Reviewerly/exHarmony huggingface dataset:
Zero-Shot Commonsense Question Answering with Cloze Translation and Consistency Optimization

Commonsense question answering (CQA) aims to test if models can answer questions regarding commonsense knowledge that everyone knows. Prior works that incorporate external knowledge bases have shown promising results, but knowledge bases are expensive to construct and are often limited to a fixed set of relations. In this paper, we instead focus on better utilizing the implicit knowledge stored in pre-trained language models. While researchers have found that the knowledge embedded in pre-trained language models can be extracted by having them fill in the blanks of carefully designed prompts for relation extraction and text classification, it remains unclear if we can adopt this paradigm in CQA where the inputs and outputs take much more flexible forms. To this end, we investigate four translation methods that can translate natural questions into cloze-style sentences to better solicit commonsense knowledge from language models, including a syntactic-based model, an unsupervised neural model, and two supervised neural models. In addition, to combine the different translation methods, we propose to encourage consistency among model predictions on different translated questions with unlabeled data. We demonstrate the effectiveness of our methods on three CQA datasets in zero-shot settings. We show that our methods are complementary to a knowledge base improved model, and combining them can lead to state-of-the-art zero-shot performance. Analyses also reveal distinct characteristics of the different cloze translation methods and provide insights on why combining them can lead to great improvements. Code/dataset is available at https://github.com/PlusLabNLP/zero_shot_cqa.
Image restoration for real-world under-display imaging

Under-display imaging technique was recently proposed to enlarge the screen-to-body ratio for full-screen devices. However, existing image restoration algorithms have difficulty generalizing to real-world under-display (UD) images, especially to images containing strong light sources. To address this issue, we propose a novel method for building a synthetic dataset (CalibPSF dataset) and introduce a two-stage neural network to solve the under-display imaging degradation problem. The CalibPSF dataset is generated using the calibrated high dynamic range point spread function (PSF) of the under-display optical system and contains various simulated light sources. The two-stage network solves the color distortion and diffraction degradation in order. We evaluate the performance of our algorithm on our captured real-world test set. Comprehensive experiments demonstrate the superiority of our method in different dynamic range scenes.
Stochastic Trust-Region Methods with Trust-Region Radius Depending on Probabilistic Models

We present a stochastic trust-region model-based framework in which its radius is related to the probabilistic models. Especially, we propose a specific algorithm, termed STRME, in which the trust-region radius depends linearly on the latest model gradient. The complexity of STRME method in non-convex, convex and strongly convex settings has all been analyzed, which matches the existing algorithms based on probabilistic properties. In addition, several numerical experiments are carried out to reveal the benefits of the proposed methods compared to the existing stochastic trust-region methods and other relevant stochastic gradient methods.
GELAB – The Cutting Edge of Grammatical Evolution

The advent of cloud-based super-computing platforms has given rise to a Data Science (DS) boom.Many types of technological problems that were once considered prohibitively expensive to tackle are now candidates for exploration.Machine Learning (ML) tools that were valued only in academic environments are quickly being embraced by industrial giants and tiny startups alike.Coupled with modern-day computing power, ML tools can be looked at as hammers that can deal with even the most stubborn nails.ML tools have become so ubiquitous that the current industrial expectation is that they should not only deliver accurate and intelligent solutions but also do so rapidly.In order to keep pace with these requirements, a new enterprise, referred to as MLOps has blossomed in recent years.MLOps combines the process of ML and DS with an agile software engineering technique to develop and deliver solutions in a fast and iterative way.One of the key challenges to this is that ML and DS tools should be efficient and have better usability characteristics than were traditionally offered.In this paper, we present a novel software for Grammatical Evolution (GE) that meets both of these expectations.Our tool, GELAB, is a toolbox for GE in Matlab which has numerous features that distinguish it from existing contemporary GE software.Firstly, it is user-friendly and its development was aimed for use by non-specialists.Secondly, it is capable of hybrid optimization, in which standard numerical optimization techniques can be added to GE.We have shown experimentally that when hybridized with meta-heuristics GELAB has an overall better performance as compared with standard GE.
Distance Bounds for Generalized Bicycle Codes

Generalized bicycle (GB) codes is a class of quantum error-correcting codes constructed from a pair of binary circulant matrices. Unlike for other simple quantum code ansätze, unrestricted GB codes may have linear distance scaling. In addition, low-density parity-check GB codes have a naturally overcomplete set of low-weight stabilizer generators, which is expected to improve their performance in the presence of syndrome measurement errors. For such GB codes with a given maximum generator weight w, we constructed upper distance bounds by mapping them to codes local in D≤w−1 dimensions, and lower existence bounds which give d≥O(n1/2). We have also conducted an exhaustive enumeration of GB codes for certain prime circulant sizes in a family of two-qubit encoding codes with row weights 4, 6, and 8; the observed distance scaling is consistent with A(w)n1/2+B(w), where n is the code length and A(w) is increasing with w.
A WeChat Official Account Reading Quantity Prediction Model Based on Text and Image Feature Extraction

