Reviewerly/exHarmony huggingface dataset:
Bidirectional Interaction Network for Person Re-Identification

Person re-identification (ReID) task aims to retrieve the same person across multiple spatially disjoint camera views. Due to huge image changes caused by various factors such as posture variation and illumination transformation, images of different persons may share the more similar appearances than images of the same one. Learning discriminative representations to distinguish details of different persons is significant for person ReID. Many existing methods learn discriminative representations resorting to a human body part location branch which requires cumbersome expert human annotations or complex network designs. In this article, a novel bidirectional interaction network is proposed to explore discriminative representations for person ReID without any human body part detection. The proposed method regards multiple convolutional features as responses to various body part properties and exploits the inter-layer interaction to mine discriminative representations for person identities. Firstly, an inter-layer bilinear pooling strategy is proposed to feasibly exploit the pairwise feature relations between two convolution layers. Secondly, to explore interaction of multiple layers, an effective bidirectional integration strategy consisting of two different multi-layer interaction processes is designed to aggregate bilinear pooling interaction of multiple convolution layers. The interaction of multiple layers is implemented in a layer-by-layer nesting policy to ensure the two interaction processes are different and complementary. Extensive experiments validate the superiority of the proposed method on four popular person ReID datasets including Market-1501, DukeMTMC-ReID, CUHK03-NP and MSMT17. Specifically, the proposed method achieves a rank-1 accuracy of 95.1% and 88.2% on Market-1501 and DukeMTMC-ReID, respectively.
A two-stage personalized recommendation based on multi-objective teaching–learning-based optimization with decomposition

Due to its successful application in information filtering and knowledge retrieval systems in the era of big data, personalized recommender system plays a significant role in meeting personalized demands of people from big data and has become a hot research hotspot. Traditional recommender systems only guarantee the recommendation accuracy and the recommendation diversity will be lost. In this paper, we propose a two-stage personalized recommendation (TSPR) algorithm based on an improved collaborative filtering (ICF) and multi-objective teaching–learning-based optimization (MOTLBO/D) with decomposition. Firstly, ICF made use of each user’ preference and social neighborhood information to obtain a candidate recommendation list for each target user. In the proposed ICF method, the jump relationship between users and their unrated items was mined to describe the characteristic behavior information of users more fully. Moreover, MOTLBO/D was adopted to simultaneously optimize accuracy and diversity of recommendation list of items for each target user. In the proposed MOTLBO/D algorithm, the learner representation strategy is designed based on attributes of the recommendation problem, and the learners are updated by an improved TLBO procedure. Finally, the simulation results on two Movielens datasets show that TSPR is effective and efficient in personalized recommender systems.
Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features

PURPOSE Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological complete response (pCR) has prognostic value depending on BC subtype. Rates of pCR, however, can be variable. Predictive modeling is desirable to help identify patients early who may have suboptimal NAC response. Here, we test and compare the predictive performances of machine learning (ML) prediction models to a standard statistical model, using clinical and pathological data. METHODS Clinical and pathological variables were collected in 431 patients, including tumor size, patient demographics, histological characteristics, molecular status, and staging information. A standard multivariable logistic regression (MLR) was developed and compared with five ML models: k-nearest neighbor classifier, random forest (RF) classifier, naive Bayes algorithm, support vector machine, and multilayer perceptron model. Model performances were measured using a receiver operating characteristic (ROC) analysis and statistically compared. RESULTS MLR predictors of NAC response included: estrogen receptor (ER) status, human epidermal growth factor-2 (HER2) status, tumor size, and Nottingham grade. The strongest MLR predictors of pCR included HER2+ versus HER2− BC (odds ratio [OR], 0.13; 95% CI, 0.07 to 0.23; P &lt; .001) and Nottingham grade G3 versus G1-2 (G1-2: OR, 0.36; 95% CI, 0.20 to 0.65; P &lt; .001). The area under the curve (AUC) for the MLR was AUC = 0.64. Among the various ML models, an RF classifier performed best, with an AUC = 0.88, sensitivity of 70.7%, and specificity of 84.6%, and included the following variables: menopausal status, ER status, HER2 status, Nottingham grade, tumor size, nodal status, and presence of inflammatory BC. CONCLUSION Modeling performances varied between standard versus ML classification methods. RF ML classifiers demonstrated the best predictive performance among all models.
A lightweight privacy-preserving and sharing scheme with dual-blockchain for intelligent pricing system of smart grid

