Reviewerly/exHarmony huggingface dataset:
FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning

Machine reading comprehension (MRC) of text data is a challenging task in Natural Language Processing (NLP), with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD) and Conversational Question Answering (CoQA). It is considered to be an effort to teach computers how to “understand” a text, and then to be able to answer questions about it using deep learning. However, until now, large-scale training on private text data and knowledge sharing has been missing for this NLP task. Hence, we present FedQAS, a privacy-preserving machine reading system capable of leveraging large-scale private data without the need to pool those datasets in a central location. The proposed approach combines transformer models and federated learning technologies. The system is developed using the FEDn framework and deployed as a proof-of-concept alliance initiative. FedQAS is flexible, language-agnostic, and allows intuitive participation and execution of local model training. In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQuAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.
Partial Label Dimensional Reduction via Semantic Difference Information and Manifold Regularization

Partial label learning is a rising weakly supervised learning framework that deals with the problem that each training instance is associated with a set of candidate labels, where only one is correct. The dimensionality reduction mechanism can effectively improve the learning system’s generalization performance. However, due to the ambiguity of the ground-truth label, it is hard to use traditional dimensionality reduction methods directly on partially labeled data sets. Existing research attempts to exploit the linear discriminant analysis (LDA) strategy to reduce the dimensionality of partial label training examples. Unfortunately, this method still suffered from false-positive labels. This paper proposed an LDA-based dimensionality reduction approach named SDIMR for partially labeled datasets. On the one hand, introduce the manifold regularization term with semantic difference information to perform dimensionality reduction procedures and simultaneously maintain the local manifold structure. On the other hand, the iterative process alternates between dimensionality reduction and disambiguation. Briefly, our approach consists of three stages: Firstly, utilize the graph structure with semantic difference information to describe the topological information in the feature space. Secondly, we propose a new objective function containing manifold regularization terms to reduce the dimensionality of original training data to generate a new training data set. Finally, the k-nearest neighbor method is employed to disambiguate the dimensionality-reduced data. Extensive experiments on artificial and real-world data sets show that our method can be comparable to state-of-the-art partial label learning algorithms.
Investigation of Using CAPTCHA Keystroke Dynamics to Enhance the Prevention of Phishing Attacks

Phishing is a cybercrime that is increasing exponentially day by day. In phishing, a phisher employs social engineering and technology to misdirect victims towards revealing their personal information, which can then be exploited. Despite ongoing research to find effective anti-phishing solutions, phishing remains a serious security problem for Internet users. In this paper, an investigation of using CAPTCHA keystroke dynamics to enhance the prevention of phishing attacks was presented. A controlled laboratory experiment was conducted, with the results indicating the proposed approach as highly effective in protecting online services from phishing attacks. The results showed a 0% false-positive rate and 17.8% false-negative rate. Overall, the proposed solution provided a practical and effective way of preventing phishing attacks.
A Probabilistic Fusion Framework for Spoofing Aware Speaker Verification

The performance of automatic speaker verification (ASV) systems could be degraded by voice spoofing attacks. Most existing works aimed to develop standalone spoofing countermeasure (CM) systems. Relatively little work targeted at developing an integrated spoofing aware speaker verification (SASV) system. In the recent SASV challenge, the organizers encourage the development of such integration by releasing official protocols and baselines. In this paper, we build a probabilistic framework for fusing the ASV and CM subsystem scores. We further propose fusion strategies for direct inference and fine-tuning to predict the SASV score based on the framework. Surprisingly, these strategies significantly improve the SASV equal error rate (EER) from 19.31% of the baseline to 1.53% on the official evaluation trials of the SASV challenge. We verify the effectiveness of our proposed components through ablation studies and provide insights with score distribution analysis.
Discriminability-Transferability Trade-Off: An Information-Theoretic Perspective

This work simultaneously considers the discriminability and transferability properties of deep representations in the typical supervised learning task, i.e., image classification. By a comprehensive temporal analysis, we observe a trade-off between these two properties. The discriminability keeps increasing with the training progressing while the transferability intensely diminishes in the later training period. From the perspective of information-bottleneck theory, we reveal that the incompatibility between discriminability and transferability is attributed to the over-compression of input information. More importantly, we investigate why and how the InfoNCE loss can alleviate the over-compression, and further present a learning framework, named contrastive temporal coding (CTC), to counteract the over-compression and alleviate the incompatibility. Extensive experiments validate that CTC successfully mitigates the incompatibility, yielding discriminative and transferable representations. Noticeable improvements are achieved on the image classification task and challenging transfer learning tasks. We hope that this work will raise the significance of the transferability property in the conventional supervised learning setting.
Classification of three-dimensional integral stimuli: Accounting for a replication and extension of Nosofsky and Palmeri (1996) with a dual discrimination invariance model.

