Reviewerly/exHarmony huggingface dataset:
Unsupervised Detailed Human Shape Estimation from Multi-view Color Images

This paper presents a novel framework to estimate detailed human body shape from color images in an unsupervised manner. It is a challenging task due to factors such as variations in human shapes, occlusion, and cloth details. The existing methods are mainly supervised and require a large number of ground truth real training data which is usually hard to obtain. To solve this problem, we propose an unsupervised detailed human shape estimation method from multi-view color images. Specifically, we first predict the depth map for the source view through robust photometric consistency with different views. Then, we predict the initial SMPL model from the color image and refine it by an iterative error feedback regressor based on point clouds of the predicted depth map. Finally, the refined SMPL model is deformed to fit the details (i.e., clothes and faces) on the point clouds to recover the detailed human shapes which are represented by adding a set of offsets to the SMPL model. The experimental results on different dataset demonstrate that our method outperforms the state-of-the-art methods and achieves higher reconstruction accuracy.
Image Forensic Tool (IFT)

Images now-a-days are often used as an authenticated proof for any cyber-crime. Images that do not remain genuine can mislead the court of law. The fast and dynamically growing technology doubts the trust in the integrity of images. Tampering mostly refers to adding or removing important features from an image without leaving any obvious trace. In earlier days, digital signatures were used to preserve the integrity, but now a days various tools are available to tamper digital signatures as well. Even in various state-of-the-art works in tamper detection, there are various restrictions in the type of inputs and the type of tampering detection. In this paper, the researchers propose a prototype model in the form of a tool that will retrieve all the image files from given digital evidence and detect tampering in the images. For various types of tampering, different tampering detection algorithms have been used. The proposed prototype will detect if tampering has been done or not and will classify the image files into groups based on the type of tampering.
Fault Injection Method for Hardware-implemented AES without Artificial Triggering

The theoretical process of fault injection attacks requires artificial triggering for target operation experimental easement. In real-world scenarios, artificial triggering depends on a powerful assumption, e.g., internal codes manipulating on the target device. Therefore, Lim et al. proposed a fault injection system that uses the I/O signals of target devices as a trigger to relax attacker assumptions. This system does not require the implementation of artificial triggering because input signals are used as a trigger in the transmission of plaintexts for fault injection attacks. The proposed fault injection system was applied in the experimental verification of an algorithm implemented in software; however, verification was not performed for a hardware-implemented algorithm. In this paper, we propose an eased triggering system that can also be used in hardware implemented algorithms. In the proposed system, Start/Done signals are used as a trigger. In addition, we propose an algorithm to analyze hardware-implemented AES, even if the fault injected range is widened.
Observable and Attention-Directing BDI Agents for Human-Autonomy Teaming

Human-autonomy teaming (HAT) scenarios feature humans and autonomous agents collaborating to meet a shared goal. For effective collaboration, the agents must be transparent and able to share important information about their operation with human teammates. We address the challenge of transparency for Belief-Desire-Intention agents defined in the Conceptual Agent Notation (CAN) language. We extend the semantics to model agents that are observable (i.e. the internal state of tasks is available), and attention-directing (i.e. specific states can be flagged to users), and provide an executable semantics via an encoding in Milner's bigraphs. Using an example of unmanned aerial vehicles, the BigraphER tool, and PRISM, we show and verify how the extensions work in practice.
Extension to CryptDB with Support for Arithmetic Expressions

Growth of cloud infrastructure has made it attractive for enterprises to rethink how and where to position their resources. CryptDB ( Raluca Ada Popa, et al.: CryptDB: Protecting Confidentiality with Encrypted Query Processing. MIT CSAIL, SOSP ‘11, October 23–26, 2011) allows storage of cloud data in encrypted form and helps mitigate privacy concerns. It performs encryption of data in layers without revealing plain-text data to the cloud vendor. While CryptDB supports simple queries, it does not appear to handle queries with arithmetic expressions. We discuss the pros and cons of a couple of schemes to address this issue and finally propose a component-based approach that provides support for queries with arithmetic expressions.
An Ensemble Approach for Modeling Process Behavior and Anomaly Detection

In recent years, malware has become more sophisticated. Security solutions based on signature-based detection and known behavior-based detection are not capable of detecting unknown malware. Anomaly detection is an effective strategy against unknown malware. In this paper we present a method to model the normal behavior of processes that run on a computer and detect whether a new process instance conforms with this normal behavior. In this paper we present unsupervised tree-based anomaly detector (UTAD) which is used in combination with autoencoder to detect anomalous process behavior. We evaluated the performance of model in separating behavior of genuine processes from one another. The ensemble model achieved good accuracy with low false positive rate.
Privacy Concerns From Single-Word Search Query Leakage From Web Browsers Through DNS

