Reviewerly/exHarmony huggingface dataset:
We Need To Talk About Random Splits

Gorman and Bedrick ( 2019) argued for using random splits rather than standard splits in NLP experiments.We argue that random splits, like standard splits, lead to overly optimistic performance estimates.We can also split data in biased or adversarial ways, e.g., training on short sentences and evaluating on long ones.Biased sampling has been used in domain adaptation to simulate real-world drift; this is known as the covariate shift assumption.In NLP, however, even worst-case splits, maximizing bias, often under-estimate the error observed on new samples of in-domain data, i.e., the data that models should minimally generalize to at test time.This invalidates the covariate shift assumption.Instead of using multiple random splits, future benchmarks should ideally include multiple, independent test sets instead; if infeasible, we argue that multiple biased splits leads to more realistic performance estimates than multiple random splits.
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning

Object recognition in the real-world requires handling long-tailed or even open-ended data. An ideal visual system needs to recognize the populated head visual concepts reliably and meanwhile efficiently learn about emerging new tail categories with a few training instances. Class-balanced many-shot learning and few-shot learning tackle one side of this problem, by either learning strong classifiers for head or learning to learn few-shot classifiers for the tail. In this paper, we investigate the problem of generalized few-shot learning (GFSL)—a model during the deployment is required to learn about tail categories with few shots and simultaneously classify the head classes. We propose the ClAssifier SynThesis LEarning (Castle), a learning framework that learns how to synthesize calibrated few-shot classifiers in addition to the multi-class classifiers of head classes with a shared neural dictionary, shedding light upon the inductive GFSL. Furthermore, we propose an adaptive version of Castle (a Castle) that adapts the head classifiers conditioned on the incoming tail training examples, yielding a framework that allows effective backward knowledge transfer. As a consequence, a Castle can handle GFSL with classes from heterogeneous domains effectively. Castle and a Castle demonstrate superior performances than existing GFSL algorithms and strong baselines on MiniImageNet as well as TieredImageNet datasets. More interestingly, they outperform previous state-of-the-art methods when evaluated with standard few-shot learning criteria.
Synthetic speech detection through short-term and long-term prediction traces

Abstract Several methods for synthetic audio speech generation have been developed in the literature through the years. With the great technological advances brought by deep learning, many novel synthetic speech techniques achieving incredible realistic results have been recently proposed. As these methods generate convincing fake human voices, they can be used in a malicious way to negatively impact on today’s society (e.g., people impersonation, fake news spreading, opinion formation). For this reason, the ability of detecting whether a speech recording is synthetic or pristine is becoming an urgent necessity. In this work, we develop a synthetic speech detector. This takes as input an audio recording, extracts a series of hand-crafted features motivated by the speech-processing literature, and classify them in either closed-set or open-set. The proposed detector is validated on a publicly available dataset consisting of 17 synthetic speech generation algorithms ranging from old fashioned vocoders to modern deep learning solutions. Results show that the proposed method outperforms recently proposed detectors in the forensics literature.
Person Re-Identification by Context-Aware Part Attention and Multi-Head Collaborative Learning

Most existing works solve the video-based person re-identification (re-ID) problem by computing the representation of each frame independently and finally aggregate the frame-level features. However, these methods often suffer from the challenging factors in videos, such as serious occlusion, background clutter and pose variation. To address these issues, we propose a novel multi-level Context-aware Part Attention (CPA) model to learn discriminative and robust local part features. It is featured in two aspects: 1) the context-aware part attention module improves the robustness by capturing the global relationship among different body parts across different video frames, and 2) the attention module is further extended to multi-level attention mechanism which enhances the discriminability by simultaneously considering low- to high-level features in different convolutional layers. In addition, we propose a novel multi-head collaborative training scheme to improve the performance, which is collaboratively supervised by multiple heads with the same structure but different parameters. It contains two consistency regularization terms, which consider both multi-head and multi-frame consistency to achieve better results. The multi-level CPA model is designed for feature extraction, while the multi-head collaborative training scheme is designed for classifier supervision. They jointly improve our re-ID model from two complementary directions. Extensive experiments demonstrate that the proposed method achieves much better or at least comparable performance compared to the state-of-the-art on four video re-ID datasets.
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples

