Reviewerly/exHarmony huggingface dataset:
Characterisation of environment type and difficulty for streamed data classification problems

SDCP require classifiers with the ability to learn and to adjust to the underlying relationships in data streams in real-time. This requirement poses a challenge to classifiers, because the learning task is no longer just to find the optimal decision boundaries, but also to track changes in the decision boundaries as new training data is received. Each SDCP can be described in terms of its environment and difficulty. The environment of an SDCP describes the rate and magnitude of changes in the decision boundaries in the data streams. On the other hand, the difficulty of an SDCP describes the availability of the data that define the decision boundaries during an environment instance. In any empirical analysis of streamed data classifiers, a set of SDCP is used. Understanding the environment and difficulty of each SDCP allows for a more holistic analysis of empirical results. This article proposes (i) a novel quantitative method for analysing the environment of SDCP, and (ii) a difficulty classification scheme based on the construction of SDCP. The proposed methods are evaluated by applying them to a benchmark suite of SDCP.
Wavelet and LSB-Based Encrypted Watermarking Approach to Hide Patient’s Information in Medical Image

Weak protection of patients’ data in medical institutions becomes a threat both for the medical institute or health care and patient. Different techniques are developing day by day to ensure the security and authentication of digital data in the medical field. Watermarking is one of the most widespread technologies to ensure the authentication of digital data and copyright protection of the user. In this paper, an algorithm has been proposed which is based on a joint watermarking and encryption technique to securely hide patient’s secret information. In the research, an initialization vector (IV) generation is proposed which is basically used to encrypt patient’s information using the advanced encryption standard (AES) technique in output feedback mode. The embedding of a watermark in the image is accomplished using a wavelet-based DWT technique and least significant bit (LSB) substitution. It has been found that the proposed algorithm is efficient and secured in many cases after observing different factors like imperceptibility, MSE, PSNR, and correlation.
Augmented Bi-path Network for Few-shot Learning

Few-shot Learning (FSL) which aims to learn from few labeled training data is becoming a popular research topic, due to the expensive labeling cost in many real-world applications. One kind of successful FSL method learns to compare the testing (query) image and training (support) image by simply concatenating the features of two images and feeding it into the neural network. However, with few labeled data in each class, the neural network has difficulty in learning or comparing the local features of two images. Such simple image-level comparison may cause serious mis-classification. To solve this problem, we propose Augmented Bi-path Network (ABNet) for learning to compare both global and local features on multi-scales. Specifically, the salient patches are extracted and embedded as the local features for every image. Then, the model learns to augment the features for better robustness. Finally, the model learns to compare global and local features separately, i.e., in two paths, before merging the similarities. Extensive experiments show that the proposed ABNet outperforms the state-of-the-art methods. Both quantitative and visual ablation studies are provided to verify that the proposed modules lead to more precise comparison results.
AVD-Net: Attention Value Decomposition Network For Deep Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) is of importance for variable real-world applications but remains more challenges like stationarity and scalability. While recently value function factorization methods have obtained empirical good results in cooperative multi-agent environment, these works mostly focus on the decomposable learning structures. Inspired by the application of attention mechanism in machine translation and other related domains, we propose an attention based approach called attention value decomposition network (AVD-Net), which capitalizes on the coordination relations between agents. AVD-Net employs centralized training with decentralized execution (CTDE) paradigm, which factorizes the joint action-value functions with only local observations and actions of agents. Our method is evaluated on multi-agent particle environment (MPE) and StarCraft micromanagement environment (SMAC). The experiment results show the strength of our approach compared to existing methods with state-of-the-art performance in cooperative scenarios.
Vision-Based Vehicle Detection and Tracking System

A robust traffic surveillance system is one of the emerging fields in the existing system; the traffic surveillance system mainly concentrates on vehicular movement for effective management of traffic. Vehicle flow detection plays a prominent role in the surveillance system. The main drawback of the existing system is it consumes more time and results in false detection due to the presence of the shadow. Hence, in this paper, mixture of Gaussian (MoG2) model is proposed to extract the foreground from the background efficiently, followed by Gaussian blur and morphological operation for efficient shadow removal. Finally, dynamic object detection is using blob detection where the vehicle is shown as a rectangular patch from which meaningful features are extracted and identified as vehicle object through blob analysis. Finally, the detected vehicles are tracked through blob tracking and counted using multiple reference lines by measuring the minimal distance between extracted features in consecutive frames. The experimental results of the proposed system show a maximum efficiency of 91%.
Adaptive Re-Balancing Network with Gate Mechanism for Long-Tailed Visual Question Answering

