Reviewerly/exHarmony huggingface dataset:
Distinguishing between natural and recolored images via lateral chromatic aberration

With the development of image colorization technique, the recolored images (RIs) become more and more authentic, making it very difficult to visually distinguish from natural images (NIs). Recently, researchers have proposed the detection methods towards recolored images. However, the current detection still has limitations such as poor generalization, large-scale training samples, high-dimensional features for training, and high computation cost. To address those issues, this paper proposes a novel method based on the lateral chromatic aberration (LCA) inconsistency and its statistical differences. Generally, RIs have fewer numbers of LCA characteristics than that of NIs, that inspire us to design the classifier for distinguishing two types of images. In particular, we propose to adopt very low 5-dimensional features to feed a classical SVM mechanism. The baseline ImageNet and Oxford datasets are used to verify the effectiveness of the proposed method, in which the performance of our proposed method rivals the prior arts.
A Comprehensive Overview on the Formation of Homomorphic Copies in Coset Graphs for the Modular Group

This work deals with the well-known group-theoretic graphs called coset graphs for the modular group G and its applications. The group action of G on real quadratic fields forms infinite coset graphs. These graphs are made up of closed paths. When M acts on the finite field Zp, the coset graph appears through the contraction of the vertices of these infinite graphs. Thus, finite coset graphs are composed of homomorphic copies of closed paths in infinite coset graphs. In this work, we have presented a comprehensive overview of the formation of homomorphic copies.
Aspect-Based Pair-Wise Opinion Generation in Chinese automotive reviews: Design of the task, dataset and model

The extraction of opinion target–word pairs from user reviews has received much attention recently, since it can provide essential information for fine-grained opinion analysis. However, according to our statistics on a large-scale dataset of Chinese reviews, about 60% reviews do not explicitly show opinion targets or words. To investigate this problem, this paper introduces a new task under fine-grained opinion analysis, named Aspect-Based Pair-wise Opinion Generation (ABPOG), which aims to generate opinion target–word pairs based on reviews and aspects. To perform this task, we develop a sequence-to-sequence model for opinion target–word pair generation by extending the pointer–generator network with two approaches: (1) an aspect-aware encoder that receives an additional aspect embedding as input to extract aspect-specific features, (2) two hierarchical decoders including a token-level GRU and a global GRU to generate opinion targets and words jointly. To empirically evaluate our task and model, we develop a multi-aspect dataset for ABPOG based on Chinese automotive reviews. Extensive experiments on our dataset show that our model outperforms several strong baselines adapted from the state-of-the-art aspect-based summarization method.
End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs

We propose a novel problem within end-toend learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting).Such dialogs are grounded in domain-specific flowcharts, which the agent is supposed to follow during the conversation.Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time.We release a dataset (FLODIAL) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts.We also design a neural model, FLONET, which uses a retrieval-augmented generation architecture to train the dialog agent.Our experiments find that FLONET can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.
Complex Valued Deep Neural Networks for Nonlinear System Modeling

Deep learning models, such as convolutional neural networks (CNN), have been successfully applied in pattern recognition and system identification recent years. But for the cases of missing data and big noises, CNN does not work well for dynamic system modeling. In this paper, complex valued convolution neural network (CVCNN) is presented for modeling nonlinear systems with large uncertainties. Novel training methods are proposed for CVCNN. Comparisons with other classical neural networks are made to show the advantages of the proposed methods.
Blockchain in Stock Market Transformation: A Systematic Literature Review

Blockchain, which is the core technology for Bit coin and other digital currencies, is a disruptive technology, especially in the Stock Exchange Market. Although blockchain has much potential in transforming the traditional stock exchange market, blockchain research in this field is still in its infancy. Business scholars began publishing studies related to blockchain in the Stock market transformation in 2015. In this research, we did a PRISMA guided systematic literature review of blockchain in the Stock market transformation from 2015 to 2019. The results show that even though there is an increase in studies, more empirical research is required to utilize the potential of blockchain technology fully. The findings also provide important insights into the current state of a blockchain scholarly study, including its top advantages and legal obstacles, the risk associated with implementing this technology, and the identification of various promising blockchain models currently being proposed in the stock market. We find that blockchain transforms the stock market remains in an early-stage research domain in terms of theoretical basis, methodological diversity, and empirically grounded work. Hence, this research identifies the research gaps and highlights areas where we need a further study that researchers can utilize.
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing.Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identified by descriptions.However, they ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions, which are treated equally in previous works.In this work, we propose Multi-View Entity Representations (MuVER), a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method.Our method achieves the state-ofthe-art performance on ZESHEL and improves the quality of candidates on three standard Entity Linking datasets 1 .
LotusSQL: SQL engine for high-performance big data systems

