Reviewerly/exHarmony huggingface dataset:
LMMS reloaded: Transformer-based sense embeddings for disambiguation and beyond

Distributional semantics based on neural approaches is a cornerstone of Natural Language Processing, with surprising connections to human meaning representation as well. Recent Transformer-based Language Models have proven capable of producing contextual word representations that reliably convey sense-specific information, simply as a product of self-supervision. Prior work has shown that these contextual representations can be used to accurately represent large sense inventories as sense embeddings, to the extent that a distance-based solution to Word Sense Disambiguation (WSD) tasks outperforms models trained specifically for the task. Still, there remains much to understand on how to use these Neural Language Models (NLMs) to produce sense embeddings that can better harness each NLM's meaning representation abilities. In this work we introduce a more principled approach to leverage information from all layers of NLMs, informed by a probing analysis on 14 NLM variants. We also emphasize the versatility of these sense embeddings in contrast to task-specific models, applying them on several sense-related tasks, besides WSD, while demonstrating improved performance using our proposed approach over prior work focused on sense embeddings. Finally, we discuss unexpected findings regarding layer and model performance variations, and potential applications for downstream tasks.
Multi-scale U-like network with attention mechanism for automatic pancreas segmentation

In recent years, the rapid development of deep neural networks has made great progress in automatic organ segmentation from abdominal CT scans. However, automatic segmentation for small organs (e.g., the pancreas) is still a challenging task. As an inconspicuous and small organ in the abdomen, the pancreas has a high degree of anatomical variability and is indistinguishable from the surrounding organs and tissues, which usually leads to a very vague boundary. Therefore, the accuracy of pancreatic segmentation is sometimes below satisfaction. In this paper, we propose a 2.5D U-net with an attention mechanism. The proposed network includes 2D convolutional layers and 3D convolutional layers, which means that it requires less computational resources than 3D segmentation models while it can capture more spatial information along the third dimension than 2D segmentation models. Then We use a cascaded framework to increase the accuracy of segmentation results. We evaluate our network on the NIH pancreas dataset and measure the segmentation accuracy by the Dice similarity coefficient (DSC). Experimental results demonstrate a better performance compared with state-of-the-art methods.
A generic LSTM neural network architecture to infer heterogeneous model transformations

Models capture relevant properties of systems. During the models’ life-cycle, they are subjected to manipulations with different goals such as managing software evolution, performing analysis, increasing developers’ productivity, and reducing human errors. Typically, these manipulation operations are implemented as model transformations. Examples of these transformations are (i) model-to-model transformations for model evolution, model refactoring, model merging, model migration, model refinement, etc., (ii) model-to-text transformations for code generation and (iii) text-to-model ones for reverse engineering. These operations are usually manually implemented, using general-purpose languages such as Java, or domain-specific languages (DSLs) such as ATL or Acceleo. Even when using such DSLs, transformations are still time-consuming and error-prone. We propose using the advances in artificial intelligence techniques to learn these manipulation operations on models and automate the process, freeing the developer from building specific pieces of code. In particular, our proposal is a generic neural network architecture suitable for heterogeneous model transformations. Our architecture comprises an encoder–decoder long short-term memory with an attention mechanism. It is fed with pairs of input–output examples and, once trained, given an input, automatically produces the expected output. We present the architecture and illustrate the feasibility and potential of our approach through its application in two main operations on models: model-to-model transformations and code generation. The results confirm that neural networks are able to faithfully learn how to perform these tasks as long as enough data are provided and no contradictory examples are given.
Towards human distance estimation using a thermal sensor array

Abstract Human distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of $$\pm 0.2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>±</mml:mo> <mml:mn>0.2</mml:mn> </mml:mrow> </mml:math> m in continuous-based estimation and $$96.8\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>96.8</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> achieved-accuracy in discrete distance estimation.
Capacity of trace decreasing quantum operations and superadditivity of coherent information for a generalized erasure channel

Abstract Losses in quantum communication lines severely affect the rates of reliable information transmission and are usually considered to be state-independent. However, the loss probability does depend on the system state in general, with the polarization dependent losses being a prominent example. Here we analyze biased trace decreasing quantum operations that assign different loss probabilities to states and introduce the concept of a generalized erasure channel. We find lower and upper bounds for the classical and quantum capacities of the generalized erasure channel as well as characterize its degradability and antidegradability. We reveal superadditivity of coherent information in the case of the polarization dependent losses, with the difference between the two-letter quantum capacity and the single-letter quantum capacity exceeding 7.197 × 10 −3 bits per qubit sent, the greatest value among qubit-input channels reported so far.
Knowledge Guided Metric Learning for Few-Shot Text Classification