Visual Question Answering (VQA) is a challenging task which requires a fine-grained semantic understanding of visual and textual contents. Existing works focus on better modality representations. However, these methods give little consideration to the long-tailed data distribution in common VQA datasets. The extreme class imbalance causes training bias to behave well in head class, but fail in tail class. Therefore, we propose a unified Adaptive Re-balancing Network (ARN) to take care of classification in both head and tail classes, exhaustively improving performance for VQA. Specifically, two training branches are introduced to per-form their own duty iteratively, which learn the universal representations first and then emphasize the tail data progressively by the re-balancing branch with adaptive learning. Meanwhile, contextual information in the question is vital for guiding accurate visual attention. Thus our network is further equipped with a novel gate mechanism to give higher weight to contextual information. The Experimental results on common benchmarks such as VQA-v2 have demonstrated the superiority of our method compared with state of the art.
Virtue Signaling: Problematizing Creative Labor Within Knowledge Socialism

This article argues that current scholarship inappropriately utilizes ‘virtue signaling’ to elicit a subjectivist form of de-valorized creative labor within knowledge socialism as a philosophy of praxis. Moreover, it argues that within institutionalized higher education (HE) contexts, knowledge socialism should be reoriented toward the promotion of prosocial-entrepreneurial ‘transitional ecosystems’ of commons-based peer production (CBPP). This sociomaterialist repositioning will outline a path forward for knowledge socialism which better understands, elicits, and valorizes the inherently prosocial-entrepreneurial creative labor contributions of precariously positioned academics within contemporary HE. In order to accomplish this aim, this article first underlines several fundamental concepts (commons-based peer production, collective intelligence, creative labor) before moving on to a more pointed philosophical discussion and critique of these concepts as underlying assumptions within knowledge socialism. Lastly, this article provides an operaismo-based reflexive inquiry into the interplay between the theory of knowledge socialism as experienced in practice within the China HE context, contributing to the development of non-exploitative knowledge cultures within CBPP creative labor processes.
A robust digital watermarking method for textile image

In this era of rapid development of network, the infringement of the textile industry is becoming more and more important, which seriously restricts the development of textiles. In this paper, a robust digital watermarking method based on chrominance and texture features of textile images is proposed. The cover image was transformed into CIE LAB color space, and the B channel of the cover image was extracted. The B channel of the carrier image was divided into blocks, and each image block was transformed by DCT. In order to better to maintain the visual effect of the image and the robustness of the watermark, the watermark is embedded in the intermediate frequency domain of DCT. Then the digital image is transferred to the textile by using the dark transfer film through the thermal transfer printing technology. The experimental results indicate that the method is robust to the attack of textile image in the process of print-canning. And the method could coordinate the contradiction between invisibility and robustness.
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization

Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot classification methods. They train an initializer across a variety of sampled learning tasks (also known as episodes) such that the initialized model can adapt quickly to new ones. However, current MAML-based algorithms have limitations in forming generalizable decision boundaries. In this paper, we propose an approach called MetaMix, which generates virtual feature-target pairs within each episode to regularize the backbone models. MetaMix can be integrated with any of the MAML-based algorithms and learn the decision boundaries generalizing better to new tasks. Experiments on the mini-ImageNet, CUB, and FC100 datasets show that MetaMix improves the performance of MAML-based algorithms and achieves state-of-the-art result when integrated with Meta-Transfer Learning.
FairFed: Cross-Device Fair Federated Learning

Federated learning (FL) is the rapidly developing machine learning technique that is used to perform collaborative model training over decentralized datasets. FL enables privacy-preserving model development whereby the datasets are scattered over a large set of data producers (i.e., devices and/or systems). These data producers train the learning models, encapsulate the model updates with differential privacy techniques, and share them to centralized systems for global aggregation. However, these centralized models are always prone to adversarial attacks (such as data-poisoning and model poisoning attacks) due to a large number of data producers. Hence, FL methods need to ensure fairness and high-quality model availability across all the participants in the underlying AI systems. In this paper, we propose a novel FL framework, called FairFed, to meet fairness and high-quality data requirements. The FairFed provides a fairness mechanism to detect adversaries across the devices and datasets in the FL network and reject their model updates. We use a Python-simulated FL framework to enable large-scale training over MNIST dataset. We simulate a cross-device model training settings to detect adversaries in the training network. We used TensorFlow Federated and Python to implement the fairness protocol, the deep neural network, and the outlier detection algorithm. We thoroughly test the proposed FairFed framework with random and uniform data distributions across the training network and compare our initial results with the baseline fairness scheme. Our proposed work shows promising results in terms of model accuracy and loss.
Application and research of heuristic search algorithm in crawler field