With the deepening of the application of information technology, the development trend of smart grid as the next generation grid has pointed the way for the development of energy internet in the power industry. To protect the privacy of participants, this paper proposes a lightweight dual-blockchain privacy protection and sharing solution for smart grid intelligent pricing systems. We design a lightweight distributed cloud storage architecture based on dual blockchains. The cloud storage architecture stores the encrypted data and returns the address, thereby protecting the privacy of the data. A private blockchain is used to map the association between real identity and pseudonym. At the same time, a new shared blockchain is created for nodes with access control to reduce cost, ensure security and access resource on-demand. Then, secure signature authentication and identity-based proxy re-encryption strategies are designed. Encryption and bilinear pair-based signature scheme is used to aggregate data to support low-cost and high-performance computing. The evaluation results show that the scheme has the advantages of low latency, low response time and small storage cost, and can meet the growing demand for secure communication of full-service data of the smart grid.
Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning

Not all supervised learning problems are described by a pair of a fixed-size input tensor and a label. In some cases, especially in medical image analysis, a label corresponds to a bag of instances (e.g. image patches), and to classify such bag, aggregation of information from all of the instances is needed. There have been several attempts to create a model working with a bag of instances, however, they are assuming that there are no dependencies within the bag and the label is connected to at least one instance. In this work, we introduce Self-Attention Attention-based MIL Pooling (SA-AbMILP) aggregation operation to account for the dependencies between instances. We conduct several experiments on MNIST, histological, microbiological, and retinal databases to show that SA-AbMILP performs better than other models. Additionally, we investigate kernel variations of Self-Attention and their influence on the results.
An improved antlion optimizer with dynamic random walk and dynamic opposite learning

Antlion optimization algorithm (ALO) has been one of the most popular meta-heuristic algorithms to solve engineering optimization problems with a fast convergence speed and steady performance. However, the long runtime and the weak capability of local optima avoidance prevent ALO from being applied in real-time and complex engineering problems. This paper proposes a dynamic random walk and dynamic opposite learning improved ALO (DALO), where a new random walk method named dynamic random walk (DRW) is presented, and a dynamic opposite learning strategy (DOL) is embedded in ALO. The DOL-inspired DRW method has a better exploration capability for converging to the best optimum due to the dynamic changing search space and is more efficient in computation due to the low complexity. The DOL strategy contributes to two steps of the ALO algorithm, i.e. DOL population initialization and DOL generation jumping, for further enhancing the algorithm to avoid premature convergence. The improvements of DRW and DOL were verified by testing benchmark problems from ALO paper, CEC 2014 and CEC 2017, and an optimal weight factor of DRW was selected by conducting a sensitivity analysis. The results, confirmed by Wilcoxon rank-sum test and Friedman test, show that DRW helps ALO greatly reduce the runtime with an excellent performance in finding the best optimum, and the DALO algorithm with the further enhancement enable by DOL is significantly superior to ALO. In addition, the results of the test on three representative engineering problems show that the ability of DALO to solve real-world problems is enhanced.
A Novel Approach to Detect Face Mask using CNN

Face detection and recognition will be considered as one of the most intriguing modalities for biometric models. Those researches are mainly required for ensuring security in a most sensitive area. This research paper has proposed a very fast image pre-processing with the mask in the center over the faces. For this system, features extraction and Convolutional Neural Network are used for classification and detection of a masked person. This research work will be carried out in three levels: preprocessing the images, cropping the images and classification of the images. This helps to detect whether the face is masked or not. A webcam or CCTV camera surveillance will record all the time and it checks whether the person is wearing a mask, if the person doesn't wear a mask then the system gives a security alert.
Model Latent Views With Multi-Center Metric Learning for Vehicle Re-Identification

Multi-view vehicle re-identification (Re-ID) aims to retrieve all images of a target vehicle from a large gallery where the vehicles are captured from non-overlapping cameras. However, the drastic variation in vehicle appearance under different viewpoints greatly affects the performance of the multi-view vehicle Re-ID model, so the key issue in multi-view vehicle Re-ID is learning an effective feature representation that is robust to both dramatic intra-class variability and small inter-class variability. To achieve this goal, we have proposed a multi-center metric learning framework for multi-view vehicle Re-ID. In our approach, we model latent views from vehicle visual appearance directly without any extra labels except ID. Firstly, we introduce several latent view clusters for a vehicle to model latent multi-view information and each view cluster has a learnable center. Then multi-view vehicle matching task can be transformed into two subproblems, cross-view matching and cross-target matching. Finally, an intra-class ranking loss with cross-view center constraint and a cross-class ranking loss with cross-vehicle center constraint are proposed to address the two subproblems, respectively. Extensive experimental evaluations on three widely used benchmarks show the superiority of the proposed framework in contrast to a series of existing state-of-the-arts.
An adaptive and opposite K-means operation based memetic algorithm for data clustering