This paper describes a study that built a neural network prediction model based on feature extraction, focusing on text analysis and image analysis of WeChat official accounts reading quantity. Based on the embedding method of the deep learning model, we extracted the text features in the title and the image features in the cover picture, explored the relationship between these features and the reading quantity, and built a neural network model based on these features to predict the reading quantity. The results show that there is a phenomenon of sentiment fusion in the text, and a sentence vector model based on Doc2Vec and a neural network model both had a good performance. This paper proposes a tool that can predict the reading quantity in advance and help administrators adjust the titles and images according to the predicted results.
COVID-19 Face Mask Detection Alert System

Study shows that mask-wearing is a critical factor in stopping the COVID-19 transmission. By the time of this article, most states have mandated face masking in public space. Therefore, real-time face mask detection becomes an essential application to prevent the spread of the pandemic. This study will present a face mask detection system that can detect and monitor mask-wearing from camera feeds and alert when there is a violation. The face mask detection algorithm uses a haar cascade classifier to find facial features from the camera feed and then utilizes it to detect the mask-wearing status. With the increasing number of cases all over the world, a system to replace humans to check masks on the faces of people is greatly needed. This system satisfies that need. This system can be employed in public places like transport stations and malls. It will be of great help in companies and huge establishments where there will be a lot of workers. Alongside this, we have used basic concepts of transfer learning in neural networks to finally output the presence or absence of a face mask in an image or a video stream. Experimental results show that our model performs well on the test data with 100 percent and 99 percent precision and recall, respectively. We are making a savvy framework that will identify whether the specific user has worn the mask or not and further tell the accuracy level of the detection. Our framework will be python and AI-based which will be safe and quick for implementation. This system will be of great help there because it is easy to obtain and store the data of the employees working in that Company and will very easily find the people who are not wearing the mask and a will be sent to that respective person to take Precautions not wearing a mask. Potential delays are analyzed, and efforts are made to reduce them to achieve real-time detection. The experiment result shows that the presented system achieves a real-time 45fps 720p Video output, with a face mask detection response of 0.15s and the accuracy of detection 99%. Keywords: Covid-19, Face Mask Detection, Alert System, CCTV DOI: 10.7176/CEIS/13-2-01 Publication date: March 31 st 2022
HG-Meta: Graph Meta-learning over Heterogeneous Graphs

Prevailing supervised graph neural networks suffer from potential performance degradation in the label sparsity case.Though increasing attention has been paid to graph fewshot learning methods for learning effective graph embeddings under the scarcity of labeled data, most existing works study homogeneous graphs while ignoring the ubiquitousness of heterogeneous graphs (HG), where multi-typed nodes are interconnected by multi-typed edges.To this end, we propose to tackle few-shot learning on HG and develop a novel model for Heterogeneous Graph Meta-learning (a.k.a.HG-Meta).Regarding the graph heterogeneity, HG-Meta firstly builds a graph encoder to aggregate heterogeneous neighbors information from multiple semantic contexts (generated by meta-paths).Secondly, to train the graph encoder with meta-learning in a few-shot scenario, HG-Meta tackles meta-task differences produced from meta-task sampling procedure on HG with a task feature scaling module and a degree based task attention module.To further alleviate low-data problem, HG-Meta leverages unlabelled information in HG with auxiliary self-supervised learning task alongside the meta-optimization process to facilitate node embedding.Extensive experiments on two HG datasets demonstrate that HG-Meta outperforms state-of-the-art methods for multiple few-shot node classification tasks.
FisheyePixPro: Self-supervised pretraining using Fisheye images for semantic segmentation

Self-supervised learning has been an active area of research in the past few years.Contrastive learning is a type of selfsupervised learning method that has achieved a significant performance improvement on image classification task.However, there has been no work done in its application to fisheye images for autonomous driving.In this paper, we propose FisheyePixPro, which is an adaption of pixel level contrastive learning method PixPro [1] for fisheye images.This is the first attempt to pretrain a contrastive learning based model, directly on fisheye images in a self-supervised approach.We evaluate the performance of learned representations on the WoodScape dataset using segmentation task.Our FisheyePixPro model achieves a 65.78 mIoU score, a significant improvement over the PixPro model.This indicates that pre-training a model on fisheye images have a better performance on a downstream task.
LogicMEM: Automatic Profile Generation for Binary-Only Memory Forensics via Logic Inference

