Reviewerly/exHarmony huggingface dataset:
A novel general kernel-based non-negative matrix factorisation approach for face recognition

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Analytical evidence of nonlinearity in qubits and continuous-variable quantum reservoir computing

Abstract The natural dynamics of complex networks can be harnessed for information processing purposes. A paradigmatic example are artificial neural networks used for machine learning. In this context, quantum reservoir computing (QRC) constitutes a natural extension of the use of classical recurrent neural networks using quantum resources for temporal information processing. Here, we explore the fundamental properties of QRC systems based on qubits and continuous variables. We provide analytical results that illustrate how nonlinearity enters the input–output map in these QRC implementations. We find that the input encoding through state initialization can serve to control the type of nonlinearity as well as the dependence on the history of the input sequences to be processed.
A Unified Deep Framework for Hand Pose Estimation and Dynamic Hand Action Recognition from First-Person RGB Videos

Understanding hand action from the first-person video has emerged recently thanks to its wide potential applications such as hand rehabilitation, augmented reality. The majority of works mainly reply on RGB images. Compared with RGB images, hand joints have certain advantages as they are robust to illuminations and appearance variation. However, previous works for hand action recognition usually employed hand joints that are manually determined. This paper presents a unified framework for both hand pose estimation and hand action recognition from first-person RGB images. First, our framework estimates 3D hand joints from every RGB image using a combination of Resnet and a Graphical convolutional network. Then, an adaptation of a SOTA method PA-ResGCN for the human skeleton is proposed for hand action recognition from estimated hand joints. Our framework takes advantage of efficient graphical networks to model graph-like human hand structure in both phases: hand pose estimation and hand action recognition. We evaluate the proposed framework on the First Person Hand Action Benchmark (FPHAB). The experiments show that the proposed framework outperforms different SOTA methods on both hand pose estimation and hand action recognition tasks.
Real-Time Magnification in Augmented Reality

With recent advances in augmented reality (AR) and computer vision it has become possible to magnify objects in real time in a user's field of view. AR object magnification can have different purposes, such as enhancing human visual capabilities with the BigHead technique, which works by up-scaling human heads to communicate important facial cues over longer distances. For this purpose, we created a prototype with a 4K camera mounted on a HoloLens 2. In this demo, we present the BigHead technique and proof of concept AR testbed to magnify heads in real-time. Further, we describe how hand gestures are detected to control the scale and position of the magnified head. We discuss the technique and implementation, and propose future research directions.
A soft computing based multi-objective optimization approach for automatic prediction of software cost models

This paper tries to extend the idea of single-objective differential evolution (DE) algorithm to a multi-objective algorithm. Most of the existing algorithms face the problem of diversity loss and convergence rate. In this paper, we propose a novel multi-objective DE algorithm to deal with this problem. In the validation process, the proposed method is validated in two steps. Firstly, the new homeostasis factor-based mutation operator incorporates multi-objective differential evolution algorithms (MODE). In this method, we use the Pareto optimality principle. We incorporate a new adaptive-based mutation operator (MODE) to create more diversity and enhance convergence rate among candidate solutions which provide better solutions to help the evolution. The effectiveness of the proposed method is evaluated on eight benchmarks of bi-objective and tri-objective test functions. Our proposed method performed well compared to the latest variants of multi-objective evolutionary algorithms (MOEAs). Secondly, the proposed method is used for an application-based test by applying it for software cost estimation. This method also incorporates multi-objective parameters, i.e., two objectives-based software cost estimation and three objectives-based software cost estimation. The proposed approach achieves better results in most software projects in terms of reducing effort and minimum error.
There’s So Much to Do and Not Enough Time to Do It! A Case for Sentiment Analysis to Derive Meaning From Open Text Using Student Reflections of Engineering Activities

Evaluators often find themselves in situations where resources to conduct thorough evaluations are limited. In this paper, we present a familiar instance where there is an overwhelming amount of open text to be analyzed under the constraints of time and personnel. In instances when timely feedback is important, the data are plentiful, and answers to the study questions carry lower consequences, we build a case for using a machine learning, in particular a sentiment analysis. We begin by explaining the rationale for the use of sentiment analysis and provide an introduction to this method. Next, we provide an example of a sentiment analysis leveraging data collected from a program evaluation of an engineering education intervention, specifically to text extracted from student reflections of course activities. Finally, limitations of sentiment analysis and related techniques are discussed as well as areas for future research.
A collaborative deep learning microservice for backdoor defenses in Industrial IoT networks

