Reviewerly/exHarmony huggingface dataset:
Key Frame Extraction Based on Frame Difference and Cluster for Person Re-identification

The features extracted by person re-identification (Re-ID) contain both spatial dimension information and time dimension information, but for video data sets, it is first necessary to select key frames as network input. In this process, problems such as data redundancy and lack often occur which make the selected key frames not representative and reduce the expressiveness of the network. At present, in using traditional features to select key frames, there are mainly methods based on SIFT, histogram, and spatio-temporal color distribution; in terms of key frame selection strategies, there are mainly algorithms based on Random, Evenly, and Frame difference. The method based on SIFT and histogram cannot accurately extract feature points for targets with smooth edges, and the amount of information loss is large; the method based on spatio-temporal color distribution requires extremely high video color. If the video resolution of the camera is poor, it is easy to loss the color distributed information; the key frames selected based on Random and Fvenly methods have great randomness; the method based on Frame difference can filter out most of the video frames with highly similar features, alleviating the problem of data redundancy. But the problem of data imbalance still exists. In response to the above problems, this paper proposes a new key frame extraction method frame difference and cluster (FDC) that integrates the idea of K-means clustering. First, the paired distance list of the difference image is calculated, and then the pre-selected frame set is generated. Then, the pre-selected frame set is unsupervised clustering, and finally the target key frame set is obtained. With the FDC method, we extract the key frame which can best represent the dynamic information of pedestrians in the video data set. This not only reduces the frame input, but also improves the computational efficiency. On the premise of ensuring the accuracy of data, we use relatively few samples to train better models on different network structures. On the mars data set, compared to the traditional Evenly and Frame difference extraction algorithms, FDC has been improved by 1.6% and 2.4% respectively.
On (simple) decision tree rank

In the decision tree computation model for Boolean functions, the depth corresponds to query complexity, and the size corresponds to storage space. The depth measure is the most well-studied one, and is known to be polynomially related to several non-computational complexity measures of functions such as certificate complexity. The size measure is also studied, but to a lesser extent. Another decision tree measure that has received very little attention is the minimal rank of the decision tree, first introduced by Ehrenfeucht and Haussler in 1989. This measure is closely related to the logarithm of the size, but is not polynomially related to depth, and hence it can reveal additional information about the complexity of a function. It is characterised by the value of a Prover-Delayer game first proposed by Pudlák and Impagliazzo in the context of tree-like resolution proofs. In this paper we study this measure further. We obtain an upper bound on depth in terms of rank and Fourier sparsity. We obtain upper and lower bounds on rank in terms of (variants of) certificate complexity. We also obtain upper and lower bounds on the rank for composed functions in terms of the depth of the outer function and the rank of the inner function. This allows us to easily recover known asympotical lower bounds on logarithm of the size for Iterated AND-OR and Iterated 3-bit Majority. We compute the rank exactly for several natural functions and use them to show that all the bounds we have obtained are tight. We also show that rank in the simple decision tree model can be used to bound query complexity, or depth, in the more general conjunctive decision tree model. Finally, we improve upon the known size lower bound for the Tribes function and conclude that in the size-rank relationship for decision trees, obtained by Ehrenfeucht and Haussler, the upper bound for Tribes is asymptotically tight.
Morphological generalization in heritage speakers: The Turkish aorist

Heritage speakers (HS) have been shown to experience difficulties with inflectional morphology (particularly with irregular morphology) and to frequently overapply regular morphology. The present study seeks to get further insight into the inflectional processes of HS by investigating how these are generalized to nonce words in language production, the first study of this kind for heritage Turkish. We specifically examined morphological generalization processes in the Turkish aorist which – unusual for this language – includes both regular and irregular forms. A written elicited-production experiment containing nonce verbs with varying degrees of similarity to existing verbs was administered to Turkish HS and native monolingually-raised Turkish speakers (MS). We also explored how well a formal model that was trained on a large lexical corpus of Turkish matches the human speakers’ performance. Our main finding is that HS employ both similarity-based and rule-based mechanisms for morphological generalization of the Turkish aorist, with subtle differences to the way these mechanisms are applied by Turkish MS.
Software Galaxies: Displaying Coding Activitiesusing a Galaxy Metaphor

