Reviewerly/exHarmony huggingface dataset:
Two-Tier 360-Degree Video Delivery Control in Multiuser Immersive Communications Systems

In the immersive communications systems, video information is stringently delivered for extended reality applications. Since users may demand various immersive experience and have different levels of view prediction accuracy, the two-tier 360-degree video delivery frame structure with dynamic transmission time interval duration needs to be designed appropriately. To maximize the system's quality of experience, two new frame structures and power control are proposed. Meanwhile, the synchronous and asynchronous cases with different computational complexities are addressed. The results demonstrate the effectiveness of the proposed design and indicate that flexible frame structure is beneficial for multiuser immersive communications.
Nexus among foreign direct investment, financial development, and sustainable economic growth: Empirical aspects from Sudan

&lt;abstract&gt; &lt;p&gt;This study examined the nexus between foreign direct investment (FDI), financial development, and sustainable economic growth in Sudan during the period of the structural adjustment program and the full Islamization of the banking and financial system that took place in the 1980s. The research provides a comprehensive analysis using the most recent time series secondary data from 1990 to 2020 and the study employed co-integration, Granger causality, and VAR error correction technique to estimate the models, to clarify the claimed relationship between FDI and its effect on the financial sector and subsequently attending a sustainable economic development in Sudan. In this research, Augmented Dickey-Fuller (ADF) unit root tests are applied to test the stationarity of data and the data was found stationary at first difference. The results of the ARDL bounds showed the existence of a long-term relationship between the FDI and other independent variables but the short-term showed otherwise. The Granger causality test implies that the past values of FDI don't significantly contribute to the prediction of sustainable economic growth. Also, results show that there's evidence of observed causality running from the country's trade openness and the financial sector's development. The implication of these results shows there is a complementary relationship between sustainable economic growth and both financial development and trade openness in the short run. Interestingly, the findings of the study show that the effect of financial development on economic growth is further enhanced by the inflows of FDI.&lt;/p&gt; &lt;/abstract&gt;
Real Image Denoising via Guided Residual Estimation and Noise Correction

Deep learning-based methods have dominated the field of image denoising with their superior performance. Most of them belong to the non-blind denoising approaches assuming that the noise is known at a specific level. However, real-world noise is complex and usually unknown. Since the distribution and level of noise are often unavailable, it will lead to severe performance degradation for non-blind denoising methods. Therefore, introducing noise levels is crucial for the challenging real-world denoising problem. Meanwhile, we observe that noise level mismatch will bring some artifacts to the denoised images. An intuitive solution is using the intermediate denoised images to correct the inaccurate noise level maps. Thus, we introduce an iterative correction scheme, yielding better results than direct noise prediction. We further propose an effective guided feature domain denoising residual network that can promote denoising for various real-world noises using iteratively denoised features, initial image features, and noise level maps. Experimental results on real-world image datasets show that the proposed method can provide excellent visual and objective performance for the real-world denoising task.
FixJS

The field of Automated Program Repair (APR) has received increasing attention in recent years both from the academic world and from leading IT companies. Its main goal is to repair software bugs automatically, thus reducing the cost of development and maintenance significantly. Recent works use state-of-the-art deep learning models to predict correct patches, for these teaching on a large amount of data is inevitable almost in every scenarios. Despite this, readily accessible data on the field is very scarce. To contribute to related research, we present FixJS, a dataset containing bug-fixing information of ~2 million commits. The commits were gathered from GitHub and processed locally to have both the buggy (before bug fixing commit) and fixed (after fix) version of the same program. We focused on JavaScript functions, as it is one of the most popular programming language globally and functions are first class objects there. The data includes more than 300,000 samples of such functions, including commit information, before/after states and 3 source code representations.
Multiauthority Attribute-Based Encryption for Assuring Data Deletion

In order to alleviate key escrow issue, the notion of multiauthority attribute-based encryption (MA-ABE) was presented, which was widely applied in cloud storage environment. In data sharing environment, secure data deletion is very crucial and challenging issue. Hence, in this article, we concentrate on verification of data deletion operation, i.e., assuring data deletion. To solve this problem, we put forward a system model, formal definition and security model of MA-ABE for assuring data deletion. Furthermore, we design a MA-ABE scheme for assuring data deletion, which is more practicable than the single authority ABE scheme. The designed scheme not only overcomes key escrow issue, but also resists collusion attack between malicious user and unauthorized user. In addition, our scheme utilizes merkle hash tree to obtain verifiable data deletion. Based on decisional bilinear Diffie-Hellman (DBDH) assumption, the scheme is proven to be secure under the selective-policy model. The experimental result indicates that the designed scheme is efficient for practical application.
Estimating Spatially-Varying Lighting in Urban Scenes with Disentangled Representation