Deep Learning shows a broad prospect in providing intelligence microservices to Industrial Internet of Things (IIoT). However, the existence of potential secure vulnerabilities limits the application of deep learning in IIoT. Therefore, how to provide secure deep learning services in IIoT applications becomes an important research topic. Among various attacks on deep neural networks (DNNs), backdoor attacks are generally recognized as the most imperceptible type, where an attacker can upload a poisoned DNN model that misbehaves only when inputs contain specific triggers. Existing defense solutions assume a defender has prior knowledge of backdoor triggers or DNN models, remaining far away from practical and flexible. To this end, this paper proposes a collaborative deep learning microservice, CoDefend, which employs strong intentional perturbation (STRIP) and cycle generative adversarial network (CycleGAN) to defend against backdoored neural networks. Compared with previous work, CoDefend has the advantages of flexibility and practicality. Empirical evaluations validate the high efficacy of CoDefend in providing secure deep learning microservices to IIoT.
Quantum Advantage for Shared Randomness Generation

Sharing correlated random variables is a resource for a number of information theoretic tasks such as privacy amplification, simultaneous message passing, secret sharing and many more. In this article, we show that to establish such a resource called shared randomness, quantum systems provide an advantage over their classical counterpart. Precisely, we show that appropriate albeit fixed measurements on a shared two-qubit state can generate correlations which cannot be obtained from any possible state on two classical bits. In a resource theoretic set-up, this feature of quantum systems can be interpreted as an advantage in winning a two players co-operative game, which we call the `non-monopolize social subsidy' game. It turns out that the quantum states leading to the desired advantage must possess non-classicality in the form of quantum discord. On the other hand, while distributing such sources of shared randomness between two parties via noisy channels, quantum channels with zero capacity as well as with classical capacity strictly less than unity perform more efficiently than the perfect classical channel. Protocols presented here are noise-robust and hence should be realizable with state-of-the-art quantum devices.
In Defense of the Learning Without Forgetting for Task Incremental Learning

Catastrophic forgetting is one of the major challenges on the road for continual learning systems, which are presented with an on-line stream of tasks. The field has attracted considerable interest and a diverse set of methods have been presented for overcoming this challenge. Learning without Forgetting (LwF) is one of the earliest and most frequently cited methods. It has the advantages of not requiring the storage of samples from the previous tasks, of implementation simplicity, and of being well-grounded by relying on knowledge distillation. However, the prevailing view is that while it shows a relatively small amount of forgetting when only two tasks are introduced, it fails to scale to long sequences of tasks. This paper challenges this view, by showing that using the right architecture along with a standard set of augmentations, the results obtained by LwF surpass the latest algorithms for task incremental scenario. This improved performance is demonstrated by an extensive set of experiments over CIFAR-100 and Tiny-ImageNet, where it is also shown that other methods can-not benefit as much from similar improvements. Our code is available at: https://github.com/guy-oren/In_defence_of_LWF
Speedup Robust Graph Structure Learning with Low-Rank Information

Recent studies have shown that graph neural networks (GNNs) are vulnerable to unnoticeable adversarial perturbations, which largely confines their deployment in many safety-critical domains. Robust graph structure learning has been proposed to improve the GNN performance in the face of adversarial attacks. In particular, the low-rank methods are utilized to purify the perturbed graphs. However, these methods are mostly computationally expensive with O(n3) time complexity and O(n2) space complexity. We propose LRGNN, a fast and robust graph structure learning framework, which exploits the low-rank property as prior knowledge to speed up optimization. To eliminate adversarial perturbation, LRGNN decouples the adjacency matrix into a low-rank component and a sparse one, and learns by minimizing the rank of the first part while suppressing the second part. Its sparse variant is formed to reduce the memory footprint further. Experimental results on various attack settings have shown LRGNN acquires comparable robustness with the state-of-the-art much more efficiently, boasting a significant advantage on large-scale graphs.
Investigating cognitive workload in concurrent speech-based information communication

Users are capable of noticing, listening, and comprehending concurrent information simultaneously, but in conventional speech-based interaction methods, systems communicate information sequentially to the users. This mismatch implies that the sequential approach may be under-utilising human perception capabilities and restricting users to seek information to sub-optimal levels. This paper reports on an experiment that investigates the cognitive workload experienced by the users when listening to a variety of combinations of information types in concurrent formats. Fifteen different combinations of concurrent information streams were investigated, and the subjective listening workload for each of the combination was measured using NASA-TLX. The results showed that the perceived workload index score varies in all concurrent combinations. The workload index score depends on the types and the amount of information presented to users. The perceived workload index score in concurrent listening remained the highest in Monolog with Interview (three concurrent talkers) combination, medium in Monolog with News Headlines (two talkers where one is intermittent) combination, and the lowest in Monolog with Music (one talker and a concurrent music stream) combination. Users descriptive feedback remained aligned with the NASA-TLX-based results. It is expected that the results of this experiment will contribute to helping digital content creators and interaction designers to communicate information more efficiently to users.
Learning Discriminative and Unbiased Representations for Few-Shot Relation Extraction

