Reviewerly/exHarmony huggingface dataset:
An Improved Secure Key Generation Using Enhanced Identity-Based Encryption for Cloud Computing in Large-Scale 5G

The demand of cloud computing and 5G networks has increased in the current scenario due to their attractive features and also the security related to the data over the cloud. In the context of cloud security, there is a number of computationally hard methods available. One of the most popular methods used to secure data over the cloud is the identity-based encryption (IBE). It is an access policy that allows only authorized users to access legible data in order to avoid a malicious attack. IBE comprises of four stages, namely, setup, key generation or extract, encryption, and decryption. Key generation is one of the important and time-consuming phases in which a security key is generated. It is a computational and decisional hard problem for generating unbreakable and nonderivable secure keys. This paper proposes an enhanced identity-based encryption approach where a secure key is generated using part of an identity bit string in order to avoid leakage of users’ identity even if an adversary or attacker decodes the key or encrypted data. Experiment results show that the prosed algorithm takes less time in the encryption and decryption as compared to the competitive approach named efficient selective-ID secure identity-based encryption approach. One of the most important features of the proposed approach is that it hides the user’s identity by using the Lagrange coefficient, which consists of a polynomial interpolation function. The security of the system depends on the hardness of computing the bilinear Diffie-Hellman problem.
Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition

Existing Scene Text Recognition (STR) methods typically use a language model to optimize the joint probability of the 1D character sequence predicted by a visual recognition (VR) model, which ignore the 2D spatial context of visual semantics within and between character instances, making them not generalize well to arbitrary shape scene text. To address this issue, we make the first attempt to perform textual reasoning based on visual semantics in this paper. Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity. Then, these subgraphs are sequentially connected by their root nodes and merged into a complete graph. Based on this graph, we devise a graph convolutional network for textual reasoning (GTR) by supervising it with a cross-entropy loss. GTR can be easily plugged in representative STR models to improve their performance owing to better textual reasoning. Specifically, we construct our model, namely S-GTR, by paralleling GTR to the language model in a segmentation-based STR baseline, which can effectively exploit the visual-linguistic complementarity via mutual learning. S-GTR sets new state-of-the-art on six challenging STR benchmarks and generalizes well to multi-linguistic datasets. Code is available at https://github.com/adeline-cs/GTR.
ThermAirGlove: A Pneumatic Glove for Thermal Perception and Material Identification in Virtual Reality

We present ThermAirGlove (TAGlove), a pneumatic glove which provides thermal feedback for users, to support the haptic experience of grabbing objects of different temperatures and materials in virtual reality (VR). The system consists of a glove with five inflatable airbags on the fingers and the palm, two temperature chambers (one hot and one cold), and the closed-loop pneumatic thermal control system. Our technical experiments showed that the highest temperature-changing speed of TAGlove system was 2.75°C/s for cooling, and the pneumatic-control mechanism could generate the thermal cues of different materials (e.g., foam, glass, copper, etc.). The user-perception experiments showed that the TAGlove system could provide five distinct levels of thermal sensation (ranging from very cool to very warm). The user-perception experiments also showed that the TAGlove could support users’ material identification among foam, glass, and copper with the average accuracy of 87.2%, with no significant difference compared to perceiving the real physical objects. The user studies on VR experience showed that using TAGlove in immersive VR could significantly improve users’ experience of presence compared to the situations without any temperature or material simulation.
Sub-Gbps key rate four-state continuous-variable quantum key distribution within metropolitan area