We present an end-to-end network for spatially-varying outdoor lighting estimation in urban scenes given a single limited field-of-view LDR image and any assigned 2D pixel position. We use three disentangled latent spaces learned by our network to represent sky light, sun light, and lighting-independent local contents respectively. At inference time, our lighting estimation network can run efficiently in an end-to-end manner by merging the global lighting and the local appearance rendered by the local appearance renderer with the predicted local silhouette. We enhance an existing synthetic dataset with more realistic material models and diverse lighting conditions for more effective training. We also capture the first real dataset with HDR labels for evaluating spatially-varying outdoor lighting estimation. Experiments on both synthetic and real datasets show that our method achieves state-of-the-art performance with more flexible editability.
R$^{2}$Fed: Resilient Reinforcement Federated Learning for Industrial Applications

Federated learning has become an emerging hot research field in industry because of its ability to perform large-scale distributed learning while preserving data privacy. However, recent studies have shown that in the actual use of federated learning, there are device heterogeneity and data not identically and independently distributed (Non-IID) characteristics between client nodes, which will affect the effect of federated learning. In this work, we propose resilient reinforcement federated learning (R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Fed), a R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Fed method, which applies reinforcement learning to federated learning and uses reinforcement learning for weighted fusion of client models instead of average fusion. We conduct experiments on object detection, object classification, and sentiment classification tasks in the context of Non-IID and heterogeneity, and the experimental results show that the R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Fed method outperforms traditional federated learning, increasing the average accuracy by 4.7%. Experiments also demonstrate that R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Fed is resilient to federation attacks.
Reflection and Rotation Symmetry Detection via Equivariant Learning

The inherent challenge of detecting symmetries stems from arbitrary orientations of symmetry patterns; a reflection symmetry mirrors itself against an axis with a specific orientation while a rotation symmetry matches its rotated copy with a specific orientation. Discovering such symmetry patterns from an image thus benefits from an equivariant feature representation, which varies consistently with reflection and rotation of the image. In this work, we introduce a group-equivariant convolutional network for symmetry detection, dubbed EquiSym, which leverages equivariant feature maps with respect to a dihedral group of reflection and rotation. The proposed network is built end-to-end with dihedrally-equivariant layers and trained to output a spatial map for reflection axes or rotation centers. We also present a new dataset, DENse and DIverse symmetry (DENDI), which mitigates limitations of existing benchmarks for reflection and rotation symmetry detection. Experiments show that our method achieves the state of the arts in symmetry detection on LDRS and DENDI datasets.
Learn From the Past: Experience Ensemble Knowledge Distillation

Traditional knowledge distillation transfers "dark knowledge" of a pre-trained teacher network to a student network, and ignores the knowledge in the training process of the teacher, which we call teacher’s experience. However, in realistic educational scenarios, learning experience is often more important than learning results. In this work, we propose a novel knowledge distillation method by integrating the teacher’s experience for knowledge transfer, named experience ensemble knowledge distillation (EEKD). We save a moderate number of intermediate models from the training process of the teacher model uniformly, and then integrate the knowledge of these intermediate models by ensemble technique. A self-attention module is used to adaptively assign weights to different intermediate models in the process of knowledge transfer. Three principles of constructing EEKD on the quality, weights and number of intermediate models are explored. A surprising conclusion is found that strong ensemble teachers do not necessarily produce strong students. The experimental results on CIFAR-100 and ImageNet show that EEKD outperforms the mainstream knowledge distillation methods and achieves the state-of-the-art. In particular, EEKD even surpasses the standard ensemble distillation on the premise of saving training cost.
Blockchain adoption for SMEs: opportunities and challenges