Memory forensic tools rely on the knowledge of kernel symbols and kernel object layouts to retrieve digital evidence and artifacts from memory dumps.This knowledge is called profile.Existing solutions for profile generation are either inconvenient or inaccurate.In this paper, we propose a logic inference approach to automatically generating a profile directly from a memory dump.It leverages the invariants existing in kernel data structures across all kernel versions and configurations to precisely locate forensics-required fields in kernel objects.We have implemented a prototype named LOGICMEM and evaluated it on memory dumps collected from mainstream Linux distributions, customized Linux kernels with random configurations, and operating systems designed for Android smartphones and embedded devices.The evaluation results show that the proposed logic inference approach is well-suited for locating forensics-required fields and achieves 100% precision and recall for mainstream Linux distributions and 100% precision and 95% recall for customized kernels with random configurations.Moreover, we show that false negatives can be eliminated with improved logic rules.We also demonstrate that LOGICMEM can generate profiles when it is otherwise difficult (if not impossible) for existing approaches, and support memory forensics tasks such as rootkit detection.
High precision reconstruction of silicon photonics chaos with stacked CNN-LSTM neural networks

Silicon-based optical chaos has many advantages, such as compatibility with complementary metal oxide semiconductor (CMOS) integration processes, ultra-small size, and high bandwidth. Generally, it is challenging to reconstruct chaos accurately because of its initial sensitivity and high complexity. Here, a stacked convolutional neural network (CNN)-long short-term memory (LSTM) neural network model is proposed to reconstruct optical chaos with high accuracy. Our network model combines the advantages of both CNN and LSTM modules. Further, a theoretical model of integrated silicon photonics micro-cavity is introduced to generate chaotic time series for use in chaotic reconstruction experiments. Accordingly, we reconstructed the one-dimensional, two-dimensional, and three-dimensional chaos. The experimental results show that our model outperforms the LSTM, gated recurrent unit (GRU), and CNN models in terms of MSE, MAE, and R-squared metrics. For example, the proposed model has the best value of this metric, with a maximum improvement of 83.29% and 49.66%. Furthermore, 1D, 2D, and 3D chaos were all significantly improved with the reconstruction tasks.
A Multi-channel Character Relationship Classification Model Based on Attention Mechanism

Relation classification is an important semantic processing task in the field of natural language processing.The deep learning technology, which combines Convolutional Neural Network and Recurrent Neural Network with attention mechanism, has always been the mainstream and state-of-art method.The LSTM model based on recurrent neural network dynamically controls the weight by gating, which can better extract the context state information in time series and effectively solve the long-standing problem of recurrent neural network.The pre-trained model BERT has also achieved excellent results in many natural language processing tasks.This paper proposes a multi-channel character relationship classification model of BERT and LSTM based on attention mechanism.Through the attention mechanism, the semantic information of the two models is fused to get the final classification result.Using this model to process the text, we can extract and classify the relationship between the characters, and finally get the relationship between the characters included in this paper.Experimental results show that the proposed method performs better than the previous deep learning model on the SemEval-2010 task 8 dataset and the COAE-2016-Task3 dataset.
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness

A notable challenge in Multi-Document Summarization (MDS) is the extremely-long length of the input.In this paper, we present an extract-then-abstract Transformer framework to overcome the problem.Specifically, we leverage pre-trained language models to construct a hierarchical extractor for salient sentence selection across documents and an abstractor for rewriting the selected contents as summaries.However, learning such a framework is challenging since the optimal contents for the abstractor are generally unknown.Previous works typically create pseudo extraction oracle to enable the supervised learning for both the extractor and the abstractor.Nevertheless, we argue that the performance of such methods could be restricted due to the insufficient information for prediction and inconsistent objectives between training and testing.To this end, we propose a loss weighting mechanism that makes the model aware of the unequal importance for the sentences not in the pseudo extraction oracle, and leverage the fine-tuned abstractor to generate summary references as auxiliary signals for learning the extractor.Moreover, we propose a reinforcement learning method that can efficiently apply to the extractor for harmonizing the optimization between training and testing.Experiment results show that our framework substantially outperforms strong baselines with comparable model sizes and achieves the best results on the Multi-News, Multi-XScience, and WikiCatSum corpora. 1
Worldwide and Brazilian scientific publications on Leishmaniasis in the first 19 years of 21st century: a bibliometric study