Breadth first search algorithm is widely used in traditional crawler programs. This kind of search algorithm takes the way of blindly searching data, and does not make use of the characteristics of crawling data. When there are many websites, the crawler will be very time-consuming. Based on the above reasons, an adaptive heuristic search algorithm is designed. According to the relevance between the page and the topic and the related information of the link, the more likely link is selected for crawling, and the estimation function is modified according to the crawling result. Experiments show that the algorithm can significantly improve the efficiency of crawler than breadth first search algorithm.
Extracting relational facts based on hybrid Syntax-Guided transformer and pointer network

Joint extraction of entities and relations from unstructured text is an essential step in constructing a knowledge base. However, relational facts in these texts are often complicated, where most of them contain overlapping triplets, making the joint extraction task still challenging. This paper proposes a novel Sequence-to-Sequence (Seq2Seq) framework to handle the overlapping issue, which models the triplet extraction as a sequence generation task. Specifically, a unique cascade structure is proposed to connect transformer and pointer network to extract entities and relations jointly. By this means, sequences can be generated in triplet-level and it speeds up the decoding process. Besides, a syntax-guided encoder is applied to integrate the sentence’s syntax structure into the transformer encoder explicitly, which helps the encoder pay more accurate attention to the syntax-related words. Extensive experiments were conducted on three public datasets, named NYT24, NYT29, and WebNLG, and the results show the validity of this model by comparing with various baselines. In addition, a pre-trained BERT model is also employed as the encoder. Then it comes up to excellent performance that the F1 scores on the three datasets surpass the strongest baseline by 5.7%, 5.6%, and 4.4%.
Formalising nominal C-unification generalised with protected variables

Abstract This work extends a rule-based specification of nominal C-unification formalised in Coq to include ‘protected variables’ that cannot be instantiated during the unification process. By introducing protected variables, we are able to reuse the C-unification simplification rules to solve nominal C-matching (as well as equality check) problems. From the algorithmic point of view, this extension is sufficient to obtain a generalised C-unification procedure; however, it cannot be formally checked by simple reuse of the original formalisation. This paper describes the additional effort necessary in order to adapt the specification of the inference rules and reuse previous formalisations. We also generalise a functional recursive nominal C-unification algorithm specified in PVS with protected variables, effectively adapting this algorithm to the tasks of nominal C-matching and nominal equality check. The PVS formalisation is applied to test the correctness of a Python manual implementation of the algorithm.
Overview of the Main Recommendation Approaches for the Scientific Articles

With the explosive growth of the data that being produced and published on the Web every day by the scientific community, it is becoming difficult for researchers to find the most appropriate scientific articles for their needs. For alleviate such information overload, the recommender systems plays a key role in allowing users to access what interests them as quickly as possible.
An Empirical Analysis of Similarity based Single Document Summarization

In current days, there is an enormous amount of information available in various resources like www, e-books, tweets, several articles, and social media posts. Human beings have very little time available to them, whereas they must obtain the necessary information from these resources due to public and business requirements. Text Summarization is one of the solutions to this problem. It is a process of shortening the length of text present in the document computationally and permit to obtain the most vital information in a very less amount of time. Therefore, a similarity-based framework is suggested to summarize a document concerning various similarity measures. Document Understanding Conference (DUC) dataset is considered for empirical validation of the model.
Bayesian Data Analysis for Software Engineering

We propose this technical briefing on "Bayesian Data Analysis for Software Engineering" (BDA-SE) for ICSE 2021. Given the broad and growing interest for statistical data analysis throughout the software en-gineering community, the top software engineering conference is a natural venue for this technical briefing. The breadth of the topic fits the time allotted for general topics, which would allow us to discuss: limitations of the current most common statistical techniques in software engineering; principles underlying Bayesian data analysis; brief overview of the Bayesian statistical framework; key guidelines to apply Bayesian data analysis in practice; practical examples of Bayesian (re-)analysis of software engineering empirical data. Bayesian statistics is not a silver bullet that will fix all the issues with empirical data analysis (in software engineering); but it can be an important step in the direction of helping empirical software engineering build solid foundations and achieve robust results.
Research on Text Summary Generation Based on Bidirectional Encoder Representation from Transformers