Abstract Continuous-variable quantum key distribution (CVQKD) has potential advantages of high secret key rate, which is very suitable for high-speed metropolitan network application. However, the secret key rates of the reported CVQKD systems are only a few Mbps over typical transmission distance so far. Here, we address the fundamental experimental problems and demonstrate a single-carrier four-state CVQKD with sub-Gbps key rate within metropolitan area. In the demonstrated four-state CVQKD using local local oscillator, an ultra-low level of excess noise is obtained and a high efficient post-processing setup is designed for practically extracting the final secure keys. Thus, the achieved secret key rates are 190.54 Mbps, 137.76 Mbps and 52.48 Mbps using linear channel assuming security analysis method and 233.87 Mbps, 133.6 Mbps and 21.53 Mbps using semidefinite programming security analysis method over transmission distances of 5 km, 10 km and 25 km, respectively. This result increases the asymptotic secret key rate to sub-Gbps level, which is sufficient to achieve the one-time pad cryptographic task. Moreover, our work shows the road for future high-rate and large-scale CVQKD deployment in secure broadband metropolitan and access networks.
Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5

COVID-19 is highly contagious, and proper wearing of a mask can hinder the spread of the virus. However, complex factors in natural scenes, including occlusion, dense, and small-scale targets, frequently lead to target misdetection and missed detection. To address these issues, this paper proposes a YOLOv5-based mask-wearing detection algorithm, YOLOv5-CBD. Firstly, the Coordinate Attention mechanism is introduced into the feature fusion process to stress critical features and decrease the impact of redundant features after feature fusion. Then, the original feature pyramid network module in the feature fusion module was replaced with a weighted bidirectional feature pyramid network to achieve efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, we combined Distance Intersection over Union with Non-Maximum Suppression to improve the missed detection of overlapping targets. Experiments show that the average detection accuracy of the YOLOv5-CBD model is 96.7%-an improvement of 2.1% compared to the baseline model (YOLOv5).
Multi-modal Siamese Network for Entity Alignment

The booming of multi-modal knowledge graphs (MMKGs) has raised the imperative demand for multi-modal entity alignment techniques, which facilitate the integration of multiple MMKGs from separate data sources. Unfortunately, prior arts harness multi-modal knowledge only via the heuristic merging of uni-modal feature embeddings. Therefore, inter-modal cues concealed in multi-modal knowledge could be largely ignored. To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect. Specifically, we first devise a multi-modal knowledge embedding module to extract visual, relational, and attribute features of entities to generate holistic entity representations for distinct MMKGs. During this procedure, we employ inter-modal enhancement mechanisms to integrate visual features to guide relational feature learning and adaptively assign attention weights to capture valuable attributes for alignment. Afterwards, we design a multi-modal contrastive learning module to achieve inter-modal enhancement fusion with avoiding the overwhelming impact of weak modalities. Experimental results on two public datasets demonstrate that our proposed MSNEA provides state-of-the-art performance with a large margin compared with competitive baselines.
Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation

Deep learning models usually suffer from the domain shift issue, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data are only available from one source domain, which is common in medical imaging applications. We tackle this problem in the context of cross-domain medical image segmentation. In this scenario, domain shifts are mainly caused by different acquisition processes. We propose a simple causality-inspired data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. They augment training images using diverse appearance transformations. 2) Further we show that spurious correlations among objects in an image are detrimental to domain robustness. These correlations might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains. We remove these spurious correlations via causal intervention. This is achieved by resampling the appearances of potentially correlated objects independently. The proposed approach is validated on three cross-domain segmentation scenarios: cross-modality (CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI segmentation, and cross-site prostate MRI segmentation. The proposed approach yields consistent performance gains compared with competitive methods when tested on unseen domains.
Recent progress in quantum photonic chips for quantum communication and internet

Recent years have witnessed significant progress in quantum communication and quantum internet with the emerging quantum photonic chips, whose characteristics of scalability, stability, and low cost, flourish and open up new possibilities in miniaturized footprints. Here, we provide an overview of the advances in quantum photonic chips for quantum communication, beginning with a summary of the prevalent photonic integrated fabrication platforms and key components for integrated quantum communication systems. We then discuss a range of quantum communication applications, such as quantum key distribution and quantum teleportation. Finally, the review culminates with a perspective on challenges towards high-performance chip-based quantum communication, as well as a glimpse into future opportunities for integrated quantum networks.
Direction Maps for Cooperative Pathfinding