Some Web browsers working on certain operating systems (OSs) specify that a single-word search query entered into the search bar be sent to the recursive resolver of the domain name system (DNS) as a DNS request that includes the word. Such behavior is unexpected for users and induces privacy concerns through single-word search query leakage. We present our survey on the conditions during which this behavior occurs. By analyzing the DNS traffic captured at the recursive resolvers, we demonstrate that a considerable number of queries are leaked and the interests of individuals or organizations can be inferred through passive eavesdroppers.
Hardware Components of the Monitoring System of Soil and Groundwater Contamination by Harmful Emissions From Vehicles

The article considers the components of the hardware monitoring system of soil and groundwater contamination by harmful emissions from vehicles. The functionality of the monitoring system is based on combination of measurement procedure of the harmful emissions concentrations in the surface atmospheric layer and mathematical modeling of the impact of these emissions on soil and groundwater contamination. The hardware scheme of the component of the vehicles exhaust gases harmful emission concentrations measurement using a mobile complex Sniffer4D Hyper-local Air Quality Analyzer is given. In particular, an example of nitrogen dioxide concentration measurement in the atmosphere surface layer is given. The architecture of the monitoring system is designed.
Imputation of Missing Values using Improved K-Means Clustering Algorithm to Attain Data Quality

A data warehouse aids in the management of large amounts of data that may be stored in order to handle user input during the computer process. The major issue with a data warehouse is to maintain the data that the user stores in good quality. Some traditional techniques can improve data quality while also increasing efficiency. Each unit of data has a unique feature that has been researched by many researchers and has an influence on data quality. This research article has enhanced the K-Means method by utilizing the Euclidean Distance metric to detect missing values from the gathered sources and replace them with closest values while maintaining the data's consistency, exactness, and quality. yThe improved data will assist developers in analysing data quality prior to data integration by allowing them to make informed decisions quickly in accordance with business requirements. Improved K-Means achieves better accuracy and requires less computational time for clustering data objects when compared to other related approaches.
Biomedical Scan Image Retrieval Using Higher Order Neural Networks

The automatic medical diagnosis that uses computer tomography or magnetic resonance images or any other medical images requires retrieving most similar images from an image repository of previous patients when an image of a particular patient is given as a query. In the existing methods, the researchers have used local bit plane decoded pattern (LBDP), local diagonal extrema pattern (LDEP), local binary pattern (LBP), and local wavelet pattern (LWP) for indexing and retrieval of biomedical images. In this proposed method, the pulse-coupled neural networks (PCNN) and unit-linking PCNN are used to extract the features. The PCNN is a single-layer two-dimensional neural network of pulse-coupled neurons. Each pixel in the image is feeding input to the corresponding neuron in the 2D network. Thus, the feature extraction is done using basic and UL-PCNN. Then to retrieve similar images, the classification is done using the neural networks. The proposed algorithm is tested for image retrieval using the mammographic image analysis society (MIAS) database and the existing approaches.
Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images

High quality data labeling is essential for improving the accuracy of deep learning applications in medical imaging. However, noisy images are not only under-represented in training datasets, but also, labeling of noisy data is low quality. Unfortunately, noisy images with poor quality labels are exacerbated by traditional data augmentation strategies. Real world images contain noise and can lead to unexpected drops in algorithm performance. In this paper, we present a non-traditional, purposeful data augmentation method to specifically transfer high quality automated labels into noisy image regions for incorporation into the training dataset. The overall approach is based on the use of paired images of the same cells in which variable image noise results in cell segmentation failures. Iteratively updating the cell segmentation model with accurate labels of noisy image areas resulted in an improvement in Dice coefficient from 77% to 86%. This was achieved by adding only 3.4% more cells to the training dataset, showing that local label transfer through graph matching is an effective augmentation strategy to improve segmentation.
Research on Performance Prediction of Technological Innovation Enterprises Based on Deep Learning

