Reviewerly/exHarmony huggingface dataset:
Accountable and Revocable Large Universe Decentralized Multi-Authority Attribute-Based Encryption for Cloud-Aided IoT

The data collected, stored, shared, and accessed across different platforms in the dynamic IoT is mostly confidential and privacy-sensitive.Data security and access control issues urgently need to be addressed.Multi-authority attribute-based encryption (MA-ABE) is seen as a potential solution for addressing data access control security concerns in the dynamic IoT since it allows for dynamic access control over encrypted data.However, the existing key abuse problem is severely destroying the security access control of MA-ABE.The existing accountable attribute-based encryption schemes only support small attributes (users) universe and single authority.Moreover, they do not support revocation.Some schemes are inefficient since they are constructed in the composite order bilinear group.In this article, the author proposes the first accountable and revocable large universe decentralized multi-authority attribute-based encryption scheme with outsourcing decryption based on prime order bilinear groups.The proposed scheme allows for the dynamic capacity expansion of attributes, users, and authorities.An audit mechanism is given to judge if the suspicious key was leaked by a malicious user or by authorities and to determine the identity of the leaker.The malicious user who divulges key can be punished by user-attribute revocation.The revocation mechanism is resistant to collusion attacks undertaken by revoked users and non-revoked users.Meanwhile, it satisfies the requirements of forward and backward security.The proposed scheme is static security in the random oracle model under the q-DPBDHE2 assumption.To save resources, the outsourced decryption module is optional for users with restricted resources.According to the results of the performance analysis, it is suited for large-scale cross-domain cooperation in the dynamic cloud-aided IoT.
DiffRNN: Differential Verification of Recurrent Neural Networks

Recurrent neural networks (RNNs) such as Long Short Term Memory (LSTM) networks have become popular in a variety of applications such as image processing, data classification, speech recognition, and as controllers in autonomous systems. In practical settings, there is often a need to deploy such RNNs on resource-constrained platforms such as mobile phones or embedded devices. As the memory footprint and energy consumption of such components become a bottleneck, there is interest in compressing and optimizing such networks using a range of heuristic techniques. However, these techniques do not guarantee the safety of the optimized network, e.g., against adversarial inputs, or equivalence of the optimized and original networks. To address this problem, we propose DiffRNN, the first differential verification method for RNNs to certify the equivalence of two structurally similar neural networks. Existing work on differential verification for ReLU-based feed-forward neural networks does not apply to RNNs where nonlinear activation functions such as Sigmoid and Tanh cannot be avoided. RNNs also pose unique challenges such as handling sequential inputs, complex feedback structures, and interactions between the gates and states. In DiffRNN, we overcome these challenges by bounding nonlinear activation functions with linear constraints and then solving constrained optimization problems to compute tight bounding boxes on non-linear surfaces in a high-dimensional space. The soundness of these bounding boxes is then proved using the dReal SMT solver. We demonstrate the practical efficacy of our technique on a variety of benchmarks and show that DiffRNN outperforms state-of-the-art RNN verification tools such as Popqorn.
roadscene2vec: A tool for extracting and embedding road scene-graphs

Recently, road scene-graph representations used in conjunction with graph learning techniques have been shown to outperform state-of-the-art deep learning techniques in tasks including action classification, risk assessment, and collision prediction. To enable the exploration of applications of road scene-graph representations, we introduce roadscene2vec: an open-source tool for extracting and embedding road scene-graphs. The goal of roadscene2vec is to enable research into the applications and capabilities of road scene-graphs by providing tools for generating scene-graphs, graph learning models to create spatio-temporal scene-graph embeddings, and tools for visualizing and analyzing scene-graph-based methodologies. The capabilities of roadscene2vec include (i) customized scene-graph generation from either video clips or data from the CARLA simulator, (ii) multiple configurable spatio-temporal graph embedding models and baseline CNN-based models, (iii) built-in functionality for using graph and sequence embeddings for risk assessment and collision prediction applications, (iv) tools for evaluating transfer learning, and (v) utilities for visualizing scene-graphs and analyzing the explainability of graph learning models. We demonstrate the utility of roadscene2vec for these use cases with experimental results and qualitative evaluations for both graph learning models and CNN-based models. roadscene2vec is available at https://github.com/AICPS/roadscene2vec.
Ensemble single image deraining network via progressive structural boosting constraints