Evolutionary algorithm (EA) incorporating with k-means local search operator represents an important approach for cluster analysis. In the existing EA approach, however, the k-means operators are usually directly employed on the individuals and generally applied with fixed intensity as well as frequency during evolution, which could significantly limit their performance. In this paper, we first introduce a hybrid EA based clustering framework such that the frequency and intensity of k-means operator could be arbitrarily configured during evolution. Then, an adaptive strategy is devised to dynamically set its frequency and intensity according to the feedback of evolution. Further, we develop an opposite search strategy to implement the proposed adaptive k-means operation, thus appropriately exploring the search space. By incorporating the above two strategies, a memetic algorithm with adaptive and opposite k-means operation is finally proposed for data clustering. The performance of the proposed method has been evaluated on a series of data sets and compared with relevant algorithms. Experimental results indicate that our proposed algorithm is generally able to deliver superior performance and outperform related methods.
A Permissioned Blockchain-Based Energy Management System for Renewable Energy Microgrids

Peer-to-peer (P2P) energy management is one of the most viable solutions to incentivize prosumers in renewable energy microgrids. As the application of blockchain expends from the finance field to energy field, blockchain technology provides a new opportunity for distributed energy systems. However, a distributed energy system based on blockchains allows any node in the whole network to read data. In many application scenarios, user privacy cannot be effectively protected, and there is a security problem that the attack cannot be traced. In this paper, we propose an energy management mode based on a permissioned blockchain for a renewable energy microgrid. The novel permissioned blockchain framework uses entity mapping with a unique identity for each enterprise, natural person, or device, in order to avoid ineligible participants to join the microgrid. Each peer entity keeps the transaction information index of the whole network, but only keeps its own specific transaction information, so they can retrieve the transaction information of other peer entities but cannot obtain the details without permission. Moreover, this model could avoid communication delays and promote plug-and-play due to the distributed nature of the permissioned blockchain. The performance of the proposed method is evaluated with a demonstration program which is designed and deployed on a Hyperledger Fabric permissioned blockchain. Simulation results show the feasibility of the proposed method, and the model is conducive to the protection privacy and P2P energy management for decentralized energy systems.
A Novel Approximate Spectral Clustering Algorithm With Dense Cores and Density Peaks

Spectral clustering is becoming more and more popular because it has good performance in discovering clusters with varying characteristics. However, it suffers from high computational cost, unstable clustering results and noises. This work presents a novel approximate spectral clustering based on dense cores and density peaks, called DCDP-ASC. It first finds a reduced data set by introducing the concept of dense cores; then defines a new distance based on the common neighborhood of dense cores and calculates geodesic distances between dense cores according to the new defined distance; after that constructs a decision graph with a parameter-free local density and geodesic distance for obtaining initial centers; finally calculates the similarity between dense cores with their new defined geodesic distance, employs normalized spectral clustering method to divide dense cores, and expands the result on dense cores to the whole data set by assigning each point to its representative. The results on some challenging data sets and the comparison of our algorithm with some other excellent methods demonstrate that the proposed method DCDP-ASC is more advantageous in identifying complex structured clusters containing a lot of noises.
The role of user-centered AR instruction in improving novice spatial cognition in a high-precision procedural task

AR instruction is a kind of virtual information presented on a human–computer interface. It allows users to view the geometric state, spatial relationship, operation method, and other information involved in the physical task, to form the spatial cognition of the current interaction process. At present, AR instructions cannot support high-precision procedural tasks. The reason is that the existing research work is to use visual elements to express the spatial relationship of physical tasks, without considering transforming the long-term accumulated potential experience of advanced users into a series of effective visual features and interaction modes, to promote the new users to quickly conceive the task intention. In this paper, a user-centered AR instruction(UcAI) is defined and tested for the first time in a procedural task. The control experiment and behavior analysis of 30 participants designing two tasks with different operation precision show that UcAI is more beneficial to improve the user's spatial cognitive ability than conventional AR instruction. Especially in the high-precision operation task, UcAI plays an important role. Our research results have a certain guiding significance for advanced AR instruction design, which extends AR technology to physical tasks with high cognitive complexity.
Are emojis processed like words?: Eye movements reveal the time course of semantic processing for emojified text