Humans can distinguish new categories very efficiently with few examples, largely due to the fact that human beings can leverage knowledge obtained from relevant tasks.However, deep learning based text classification model tends to struggle to achieve satisfactory performance when labeled data are scarce.Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge.A novel parameter generator network is investigated to this end, which is able to use the external knowledge to generate different metrics for different tasks.Armed with this network, similar tasks can use similar metrics while different tasks use different metrics.Through experiments, we demonstrate that our method outperforms the SoTA few-shot text classification models.
IoT garment for remote elderly care network

The elderly is a continuous growth sector thanks to the life expectancy increase in Western society. This sector is especially at risk from the appearance of respiratory diseases and, therefore, is the most affected sector in the COVID-19 epidemic. Many of these elderly require continuous care in residences or by specialized caregivers, but these personal contacts put this sector at risk. In this work, an IoT system for elderly remote monitoring is studied, designed, developed and tested. This system is composed by a smart garment that records information from various physiological sensors in order to detect falls, sudden changes in body temperature, heart problems and heat stroke; This information is sent to a cloud server through a gateway located in the patient’s residence, allowing to real-time monitor remotely patient’s activity using a customized App, as well as receiving alerts in dangerous situations. This system has been tested with professional caregivers, obtaining usability and functionality surveys; and, in addition, a detailed power-consumption study has been carried out. The results, compared with other similar systems, demonstrate that the proposed one is useful, usable, works in real time and has a decent power consumption that allows the patient to carry it during all day without charging the battery.
Multilevel information fusion for cryptographic substitution box construction based on inevitable random noise in medical imaging

Block cipher has been a standout amongst the most reliable option by which data security is accomplished. Block cipher strength against various attacks relies on substitution boxes. In literature, extensively algebraic structures, and chaotic systems-based techniques are available to design the cryptographic substitution boxes. Although, algebraic and chaotic systems-based approaches have favorable characteristics for the design of substitution boxes, but on the other side researchers have also pointed weaknesses in these approaches. First-time multilevel information fusion is introduced to construct the substitution boxes, having four layers; Multi Sources, Multi Features, Nonlinear Multi Features Whitening and Substitution Boxes Construction. Our proposed design does not hold the weakness of algebraic structures and chaotic systems because our novel s-box construction relies on the strength of true random numbers. In our proposed method true random numbers are generated from the inevitable random noise of medical imaging. The proposed design passes all the substitution box security evaluation criteria including Nonlinearity, Bit Independence Criterion (BIC), Strict Avalanche Criterion (SAC), Differential Approximation Probability (DP), Linear Approximation Probability (LP), and statistical tests, including resistance to Differential Attack, Correlation Analysis, 2D, 3D histogram analysis. The outcomes of the evaluation criteria validate that the proposed substitution boxes are effective for block ciphers; furthermore, the proposed substitution boxes attain better cryptographic strength as compared to very recent state-of-the-art techniques.
Maximum relative entropy of coherence for quantum channels

Based on the resource theory for quantifying the coherence of quantum channels, we introduce a new coherence quantifier for quantum channels via maximum relative entropy. We prove that the maximum relative entropy for coherence of quantum channels is directly related to the maximally coherent channels under a particular class of superoperations, which results in an operational interpretation of the maximum relative entropy for coherence of quantum channels. We also introduce the conception of sub-superchannels and sub-superchannel discrimination. For any quantum channels, we show that the advantage of quantum channels in sub-superchannel discrimination can be exactly characterized by the maximum relative entropy of coherence for quantum channels. Similar to the maximum relative entropy of coherence for channels, the robustness of coherence for quantum channels has also been investigated. We show that the maximum relative entropy of coherence for channels provides new operational interpretations of robustness of coherence for quantum channels and illustrates the equivalence of the dephasing-covariant superchannels, incoherent superchannels, and strictly incoherent superchannels in these two operational tasks.
AHWR-Net: offline handwritten amharic word recognition using convolutional recurrent neural network