Image deraining is an extensively researched topic in low-level computer vision community. During the past, sufficient works have been proposed to address this problem. Though great improvements these methods have achieved, no derain network can confidently declare that it can solve rain removal problem perfectly. Single complex model may lead to overfitting, while simple model is too weak to achieve clear result. Therefore, in this paper, inspired from classic boosting idea, we have proposed an effective ensemble derain framework to aggregate multiple simple weak drain models to obtain a strong derain model. Cascade structural weighting-map are computed for adaptively emphasizing the quality of local derain regions. Struct-absolution losses are proposed to account for pixel-wise and local region-wise differences, and to facilitate embedding boosting idea into network training. The comprehensive experiments on public derain datasets and high-level vision tasks validate that our proposed model which just utilizes three generally weak derain subnets can achieve much better performance than compared state-of-the-art methods. Our ensemble framework has enough capacity that any state-of-the-art DL-based models can be taken as sub-modules to solve rain removal of multiple types within a single framework.
A compact compound sinusoidal differential evolution algorithm for solving optimisation problems in memory-constrained environments

In this paper, a new compact algorithm is proposed. Two sinusoidal formulas are used to automatically adjust the crossover rate and the mutation scaling factor in the compact Differential Evolution (cDE) metaheuristic. The proposed algorithm, called Compound Sinusoidal cDE, CScDE, is compared to seven state-of-the-art compact algorithms on the well-known BBOB test-bed, the CEC-2014 test suite for continuous optimisation, as well as five real-world optimisation problems chosen from the CEC-2011 benchmarks. The CScDE algorithm outperformed its competitors for most problem categories and over most dimensions. It is also compared to some well-established population-based metaheuristics.
SONIC: A Sparse Neural Network Inference Accelerator with Silicon Photonics for Energy-Efficient Deep Learning

Sparse neural networks can greatly facilitate the deployment of neural networks on resource-constrained platforms as they offer compact model sizes while retaining inference accuracy. Because of the sparsity in parameter matrices, sparse neural networks can, in principle, be exploited in accelerator architectures for improved energy-efficiency and latency. However, to realize these improvements in practice, there is a need to explore sparsity-aware hardware-software co-design. In this paper, we propose a novel silicon photonics-based sparse neural network inference accelerator called SONIC. SONIC takes advantage of the high energy-efficiency and low latency of photonic devices along with software co-optimization to accelerate sparse neural networks. Our experimental analysis shows that SONIC can achieve up to 5.8* better performance-per-watt and 8.4* lower energy-per-bit than state-of-the-art sparse electronic neural network accelerators; and up to 13.8* better performance-per-watt and 27.6* lower energy-per-bit than the best known photonic neural network accelerators.
Harmonious Textual Layout Generation Over Natural Images via Deep Aesthetics Learning

Automatic typography is important because it helps designers avoid highly repetitive tasks and amateur users achieve high-quality textual layout designs. However, there are often many parameters and complicated aesthetic rules that need to be adjusted in automatic typography work. In this paper, we propose an efficient deep aesthetics learning approach to generate harmonious textual layout over natural images, which can be decomposed into two stages, saliency-aware text region proposal and aesthetics-based textual layout selection. Our method incorporates both semantic features and visual perception principles. First, we propose a semantic visual saliency detection network combined with a text region proposal algorithm to generate candidate text anchors with various positions and sizes. Second, a discriminative deep aesthetics scoring model is developed to assess the aesthetic quality of the candidate textual layouts. We build a new Textual Layout Aesthetics dataset with dense annotations of each image and design a reasonable evaluation metric to compare our method with richer baselines. The results demonstrate that our method can generate harmonious textual layouts in various actual scenarios with better performance.
Cross-Modal Image-Recipe Retrieval via Intra- and Inter-Modality Hybrid Fusion

In recent years, the Internet has stimulated the explosion of multimedia data. Food-related cooking videos, images, and recipes promote the rapid development of food computing. Image-recipe retrieval is an important sub-task in the field of cross-modal retrieval, which focuses on the measurement of the association between food image and recipe (title, ingredients, instructions). Although the existing methods have proposed some feasible solutions to achieve the goal of Image-recipe retrieval, there are still the following issues: 1) complex model structure and time-consuming training process. 2) the lack of information interaction within modalities and information integration between images and recipes. To this end, we propose a novel lightweight framework namedIntra- and Inter-Modality Hybrid Fusion (IMHF). Our IMHF model abandons a separate deep vision encoder and utilizes the transformer module to unify the visual and text features. In this way, valuable information from images and recipes can be condensed and the direct information interaction between the two modalities can be promoted. Both the intra- and inter-modality fusion can be realized. Extensive experiment results on the large-scale benchmark dataset Recipe1M demonstrate that our model IMHF with a lightweight architecture is superior to the state-of-the-art approaches.
MSAR‐DefogNet: Lightweight cloud removal network for high resolution remote sensing images based on multi scale convolution