Cooperative behavior is a desired trait in many fields from computer games to robotics. Yet, achieving cooperative behavior is often difficult, as maintaining shared information about the dynamics of agents in the world can be complex. We focus on the specific task of cooperative pathfinding and introduce a new approach based on the idea of "direction maps" that learns about the movement of agents in the world. This learned data then is used to produce implicit cooperation between agents. This approach is less expensive and has better performance than several existing cooperative algorithms.
Morphological evaluation and sentiment analysis of Punjabi text using deep learning classification

Morphological processing of Indian languages is one of the most escalating fields in the era of Natural Language Processing (NLP) since the last decade. The evaluation of Asian languages is a highly relevant field in the times of text mining and information retrieval. The morphological evaluation of a text can be employed for extraction and classification of knowledge. This paper amalgamates morphological evaluation and sentiment prediction of Punjabi language text. The textual data for Punjabi language is concerned with farmer suicide cases reported for Punjab state of India. The pre-processing phase of this study involves morphological evaluation and normalization of Punjabi words to their respective canonical forms. The next phase carries out training and testing of deep neural network model on refined Punjabi tokens obtained from the earlier phase. The proposed model classifies Punjabi tokens into four negatively oriented classes tailored for farmer suicide cases. The average accuracies of sentiment prediction obtained after 10-fold cross validation are 93.85%, 88.53%, 83.3%, and 95.45% for the four respective classes. The proposed framework yields satisfactory results on 275 Punjabi text documents with the overall accuracy of 90.29% for sentiment classification.
Incremental Class Learning for Hierarchical Classification

Objects can be described in hierarchical semantics, and people also perceive them this way. It leads to the need for hierarchical classification in machine learning. On the other hand, when a new data that belongs to a new class is given, the existing classification methods should be retrained for all data including the new data. To deal with these issues, we propose an adaptive resonance theory-supervised predictive mapping for hierarchical classification (ARTMAP-HC) network that allows incremental class learning for raw data without normalization in advance. Our proposed ARTMAP-HC is composed of hierarchically stacked modules, and each module incorporates two fuzzy ARTMAP networks. Regardless of the level of the class hierarchy and the number of classes for each level, ARTMAP-HC is able to incrementally learn sequentially added input data belonging to new classes. By using a novel online normalization process, ARTMAP-HC can classify the new data without prior knowledge of the maximum value of the dataset. By adopting the prior labels appending process, the class dependency between class hierarchy levels is reflected in ARTMAP-HC. The effectiveness of the proposed ARTMAP-HC is validated through experiments on hierarchical classification datasets. To demonstrate the applicability, ARTMAP-HC is applied to a multimedia recommendation system for digital storytelling.
Security Strategy for Virtual Machine Allocation in Cloud Computing

In the cloud infrastructure, the co-resident attack is a critical security threat. Through virtualization technology provided by Cloud Service Provider, tenants’ virtual machines (VMs) are possible to be allocated on the same host. Multi-tenant environment provides malicious tenants an opportunity to launch the co-resident attack and steal other tenants’ information by side channels. To prevent this type of attack, previous works mostly pay attention to eliminating side channels and few of them study VM deployment strategy. Hence, we focus on deploying VMs with a secure and effective allocation strategy to reduce the probability of VM co-residence. A novel VM allocation strategy is proposed with three optimization objectives including security, load balancing and energy consumption. Finally, we implement our VM allocation strategy and prove its effectiveness on the simulation platform CloudSim.
Design of Fuzzy Controllers for Embedded Systems With JFML