Abstract Amharic ("Image missing"<!-- image only, no MathML or LaTex -->) is the official language of the Federal Government of Ethiopia, with more than 27 million speakers. It uses an Ethiopic script, which has 238 core and 27 labialized characters. It is a low-resourced language, and a few attempts have been made so far for its handwritten text recognition. However, Amharic handwritten text recognition is challenging due to the very high similarity between characters. This paper presents a convolutional recurrent neural networks based offline handwritten Amharic word recognition system. The proposed framework comprises convolutional neural networks (CNNs) for feature extraction from input word images, recurrent neural network (RNNs) for sequence encoding, and connectionist temporal classification as a loss function. We designed a custom CNN model and compared its performance with three different state-of-the-art CNN models, including DenseNet-121, ResNet-50 and VGG-19 after modifying their architectures to fit our problem domain, for robust feature extraction from handwritten Amharic word images. We have conducted detailed experiments with different CNN and RNN architectures, input word image sizes, and applied data augmentation techniques to enhance performance of the proposed models. We have prepared a handwritten Amharic word dataset, HARD-I, which is available publicly for researchers. From the experiments on various recognition models using our dataset, a WER of 5.24 % and CER of 1.15 % were achieved using our best-performing recognition model. The proposed models achieve a competitive performance compared to existing models for offline handwritten Amharic word recognition.
Detecting Small Objects in Thermal Images Using Single-Shot Detector

SSD (Single Shot Multibox Detector) is one of the most successful object detectors for its high accuracy and fast speed. However, the features from shallow layer (mainly Conv4_3) of SSD lack semantic information, resulting in poor performance in small objects. In this paper, we proposed DDSSD (Dilation and Deconvolution Single Shot Multibox Detector), an enhanced SSD with a novel feature fusion module which can improve the performance over SSD for small object detection. In the feature fusion module, dilation convolution module is utilized to enlarge the receptive field of features from shallow layer and deconvolution module is adopted to increase the size of feature maps from high layer. Our network achieves 79.7% mAP on PASCAL VOC2007 test and 28.3% mmAP on MS COCO test-dev at 41 FPS with only 300x300 input using a single Nvidia 1080 GPU. Especially, for small objects, DDSSD achieves 10.5% on MS COCO and 22.8% on FLIR thermal dataset, outperforming a lot of state-of-the-art object detection algorithms in both aspects of accuracy and speed.
Multi-Speaker ASR Combining Non-Autoregressive Conformer CTC and Conditional Speaker Chain

Non-autoregressive (NAR) models have achieved a large inference computation reduction and comparable results with autoregressive (AR) models on various sequence to sequence tasks. However, there has been limited research aiming to explore the NAR approaches on sequence to multi-sequence problems, like multi-speaker automatic speech recognition (ASR). In this study, we extend our proposed conditional chain model to NAR multi-speaker ASR. Specifically, the output of each speaker is inferred one-by-one using both the input mixture speech and previously-estimated conditional speaker features. In each step, a NAR connectionist temporal classification (CTC) encoder is used to perform parallel computation. With this design, the total inference steps will be restricted to the number of mixed speakers. Besides, we also adopt the Conformer and incorporate an intermediate CTC loss to improve the performance. Experiments on WSJ0-Mix and LibriMix corpora show that our model outperforms other NAR models with only a slight increase of latency, achieving WERs of 22.3% and 24.9%, respectively. Moreover, by including the data of variable numbers of speakers, our model can even better than the PIT-Conformer AR model with only 1/7 latency, obtaining WERs of 19.9% and 34.3% on WSJ0-2mix and WSJ0-3mix sets. All of our codes are publicly available at this https URL.
PrivBV: Distance-aware encoding for distributed data with local differential privacy

Recently, local differential privacy (LDP) has been used as the de facto standard for data sharing and analyzing with high-level privacy guarantees. Existing LDP-based mechanisms mainly focus on learning statistical information about the entire population from sensitive data. For the first time in the literature, we use LDP for distance estimation between distributed datato support more complicated data analysis. Specifically, we propose PrivBV—a locally differentially private bit vector mechanism with a distance-aware property in the anonymized space. We also present an optimization strategy for reducing privacy leakage in the high-dimensional space. The distance-aware property of PrivBV brings new insights into complicated data analysis in distributed environments. As study cases, we show the feasibility of applying PrivBV to privacy-preserving record linkage and non-interactive clustering. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed scheme.
Remotely Establishing Polarization Entanglement Over Noisy Polarization Channels