Leishmaniasis is a set of tropical diseases widely distributed throughout the world. Publications concerning different aspects of leishmaniasis have shown an increasing trend in recent years, especially in Brazil, where this disease has been under continuous expansion. The present study analyzes the data and assess trends in publications on leishmaniasis in Brazil and worldwide.The bibliometric analysis was performed on the Scopus database, addressing data on scientific publications about leishmaniasis in the first nineteen years of the 21st century. The tendency of the annual number of publications was analyzed by mean of linear regression equations.The literature search identified 19.317 publications. Brazil was the country with the largest number of studies (24.88%). The annual growth rate for Brazilian publications was 4.66%, whereas worldwide publications grew 2.96%. The leading ten international authors contributed to 8.71% of the publications, whereas the ten principal Brazilian authors contributed to 4.31%. The largest collaboration network between authors was between Brazil and the United States. Oswaldo Cruz Foundation occupied the premier position in both worldwide and Brazilian ranking in number of publications. The international journal with the largest number of publications and SCImago Journal Rank indicator was PLOS Neglected Tropical Diseases. The Journal of Immunology had the highest H-index. Most of the research covered topics related to immunology and molecular biology.The data show relevant characteristics of the scientific production on leishmaniasis and demonstrate the fundamental role played by Brazil in the production of knowledge in this area of study.
secml: Secure and explainable machine learning in Python

<h2>Abstract</h2> We present secml, an open-source Python library for secure and explainable machine learning. It implements the most popular attacks against machine learning, including test-time evasion attacks to generate adversarial examples against deep neural networks and training-time poisoning attacks against support vector machines and many other algorithms. These attacks enable evaluating the security of learning algorithms and the corresponding defenses under both white-box and black-box threat models. To this end, secml provides built-in functions to compute security evaluation curves, showing how quickly classification performance decreases against increasing adversarial perturbations of the input data. secml also includes explainability methods to help understand why adversarial attacks succeed against a given model, by visualizing the most influential features and training prototypes contributing to each decision. It is distributed under the Apache License 2.0 and hosted at https://github.com/pralab/secml.
Learners' Autonomous Learning Behavior in Distance Reading Based on Big Data

Some learners couldn’t get ideal learning results from distance education, to find out the root of this problem, their learning behavior and initiative should be analyzed in real time. However, in existing research results, the collected distance learning behavior data of learners are not pertinent enough, the extraction methods of big data are not proper enough, and the analysis models of learning behavior are not scientific enough. For these reasons, this paper analyzed the autonomous learning behavior of learners during distance reading based on big data, and gave the analysis contents and methods. In the text, a sequence diagram transformation method that is self-adaptive to long and short sequences had been introduced to process the learning behavior data of learners, who were then classified according to the features of different autonomous learning behavior sequences. Then, an attribute reduction algorithm based on improved Bayesian fuzzy rough set was adopted for attribute reduction, and the behavior indexes that are closely related to the autonomous learning effect of learners were selected for correlation analysis. After that, this paper proposed a method for detecting non-autonomous learning behavior based on multiple time scales, combining short-term autonomous learning pattern with long-term autonomous learning behavior and resource access behavior, this paper also analyzed the typical autonomous learning pattern of learners through the learning of hidden layer features. At last, experimental results proved the effectiveness of the proposed analysis method.
A New Framework Containing Convolution and Pooling Circuits for Image Processing and Deep Learning Applications with Quantum Computing Implementation

The resource need for deep learning and quantum computers' high computing power potential encourage collaboration between the two fields. Today, variational quantum circuits are used to perform the convolution operation with quantum computing. However, the results produced by variational circuits do not show a direct resemblance to the classical convolution operation. Because classical data is encoded into quantum data with their exact values in value-encoded methods, in contrast to variational quantum circuits, arithmetical operations can be applied with high accuracy. In this study, value-encoded quantum circuits for convolution and pooling operations are proposed to apply deep learning in quantum computers in a traditional and proven way. To construct the convolution and pooling operations, some modules such as addition, multiplication, division, and comparison are created. In addition, a window-based framework for quantum image processing applications is proposed. The generated convolution and pooling circuits are simulated on the IBM QISKIT simulator in parallel thanks to the proposed framework. The obtained results are verified by the expected results. Due to the limitations of quantum simulators and computers in the NISQ era, the used grayscale images are resized to 8x8 and the resolution of the images is reduced to 3 qubits. With developing the quantum technologies, the proposed approach can be applied for bigger and higher resolution images. Although the proposed method causes more qubit usage and circuit depth compared to variational convolutional circuits, the results they produce are exactly the same as the classical convolution process.