Small and medium-sized enterprises (SMEs) are the backbone of economies and are recognized as an engine of sustainable economic development in developed and developing countries. Nonetheless, SMEs still face many challenges due to market globalization and escalating competition. Thanks to its salient features, blockchain is a promising technology that could provide cost-efficient and transparent solutions to these challenges. This paper provides insights and explores the opportunities and challenges of incorporating Blockchain technology in SMEs. Further, we present some established SME-focused projects, initiatives, and blockchain platforms. Finally, we point out some open research issues and outline future research directions.
Blockchain-Assisted Secure Intra/Inter-Domain Authorization and Authentication for Internet of Things

Multidomain Internet of Things (IoT) is faced with serious domain interoperability (DI) and compatibility issues since different intradomain authorization and authentication (A&A) mechanisms are deployed without the consideration of interdomain A&A. This article proposes a blockchain-assisted scheme to achieve flexible intra- and inter-domain A&A simultaneously and seamlessly. Specifically, we first design a contract-based mutual access control agreement on top of a consortium blockchain, where domain managers can manage their access permission without any trusted parties. Based on the agreement, a secure and privacy-preserving authentication protocol is further proposed by tailoring one-out-of-many proof techniques, which enables IoT devices to anonymously access authorized IoT domains. We additionally design a voting-based protocol by using a threshold-based cryptosystem. The protocol allows domain managers to transparently audit resource access with the assistance of the blockchain. Detailed security analysis demonstrates that the proposed scheme achieves the security properties, such as DI, privacy protection, and accountability. Finally, we develop two proof-of-concept prototypes in a physical testbed and virtual machine, respectively, based on an open-source blockchain platform to show our scheme’s efficiency in terms of computation and communication overhead.
Fast 3D Sparse Topological Skeleton Graph Generation for Mobile Robot Global Planning

In recent years, mobile robots are becoming ambitious and deployed in large-scale scenarios. Serving as a high-level understanding of environments, a sparse skeleton graph is beneficial for more efficient global planning. Currently, existing solutions for skeleton graph generation suffer from several major limitations, including poor adaptiveness to different map representations, dependency on robot inspection trajectories and high computational overhead. In this paper, we propose an efficient and flexible algorithm generating a trajectory-independent 3D sparse topological skeleton graph capturing the spatial structure of the free space. In our method, an efficient ray sampling and validating mechanism are adopted to find distinctive free space regions, which contributes to skeleton graph vertices, with traversability between adjacent vertices as edges. A cycle formation scheme is also utilized to maintain skeleton graph compactness. Benchmark comparison with state-of-the-art works demonstrates that our approach generates sparse graphs in a substantially shorter time, giving high-quality global planning paths. Experiments conducted in real-world maps further validate the capability of our method in real-world scenarios. Our method will be made open source to benefit the community.
Few-shot Backdoor Defense Using Shapley Estimation

Deep neural networks have achieved impressive performance in a variety of tasks over the last decade, such as autonomous driving, face recognition, and medical diagnosis. However, prior works show that deep neural networks are easily manipulated into specific, attacker-decided behaviors in the inference stage by backdoor attacks which inject malicious small hidden triggers into model training, raising serious security threats. To determine the triggered neurons and protect against backdoor attacks, we exploit Shapley value and develop a new approach called Shapley Pruning (ShapPruning) that successfully mitigates backdoor attacks from models in a data-insufficient situation (1 image per class or even free of data). Considering the interaction between neurons, ShapPruning identifies the few infected neurons (under 1 % of all neurons) and manages to protect the model's structure and accuracy after pruning as many infected neurons as possible. To accelerate ShapPruning, we further propose discarding threshold and ∊ -greedy strategy to accelerate Shapley estimation, making it possible to repair poisoned models with only several minutes. Experiments demonstrate the effectiveness and robustness of our method against various attacks and tasks compared to existing methods.
Perceived Challenges in Benefits Management-A Study of Public Sector Information Systems Engineering Projects

The field of benefits management gives guidelines on how to plan and realize benefits throughout the life-cycle of a system. However, realizing benefits from information systems projects has proven to be challenging in practice. In this paper, we investigate specific benefits management challenges as perceived by practitioners involved in information systems engineering projects. We conducted 22 interviews with respondents representing nine public sector projects, where challenges in managing benefits were elicited and identified. We elicited six specific benefits management challenges: A - Identifying and describing benefits, B - Alignment of work with planned benefits, C - Reception and acceptance of the planned benefits, D - Organizational issues, E - Alternative or competing solutions, F - Measuring and evaluating benefits. Overlaying these challenges with current normative models on benefits management, we find that: 1. Normative models on benefits management lack sufficient guidance on operative work on how to create information systems fit for realizing benefits and how to introduce these solutions to ensure benefits realization, and 2. Normative models on benefits management do not explicitly leverage the rapid project learning promoted by modern engineering methods. We conclude that more specific benefits management models should be elaborated, which are integrated into modern information systems engineering practices. This will enable best practices on the continuous adjustments of cost and scope according to evolving knowledge in projects to also be adapted to the management of benefits.
Human-Centric Image Cropping with Partition-Aware and Content-Preserving Features

