Reviewerly/exHarmony huggingface dataset:
SAN-GAL: Spatial Attention Network Guided by Attribute Label for Person Re-identification

Person Re-identification (Re-ID) is aimed at solving the matching problem of the same pedestrian at a different time and in different places. Due to the cross-device condition, the appearance of different pedestrians may have a high degree of similarity; at this time, using the global features of pedestrians to match often cannot achieve good results. In order to solve these problems, we designed a Spatial Attention Network Guided by Attribute Label (SAN-GAL), which is a dual-trace network containing both attribute classification and Re-ID. Different from the previous approach of simply adding a branch of attribute binary classification network, our SAN-GAL is mainly divided into two connecting steps. First, with attribute labels as guidance, we generate Attribute Attention Heat map (AAH) through Grad-CAM algorithm to accurately locate fine-grained attribute areas of pedestrians. Then, the Attribute Spatial Attention Module (ASAM) is constructed according to the AHH which is taken as the prior knowledge and introduced into the Re-ID network to assist in the discrimination of the Re-ID task. In particular, our SAN-GAL network can integrate the local attribute information and global ID information of pedestrians without introducing additional attribute region annotation, which has good flexibility and adaptability. The test results on Market1501 and DukeMTMC-reID show that our SAN-GAL can achieve good results and can achieve 85.8% Rank-1 accuracy on DukeMTMC-reID dataset, which is obviously competitive compared with most Re-ID algorithms.
Improving Relation Classification with Multi-graph GCN

As a basis task in the field of Natural Language Processing (NLP), relation extraction task aims to extract the relation between two entities in a text. Most existing models rely on a single semantic feature of the sentence for relation classification. In this paper, we present MGGCM model, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages two distinct graphs which are the dependency tree path and the relation-entity graph respectively. In this model, we integrate both semantic features and structural features to enhance the performance of relation extraction model. We encode the sentence through BiLSTM, obtain its structural features by GCN, and pay more attention to the entity information which is related to the target entity pair, and finally fuse the features to obtain the classification results. We test our model on the SemEval 2010 relation classification task, and achieve an F1-score of 85.7%, higher than competing methods in literature.
Bounds for Learning Lossless Source Coding

This paper asks a basic question: how much training is required to beat a universal source coder? Traditionally, there have been two types of source coders: fixed, optimum coders such as Huffman coders; and universal source coders, such as Lempel-Ziv. The paper considers a third type of source coders: learned coders. These are coders that are trained on data of a particular type, and then used to encode new data of that type. This is a type of coder that has recently become popular for (lossy) image and video coding. The paper evaluates two criteria for performance of learned coders: the average performance over training data, and a guaranteed performance for all training except for some error probability Pe, which is PAC learning. In both cases the coders are evaluated with respect to redundancy. The paper considers the independent identically distributed (IID) binary case and binary Markov chains. In both cases it is shown that the amount of training data required is very moderate: to code sequences of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l$</tex> the amount of training data required to beat a universal source coder is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m=K\frac{l}{\log l}$</tex> , where the constant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> depends on the case considered.
Assessing the Impact of Imputation on the Interpretations of Prediction Models: A Case Study on Mortality Prediction for Patients with Acute Myocardial Infarction.

Acute myocardial infarction poses significant health risks and financial burden on healthcare and families. Prediction of mortality risk among AM! patients using rich electronic health record (EHR) data can potentially save lives and healthcare costs. Nevertheless, EHR-based prediction models usually use a missing data imputation method without considering its impact on the performance and interpretability of the model, hampering its real-world applicability in the healthcare setting. This study examines the impact of different methods for imputing missing values in EHR data on both the performance and the interpretations of predictive models. Our results showed that a small standard deviation in root mean squared error across different runs of an imputation method does not necessarily imply a small standard deviation in the prediction models' performance and interpretation. We also showed that the level of missingness and the imputation method used can have a significant impact on the interpretation of the models.
Strictly In-Place Algorithms for Permuting and Inverting Permutations