In recent years, Apache Spark has become the de facto standard for big data processing. SparkSQL is a module offering support for relational analysis on Spark with Structured Query Language (SQL). SparkSQL provides convenient data processing interfaces. Despite its efficient optimizer, SparkSQL still suffers from the inefficiency of Spark resulting from Java virtual machine and the unnecessary data serialization and deserialization. Adopting native languages such as C++ could help to avoid such bottlenecks. Benefiting from a bare-metal runtime environment and template usage, systems with C++ interfaces usually achieve superior performance. However, the complexity of native languages also increases the required programming and debugging efforts. In this work, we present LotusSQL, an engine to provide SQL support for dataset abstraction on a native backend Lotus. We employ a convenient SQL processing framework to deal with frontend jobs. Advanced query optimization technologies are added to improve the quality of execution plans. Above the storage design and user interface of the compute engine, LotusSQL implements a set of structured dataset operations with high efficiency and integrates them with the frontend. Evaluation results show that LotusSQL achieves a speedup of up to 9× in certain queries and outperforms Spark SQL in a standard query benchmark by more than 2× on average.
The pitfalls of using Gaussian Process Regression for normative modeling

Normative modeling, a group of methods used to quantify an individual’s deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input domain, which at face value makes it an attractive method to normalize the cohort heterogeneity where the deviation between predicted value and true observation is divided by the derived uncertainty directly from Gaussian Processes Regression. However, we show that the uncertainty directly from Gaussian Processes Regression is irrelevant to the cohort heterogeneity in general.
The Evaluation Model of College Students’ Mental Health in the Environment of Independent Entrepreneurship Using Neural Network Technology

In recent years, the employment of college students is becoming more and more prominent; no matter for the society, universities, college students themselves, and their families have formed a huge pressure, in the current situation, the success rate of college students to start their own business is not high; one of the important reasons is that college students generally have defects in entrepreneurial psychology. Therefore, effective evaluation of college students’ mental health under the environment of independent entrepreneurship is conducive to comprehensively improving the quality of talent training in colleges and universities. In this paper, we propose a novel three-channel multifeature fusion network based on neural network technology to identify and predict college students’ mental health problems in the self-entrepreneurship environment. Specifically, we first extract the behavior characteristics, visual characteristics, and social relations as a three-channel network input. Second, in view of the behavior characteristic, we use the length of the memory deep context dependent on network access. In view of visual features, we use the convolution neural network to face emotional characteristics and characteristics of social relations. The feature concat strategy is used for feature fusion. The experimental results on real datasets show that the method in this paper is effective, and it is expected to propose a new solution for college students’ mental health assessment.
XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge

Cross-lingual pre-training has achieved great successes using monolingual and bilingual plain text corpora. However, most pre-trained models neglect multilingual knowledge, which is language agnostic but comprises abundant cross-lingual structure alignment. In this paper, we propose XLM-K, a cross-lingual language model incorporating multilingual knowledge in pre-training. XLM-K augments existing multilingual pre-training with two knowledge tasks, namely Masked Entity Prediction Task and Object Entailment Task. We evaluate XLM-K on MLQA, NER and XNLI. Experimental results clearly demonstrate significant improvements over existing multilingual language models. The results on MLQA and NER exhibit the superiority of XLM-K in knowledge related tasks. The success in XNLI shows a better cross-lingual transferability obtained in XLM-K. What is more, we provide a detailed probing analysis to confirm the desired knowledge captured in our pre-training regimen. The code is available at https://github.com/microsoft/Unicoder/tree/master/pretraining/xlmk.
Face SSD: A Real-time Face Detector based on SSD