The quest for determining the degree of learning difficulty associated with different types of categories has been instrumental in our understanding of human categorization behavior and, more broadly, human generalization. For instance, we now know that the topological nature of the dimensions (e.g., whether these are integral or separable) that define the family of categories generated with three binary dimensions yield two different learning difficulty orderings. In our study, for the first time, we replicated one such classic ordering involving integral dimensions and explored the impact that the choice of dimensional values, along with the nature of the stimulus presentation, had on learning difficulty during the learning phase of our four experiments. Two standard orderings were observed in our investigation which were consistent with the hypothesis that multilevel discrimination plays a key role in concept learning. We accounted for our empirical results by making this role explicit at three levels, the feature, object, and category structure levels, using a dual discrimination invariance model (DDIM) derived from the core invariance law of generalized invariance structure theory (GIST). The model involves a "discrimination switch" via what is referred to in GIST as the "tau discrimination threshold" where two distinct extreme degrees of discrimination yield two fundamental learning difficulty orderings able to account for results from our experiments without free parameters. We then show how the DDIM is not only a more accurate and general predictor than the best alternatives but also provides a plausible and tenable cognitive mechanism for understanding these behaviors. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Facial recognition law in China

Although the prevalence of facial recognition-based COVID-19 surveillance tools and techniques, China does not have a facial recognition law to protect its residents’ facial data. Oftentimes, neither the public nor the government knows where people’s facial images are stored, how they have been used, who might use or misuse them, and to what extent. This reality is alarming, particularly factoring in the wide range of unintended consequences already caused by good-intentioned measures and mandates amid the pandemic. Biometric data are matters of personal rights and national security. In light of worrisome technologies such as deep-fake pornography, the protection of biometric data is also central to the protection of the dignity of the citizens and the government, if not the industry as well. This paper discusses the urgent need for the Chinese government to establish rigorous and timely facial recognition laws to protect the public’s privacy, security, and dignity amid COVID-19 and beyond.
Automatic Image Caption Generation Based on Some Machine Learning Algorithms

This paper is dedicated to machine learning, the branches of machine learning, which include the methods for solving this issue, and the practical implementation of the solution to the automatic image description generation. Automatic image caption generation is one of the frequent goals of computer vision. Image description generation models must solve a larger number of complex problems to have this task successfully solved. The objects in the image must be detected and recognized, after which a logical and syntactically correct textual description is generated. For that reason, description generation is a complex problem. It is an extremely important challenge for machine learning algorithms because it represents an impersonation of a complicated human ability to encapsulate huge amounts of highlighted visual pieces of information in descriptive language. The results of the generated descriptions are compared depending on the used pretrained convolutional networks. The BLEU metrics are used to calculate the quality of the image description. Although the solution to the problem of image description automatic generation does provide us with good results, there is yet room for improvement since there are images that are not adequately described.
Smartphone Based Human Activity Recognition Using 1D Lightweight Convolutional Neural Network

Smartphones are an obvious platform for the de-ployment of the Human Activity Recognition(HAR) system. But, they are limited in terms of processing power, energy and storage space. Therefore, there is a need to make lightweight deep learning models that can be run within these constraints. In this paper, we propose a one-dimensional lightweight Convolutional Neural Network(CNN) that can be operated on smartphones. In the proposed one-dimensional lightweight CNN model, pruning and quantization are used to compress CNN model size without significant accuracy losses. The experimental result shows that the proposed CNN model was proven to be successful accuracy while maintaining their performance even after quantization and pruning.
A new occluded face recognition framework with combination of both Deocclusion and feature filtering methods

