods significantly in underwater image enhancement on a single GPU with 24G RAM. Codes are available at https://github.com/wyw0112/UHD-SFNet .
Multi-Branch Network with Ensemble Learning for Text Removal in the Wild

The scene text removal (STR) is a task to substitute text regions with visually realistic backgrounds. Due to the diversity of scene text and the intricacy of background, earlier STR approaches may not successfully remove scene text. We discovered that different networks produce different text removal results. Thus, we present a novel STR approach with a multi-branch network to entirely erase the text while maintaining the integrity of the backgrounds. The main branch preserves high-resolution texture information, while two sub-branches learn multi-scale semantic features. The complementary erasure networks are integrated with two ensemble learning fusion mechanisms: a feature-level fusion and an image-level fusion. Additionally, we propose a patch attention module to perceive text location and generate text attention features. Our method outperforms state-of-the-art approaches on both real-world and synthetic datasets, improving PSNR by 1.78 dB in the SCUT-EnsText dataset and 4.45 dB in the SCUT-Syn dataset.
PromptLearner-CLIP: Contrastive Multi-Modal Action Representation Learning with Context Optimization

An action contains rich multi-modal information, and current methods generally map the action class to a digital number as supervised information to train models. However, numerical labels cannot describe the semantic content contained in the action. This paper proposes PromptLearner-CLIP for action recognition, where the text pathway uses PromptLearner to automatically learn the text content of prompt as the input and calculates the semantic features of actions, and the vision pathway takes video data as the input to learn the visual features of actions. To strengthen the interaction between features of different modalities, this paper proposes a multi-modal information interaction module that utilizes Graph Neural Network(GNN) to process both the semantic features of text content and the visual features of a video. In addition, the single-modal video classification problem is transformed into a multi-modal video-text matching problem. Multi-modal contrastive learning is used to disclose the feature distance of the same but different modalities samples. The experimental results showed that PromptLearner-CLIP could utilize the textual semantic information to significantly improve the performance of various single-modal backbone networks on action recognition and achieved top-tier results on Kinetics400, UCF101, and HMDB51 datasets. Code is available at https://github.com/ZhenxingZheng/PromptLearner .
Action Representing by Constrained Conditional Mutual Information

Contrastive learning achieves a remarkable performance for representation learning by constructing the InfoNCE loss function. It enables learned representations to describe the invariance in data transformation without labels. Contrastive learning also been employed in self-supervised learning of action recognition. However, this kind of method fails to introduce assumptions according to human knowledge about the prior distribution of representations in the training process. For solving this problem, this paper proposes a self-supervised learning framework, which can achieve different self-supervised learning methods by choosing different assumptions about the prior distribution of representations, while still learning the description of invariance in data transformation as contrastive learning. This framework minimizes the CCMI (Constrained Conditional Mutual Information) loss function, which represents the conditional mutual information between input augmented samples of the same sample and the output representations of the encoder while the prior distribution of representations is constrained. By theoretical analysis of the framework, it is proved that traditional contrastive learning by InfoNCE is a special case without human knowledge constraint of this framework. The Gaussian Mixture Model on Unit Hyper-sphere is chosen as the representation prior distribution to achieve the self-supervised method called CoMInG. Compared with the existing methods, the performance of the learned representation by this method in the downstream task of action recognition is significantly improved.
Boosting Dense Long-Tailed Object Detection from Data-Centric View

Several re-sampling and re-weighting approaches have been proposed in recent literature to address long-tailed object detection. However, state-of-the-art approaches still struggle on the rare class. From data-centric view, this is due to few training data of the rare class and data imbalance. Some data augmentations which could generate more training data perform well in general object detection, while they are hardly leveraged in long-tailed object detection. We reveal that the real culprit lies in the fact that data imbalance has not been alleviated or even intensified. In this paper, we propose REDet: a rare data centric detection framework which could simultaneously generate training data of the rare class and deal with data imbalance. Our REDet contains data operations at two levels. At the instance-level, Copy-Move data augmentation could independently rebalance the number of instances of different classes according to their rarity. Specifically, we copy instances of the rare class in an image and then move them to other locations in the same image. At the anchor-level, to generate more supervision for the rare class within a reasonable range, we propose Long-Tailed Training Sample Selection (LTTSS) to dynamically determine the corresponding positive samples for each instance based on the rarity of the class. Comprehensive experiments performed on the challenging LVIS v1 dataset demonstrate the effectiveness of our proposed approach. We achieve an overall 30.2% AP and obtain significant performance improvements on the rare class.
SST-VLM: Sparse Sampling-Twice Inspired Video-Language Model

Most existing video-language modeling methods densely sample dozens (or even hundreds) of video clips from each raw video to learn the video representation for text-to-video retrieval. This paradigm requires high computational overload. Therefore, sparse sampling-based methods are proposed recently, which only sample a handful of video clips with short time duration from each raw video. However, they still struggle to learn a reliable video embedding with fragmented clips per raw video. To overcome this challenge, we present a novel video-language model called SST-VLM inspired by a Sparse Sampling-Twice (SST) strategy, where each raw video is represented with only two holistic video clips (each has a few frames, but throughout the entire video). For training our SST-VLM, we propose a new Dual Cross-modal MoCo (Dual X-MoCo) algorithm, which includes two cross-modal MoCo modules to respectively model the two clip-text pairs (for each video-text input). In addition to the classic cross-modal contrastive objective, we devise a clip-level alignment objective to obtain more consistent retrieval performance by aligning the prediction distributions of the two video clips (based on the negative queues of MoCo). Extensive experiments show that our SST-VLM achieves new state-of-the-art in text-to-video retrieval.
Confidence-Calibrated Face Image Forgery Detection with Contrastive Representation Distillation

Face forgery detection has been increasingly investigated due to the great success of various deepfake techniques. While most existing face forgery detection methods have achieved excellent results on the test split of the same dataset or the same type of manipulations, they often do not work well on unseen datasets or unseen manipulations due to the issue of model generalization. Therefore, in this paper, we propose a novel contrastive distillation calibration (CDC) framework, which distills the contrastive representations with confidence calibration to address this generalization issue. Different from previous methods that equally treat the two forgery types, Face Swapping and Face Reenactment, we devise a dual-teacher module where the knowledge is separately learned for each forgery type. A contrastive representation learning strategy is further presented to enhance the representations of diverse forgery artifacts. To prevent the proposed model from being overconfident, we propose a novel Kullback-Leibler divergence loss with dynamic weights to moderate the dual-teacher’s outputs. In addition, we introduce label smoothing to calibrate the model confidence with the target outputs. Extensive experiments on three popular datasets show that our proposed method achieves the state-of-the-art performance for cross-dataset face forgery detection.
MSF$$^2$$DN: Multi Scale Feature Fusion Dehazing Network with Dense Connection

Single image dehazing is a challenging problem in computer vision. Previous work has mostly focused on designing new encoder and decoder in common network architectures, while neglecting the connection between the two. In this paper, we propose a multi scale feature fusion dehazing network based on dense connection, MSF $$^2$$ DN. The design principle of this network is to make full use of dense connection to achieve efficient reuse of features. On the one hand, we use a dense connection inside the base module of the encoder-decoder to fuse the features of different convolutional layers several times, and on the other hand, we design a simple multi-stream feature fusion module which fuses the features of different stages after uniform scaling and feeds them into the base module of the decoder for enhancement. Numerous experiments have demonstrated that our network outperforms the existing state-of-the-art networks.