This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS). The method trains a model to minimize a consistency loss, which ensures that different views of the same unlabeled instance are assigned similar pseudo-labels. The pseudo-labels are generated non-parametrically, by comparing the representations of the image views to those of a set of randomly sampled labeled images. The distance between the view representations and labeled representations is used to provide a weighting over class labels, which we interpret as a soft pseudo-label. By non-parametrically incorporating labeled samples in this way, PAWS extends the distance-metric loss used in self-supervised methods such as BYOL and SwAV to the semi-supervised setting. Despite the simplicity of the approach, PAWS outperforms other semi-supervised methods across architectures, setting a new state-of-the-art for a ResNet-50 on ImageNet trained with either 10% or 1% of the labels, reaching 75.5% and 66.5% top-1 respectively. than the previous best methods. PAWS requires 4× to 12× less training
Self-attention feature fusion network for semantic segmentation

Capturing rich contextual information is very helpful for semantic segmentation. In addition, the effective use of low-level details and high-level semantics is crucial for semantic segmentation. In this paper, we start from these two aspects, and we propose a self-attention feature fusion network for semantic segmentation (SA-FFNet) to improve semantic segmentation performance. Specifically, we introduced the vertical and horizontal compression attention module (VH-CAM) and the unequal channel pyramid pooling module (UC-PPM). The previous position attention module, such as the position attention module (PAM) in DANet, calculates the similarity between each pixel and all other pixels. However, the amount of information contained in a single isolated pixel is too small, so the resulting position attention weight is not perfect. Our approach is to compress a feature map from both vertical and horizontal directions so that the information contained in each pixel will be richer, and the spatial feature map obtained from this will be better. Experiments show that the position attention weight generated by our vertical and horizontal compression attention module is better than PAM in DANet. Additionally, our UC-PPM on each decoder can provide high-level rich semantic information to guide the selection of low-level feature maps. The proposed model achieves a 76.42% mean IoU on PASCAL VOC2012 and a 73.13% mean IoU on Cityscapes.
A substitution box generator, its analysis, and applications in image encryption

A substitution box (S-box) is the only non-linear component in many cryptosystems. Recently, S-box generators have been used intensively in many modern cryptosystems to obtain ciphertext that is secure against modern cryptanalysis. The purpose of these generators is to (i) efficiently generate (ii) dynamic S-boxes (iii) with optimal cryptographic properties (iv) that can be used as a group to create a secure ciphertext. However, no formal methods have been introduced that can analyze if an S-box generator can satisfy conditions (i), (ii) and (iv). The aim of this paper is fourfold: we formally discuss five methods to test if an S-box generator satisfies (i), (ii) and (iv). In the second part, we propose a new S-box generator based on isomorphic ordered elliptic curves that satisfies conditions (i)-(iv). In the third part, we conduct a detailed comparison of our S-box generator with some of the state-of-the-art S-box generators. From the comparison, we conclude that our generator is more secure and suitable for encryption. In the fourth part, we discuss an application of the proposed S-box generator for image encryption. Experimental results reveal that our S-box generator can generate a highly secure ciphertext for a plain text with a high correlation.
A dynamic simulation approach to support the evaluation of cyber risks and security investments in SMEs

The growing amount of cyberspace threats highlights the need to evaluate cybersecurity risks and to plan for effective investments. One internationally recognized document for cybersecurity risk management is the framework for Improving Critical Infrastructure Cybersecurity by the US National Institute of Standards and Technology (NIST). It provides guidelines, best practices and standards for cybersecurity risk management. Nevertheless, as other self-assessment frameworks, it produces a static view of an organization's cyber posture and does not capture the dynamics of organizational changes and cyberattacks. Moreover, the current situation sees small and medium enterprises (SMEs) in a critical position since they need to manage their cybersecurity while usually not being skilled or equipped enough to internalize this process. Therefore, there is a need for a practical and easily applicable model able to identify a cybersecurity risk profile and its dynamics. This study proposes a system dynamics methodology and tool (SMECRA - SME Cyber Risk Assessment) for supporting cybersecurity investment decisions for SMEs through the evaluation of cyber risk and previous investments. SMECRA addresses dynamic organizational complexity and can be used to assess cyber risks and related dynamics over time. Three case studies demonstrate its capability to assess a SME's cybersecurity status and to evaluate investments impacts on an organization's risk profile, raising cybersecurity awareness. This study is important for SMEs wishing to manage their own cybersecurity risk and for insurance companies in their economic evaluation of residual risks that SMEs wish to externalize.
Multiscale attention guided U-Net architecture for cardiac segmentation in short-axis MRI images