High resolution remote sensing image cloud removal can bring a lot of convenience for human activities. However, the existing cloud removal algorithms have a variety of disadvantages. First of all, they have the disadvantages of long computing time and large consumption of computing resources. Secondly, the effect of recovery needs to be improved. In order to improve the above two points, a near real-time effective algorithm is proposed, namely MSAR-Defognet (multiple scale attention residual network using for cloud remove), which consumes less computing power and space and has superior cloud removal effect. On the one hand, several different large-scale filters are chosen to extract the weak information effectively, while can save the computing power and shorten the image processing time. On the other hand, the fine-grained convolution residual block with channel attention mechanism is used to enhance the network's ability to extract cloud features. In addition, a data set which is closer to the real cloud shape and has higher richness to train the cloud removal network, so that the parameters obtained by training have stronger robustness and can adaptively remove clouds with different thickness. Experiments show that, compared with other advanced network models, the network not only has the advantage of fast processing speed, but also has better image restoration effect in high-resolution remote sensing image restoration. It can meet the requirements of many hard real-time tasks, so that remote sensing images can play a greater value for human activities.
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data

We study distributed stochastic gradient descent (SGD) in the master-worker architecture under Byzantine attacks. We consider the heterogeneous data model, where different workers may have different local datasets, and we do not make any probabilistic assumptions on data generation. At the core of our algorithm, we use the polynomial-time outlier-filtering procedure for robust mean estimation proposed by Steinhardt et al. (ITCS 2018) to filter-out corrupt gradients. In order to be able to apply their filtering procedure in our heterogeneous data setting where workers compute stochastic gradients, we derive a new matrix concentration result, which may be of independent interest. We provide convergence analyses for smooth strongly-convex and non-convex objectives and show that our convergence rates match that of vanilla SGD in the Byzantine-free setting. In order to bound the heterogeneity, we assume that the gradients at different workers have bounded deviation from each other, and we also provide concrete bounds on this deviation in the statistical heterogeneous data model.
I-AID: Identifying Actionable Information From Disaster-Related Tweets

Social media plays a significant role in disaster management by providing valuable data about affected people, donations and help requests. Recent studies highlight the need to filter information on social media into fine-grained content labels. However, identifying useful information from massive amounts of social media posts during a crisis is a challenging task. In this paper, we propose I-AID, a multimodel approach to automatically categorize tweets into multi-label information types and filter critical information from the enormous volume of social media data. I-AID incorporates three main components: i) a BERT-based encoder to capture the semantics of a tweet and represent as a low-dimensional vector, ii) a graph attention network (GAT) to apprehend correlations between tweets’ words/entities and the corresponding information types, and iii) a Relation Network as a learnable distance metric to compute the similarity between tweets and their corresponding information types in a supervised way. We conducted several experiments on two real publicly-available datasets. Our results indicate that I-AID outperforms state-ofthe- art approaches in terms of weighted average F1 score by +6% and +4% on the TREC-IS dataset and COVID-19 Tweets, respectively.
The role of an ant colony optimisation algorithm in solving the major issues of the cloud computing

There are many issues and problems in cloud computing that researchers try to solve by using different techniques. Most of the cloud challenges are NP-hard problems; therefore, many meta-heuristic techniques have been used for solving these challenges. As a famous and powerful meta-heuristic algorithm, the Ant Colony Optimisation (ACO) algorithm has been recently used for solving many challenges in the cloud. However, in spite of the ACO potency for solving optimisation problems, its application in solving cloud issues in the form of a review article has not been studied so far. Therefore, this paper provides a complete and detailed study of the different types of ACO algorithms for solving the important problems and issues in cloud computing. Also, the number of published papers for various publishers and different years is shown. In this paper, available challenges are classified into different groups, including scheduling, resource allocation, load balancing, consolidation, virtual machine placement, service composition, energy consumption, and replication. Then, some of the selected important techniques from each category by applying the selection process are presented. Besides, this study shows the comparison of the reviewed approaches and also it highlights their principal elements. Finally, it highlights the relevant open issues and some clues to explain the difficulties. The results revealed that there are still some challenges in the cloud environments that the ACO is not applied to solve.
Privacy Preservation of User Identity in Contact Tracing for COVID-19-Like Pandemics Using Edge Computing