For Chinese automatic summarization, most of the generation methods are extractive, and the generative summary is not smooth, incoherent, and covers incomplete information. Compared with the traditional sequence-to-sequence model, Generative Adversarial Network (GAN) uses a reinforcement learning strategy The use of discriminator to guide generation has achieved good results in text generation. This paper proposes a pre-training method based on Bidirectional Encoder Representation from Transformers (BERT) and combined with LeakGAN model to generate abstracts. Firstly, using the bidirectional encoding characteristics of the BERT model, it can retain the original information well, and has a better effect when extracting features of words in the context to generate high-quality word vectors; secondly, for the current supervised generative model Both have the training problem of maximum likelihood estimation. This article uses the LeakGAN model that can decompose the task into different levels of sub-strategies, and uses hierarchical reinforcement learning to solve the characteristics of sparse rewards and generate a more accurate summary.
A Comparative Analysis of Filters for Noise Reduction and Smoothening of Brain MRI Images

Image pre-processing is used to improve the quality of image by removing irrelevant and unwanted parts of the image such as noise. Medical images generally suffer from noise, and this noisy effect is due to the inherent properties of the medical devices such as magnetic field generated by the scanners or may be due to the vibrations and loud sound generated during Magnetic Resonance Image (MRI) scan. MRI images suffer due to radiation and physical movements of magnetic parts of the devices which are responsible for image noise in MRI. The MRI device uses strong magnetic field to create images and hence images usually suffer from different types of noises such as Gaussian, Salt and pepper, speckle etc. Removing such noise from brain MRI images is a big challenge for researchers to have better visualization of the images to accurately identify brain tumor. The core objective of this paper is to discuss different methods used in pre-processing of the brain MRI images and compare the quality of the resultant images generated by different pre-processing techniques. This paper discusses different types of noises and filters suitable for these noises by comparing the histograms of various filters.
Semi-Supervised Learning For Signal Recognition With Sparsity And Robust Promotion

Due to the emergence of deep learning, signal recognition has made great strides in performance improvement. The success of most deep learning methods relies on the accessibility of abundant labelled training data. However, the annotation of signals is quite expensive, making it challenging to train deep learning models substantially. This calls for the development of semi-supervised learning (SSL) method to fully utilize the unlabelled data to assist the training of deep learning models. To achieve this goal, three types of loss function tailored to the task of signal recognition are carefully designed in this paper. Together with the novel design of neural network structure, the proposed SSL method can effectively extract the information from unlabelled training data and thus overcome the difficulty of insufficient training. Extensive numerical results using real-world signal datasets are presented to show the remarkable performance of the proposed SSL method.
A New Method for information security risk management in big data environment

In recent years, the social security problems are becoming more and more complex, and the public security work is arduous. The traditional governance mode needs more transformation and bigger upgrading. At present, the police information processing work lacks of effective information data support. By using advanced technologies such as big data and artificial intelligence, the police information big data application platform is constructed to more effectively gather information related to the police situation, dig deeply into the police information data resources, and expand the application according to police information business. In order to achieve a more comprehensive source of police information, more efficient business processing, and more positive service to the grassroots, we need accurate prediction and decision support. So as to promote the transformation of public security policing mode from extensive manpower type to intelligent fine type, this paper propose a new method based private matching protocol for information security risk management in big data environment, and provide security analysis at the last.
Knowledge Transfer across Imaging Modalities Via Simultaneous Learning of Adaptive Autoencoders for High-Fidelity Mobile Robot Vision

Enabling mobile robots for solving challenging and diverse shape, texture, and motion related tasks with high fidelity vision requires the integration of novel multimodal imaging sensors and advanced fusion techniques. However, it is associated with high cost, power, hardware modification, and computing requirements which limit its scalability. In this paper, we propose a novel Simultaneously Learned Auto Encoder Domain Adaptation (SAEDA)-based transfer learning technique to empower noisy sensing with advanced sensor suite capabilities. In this regard, SAEDA trains both source and target auto-encoders together on a single graph to obtain the domain invariant feature space between the source and target domains on simultaneously collected data. Then, it uses the domain invariant feature space to transfer knowledge between different signal modalities. The evaluation has been done on two collected datasets (LiDAR and Radar) and one existing dataset (LiDAR, Radar and Video) which provides a significant improvement in quadruped robot-based classification (home floor and human activity recognition) and regression (surface roughness estimation) problems. We also integrate our sensor suite and SAEDA framework on two real-time systems (vacuum cleaning and Mini-Cheetah quadruped robots) for studying the feasibility and usability.