Few-shot relation extraction (FSRE) aims to predict the relation for a pair of entities in a sentence by exploring a few labeled instances for each relation type. Current methods mainly rely on meta-learning to learn generalized representations by optimizing the network parameters based on various collections of tasks sampled from training data. However, these methods may suffer from two main issues. 1) Insufficient supervision of meta-learning to learn discriminative representations on very few training instances, which are sampled from a large amount of base class data. 2) Spurious correlations between entities and relation types due to the biased training procedure that focuses more on entity pair rather than context. To learn more discriminative and unbiased representations for FSRE, this paper proposes a two-stage approach via supervised contrastive learning and sentence- and entity-level prototypical networks. In the first (pre-training) stage, we introduce a supervised contrastive pre-training method, which is able to yield more discriminative representations by learning from the entire training instances, such that the semantically related representations are close to each other, and far away otherwise. In the second (meta-learning) stage, we propose a novel sentence- and entity-level prototypical network equipped with fine-grained feature-wise fusion strategy to learn unbiased representations, where the networks are initialized with the parameters trained in the first stage. Specifically, the proposed network consists of a sentence branch and an entity branch, taking entire sentences and entity mentions as inputs, respectively. The entity branch explicitly captures the correlation between entity pairs and relations, and then dynamically adjusts the sentence branch's prediction distributions. By doing so, the spurious correlations issue caused by biased training samples can be properly mitigated. Extensive experiments on two FSRE benchmarks demonstrate the effectiveness of our approach.
DORA THE EXPLORER

We demonstrate DORA THE EXPLORER, a system that guides users in finding items of interest in a very large data set. DORA THE EXPLORER provides users with the full spectrum of exploration modes and is driven by Data Familiarity or Curiosity, as well as User Interventions. DORA THE EXPLORER is able to handle data and search scenario complexity, i.e., the difficulty to find scattered/clustered individual records in the data set, and user ability to express what s/he needs. DORA THE EXPLORER relies on Deep Reinforcement Learning that combines intrinsic (curiosity) and extrinsic (familiarity) rewards. DORA's main goal is to support scientific discovery from data. We describe the system architecture and illustrate it with three demonstration scenarios on a 2.6 mil-lion galaxies SDSS, a large sky survey data set1. A video of DORA THE EXPLORER is available at https://bit.ly/dora-demo, the codehttps://github.com/apersonnaz/rl-guided-galaxy-exploration, and the application at https://bit.ly/dora-application
Adversarial Domain Adaptation for Cross-lingual Information Retrieval with Multilingual BERT

Transformer-based language models (e.g. BERT, RoBERT, GPT, etc) have shown remarkable performance in many natural language processing tasks and their multilingual variants make it easier to handle cross-lingual tasks without using machine translation system. In this paper, we apply multilingual BERT in cross-lingual information retrieval (CLIR) task with triplet loss to learn the relevance between queries and documents written in different languages. Moreover, we align the token embeddings from different languages via adversarial networks to help the language model to learn cross-lingual sentence representation. We achieve the state-of-the-art result on the newly published CLIR dataset: CLIRMatrix. Furthermore, we show that the adversarial multilingual BERT can also get the competitive result in the zero-shot setting in some specific languages when we are lack of CLIR training data in a specific language.
An analog electronic emulator of non-linear dynamics in optical microring resonators

The microring resonator is a ubiquitous building block of optical integrated circuits. Owing to its unique non-linear properties, it appears well-suited as a node in the realization of photonic networks, such as spiking neural networks. However, experimental investigation requires complex setups, incurring considerable device fabrication times and costs. Inherent physical constraints limit the ranges of the parameters determining the timescales and strengths of the non-linear behaviors. Moreover, on-the-fly changes to these parameters and node couplings are not easily realizable. On the other hand, numerical simulations are computationally heavy, particularly for large networks, and do not entirely replace measurement on a physical apparatus. To mitigate this problem, we introduce and realize an analog electronic emulator that implements dynamics, attempting to reproduce the self-pulsing phenomenon in an optical microresonator. It can be readily constructed with off-the-shelf components, and is well-suited for building complex networks. The circuit is explicitly based on a mathematical model of the microresonator, subject to some approximations, parameter adjustments and rescalings. Initial results comparing experimental data from the emulator and a representative silicon photonic device plus numerical simulations suggest a satisfactory level of agreement in the temporal dynamics and response to parametric sweeps when scaled for the different response times. This work exemplifies the relevance of explicitly establishing correspondences between physical systems having widely different features but governed by similar dynamics.
Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance

The separation assurance task will be extremely challenging for air traffic controllers in a complex and high-density airspace environment. Deep reinforcement learning (DRL) was used to develop an autonomous separation assurance framework in our previous work where the learned model advised speed maneuvers. In order to improve the safety of this model in unseen environments with uncertainties, in this work we propose a safety module for DRL in autonomous separation assurance applications. The proposed module directly addresses both model uncertainty and state uncertainty to improve safety. Our safety module consists of two sub-modules: (1) the state safety sub-module is based on the execution-time data augmentation method to introduce state disturbances in the model input state; (2) the model safety sub-module is a Monte-Carlo dropout extension that learns the posterior distribution of the DRL model policy. We demonstrate the effectiveness of the two sub-modules in an open-source air traffic simulator with challenging environment settings. Through extensive numerical experiments, our results show that the proposed sub-safety modules help the DRL agent significantly improve its safety performance in an autonomous separation assurance task.
A Survey on Automatic Text Summarization Techniques

Abstract In recent years, there is a tremendous explosion in the amount of text data on the internet and in the archives of news articles, scientific papers, legal documents and even in online product reviews. Text summarization is playing an important role in automatic content creation, minutes of meeting generation, helping disabled people and also for quick online document reading. To achieve these, several automation techniques have been proposed in various researches. In this regard, performing an exclusive survey on different methods, approaches of automatic text summarization which are published in different articles in most recent three years.
Learning When to Stop: A Mutual Information Approach to Prevent Overfitting in Profiled Side-Channel Analysis

Today, deep neural networks are a common choice for conducting the profiled side-channel analysis. Unfortunately, it is not trivial to find neural network hyperparameters that would result in top-performing attacks. The hyperparameter leading the training process is the number of epochs during which the training happens. If the training is too short, the network does not reach its full capacity, while if the training is too long, the network overfits and cannot generalize to unseen examples. In this paper, we tackle the problem of determining the correct epoch to stop the training in the deep learning-based side-channel analysis. We demonstrate that the amount of information, or, more precisely, mutual information transferred to the output layer, can be measured and used as a reference metric to determine the epoch at which the network offers optimal generalization. To validate the proposed methodology, we provide extensive experimental results.
KAPRE: Key-aggregate proxy re-encryption for secure and flexible data sharing in cloud storage

Key-aggregate cryptosystems (KAC) have attracted significant attention from the research community because of their elegance and efficiency in enforcing predefined access control policies for outsourced data. The data owner computes a constant-size aggregate key that is capable of decrypting a subset of outsourced data items. Based on the access control policy, the data owner securely transmits the aggregate key to a user authorized for the corresponding subset of data items. For practical access control scenarios, a KAC needs to satisfy additional flexibility requirements. We propose a practically motivated, novel cryptographic primitive called key-aggregate proxy re-encryption that allows temporary delegation of decryption capabilities of an aggregate key to one or more other aggregate keys without carrying out any secure transmissions. The existing key-aggregate cryptosystems face two important issues in highly dynamic environments, namely the non-revocability of aggregate keys and the need to securely transmit the aggregate key(s) to enhance the access capabilities of the user(s). The proposed key-aggregate proxy re-encryption is a significant enhancement to the existing KACs in that it features temporary delegation of decryption capabilities without needing any secure transmissions for carrying out or revoking the temporary delegation(s). We propose two variants of key-aggregate proxy re-encryption. The first variant delegates decryption capabilities of an aggregate key to a set of aggregate keys and the second variant delegates decryption capabilities of one aggregate key to another unique aggregate key. We present formal security definitions of both the proposed variants of key-aggregate proxy re-encryption under chosen-plaintext and chosen-ciphertext attacks. We present concrete constructions for both the variants of key-aggregate proxy re-encryption and formally prove the chosen-ciphertext security in the random oracle model. Also, we show that both of our constructions satisfy the temporary delegation property. Finally, we analyze the performance of our key-aggregate proxy re-encryption schemes to confirm their practical applicability.