Pandemic and infectious disease outbreaks put pressure on health authorities and require lockdowns.These outbreaks, which strain limited healthcare resources, must be swiftly controlled and monitored.A large number of healthcare authorities are currently investigating automated systems to support outbreak monitoring and control.However, current contact tracing systems face many privacy, participation, and power constraints.Furthermore, elderly or less financially able individuals often cannot participate in automated contact tracing due to not owning a smartphone.This paper proposes a new system that enables health authorities to track exposure among individuals participating in the automated system, aid health authorities in interviewing non-participating individuals, and minimize the processing required by offloading to nearby edge computing devices.The proposed system utilizes edge servers to assist health authorities in tracking users who withdraw from or are unable to use contact tracing.Edge computing devices have access to more contextual information, resulting in minimal data collection and thus enabling businesses, houses, and offices to participate in contact tracing as locations.Edge computing devices enable location-based data collection of contact tracing data using proximity-based sensors for offices, homes, and shops, thereby assisting health authorities to notify users of exposure without disclosing the identities of businesses or individuals.Moreover, the proposed system reduces the overall power for end users up to 97% by delegating contact tracing to nearby edge computing devices.In addition, the system mitigates data poisoning attacks that target individuals' smartphones, edge devices, or cloud servers by utilizing blockchain to store contacts, delegations, and identities.
A Pairing‐Free Certificateless Signcryption Scheme for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) create an vital platform for communication between vehicles, which can realize accident warning, auxiliary driving, road traffic information query, passenger communication and other applications. While providing convenient services for people, VANETs also bring some security risks. Security and privacy are the primary issues in the research of VANETs. Signcryption is an ideal way to transfer messages in a logical step in a secure and authenticated way. We design a Certificateless signcryption (CLSC) scheme to provide confidentiality, authentication, integrity, non-repudiation and user privacy preservation for the information transmitted between vehicle communication units. We demonstrate the confidentiality and unforgeability of the proposed scheme in the random oracle model. Moreover, compared with the existing CLSC schemes, ours realizes the perfect combination of efficiency, security and privacy, and it is particularly well adapted to the secure communication of vehicle networks.
Navigating Interactive Story Spaces. The Architecture of Interactive Narratives in Online Journalism

Over the past decade, journalists have created in-depth interactive narratives to provide an alternative to the relentless 24-hour news cycle. Combining different media forms, such as text, audio, video, and data visualisation with the interactive possibilities of digital media, these narratives involve users in the narrative in new ways. In journalism studies, the convergence of different media forms in this manner has gained significant attention. However, interactivity as part of this form has been left underappreciated. In this study, we scrutinise how navigational structure, expressed as navigational cues, shapes user agency in their individual explorations of the narrative. By approaching interactive narratives as story spaces with unique interactive architectures, in this article, we reconstruct the architecture of five Dutch interactive narratives using the walkthrough method. We find that the extensiveness of the interactive architectures can be described on a continuum between closed and open navigational structures that predetermine and thus shape users’ trajectories in diverse ways.
A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods. The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems. Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data. The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers. Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.
Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs

We tackle the problem of weakly-supervised conversational Question Answering over large Knowledge Graphs using a neural semantic parsing approach.We introduce a new Logical Form (LF) grammar that can model a wide range of queries on the graph while remaining sufficiently simple to generate supervision data efficiently.Our Transformer-based model takes a JSON-like structure as input, allowing us to easily incorporate both Knowledge Graph and conversational contexts.This structured input is transformed to lists of embeddings and then fed to standard attention layers.We validate our approach, both in terms of grammar coverage and LF execution accuracy, on two publicly available datasets, CSQA and ConvQuestions, both grounded in Wikidata.On CSQA, our approach increases the coverage from 80% to 96.2%, and the LF execution accuracy from 70.6% to 75.6%, with respect to previous state-of-the-art results.On ConvQuestions, we achieve competitive results with respect to the state-of-the-art.