This paper has been supported in part by the Spanish Ministry of Economyand Competitiveness (Projects TIN2017-89517-P, TIN2015-68454-R,TIN2017-84796-C2-1-R, and TIN2017-90773-REDT) and the AndalusianGovernment. In addition, Jose M. Alonso is Ramon y Cajal Researcher(RYC-2016-19802). Financial support from the Galician Ministry of Education(grants ED431F 2018/02, GRC2014/030 and accreditation 2016-2019,ED431G/08), co-funded by the European Regional Development Fund(ERDF/FEDER program), is also gratefully acknowledged.
Non-Profiled Deep Learning-based Side-Channel attacks with Sensitivity Analysis

Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis [MPP16]. Until now, studies have been focused on applying Deep Learning techniques to perform Profiled Side-Channel attacks where an attacker has a full control of a profiling device and is able to collect a large amount of traces for different key values in order to characterize the device leakage prior to the attack. In this paper we introduce a new method to apply Deep Learning techniques in a Non-Profiled context, where an attacker can only collect a limited number of side-channel traces for a fixed unknown key value from a closed device. We show that by combining key guesses with observations of Deep Learning metrics, it is possible to recover information about the secret key. The main interest of this method is that it is possible to use the power of Deep Learning and Neural Networks in a Non-Profiled scenario. We show that it is possible to exploit the translation-invariance property of Convolutional Neural Networks [CDP17] against de-synchronized traces also during Non-Profiled side-channel attacks. In this case, we show that this method can outperform classic Non-Profiled attacks such as Correlation Power Analysis. We also highlight that it is possible to break masked implementations in black-box, without leakages combination pre-preprocessing and with no assumptions nor knowledge about the masking implementation. To carry the attack, we introduce metrics based on Sensitivity Analysis that can reveal both the secret key value as well as points of interest, such as leakages and masks locations in the traces. The results of our experiments demonstrate the interests of this new method and show that this attack can be performed in practice.
Tree-based Machine Learning Methods for Survey Research.

Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.
Subspace-Based Direct Visual Servoing

To date, most of visual servoing approaches have relied on the geometric features that have to be tracked and matched in the image. Recent works have highlighted the importance of taking into account the photometric information of the entire images. This leads to direct visual servoing (DVS) approaches. The main disadvantage of DVS is its small convergence domain compared to conventional techniques, which is due to the high non-linearities of the cost function to be minimized. In this letter, we propose to project the image on an orthogonal basis (PCA) and then servo on either images reconstructed from this new compact set of coordinates or directly on these coordinates used as visual features. In both cases, we exhibit the analytical formulation of the interaction matrix. We show that these approaches feature a better behavior than the classical photometric visual servoing scheme allowing larger displacements and a satisfactory decrease of the error norm thanks to a well-modeled interaction matrix.
A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.
Deep Recurrent Neural Networks Based Obstacle Avoidance Control for Redundant Manipulators

Obstacle avoidance is an important subject in the control of robot manipulators, but is remains challenging for robots with redundant degrees of freedom, especially when there exist complex physical constraints. In this paper, we propose a novel controller based on deep recurrent neural networks. By abstracting robots and obstacles into critical point sets respectively, the distance between the robot and obstacles can be described in a simpler way, then the obstacle avoidance strategy is established in form of inequality constraints by general class-K functions. Using minimal-velocity-norm (MVN) scheme, the control problem is formulated as a quadratic-programming case under multiple constraints. Then a deep recurrent neural network considering system models is established to solve the QP problem online. Theoretical conduction and numerical simulations show that the controller is capable of avoiding static or dynamic obstacles, while tracking the predefined trajectories under physical constraints.
Neural Network Attributions: A Causal Perspective

We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architecture is viewed as a Structural Causal Model, and a methodology to compute the causal effect of each feature on the output is presented. With reasonable assumptions on the causal structure of the input data, we propose algorithms to efficiently compute the causal effects, as well as scale the approach to data with large dimensionality. We also show how this method can be used for recurrent neural networks. We report experimental results on both simulated and real datasets showcasing the promise and usefulness of the proposed algorithm.