Emojis have many functions that support reading. Most obviously, they convey semantic information and support reading comprehension (Lo, CyberPsychology & Behavior, 11[5], 595–597, 2008; Riordan, Computers in Human Behavior, 76, 75–86, 2017b). However, it is undetermined whether emojis recruit the same perceptual and cognitive processes for identification and integration during reading as do words. To investigate whether emojis are processed like words, we used eye tracking to examine the time course of semantic processing of emojis during reading. Materials consisted of sentences containing a target word (e.g., coffee in the sentence “My tall coffee is just the right temperature”) when there was no emoji present and when there was a semantically congruent (i.e., synonymous) emoji (e.g., the cup of coffee emoji, ) or an incongruent emoji (e.g., the beer mug emoji, ) present at the end of the sentence. Similar to congruency effects with words, congruent emojis were fixated for shorter periods and were less likely to be refixated than were incongruent emojis. In addition, congruent emojis were more frequently skipped than incongruent emojis, which suggests that semantic aspects of emoji processing begin in the parafovea. Finally, the presence of an emoji, relative to its absence increased target-word skipping rates and reduced total time on target words. We discuss the implications of our findings for models of eye-movement control during reading.
Case Study Research in Software Engineering—It is a Case, and it is a Study, but is it a Case Study?

Case studies are regularly published in the software engineering literature, and guidelines for conducting case studies are available. Based on a perception that the label "case study" is assigned to studies that are not case studies, an investigation has been conducted. The aim was to investigate whether or not the label "case study" is correctly used in software engineering research. To address the objective, 100 recent articles found through Scopus when searching for case studies in software engineering have been investigated and classified. Unfortunately, the perception of misuse of the label "case study" is correct. Close to 50% of the articles investigated were judged as not being case studies according to the definition of a case study. We either need to ensure correct use of the label "case study", or we need another label for its definition. Given that "case study" is a well-established label, it is probably impossible to change the label. Thus, we introduce an alternative definition of case study emphasising its real-life context, and urge researchers to carefully follow the definition of different research methods when presenting their research.
Code to comment "translation"

The relationship of comments to code, and in particular, the task of generating useful comments given the code, has long been of interest. The earliest approaches have been based on strong syntactic theories of comment-structures, and relied on textual templates. More recently, researchers have applied deep-learning methods to this task---specifically, trainable generative translation models which are known to work very well for Natural Language translation (e.g., from German to English). We carefully examine the underlying assumption here: that the task of generating comments sufficiently resembles the task of translating between natural languages, and so similar models and evaluation metrics could be used. We analyze several recent code-comment datasets for this task: CodeNN, DeepCom, FunCom, and DocString. We compare them with WMT19, a standard dataset frequently used to train state-of-the-art natural language translators. We found some interesting differences between the code-comment data and the WMT19 natural language data. Next, we describe and conduct some studies to calibrate BLEU (which is commonly used as a measure of comment quality). using "affinity pairs" of methods, from different projects, in the same project, in the same class, etc; Our study suggests that the current performance on some datasets might need to be improved substantially. We also argue that fairly naive information retrieval (IR) methods do well enough at this task to be considered a reasonable baseline. Finally, we make some suggestions on how our findings might be used in future research in this area.
Quantum state discrimination using noisy quantum neural networks

Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, applicable on near-term quantum devices as it fulfils the above criteria. We find that when simulating gradient calculation on a noisy device, a large number of parameters is disadvantageous. By introducing a new smaller circuit ansatz we overcome this limitation, and find that the QNN performs well at noise levels of current quantum hardware. We also show that networks trained at higher noise levels can still converge to useful parameters. Our findings show that noisy quantum computers can be used in applications for state discrimination and for classifiers of the output of quantum generative adversarial networks.
The MIDAS Touch: Accurate and Scalable Missing-Data Imputation with Deep Learning

Abstract Principled methods for analyzing missing values, based chiefly on multiple imputation, have become increasingly popular yet can struggle to handle the kinds of large and complex data that are also becoming common. We propose an accurate, fast, and scalable approach to multiple imputation, which we call MIDAS (Multiple Imputation with Denoising Autoencoders). MIDAS employs a class of unsupervised neural networks known as denoising autoencoders, which are designed to reduce dimensionality by corrupting and attempting to reconstruct a subset of data. We repurpose denoising autoencoders for multiple imputation by treating missing values as an additional portion of corrupted data and drawing imputations from a model trained to minimize the reconstruction error on the originally observed portion. Systematic tests on simulated as well as real social science data, together with an applied example involving a large-scale electoral survey, illustrate MIDAS’s accuracy and efficiency across a range of settings. We provide open-source software for implementing MIDAS.