Software visualization uses metaphors to depict software system and software development data that usually has no inherent gestalt. The choice of a fitting metaphor for visual display is researched broadly, but deriving a layout based on similarity is still challenging. We present a novel approach to 3D software visualization called Software Galaxy. Our layout is based on applying Latent Dirichlet Allocation on source code documents. We utilize a metaphor inspired from astronomy for depicting software metrics for single documents and clusters of documents. Our first experiments indicate that a 3D visualization capturing semantic relatedness can be beneficial for standard program comprehension tasks.
Automatic colorization of black and white images using convolutional neural networks

In recent years, image colorization has become increasingly common in video and image restoration. Automatic colorization approaches based on deep learning have recently shown to be very successful. Researchers are very excited to dig up a lot of knowledge from our history as technology advances rapidly. Still, most of the bits of data from the past are in black and white images, which are becoming a little inconvenient compared to current surroundings. Image colorization is the process of applying colours to grayscale images, which used to be a time-consuming and labor-intensive task involving a lot of human effort. The colorization of black and white images is a prevalent issue in the machine learning and computer vision communities. This method previously involved considerable user intervention, such as putting multiple color scribbles, looking at similar images, performing segmentation, and Previous ways for colorizing grayscale images relied on manual effort. The results were often undersaturated and unlikely to be true colorizations. We proposed a novel approach that uses Deep Learning and Convolutional Neural Networks (DLCNN) to color the picture from grayscale automatically. We have taken the image net dataset and convert all of the images from RGB to Lab colour space. We feed the network channel as a grayscale input, and the network will have to learn to predict the values of and channels. According to experimental results, the proposed DLCNN outperforms current methods on various performance indicators and achieves state-of-the-art efficiency.
Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising

Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately.
Human Fall Detection in Depth-Videos Using Temporal Templates and Convolutional Neural Networks

The task of Human Fall detection is taken up to develop efficient technology to assist humans in activities of their daily living. In homes, the aged people are more prone to fall with improper gait, due to weakened muscles, lack of balance, sensation and co-ordination. The toddlers frequently fall down as the above abilities are not yet well developed in them. The elderly people and the toddlers need constant watch in homes by a system to alert the care-takers immediately after a fall, that makes our homes smart and safe. This research addresses this problem. In this work, we use motion information obtained from video to compute new temporal templates, which are in turn used by a convolutional neural network to recognize the human actions. A new representation capturing the pose of the subject over a period of time is proposed for human action recognition. This temporal template representation computed at the beginning and at the end of the fall-event is explored in this work for fall detection. The ConvNet feature extracted from these temporal templates are used by an extreme learning machine (ELM) for action recognition. The efficacy of the proposed approach is demonstrated on SDU Fall detection, UP-Fall, UR Fall, and MIVIA action datasets.
A Modular Quantum Key Distribution Software Stack for Rapid Experimental Prototyping

Quantum Key Distribution (QKD) is a rapidly advancing field, yet there is often a divide between theory, experiment, and practice. One difficulty in evaluating novel QKD protocols is in the post-processing needed. We are reporting here on the current development of a modular QKD software stack researchers can use to evaluate the performance of new QKD protocols, either using real hardware or simulated. We are designing our system to be modular and extendable allowing for rapid prototyping of new systems.
Digital Image Watermarking Technique Using Arnold Transform and Lifting

In this paper, we propose a digital image watermarking scheme using Arnold transform. To implement this, a scrambled binary image is embedded into the coefficient of host images via Lifting wavelet transform. Then inverse wavelet transform is applied to get the watermarked image. We have applied to watermark in the transform domain instead of the spatial domain. This is since in a spatial domain the pixels of the cover image is directly manipulated, in contrast with transform domain where the coefficient of pixels is modified after applying different transformation technique that ultimately increases imperceptibility of the scheme. Moreover, nowadays most of the images are compressed in wavelet domain like JPEG 2000. Another important feature of the proposed algorithm is the Arnold scrambling technique which increases the security of the scheme. It is difficult for the attacker to derive the original watermark as well as retrieve the cover image. The proposed digital watermarking system is tested over a large number of standard grayscale (512×512) benchmark images to provide good imperceptibility and robustness. In our case, we have seen that the proposed technique offers the average value of peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSI) before decoding and after decoding are 49.93, 0.97, 51.72 and 0.976721 respectively. The experimental results show that the proposed scheme is robust against different types of attacks.
Object Detection in unmanned vehicle with End-to End Edge-Enhanced GAN and Object Detector Network