We revisit the problem of permuting an array of length n according to a given permutation in place, that is, using only a small number of bits of extra storage. Fich, Munro and Poblete [FOCS 1990, SICOMP 1995] obtained an elegant $$\mathcal {O}(n\log n)$$ -time algorithm using only $$\mathcal {O}(\log ^{2}n)$$ bits of extra space for this basic problem by designing a procedure that scans the permutation and outputs exactly one element from each of its cycles. However, in the strict sense in place should be understood as using only an asymptotically optimal $$\mathcal {O}(\log n)$$ bits of extra space, or storing a constant number of indices. The problem of permuting in this version is, in fact, a well-known interview question, with the expected solution being a quadratic-time algorithm. Surprisingly, no faster algorithm seems to be known in the literature. Our first contribution is a strictly in-place generalisation of the method of Fich et al. that works in $$\mathcal {O}_{\varepsilon }(n^{1+\varepsilon })$$ time, for any $$\varepsilon > 0$$ . Then, we build on this generalisation to obtain a strictly in-place algorithm for inverting a given permutation on n elements working in the same complexity. This is a significant improvement on a recent result of Guśpiel [arXiv 2019], who designed an $$\mathcal {O}(n^{1.5})$$ -time algorithm.
Masking Hidden Messages under Noise in Steganography Problems

The authors consider the problem of masking a hidden message in HUGO stegosystems under natural noise in the communication channel using discrete chaotic Arnold’s cat map and Baker’s map, which are iterative reversible discrete transformations in highly undetectable HUGO stegosystems. To estimate the chaotic state of a hidden message represented by a digital still image, the authors introduce the concept of the chaotic coefficient, which is a numerical indicator of the entropy of the probability of disordered pixels. Furthermore, the authors propose a method for determining the maximum value of the chaotic coefficient corresponding to the maximum chaotic state of the hidden image.
Analysis of Challenges in Modern Network Forensic Framework

Network forensics can be an expansion associated with network security design which typically emphasizes avoidance and detection of community assaults. It covers the necessity for dedicated investigative abilities. When you look at the design, this indeed currently allows investigating harmful behavior in communities. It will help organizations to examine external and community this is undoubtedly around. It is also important for police force investigations. Network forensic techniques can be used to identify the source of the intrusion and the intruder’s location. Forensics can resolve many cybercrime cases using the methods of network forensics. These methods can extract intruder’s information, the nature of the intrusion, and how it can be prevented in the future. These techniques can also be used to avoid attacks in near future. Modern network forensic techniques face several challenges that must be resolved to improve the forensic methods. Some of the key challenges include high storage speed, the requirement of ample storage space, data integrity, data privacy, access to IP address, and location of data extraction. The details concerning these challenges are provided with potential solutions to these challenges. In general, the network forensic tools and techniques cannot be improved without addressing these challenges of the forensic network. This paper proposed a thematic taxonomy of classifications of network forensic techniques based on extensive. The classification has been carried out based on the target datasets and implementation techniques while performing forensic investigations. For this purpose, qualitative methods have been used to develop thematic taxonomy. The distinct objectives of this study include accessibility to the network infrastructure and artifacts and collection of evidence against the intruder using network forensic techniques to communicate the information related to network attacks with minimum false-negative results. It will help organizations to investigate external and internal causes of network security attacks.
Qualitative Invariant Effects Arise from Neural Constraints: Common Architecture and Sources of Individual Differences

The distinction between qualitative and quantitative effects can be meaningfully related to the distinction between “architecture” and “model” in computational cognitive science. In turn, this distinction can be related to differences between the invariant systems-level organization of the human brain and individual differences in structural and functional activity. Taken together, this presents an iterative new way to answer Newell’s “20 Questions” problem and to systematize psychological effects as belonging to either architecture or the individual variations within it. Although some limits to this approach exist (for example, Individual differences in strategy might affect whether an effect is qualitative or quantitative), the approach might also shed light on how to account for special populations (neurological or psychological patients) within the same framework.
The Role of AI in Cervical Cancer Screening

In the last few years internet-based technologies played an important role in reinventing various medical procedures and facilitating quick access to medical services and care, particularly in the remote areas of China. The use of artificial intelligence and cloud computing in clinical laboratory setting for slide analysis contributed to standardized cytology and pathology diagnosis but more importantly slide analysis with artificial intelligence has a huge potential to compensate for a country wide lack of pathologists and systematic quality control. While well-established automated slide scanning is already in use, we added intelligent algorithms located in a secure cloud for the better slide readings, and mobile phone microscopes to capture those regions of Hubei province where laboratory infrastructure is supported by high-speed internet and 5G networks. These technological advances allowed us to bring an important pathology expertise across the large areas of China.
SP-DARTS: Synchronous Progressive Differentiable Neural Architecture Search for Image Classification