Image cropping aims to find visually appealing crops in an image, which is an important yet challenging task. In this paper, we consider a specific and practical application: human-centric image cropping, which focuses on the depiction of a person. To this end, we propose a human-centric image cropping method with two novel feature designs for the candidate crop: partition-aware feature and content-preserving feature. For partition-aware feature, we divide the whole image into nine partitions based on the human bounding box and treat different partitions in a candidate crop differently conditioned on the human information. For content-preserving feature, we predict a heatmap indicating the important content to be included in a good crop, and extract the geometric relation between the heatmap and a candidate crop. Extensive experiments demonstrate that our method can perform favorably against state-of-the-art image cropping methods on human-centric image cropping task. Code is available at https://github.com/bcmi/Human-Centric-Image-Cropping .
Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations

Learning visual concepts from raw images without strong supervision is a challenging task. In this work, we show the advantages of prototype representations for understanding and revising the latent space of neural concept learners. For this purpose, we introduce interactive Concept Swapping Networks (iCSNs), a novel framework for learning concept-grounded representations via weak supervision and implicit prototype representations. iCSNs learn to bind conceptual information to specific prototype slots by swapping the latent representations of paired images. This semantically grounded and discrete latent space facilitates human understanding and human-machine interaction. We support this claim by conducting experiments on our novel data set “Elementary Concept Reasoning” (ECR), focusing on visual concepts shared by geometric objects. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code available at: https://github.com/ml-research/ XIConceptLearning
Learning Spatial-Preserved Skeleton Representations for Few-Shot Action Recognition

Few-shot action recognition aims to recognize few-labeled novel action classes and attracts growing attentions due to practical significance. Human skeletons provide explainable and data-efficient representation for this problem by explicitly modeling spatial-temporal relations among skeleton joints. However, existing skeleton-based spatial-temporal models tend to deteriorate the positional distinguishability of joints, which leads to fuzzy spatial matching and poor explainability. To address these issues, we propose a novel spatial matching strategy consisting of spatial disentanglement and spatial activation. The motivation behind spatial disentanglement is that we find more spatial information for leaf nodes (e.g., the "hand" joint) is beneficial to increase representation diversity for skeleton matching. To achieve spatial disentanglement, we encourage the skeletons to be represented in a full rank space with rank maximization constraint. Finally, an attention based spatial activation mechanism is introduced to incorporate the disentanglement, by adaptively adjusting the disentangled joints according to matching pairs. Extensive experiments on three skeleton benchmarks demonstrate that the proposed spatial matching strategy can be effectively inserted into existing temporal alignment frameworks, achieving considerable performance improvements as well as inherent explainability.
DSFNet: A Distributed Sensors Fusion Network for Action Recognition

Human action recognition (HAR) has become a hot topic in the field of computer vision and pattern recognition due to its wide range of application prospects. In most deep learning and multisensor data-based action recognition works, sequences from sensors are first stacked into 2-D images and then fed into convolutional neural network (CNN) models to extract features. This approach based on data-level fusion learns different sensor data features but loses the uniqueness of different sensor data. In this article, we propose a new deep learning model called distributed sensors fusion network (DSFNet). For the property that acceleration sequences can reflect motion trends, we first learn the features of different sensors and then use the Transformer encoder module to model the dependencies of multisensor actions and extract features. For the property that angular velocity can reflect the direction and velocity of the local pose, we first learn the features of a single sensor in different motion directions in time sequence and then learn the output feature maps of multisensor features in time sequence and extract the features. Finally, the outputs of different data modalities are used for decision-level fusion, which significantly improves the performance of the model. We evaluate the performance of the proposed model on the self-built dataset Changzhou University: a comprehensive multi-modal human action dataset (CZU-MHAD), and the experimental results show that the DSFNet model outperforms the existing methods.