Background and Objective: Automatic cardiac segmentation plays an utmost role in the diagnosis and quantification of cardiovascular diseases. Methods: This paper proposes a new cardiac segmentation method in short-axis Magnetic Resonance Imaging (MRI) images, called attention U-Net architecture with input image pyramid and deep supervised output layers (AID), which can fully-automatically learn to pay attention to target structures of various sizes and shapes. During each training process, the model continues to learn how to emphasize the desired features and suppress irrelevant areas in the original images, effectively improving the accuracy of cardiac segmentation. At the same time, we introduce the Focal Tversky Loss (FTL), which can effectively solve the problem of high imbalance in the amount of data between the target class and the background class during cardiac image segmentation. In order to obtain a better representation of intermediate features, we add a multi-scale input pyramid to the attention network. Results: The proposed cardiac segmentation technique is tested on the public Left Ventricle Segmentation Challenge (LVSC) dataset, which is shown to achieve 0.75, 0.87 and 0.92 for Jaccard Index, Sensitivity and Specificity, respectively. Experimental results demonstrate that the proposed method is able to improve the segmentation accuracy compared with the standard U-Net, and achieves comparable performance to the most advanced fully-automated methods. Conclusions: Given its effectiveness and advantages, the proposed method can facilitate cardiac segmentation in short-axis MRI images in clinical practice.
An Investigation into Smart Contract Deployment on Ethereum Platform Using Web3.js and Solidity Using Blockchain

Blockchain is a decentralized distributed platform, where multiple peers can exchange digital assets securely all over the globe without third party based on trustiness. In blockchain, blocks are connected by using hash that maintains the order of transaction. In this paper, we have proposed a novel approach of understanding proof of work and proof of stake which are considered as consensus algorithms that are used to manage and control blocks on distributed platform. Here, the paper also presents the Ethereum, a public blockchain platform which is used to implement decentralized application over the Internet using a contract (agreement) in digital form written by using solidity language that defines the way to exchange digital assets is called smart contact. Lastly, the paper concludes with implementation methodology for creation, deployment on Ethereum blockchain, and interaction of smart contract using Node.js, Web3 library, and Infura API.
Lightweight Public Key Authenticated Encryption With Keyword Search Against Adaptively-Chosen-Targets Adversaries for Mobile Devices

Cloud storage services have grown extensively in recent years. For security and privacy purposes, sensitive data need to be outsourced to clouds in encrypted form. Searchable public key encryption (SPKE) enables data ciphertexts to be retrieved by keyword(s) without decryption. Unfortunately, most of the existing SPKE schemes cannot withstand the keyword guessing attack. To combat such attack, public key authenticated encryption with keyword search (PAEKS) was presented. However, the existing PAEKS schemes were proven secure under a designated-targets security model, in which an adversary only can attack a sender and a recipient designated by the challenger. Our cryptanalysis indicates that such a scheme may be insecure against the practical attacks where the adversaries choose their targets by themselves. To fight against adaptively-chosen-targets adversaries, we refine the adversary model for PAEKS by permitting the adversaries to choose their targets adaptively, and then formalize the security definitions under the improved security model. After that, we devise a lightweight PAEKS scheme that avoids the time-consuming bilinear pairing operations and give the security proofs. The comparisons show that it outperforms the existing bilinear pairing-based PAEKS schemes in both the computation and communication performance, and therefore is more suitable for the resource-constrained mobile devices.
Location Privacy-Preserving Task Recommendation With Geometric Range Query in Mobile Crowdsensing

In mobile crowdsensing, location-based task recommendation requires each data requester to submit a task-related geometric range to crowdsensing service providers such that they can match suitable workers within this range. Generally, a trusted server (i.e., database owner) should be deployed to protect location privacy during the process, which is not desirable in practice. In this paper, we propose the location privacy-preserving task recommendation (PPTR) schemes with geometric range query in mobile crowdsensing without the trusted database owner. Specifically, we first propose a PPTR scheme with linear search complexity, named PPTR-L, based on a two-server model. By leveraging techniques of polynomial fitting and randomizable matrix multiplication, PPTR-L enables the service provider to find the workers located in the data requester’s arbitrary geometric query range without disclosing the sensitive location privacy. To further improve query efficiency, we design a novel data structure for task recommendation and propose PPTR-F to achieve faster-than-linear search complexity. Through security analysis, it is shown that our schemes can protect the confidentiality of workers’ locations and data requesters’ queries. Extensive experiments are performed to demonstrate that our schemes can achieve high computational efficiency in terms of geometric range query.
A Capsule Network Based Approach for Detection of Audio Spoofing Attacks