The faithful distribution of entanglement over noisy channels is a vital prerequisite for many quantum technological applications. Quantum information can be encoded in different degrees of freedom (DOF) of photons, where each encoding comes with its own advantages and disadvantages with respect to noise resilience and practicality in manipulation. In this work, we experimentally implement a deterministic entanglement purification protocol that allows us to faithfully distribute entanglement in one DOF over a noisy channel and then remotely transfer it to another DOF for manipulation. In particular, we distribute robust energy-time entanglement and transfer it to polarization entanglement at the communicating parties. The remotely obtained polarization state is independent of the polarization noise during distribution and reaches fidelities to a Bell state of up to $97.6\mathrm{%}$. Our scheme enables robust and efficient polarization entanglement distribution in the presence of arbitrary polarization noise, which is relevant for future large-scale quantum networks.
Towards Round-Optimal Secure Multiparty Computations: Multikey FHE Without a CRS

Multikey fully homomorphic encryption (MFHE) allows homomorphic operations between ciphertexts encrypted under different keys. In applications for secure multiparty computation (MPC) protocols, MFHE can be more advantageous than usual fully homomorphic encryption (FHE) since users do not need to agree with a common public key before the computation when using MFHE. In EUROCRYPT 2016, Mukherjee and Wichs constructed a secure MPC protocol in only two rounds via MFHE which deals with a common random/reference string (CRS) in key generation. After then, Brakerski et al. replaced the role of CRS with the distributed setup for CRS calculation to form a four round secure MPC protocol. Thus, recent improvements in round complexity of MPC protocols have been made using MFHE. In this paper, we go further to obtain round-efficient and secure MPC protocols. The underlying MFHE schemes in previous works still involve the common value, CRS, it seems to weaken the power of using MFHE to allow users to independently generate their own keys. Therefore, we resolve the issue by constructing an MFHE scheme without CRS based on LWE assumption, and then we obtain a secure MPC protocol against semi-malicious security in three rounds. We also define a new security notion “multikey-CPA security” to prove that a multikey ciphertext cannot be decrypted unless all the secret keys are gathered and our scheme is multikey-CPA secure.
Website fingerprinting on early QUIC traffic

Cryptographic protocols have been widely used to protect the user's privacy and avoid exposing private information. QUIC (Quick UDP Internet Connections), including the version originally designed by Google (GQUIC) and the version standardized by IETF (IQUIC), as alternatives to the traditional HTTP, demonstrate their unique transmission characteristics: based on UDP for encrypted resource transmitting, accelerating web page rendering. However, existing encrypted transmission schemes based on TCP are vulnerable to website fingerprinting (WFP) attacks, allowing adversaries to infer the users' visited websites by eavesdropping on the transmission channel. Whether GQUIC and IQUIC can effectively resist such attacks is worth investigating. In this paper, we study the vulnerabilities of GQUIC, IQUIC, and HTTPS to WFP attacks from the perspective of traffic analysis. Extensive experiments show that, in the early traffic scenario, GQUIC is the most vulnerable to WFP attacks among GQUIC, IQUIC, and HTTPS, while IQUIC is more vulnerable than HTTPS, but the vulnerability of the three protocols is similar in the normal full traffic scenario. Features transferring analysis shows that most features are transferable between protocols when on normal full traffic scenario. However, combining with the qualitative analysis of latent feature representation, we find that the transferring is inefficient when on early traffic, as GQUIC, IQUIC, and HTTPS show the significantly different magnitude of variation in the traffic distribution on early traffic. By upgrading the one-time WFP attacks to multiple WFP Top-a attacks, we find that the attack accuracy on GQUIC and IQUIC reach 95.4% and 95.5%, respectively, with only 40 packets and just using simple features, whereas reach only 60.7% when on HTTPS. We also demonstrate that the vulnerability of IQUIC is only slightly dependent on the network environment.
Text-Based Localization of Moments in a Video Corpus

Prior works on text-based video moment localization focus on temporally grounding the textual query in an untrimmed video. These works assume that the relevant video is already known and attempt to localize the moment on that relevant video only. Different from such works, we relax this assumption and address the task of localizing moments in a corpus of videos for a given sentence query. This task poses a unique challenge as the system is required to perform: 2) retrieval of the relevant video where only a segment of the video corresponds with the queried sentence, 2) temporal localization of moment in the relevant video based on sentence query. Towards overcoming this challenge, we propose Hierarchical Moment Alignment Network (HMAN) which learns an effective joint embedding space for moments and sentences. In addition to learning subtle differences between intra-video moments, HMAN focuses on distinguishing inter-video global semantic concepts based on sentence queries. Qualitative and quantitative results on three benchmark text-based video moment retrieval datasets - Charades-STA, DiDeMo, and ActivityNet Captions - demonstrate that our method achieves promising performance on the proposed task of temporal localization of moments in a corpus of videos.