Face detection has made substantial progress in recent years. In many applications, face detectors must run on mobile devices or embedded devices. Due to the limited computing resource in such scenarios, it is usually difficult to meet the need of both accuracy and speed. Most detectors cannot detect small face accurately or the detection speed will slow down. To address this challenge, we propose a novel face detector in which the basic framework is a single shot multibox detector (SSD), and name it Face SSD. In order to accelerate the detection speed of Face SSD and improve the detection accuracy, we make contributions in the following three aspects: we improved the structure of ShuffleNet V2 and use it as the backbone network; then we proposed a modified prediction module (MPM) to improve recall of small faces; finally we introduced a scale-equitable face detection framework to match face better. Experimental results show that Face SSD runs 40 frames per second (FPS) on CPU and 110 FPS on GPU. We train Face SSD on WIDER FACE - a face detection benchmark. The detector has also achieved excellent performance on FDDB, a benchmark for face detection in unconstrained settings.
Mammography Lesion Detection Using an Improved GrowCut Algorithm

Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have been proposed so far and, regardless of the algorithms, they all share a step: segmentation. That is, identifying the region of interest in order to further analyze and classify it either as benign or malignant. However, due to the different types of mammary tissues, mammography segmentation can prove to be a difficult task. Various techniques of mammography segmentation have been proposed so far. Yet, since obtaining the ground-truth for mam-mographic images might be problematic, recent literature leans towards unsupervised techniques. In this paper we present a segmentation approach based on the GrowCut algorithm. The original method starts with a number of seed points inside and outside the region of interest, selected by a human expert, and iterates over the pixels multiple times, until it reaches a stable state where all the pixels have been assigned to a class. Our proposal aims to reduce the human intervention by eliminating the need of selecting initial background seeds and, also, to reduce the computational time by limiting the process of parsing the entire image to a small, fixed number of iterations. The proposed approach was compared to the original method, using three variants: (1) automatically constructing the initial background seeds surrounding the foreground ones; (2) using mammograms's background as initial background seeds; (3) not using initial background seeds. Experimental results obtained for the Mini-MIAS dataset show that, for the variations that use background seeds outside the breast and that do not use any background seeds, our approach yields much better results than the original method.
Variational Quantum-Based Simulation of Waveguide Modes

Variational quantum algorithms are one of the most promising methods that can be implemented on noisy intermediate-scale quantum (NISQ) machines to achieve a quantum advantage over classical computers. This article describes the use of a variational quantum algorithm in conjunction with the finite difference method for the calculation of propagation modes of an electromagnetic wave in a hollow metallic waveguide. The two-dimensional (2D) waveguide problem, described by the Helmholtz equation, is approximated by a system of linear equations, whose solutions are expressed in terms of simple quantum expectation values that can be evaluated efficiently on quantum hardware. Numerical examples are presented to validate the proposed method for solving 2D waveguide problems.
Utility Optimization of Federated Learning with Differential Privacy

Secure and trusted cross-platform knowledge sharing is significant for modern intelligent data analysis. To address the trade-off problems between privacy and utility in complex federated learning, a novel differentially private federated learning framework is proposed. First, the impact of data heterogeneity of participants on global model accuracy is analyzed quantitatively based on 1-Wasserstein distance. Then, we design a multilevel and multiparticipant dynamic allocation method of privacy budget to reduce the injected noise, and the utility can be improved efficiently. Finally, they are integrated, and a novel adaptive differentially private federated learning algorithm (A-DPFL) is designed. Comprehensive experiments on redefined non-I.I.D MNIST and CIFAR-10 datasets are conducted, and the results demonstrate the superiority of model accuracy, convergence, and robustness.
Optimized Implementation of SM4 on AVR Microcontrollers, RISC-V Processors, and ARM Processors

At 2003, the SM4 block cipher was introduced that is a Chinese domestic cryptographic. It is mandated in the Chinese National Standard for Wireless LAN Wired Authentication and Privacy Infrastructure (WAPI), because the algorithm was developed for use in wireless sensor networks to provide safety network environment. The SM4 block cipher uses a 128-bit block size and a 32-bit round key. It consists of 32 rounds and one reverse translation R. In this paper, we present the optimized implementation of the SM4 block cipher on 8-bit AVR microcontrollers, which are widely used in wireless sensor devices; the optimized implementation of the SM4 block cipher on 32-bit RISC-V processors, which are open-source-based computer architectures, and the optimized implementation of SM4 on 64-bit ARM processors with parallel computation, which are widely used in smartphones and tablets. In the AVR microcontroller, three versions are implemented for various purposes, including speed-optimization, memory-optimization, and code size-optimization. As a result, the speed-optimization, memory-optimization, and code size-optimization versions achieved 205.2 cycles per byte, 213.3 cycles per byte, and 207.4 cycles per byte, respectively. This is faster than the reference implementation written in C language (1670.7 cycles per byte). The implementation on 32-bit RISC-V processors achieved 128.8 cycles per byte. This is faster than the reference implementation written in C language (345.7 cycles per byte). The implementation on 64-bit ARM processors achieved 8.62 cycles per byte. This is faster than the reference implementation written in C language (120.07 cycles per byte).
A Novel Hybrid Particle Swarm Optimization and Sine Cosine Algorithm for Seismic Optimization of Retaining Structures