Differentiable neural architecture search (DARTS) is now a widely disseminated weight-sharing neural architecture search method and it consists of two stages: search and evaluation. However, the original DARTS suffers from some well-known shortcomings. Firstly, the width and depth of the network, as well as the operation of two stages are discontinuous, which causes a performance collapse. Secondly, DARTS has a high computational overhead. In this paper, we propose a synchronous progressive approach to solve the discontinuity problem for network depth and width and we use the 0-1 loss function to alleviate the discontinuity problem caused by the discretization of operation. The computational overhead is reduced by using the partial channel connection. Besides, we also discuss and propose a solution to the aggregation of skip operations during the search process of DARTS. We conduct extensive experiments on CIFAR-10 and WANFANG datasets, specifically, our approach reduces search time significantly (from 1.5 to 0.1 GPU days) and improves the accuracy of image recognition.
Research on innovative Design of Classroom Situational Teaching : — —Take higher Dance education as an example

Classroom situational teaching is one of the more common teaching methods in classroom teaching. Virtual reality technology, as a new computer technology with the development of multimedia technology, is developing rapidly in the field of education, which is of great value to higher education. The application of video conference (VC) and virtual reality technology (VR) in the field of education is developing rapidly, which is of great value to higher education teaching. This paper attempts to break the traditional teaching mode and combine the innovative design research of classroom situational teaching to provide more perspectives and ideas for the diversified development of innovative digital design research.
The Development and New Direction of Digital Image Stenography

In order to promote the development of digital image steganography, the status and new direction of digital image stenography is analyzed. This paper summarized the development of traditional digital image steganography and illustrated that steganography based on adversarial example and steganography based on Generative Adversarial Networks are new directions in the field. Besides, the importance of digital image steganography and steganography analysis in the field of information security is also analyzed, and the method of steganography analysis for detecting and judging the secret information contained in the image is discussed. The paper also analyzed the current problem and proposed the future improvement direction.
Optimal representation to High Order Random Boolean kSatisability via Election Algorithm as Heuristic Search Approach in Hopeld Neural Networks

This study proposed a hybridization of higher-order Random Boolean kSatisfiability (RANkSAT) with the Hopfield neural network (HNN) as a neuro-dynamical model designed to reflect knowledge efficiently. The learning process of the Hopfield neural network (HNN) has undergone significant changes and improvements according to various types of optimization problems. However, the HNN model is associated with some limitations which include storage capacity and being easily trapped to the local minimum solution. The Election algorithm (EA) is proposed to improve the learning phase of HNN for optimal Random Boolean kSatisfiability (RANkSAT) representation in higher order. The main source of inspiration for the Election Algorithm (EA) is its ability to extend the power and rule of political parties beyond their borders when seeking endorsement. The main purpose is to utilize the optimization capacity of EA to accelerate the learning phase of HNN for optimal random k Satisfiability representation. The global minima ratio (mR) and statistical error accumulations (SEA) during the training process were used to evaluate the proposed model performance. The result of this study revealed that our proposed EA-HNN-RANkSAT outperformed ABC-HNN-RANkSAT and ES-HNN-RANkSAT models in terms of mR and SEA.This study will further be extended to accommodate a novel field of Reverse analysis (RA) which involves data mining techniques to analyse real-life problems.
Environmental costs related to cryptocurrency mining: ensuring that innovation does not happen at the expense of the environment.