High-tech enterprises are the leaders in promoting economic development. The study of the relationship between their scientific and technological innovation capabilities and corporate performance is of far-reaching practical significance for guiding companies to formulate independent innovation strategies scientifically, improving their independent innovation capabilities, and promoting further transformation into an innovative country. In view of the large-scale technological innovation enterprise network, the traditional technological innovation enterprise performance prediction method cannot fully reflect the real-time technological innovation enterprise status. Aiming at the deficiencies of the existing short-term technology innovation enterprise forecasting methods, this paper proposes a technology innovation enterprise performance forecasting method based on deep learning. I analyze the temporal and spatial characteristics of the data of technological innovation enterprises and divide the data according to the temporal characteristics of technological innovation enterprises. According to the spatial relevance of technological innovation enterprises, grouping is carried out by setting different correlation coefficient thresholds. The method of spectral decomposition is used to divide the data of scientific and technological innovation enterprises into trend items and random fluctuation items, to decompose the matrix of scientific and technological innovation enterprises, and to construct a compressed matrix using correlation. Using the deep belief network model in deep learning combined with support vector regression to establish a prediction model for technological innovation enterprises, this paper proposes a convolutional neural network model for performance prediction of scientific and technological innovation enterprises. Through the convolution operation and subsampling operation based on the concept of local window, the feature learning from the local to the whole is completed. This article uses the Naive Bayes model, logistic regression model, support vector regression model, and other mainstream methods to predict and compare the performance of technological innovation enterprises. I use the dropout method to reduce the impact of overfitting during training. The experimental results show that the deep neural network model method used in this article can achieve better prediction results than mainstream methods under the same characteristics. The experimental results on the data set confirm that the method of performance prediction of technology innovation enterprises based on deep learning used in this paper can effectively improve the results of performance prediction of technology innovation enterprises.
Maintaining the Data Integrity and Data Replication in Cloud using Modified Genetic Algorithm (Mga) and Greedy Search Algorithm (Gsa)

Cloud computing is an emerging paradigm thatprovides computing, communication and storage resourcesas a service over a network. Data Integrity is an important aspect which ensures the quality of the data stored in the cloud. Data Replication is the key factor which restores the corrupted data stor
Single-Photon Source with Near-Millisecond Memory based on Room-Temperature Atomic Vapour

Single-photon sources with built-in memory are prospective building blocks for long distance quantum communication [1] . Room-temperature systems offer advantages thanks to their technical simplicity and absence of cryogenic requirements. Atomic vapour cells are room-temperature candidates, however thermal atomic motion limits their coherence time. Reducing the motional effects with buffer gas typically leads to coherence times of microseconds, severely limiting the range of quantum networks.
Research on Stereo Vision Technology Based on Improved Region Growing Method

Abstract Image segmentation is the cornerstone of image analysis and image processing, its main difficulty is the ill-posedness of image segmentation. The region growing method is the most commonly used method in image segmentation. Its advantages are fast calculation speed, lower algorithm difficulty, and easy understanding. This article uses the area growing algorithm and TOF combined with binocular fusion technology. Discussed how to select the seed points in the region growing method. The algorithm proposed in this paper has high accuracy and high matching quality in the boundary area of the object and the area with large difference in depth. It has both matching time and reliability. It has good results and overcomes the limitations of the active and passive distance methods in the vision system.
Interval Model for Assessing the Position of the Recurrent Laryngeal Nerve at the Site of Surgery Wound During Thyroid Surgery

The problem of assessing the position of the of the recurrent laryngeal nerve is considered in the article. This problem occurs during surgery on the thyroid gland. The basis for solving this problem is a well-known technology that involves stimulation the tissues of the surgical wound by a pulsed current with an root mean square (RMS) of current strength value of up to 2 mA and evaluating the results of stimulation by analyzing the information signal that occurs due to stretching vocal cords. The basis of this technology is the mathematical model (interval model) proposed in the article, which defines the distribution of the parameters of the information signal depending on the point of stimulation on the surgical wound. A method of structural and parametric identification of this interval model which are based on the behavioral model of artificial bee colony is proposed. An interval model has been developed to identify the position of the recurrent laryngeal nerve at the site of surgery wound based on a sample of data for one patient.
Multi-malicious nodes double-spending attack blacklist management model

The double-spending attack on the blockchain has been extensively studied, and many research results have been obtained. However, most of these researches are the improvement and prevention for current attack algorithms, and the double-spending attack among multiple malicious nodes is also a valuable research direction. This article further analyzes on the basis of current researches, and proposes a multi-malicious node double-spending attack blacklist management model based on the malicious behavior from different malicious nodes. Firstly, studying the double-spending attacks model to get the mathematical rules between multiple malicious nodes. Then, electing credible nodes to build a blacklist management model. Finally, malicious nodes are extracted through the blacklist single-judgment and double-judgment strategies, which fundamentally block the nodes from further doing evil. Simulation experiments show that the blacklist management model can effectively resist double-spending attacks from multi-malicious nodes.
Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements

We propose a new family of adaptive first-order methods for a class of convex minimization problems that may fail to be Lipschitz continuous or smooth in the standard sense. Specifically, motivated by a recent flurry of activity on non-Lipschitz (NoLips) optimization, we consider problems that are continuous or smooth relative to a reference Bregman function-as opposed to a global, ambient norm (Euclidean or otherwise). These conditions encompass a wide range of problems with singular objective, such as Fisher markets, Poisson tomography, D-design, and the like. In this setting, the application of existing order-optimal adaptive methods-like UnixGrad or AcceleGrad-is not possible, especially in the presence of randomness and uncertainty. The proposed method, adaptive mirror descent (AdaMir), aims to close this gap by concurrently achieving min-max optimal rates in problems that are relatively continuous or smooth, including stochastic ones.
Interval Discrete Equation as a Model of Soil and Groundwater Contamination by Nitrogen Dioxide and Nitrogen Acid

The article presents the results of the construction of interval discrete equation as a model of soil and groundwater contamination. The problem of modeling the processes of soil contamination with nitrogen dioxide and salts of nitric acid is considered. It is proposed to use a difference operator to model these contamination processes. The method of identification of this model, which is based on the behavioral models of the bee colony, is presented. An example of constructing an interval model of soil and groundwater contamination by nitrogen dioxide and nitrogen acid is considered. The obtained results are compared with the results given in the paper, where the mathematical model is built on the basis of a differential equation. Their coincidence with accuracy to measurement errors is shown.
Research on the Design of College English Learning Vocabulary APP Based on Corpus

Under the tide of college English reform in China, in order to adapt to foreign language teaching in the era of big data, this research has designed a corpus-based college English vocabulary APP by using mobile terminals as the medium, guiding students to learn independently through human-computer interaction, and promoting corpus in English teaching. The corpus-based APP will cultivate students’ independent learning and innovation, improve teaching efficiency and quality, realize the scientific and standardization of college English curriculum structure, and achieve the educational pursuit of letting students “be the masters of learning”.
Research on Infrared Image Target Detection Technology Based on YOLOv3 and Computer Vision

Target detection technology is one of the basic topics in the field of computer vision, and it is also a hot spot, with a very broad application market. However, most of the current target detection technologies based on deep learning are aimed at visible light imaging technology, and there are very few researches on infrared imaging technology. Target detection based on deep learning implements the learning of more features by abstracting, extracting, processing and integrating the essential features of a large number of samples. Therefore, the use of deep learning target detection algorithms for infrared image pedestrian detection applications can make up for the shortcomings of traditional detection methods. YOLOv3 is currently a relatively balanced recognition algorithm. This article analyzes the principles and characteristics of the YOLOv3 series of algorithms to optimize multi-scale detection, which improves the detection accuracy and achieves a relative balance between detection accuracy and speed to a certain extent. This research hopes to provide efficient and feasible solutions and solutions for infrared target detection and recognition in the air through the application of deep learning technology.
Research and application of small target detection method

Abstract With the advent of the big data era, more and more data are used in image detection. In order to solve the problem of small target detection in traditional image detection algorithms, this paper proposes an improved target detection algorithm based on YOLO-v5. The algorithm proposes to connect two models in series, using the former model for target pre-positioning, and the latter model for target detection. This method trains two different model parameters, tests them separately, and finally connects them for target detection. The test results show that the improved algorithm improves the ability in small target detection, and at the same time has a speed advantage.
Unsupervised Domain Adaptation with Multi-kernel MMD

In this work, we propose a method to solve the problem of unsupervised domain adaptation. Most of existing works are based on adversarial learning method, which obtains the features of inputs through a feature extraction network, and distinguishes the features by a domain classifier that can generate domain-invariant features. The domain-invariant features is used as the input of a classifier (fully connected network) to get the final category of the image. However, in fact, each layer of the fully connected network can be regarded as the domain invariant feature. Consequently, we employ the last layer as domain-invariant features which can also be considered as a probability distribution. Finally, the domain discrepancies between the distribution in source domain and target domain is measured by multi-kernel MMD, which is what we need to minimize. Experimental evidences show that the proposed method achieves satisfactory results on standard domain adapatation benchmarks.