This study introduces an effective hybrid optimization algorithm, namely Particle Swarm Sine Cosine Algorithm (PSSCA) for numerical function optimization and automating optimum design of retaining structures under seismic loads. The new algorithm employs the dynamic behavior of sine and cosine functions in the velocity updating operation of particle swarm optimization (PSO) to achieve faster convergence and better accuracy of final solution without getting trapped in local minima. The proposed algorithm is tested over a set of 16 benchmark functions and the results are compared with other well-known algorithms in the field of optimization. For seismic optimization of retaining structure, Mononobe-Okabe method is employed for dynamic loading condition and total construction cost of the structure is considered as the objective function. Finally, optimization of two retaining structures under static and seismic loading are considered from the literature. As results demonstrate, the PSSCA is superior and it could generate better optimal solutions compared with other competitive algorithms.
Data Pre Processing for Machine Learning Models using Python Libraries

Data pre-processing is the process of transforming the raw data into useful dataset. Data pre-processing is one of the most important phase of any machine learning model because the quality and efficiency of any machine learning model directly depends upon the data-set, if we skip this step and design a model with data sets containing missing values then the model we have designed will not be that efficient and will be inconsistent model. This paper describes the methodology for pre-processing the data in seven sequence of steps using python powerful libraries which are open source machine learning libraries that support both supervised and unsupervised learning like pandas is a high level data manipulation tool, scikit learn which provides various tools for model fitting, data pre-processing, model selection and many other utilities. These steps include dealing with missing value, categorical values, importing data sets etc. This analysis helps in cleaning and transforming the datasets which future applied to any learning model and produce a efficient machine learning model.
Budget-Aware Online Control of Edge Federated Learning on Streaming Data With Stochastic Inputs

Performing federated learning continuously in edge networks while training data are dynamically and unpredictably streamed to the devices faces critical challenges, including the global model convergence, the long-term resource budget, and the uncertain stochastic network and execution environment. We formulate an integer program to capture all these challenges, which minimizes the cumulative total latency of stream learning on device and federated learning between devices and the edge server. We then decouple the problem, design an online learning algorithm for controlling the number of local model updates via a convex-concave reformulation and rectified gradient-descent steps, and design a bandit learning algorithm for selecting the edge server for global model aggregations by incorporating the budget information to strike the exploit-explore balance. We rigorously prove the sub-linear regret regarding the optimization objective and the sub-linear constraint violation regarding the maximal on-device load, while guaranteeing the convergence of the global model trained. Extensive evaluations with real-world training data and input traces confirm the empirical superiority of our approach over multiple state-of-the-art algorithms.
Avoiding gender ambiguous pronouns in French

Across many languages, pronouns are the most frequently produced referring expressions. We examined whether and how speakers avoid referential ambiguity that arises when the gender of a pronoun is compatible with more than one entity in the context in French. Experiment 1 showed that speakers use fewer pronouns when human referents have the same gender than when they had different genders, but grammatical gender congruence between inanimate referents did not result in fewer pronouns. Experiment 2 showed that semantic similarity between non-human referents can enhance the likelihood that speakers avoid grammatical-gender ambiguous pronouns. Experiment 3 pitched grammatical gender ambiguity avoidance against the referents' competition in the non-linguistic context, showing that when speakers can base their pronoun choice on non-linguistic competition, they ignore the pronoun's grammatical gender ambiguity even when the referents are semantically related. The results thus indicated that speakers preferentially produce referring expressions based on non-linguistic information; they are more likely to be affected by the referents' non-linguistic similarity than by the linguistic ambiguity of a pronoun.