This paper proposes an efficient method for image detection for unmanned cars based on vision, and solves the problem of false localization for unmanned cars. The current SR methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, we add Edge enhancement network (EEN) to GAN network to recover the high-frequency edge details. For the problem of false localization, we build a model of the bounding box of YOLOv3 with a Gaussian parameter and redesign the loss function. By using the predicted localization uncertainty and edge enhancement network, during the detection process, the proposed schemes can significantly reduce the FP and recover the high-frequency edge details. Compared to a conventional YOLOv3, the proposed algorithm, End-to-End Edge-Enhanced GAN and Object Detector Network improves the mean average precision by 4.2 on the COCO datasets.
High precision FPGA real-time linear structured light imaging system

A high-speed real-time structured light imaging system is presented in this paper. The improved linear structured light imaging method is transplanted to the FPGA hardware system, and the imaging frequency is greatly increased. The best imaging effect can be obtained when imaging objects with different reflectivity. Experiments show that this method can achieve outstanding imaging effect for complex objects, and the imaging frame rate can reach 60 Fps.
Aliasing fringe pattern denoising based on deep learning

Wavelength tuning laser interferometry can measure the front and rear surface profile and thickness variation of transparent plate at one time. Separating the collected aliasing fringe patterns containing multi-surface interference information can obtain the surface shape information of each surface of the transparent plate. However, in the process of image acquisition and transmission, it will inevitably be affected by noise, and the existence of noise will affect the separation of multi surface shape information, and further affect the recovery of each surface phase and the accurate acquisition of three-dimensional shape. In this paper, a noise reduction method of aliasing fringe pattern based on convolutional neural network is proposed. The simulation data and experimental fringe patterns show that the network can effectively improve the quality of fringe patterns, has faster calculation speed.
A Route Optimization Scheme based on Improved Simulated Annealing Algorithm

The simulated annealing (SA) algorithm can be employed to solve the traveling salesman problem (TSP). When the scale of the problem increases, the convergence of SA will be extremely slow. To address this issue, we propose an improved SA (ISA) algorithm, which can adaptively search an optimal solution with multi-modal pathway variants, and introduces jump-cooling and quadratic variation mechanism during the search process to improve the convergence speed. A quadratic annealing is also introduced at the end of the algorithm to prevent from falling into a local optimum. A historical optimal solution memory is proposed to ensure that the optimal solution will not be lost due to probabilistic acceptance of poorer solutions. The results of comparative experiments on different data sets of TSP benchmarks, which are obtained from TSPLIB, justify that ISA outperforms SA as well as other heuristics in terms of faster convergence speed and higher search accuracy. We also evaluate and show the efficiency and optimization of ISA by real data, e.g., Hebei province tour data.
Medical Knowledge Graphs in the Discovery of Future Research Collaborations

This chapter introduces a framework that is based on a novel graph-based text representation method and combines graph-based feature selection, text categorization and link prediction to advance the discovery of future research collaborations. Our approach integrates into a single knowledge graph both structured and unstructured textual data through a novel representation of multiple scientific documents. The Neo4j graph database is used for the representation of the proposed scientific knowledge graph. For the implementation of our approach, we use the Python programming language and the scikit-learn machine learning library. We assess our approach against classical link prediction algorithms using accuracy, recall and precision as our performance metrics. Our experiments achieve state-of-the-art accuracy in the task of predicting future research collaborations. The experimentations reported in this chapter use the COVID-19 Open Research Dataset.
An Improved Tri-Training Based Named Entity Identification Approach for Legal Knowledgebase of Properties Involved in Criminal Cases