Face recognition plays the significant role in many human-computer interaction decvices and applications, whose access control systems are based on the verification of face biometrical features. Though great improvement in the recognition performances have been achieved, when under some specific conditions like faces with occlusions, the performance would suffer a severe drop. Occlusion is one of the most significant reasons for the performance degrade of the existing general face recognition systems. The biggest problem in occluded face recognition (OFR) lies in the lack of the occluded face data. To mitigate this problem, this paper has proposed one new OFR network DOMG-OFR (Dynamic Occlusion Mask Generator based Occluded Face Recognition), which keeps trying to generate the most informative occluded face training samples on feature level dynamically, in this way, the recognition model would always be fed with the most valuable training samples so as to save the labor in preparing the synthetic data while simultaneously improving the training efficiency. Besides, this paper also proposes one new module called Decision Module (DM) in an attempt to combine both the merits of the two mainstream methodologies in OFR which are face image reconstruction based methodologies and the face feature filtering based methodologies. Furthermore, to enable the existing face deocclusion methods that mostly target at near frontal faces to work well on faces under large poses, one head pose aware deocclusion pipeline based on the Condition Generative Adversarial Network (CGAN) is proposed. In the experimental parts, we have also investigated the effects of the occlusions upon face recognition performance, and the validity and the efficiency of our proposed Decision based OFR pipeline has been fully proved. Through comparing both the verification and the recognition performance upon both the real occluded face datasets and the synthetic occluded face datasets with other existing works, our proposed OFR architecture has demonstrated obvious advantages over other works.
Quantum channel correction outperforming direct transmission

Abstract Long-distance optical quantum channels are necessarily lossy, leading to errors in transmitted quantum information, entanglement degradation and, ultimately, poor protocol performance. Quantum states carrying information in the channel can be probabilistically amplified to compensate for loss, but are destroyed when amplification fails. Quantum correction of the channel itself is therefore required, but break-even performance—where arbitrary states can be better transmitted through a corrected channel than an uncorrected one—has so far remained out of reach. Here we perform distillation by heralded amplification to improve a noisy entanglement channel. We subsequently employ entanglement swapping to demonstrate that arbitrary quantum information transmission is unconditionally improved—i.e., without relying on postselection or post-processing of data—compared to the uncorrected channel. In this way, it represents realization of a genuine quantum relay. Our channel correction for single-mode quantum states will find use in quantum repeater, communication and metrology applications.
ReflecTouch: Detecting Grasp Posture of Smartphone Using Corneal Reflection Images

By sensing how a user is holding a smartphone, adaptive user interfaces are possible such as those that automatically switch the displayed content and position of graphical user interface (GUI) components following how the phone is being held. We propose ReflecTouch, a novel method for detecting how a smartphone is being held by capturing images of the smartphone screen reflected on the cornea with a built-in front camera. In these images, the areas where the user places their fingers on the screen appear as shadows, which makes it possible to estimate the grasp posture. Since most smartphones have a front camera, this method can be used regardless of the device model; in addition, no additional sensor or hardware is required. We conducted data collection experiments to verify the classification accuracy of the proposed method for six different grasp postures, and the accuracy was 85%.
Few shot learning-based fast adaptation for human activity recognition

Sensor-based human activity recognition (HAR) is a common application in the fields of mobile computing and pattern recognition. Existing approaches and models of HAR can present ideal recognition performance only in well-designed, specific, and deterministic scenarios. However, in real scenes, new types of activities, new human bodies that performs activities, and other new situations are encountered. When new situations arise, it is difficult to collect sufficient and high-quality data in time. Thus, the existing approaches and models suffer from a lack of interoperability and scalability. To address these challenges, this study proposes a Model-agnostic Meta Learnings(MAML)-Coarse-Fine Convolutional Neural Networks(CFCNN) mixed task strategy to achieve fast adaptation to the human activity recognition under new situations. This is a novel method that incorporates shot learning to recognize tasks in situations where several kinds of new scenarios exist. First, some modifications were carried out on traditional MAML for multi-scale feature extraction, and a mixed task strategy was adopted during training. The proposed method improves the generalization ability of the model, and is capable of learning quickly when dealing with diffirent new tasks. Finally, the model was compared with other models when facing two new scenes of a new activity category and a new human body. The results showed that the accuracy of the proposed MAML-CFCNN based on the mixed task strategy is higher than that of the state-of-the-art methods, including MAML, First-Order MAML (FOMAML), REPTILE, and so on.
Probing phoneme, language and speaker information in unsupervised speech representations