Audio spoofing attacks not only increasingly pose a threat to automatic speaker verification systems but also have the potential to destabilize national security (e.g., by creating fake audio of influential politicians). The main purpose of anti-spoofing is to detect fake audios synthesized by advanced methods, while current algorithms using convolutional neural networks as classifiers exposed poor generalization to the unknown attacks. In this paper, as the first attempt, we introduce a capsule network to enhance the generalization of the detection system. To make the capsule network suitable for anti-spoofing tasks, we modified the original dynamic routing algorithm to force the model to pay more attention to artifacts and thus yield better detection performance for text-to-speech/voice conversion attacks. Furthermore, replay attack detection is also investigated, and the results indicate that our proposed approach is also highly capable of detecting replay attacks.
Automated Query Reformulation for Efficient Search Based on Query Logs From Stack Overflow

As a popular Q&A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are two gaps leading to poor search results: the gap between the user's intention and the textual query, and the semantic gap between the query and the post content. Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. As query reformulation is tedious for developers, especially for novices, we propose an automated software-specific query reformulation approach based on deep learning. With query logs provided by Stack Overflow, we construct a large-scale query reformulation corpus, including the original queries and corresponding reformulated ones. Our approach trains a Transformer model that can automatically generate candidate reformulated queries when given the user's original query. The evaluation results show that our approach outperforms five state-of-the-art baselines, and achieves a 5.6% to 33.5% boost in terms of ExactMatch and a 4.8% to 14.4% boost in terms of GLEU.
Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency

Training datasets fundamentally impact the performance of machine learning (ML) systems. Any biases introduced during training (implicit or explicit) are often reflected in the system's behaviors leading to questions about fairness and loss of trust in the system. Yet, information on training data is rarely communicated to stakeholders. In this work, we explore the concept of data-centric explanations for ML systems that describe the training data to end-users. Through a formative study, we investigate the potential utility of such an approach, including the information about training data that participants find most compelling. In a second study, we investigate reactions to our explanations across four different system scenarios. Our results suggest that data-centric explanations have the potential to impact how users judge the trustworthiness of a system and to assist users in assessing fairness. We discuss the implications of our findings for designing explanations to support users' perceptions of ML systems.
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods

3D semantic segmentation is a fundamental task for robotic and autonomous driving applications. Recent works have been focused on using deep learning techniques, whereas developing fine-annotated 3D LiDAR datasets is extremely labor intensive and requires professional skills. The performance limitation caused by insufficient datasets is called data hunger problem. This research provides a comprehensive survey on the question: are we hungry for 3D LiDAR data for semantic segmentation? The studies are conducted at three levels. First, a broad review to the main 3D LiDAR datasets is conducted, followed by a statistical analysis on three representative datasets to gain an in-depth view on the datasets’ size, diversity and quality, which are the critical factors in learning deep models. Second, an organized survey of 3D semantic segmentation methods is given with a focus on the mainstream of the latest research trend using deep learning techniques, followed by a systematic survey to the existing efforts to solve the data hunger problem. Finally, an insightful discussion of the remaining problems on both methodological and datasets’ viewpoints, and the open questions on dataset bias, domain and semantic gap are given, leading to potential topics in future works. To the best of our knowledge, this is the first work to study the data hunger problem for 3D semantic segmentation using deep learning techniques, which are addressed in both methodological and dataset review, and we share findings and discussions through a comprehensive dataset analysis.
An adjustable visual image cryptosystem based on 6D hyperchaotic system and compressive sensing

In this paper, an efficient and adjustable visual image encryption scheme is proposed by combining a 6D hyperchaotic system, compressive sensing, and Bezier curve embedding. First, the plain image is sparse by discrete wavelet transform (DWT). Then, the sparse image is encrypted and compressed through game-of-life (GOL) hybrid scrambling and compressive sensing into a cipher image. Next, Bezier curve embedding is utilized to embed the cipher image into the carrier image in wavelet domain. After these operations, the final visually meaningful steganographic image is generated. Additionally, the frequency-domain information of the plain image is used to generate the initial values of the 6D hyperchaotic system in scrambling process, which makes the proposed encryption scheme able to effectively resist the chosen-plaintext attacks (CPA) and the known-plaintext attacks (KPA). Moreover, our scheme exhibits excellent adjustable performance compared with existing related schemes. Ultimately, simulation results and comprehensive performance analyses demonstrate that the scheme proposed in this paper has high decryption quality, visual security, robustness, and operating efficiency.