The advent of DLTs, especially cryptocurrencies, has revolutionized the financial market, introduced the concrete possibility of smart contracts, increased the cybersecurity of networks, and contributed to the evolution of financial transactions. Along with this evolution, however, came environmental problems stemming from the high energy cost of the PoW process used to achieve consensus in most cryptocurrencies. If measures are not taken to discourage the use of energy-intensive processes, the environmental and social costs of mining will soon outweigh the wealth made possible by this new technology. This paper aims to raise awareness about the high energy cost associated with the cryptocurrency mining process, expose options to counterbalance the environmental damage from this practice, and encourage developers, manufacturers, and miners to adopt systems that comply with international environmental legislation, especially the Paris Agreement.
Efficient Inference of Large-Scale and Lightweight Convolutional Neural Networks on FPGA

Convolutional neural networks (CNNs) have achieved significant accuracy improvement in many intelligent applications at the cost of intensive convolution operations and massive data movements. To efficiently deploy CNNs on low power embedded platforms in real time, the depthwise separable convolution has been proposed to replace the standard convolution, especially in lightweight CNNs, which remarkably reduces the computation complexity and model size. However, it is difficult for a general convolution engine to obtain the theoretical performance improvement as the decreased data dependency of depthwise convolution significantly reduces the data reuse opportunity. To address this issue, a flexible and highperformance accelerator based on FPGA is proposed to efficiently process the inference of both large-scale and lightweight CNNs. Firstly, by sharing the activation dataflow between the depthwise convolution and pooling layers, the control logic and data bus of the two layers are reused to maximize the data utilization and minimize the logic overhead. Furthermore, these two layers can be processed either directly after standard convolutions to eliminate the external memory accesses or independently to gain better flexibility. Thirdly, a performance model is proposed to automatically explore the optimal design options of the accelerator. The proposed hardware accelerator is evaluated on Intel Arria 10 SoC FPGA and demonstrates state-of-the-art performance on both large-scale CNNs, e.g., VGG, and lightweight ones, e.g., MobileNet.
An Accurate Speckle 3D Reconstruction System Based on Binocular Endoscope

As the credibility and accuracy of binocular endoscope reconstruction are significantly affected by narrow baseline and large distortion of taken pictures, we develop a binocular endoscopic speckle system to address these limitations. The system consists of a projector and a binocular endoscope. First, the binocular endoscope captures the object surface, which illuminated by a projector with pseudo-random speckles. Then, we proposed a high-precision calibration method based on three-dimensional to improve the calibration precision of the system. Finally, in order to enhance the exactness and reliability of the stereo matching, Semi-Global Matching (SGM) algorithm is studied and improved. This paper aims to reduce the deviation and improve the 3D measurement accuracy of the system. In comparison with traditional stereo reconstruction, the reconstruction accuracy of cylinder and plane was improved 31.2% and 34.0% respectively.
Multi-objective robust design approach usage in integration of bond graph and genetic programming

In this paper, a new hybrid algorithm, based on multi-objective genetic programming and bond graphs in combination with Monte Carlo simulation, is proposed for Pareto optimal robust design of dynamical systems. For this purpose, first, an embryo model is considered for a dynamical system. Then, genetic programming is used to help this embryo model evolve through generations and achieve an optimum model. It is clear that without considering the uncertain parameters of the designing system, unwanted behaviors could occur. To overcome this, Monte Carlo simulation has been used to minimize the variations of objective functions by generating some samples around the nominal values of the uncertain parameters. In other words, the mean and variance of the objective function are minimized simultaneously through sampling, generated by Monte Carlo simulation, to reach a robust behavior. The proposed algorithm has been employed for robust design of two case studies, namely a closed-loop typewriter drive system and an optimum controller for quarter-car suspension system. A comparison between the results obtained using the proposed algorithm and those obtained through other approaches in the literature shows that more robust behavior in the presence of uncertainties can be observed in the proposed method.
Sparse Document Analysis Using Beta-Liouville Naive Bayes with Vocabulary Knowledge

Smoothing the parameters of multinomial distributions is an important concern in statistical inference tasks. In this paper, we present a new smoothing prior for the Multinomial Naive Bayes classifier. Our approach takes advantage of the Beta-Liouville distribution for the estimation of the multinomial parameters. Dealing with sparse documents, we exploit vocabulary knowledge to define two distinct priors over the “observed” and the “unseen” words. We analyze the problem of large-scale and sparse data by enhancing Multinomial Naive Bayes classifier through smoothing the estimation of words with a Beta-scale. Our approach is evaluated on two different challenging applications with sparse and large-scale documents namely: emotion intensity analysis and hate speech detection. Experiments on real-world datasets show the effectiveness of our proposed classifier compared to the related-work methods.