The legal knowledgebase of properties involved in criminal cases aims to automatically complete the knowledge fusion related to the disposal of case-involved properties in criminal cases based on the existing laws and regulations, and provide support to the case handlers of law enforcement and judicial units such as public prosecutors and law enforcement in judicial practice. In this paper, we explore semi-supervised learning based on tri-training for named entity identification of case-related property knowledgebase based on a small amount of annotated data to reduce the workload of corpus annotation. To overcome the problem that the original tri-training method tends to degenerate into a single learner when the amount of training data is small, we propose an improved tri-training method. Experiments show that our proposed method can effectively improve the performance of named entity recognition compared to the traditional tri-training scheme.
Analysis of Cumulant Coefficients of Two-Component Mixtures of Shifted Non-Gaussian Distributions

The dependence of the cumulant coefficients of two-component mixtures of shifted non- Gaussian distributions on the weight coefficient is analyzed and conditions are determined under which the cumulant coefficients of any orders are equal to zero. The dependence of the cumulant coefficients of two-component mixtures on the shear parameter is investigated and the parameter values are determined at which the cumulant coefficients of any orders have extrema and zeros. The dependence of the skewness and excess kurtosis of a two-component mixture of shifted Gumbel distributions of type 1 on the weight coefficient and the shear parameter is investigated and their values are obtained at which the skewness and excess kurtosis of the mixture are equal to zero. The features of computer modeling of random variables, the probability density of which is a two-component mixture of shifted distributions, are considered.
An integrated decision-making framework for sustainable data center operation through intelligent load scheduling

Abstract Intelligent load scheduling is an emerging approach that has the potential to facilitate extreme sustainable data center (DC) operation. However, scarcity of straightforward tools in the public domain challenges decision makers performing quantitative analysis of the DC load planning and its potential benefits. In this work, a novel integrated decision-making framework was developed to address this issue, which provides the basis for the multi-objective optimization of carbon-, water-, and economic-intelligent load scheduling. The proposed framework was demonstrated with a case study DC in California, which showed the usefulness of the proposed framework in informing sustainable DC operations.
Mapping the Educational Applications of Augmented Reality Research using A Bibliometric Analysis

The use of technology in education is increasingly popular, especially during the COVID-19 pandemic, where all sectors are affected, especially education. The limitations of learning interactions such as practicum in the Lab become one of the obstacles during the learning process. Educational Applications of Augmented Reality is a solution to this challenge. The purpose of this study is to reveal research trends over the last twenty years with bibliometric analysis to examine the results of articles related to the use of Educational Applications of Augmented Reality (AR). For bibliometric mapping analysis, 943 articles were accessed and for content analysis from the Scopus database from 2001 to 2020. This study visually maps scientific publications in indexed digital techniques with bibliometric analysis in various countries. This study collects data from the Scopus database, uses the Scopus website analysis feature, and visualizes the bibliometric network using Vosviewer. The method used consists of five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the articles most published and indexed by Scopus, papers published by researchers in the United States (US) have the highest number of publications (107), followed by Spain (91) and Italy with 43 academic publications. Computers and Education is the most cited journal, with 845 citations. This data was taken in July 2021. The processed data shows the pattern and trend of increasing international publications in Educational Applications of Augmented Reality, namely the Scopus index.
Privacy preserving event based transaction system in a decentralized environment

In this paper, we present the design and implementation of a privacy preserving event based UTXO (Unspent Transaction Output) transaction system. Unlike the existing approaches that often depend on smart contracts where digital assets are first locked in a vault, and then released according to event triggers, the event based transaction system encodes event outcome as part of the UTXO note and safeguards event privacy by shielding it with zero-knowledge proof based protocols such that associations between UTXO notes and events are hidden from the validators. Without relying on any triggering mechanism, the proposed transaction system separates event processing from the transaction processing where confidential event based UTXO notes (event based UTXOs or conditional UTXOs) can be transferred freely with full privacy in an asynchronous manner, only with their asset values conditional to the linked event outcomes. The main advantage of such design is that it enables free trade of event based digital assets and prevents the assets from being locked. We implemented the proposed transaction system by extending the Zerocoin data model and protocols. The system is implemented and evaluated using xJsnark.