Unsupervised models of representations based on Contrastive Predictive Coding (CPC)[1] are primarily used in spoken language modelling in that they encode phonetic information. In this study, we ask what other types of information are present in CPC speech representations. We focus on three categories: phone class, gender and language, and compare monolingual and bilingual models. Using qualitative and quantitative tools, we find that both gender and phone class information are present in both types of models. Language information, however, is very salient in the bilingual model only, suggesting CPC models learn to discriminate languages when trained on multiple languages. Some language information can also be retrieved from monolingual models, but it is more diffused across all features. These patterns hold when analyses are carried on the discrete units from a downstream clustering model. However, although there is no effect of the number of target clusters on phone class and language information, more gender information is encoded with more clusters. Finally, we find that there is some cost to being exposed to two languages on a downstream phoneme discrimination task.
Adaptive Filter-Based Approach for PHY Fingerprints Extraction of Fast Ethernet Network

Access control is a common part of network security measure. However, the existing access control mechanism is mostly limited to distinguish users based on digital authentication methods such as MAC addresses, which are vulnerable to forgery and counterfeiting attacks. Using distinguishable physical-layer (PHY) based on fingerprints from devices and MAC-based ID verification is a popular method for enhancing network security. Extracting device finger-prints is the crucial step in this enhanced security method. In this paper, different from the existing fingerprint extraction method based on the preamble, we propose an adaptive filter-based method of fingerprint extraction. The method utilizes more signal regions to extract finer fingerprints, which is not limited to the preamble. Our results show that with a filter order of 65 and 20 times averaging, among 13 devices can be classified with 98% accuracy under the Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML) classifier.
U-Net: A valuable encoder-decoder architecture for liver tumors segmentation in CT images

This study proposes a new predictive segmentation method for liver tumors detection using computed tomography (CT) liver images. In the medical imaging field, the exact localization of metastasis lesions after acquisition faces persistent problems both for diagnostic aid and treatment effectiveness. Therefore, the improvement in the diagnostic process is substantially crucial in order to increase the success chance of the management and the therapeutic follow-up. The proposed procedure highlights a computerized approach based on an encoder–decoder structure in order to provide volumetric analysis of pathologic tumors. Specifically, we developed an automatic algorithm for the liver tumors defect segmentation through the Seg-Net and U-Net architectures from metastasis CT images. In this study, we collected a dataset of 200 pathologically confirmed metastasis cancer cases. A total of 8,297 CT image slices of these cases were used developing and optimizing the proposed segmentation architecture. The model was trained and validated using 170 and 30 cases or 85% and 15% of the CT image data, respectively. Study results demonstrate the strength of the proposed approach that reveals the superlative segmentation performance as evaluated using following indices including F1-score = 0.9573, Recall = 0.9520, IOU = 0.9654, Binary cross entropy = 0.0032 and p-value &lt;0.05, respectively. In comparison to state-of-the-art techniques, the proposed method yields a higher precision rate by specifying metastasis tumor position.
The Data Trust Solution to Data Sharing Problems

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A Semi-Supervised Reservoir Computing System Based on Tapered Whisker for Mobile Robot Terrain Identification and Roughness Estimation

Identifying terrain type is important for safely operating robots exploration in unstructured environments. In this letter, we firstly proposed a novel tapered whisker-based semi-supervised reservoir computing (TWSSRC) system for improving terrain classification and terrain property estimation for traversability assessment with low computing cost. Three Hall sensors are used to capture the vibration at different locations of the tapered whisker. It could provide morphological computation power to achieve frequency separation in the time domain simultaneously without any data procession and only with the help of an additional simple linear regression, different signals can be classified. The movement of the robot on different types of terrain will result in different vibration behaviors of the whiskers and the whiskered robot can learn from prior physical experiences through cost-efficient self-supervised reservoir computing to achieve auto-labeling of new terrain and terrain classification. Experimental results demonstrate that this method can achieve good performance when the robot encounters new terrain and can accurately estimate the property of the unknown terrain surface at different robot speeds.
ECO v1: Towards Event-Centric Opinion Mining

Events are considered as the fundamental building blocks of the world.Mining event-centric opinions can benefit decision making, people communication, and social good.Unfortunately, there is little literature addressing eventcentric opinion mining, although which significantly diverges from the well-studied entitycentric opinion mining in connotation, structure, and expression.In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory.We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework.Experiment results show that eventcentric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies.