Reviewerly/exHarmony huggingface dataset:
Federated and Transfer Learning-Empowered Intrusion Detection for IoT Applications

The Internet of Things (IoT) can be described as a considerable number of sensors and physical devices connected to different applications, supported with networking technologies to communicate with other devices and the Internet. With the growing number of IoT users, emerging services, and the need for high availability and data exchange, cyberattacks on those applications have increased in recent years. Therefore, securing IoT applications has allured particular consideration from the industry and research fields. This article illustrates and comprehensively analyzes the effectiveness of using FL/TL trending techniques used with different Machine Learning (ML) and Deep Learning (DL) algorithms to drive the Intrusion Detection Systems (IDS) to secure the IoT applications. The Internet of Medical Things (IoMT) is considered in this article as a use case in which we have demonstrated that using federated and transfer learning can improve model performance, increase learning process speed, reduce the amount of data needed to be trained, and preserve the user's data privacy compared with the traditional learning approaches.
Quality-Aware Network for Human Parsing

How to estimate the quality of the network output is an important issue, and currently there is no effective solution in the field of human parsing. To solve this problem, this work proposes a statistical method based on the output probability map to calculate the pixel classification quality, which is called pixel score. In addition, the Quality-Aware Module (QAM) is proposed to fuse the different quality information, the purpose of which is to estimate the quality of human parsing results. We combine QAM with a concise and effective network design to propose Quality-Aware Network (QANet) for human parsing. Benefiting from the superiority of QAM and QANet, we achieve the best performance on three multiple and one single human parsing benchmarks, including CIHP, MHP-v2, Pascal-Person-Part, ATR and LIP. Without increasing the training and inference time, QAM improves the AP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\text{r}$</tex-math></inline-formula> criterion by more than 10 points in the multiple human parsing task. QAM can be extended to other tasks with good quality estimation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> instance segmentation. Specifically, QAM improves Mask R-CNN by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\scriptstyle \sim$</tex-math></inline-formula> 1% mAP on COCO and LVISv1.0 datasets. Based on the proposed QAM and QANet, our overall system wins 1st place in CVPR2021 L2ID High-resolution Human Parsing (HRHP) Challenge, and 2nd in CVPR2021 PIC Short-video Face Parsing (SFP) Challenge. Code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/soeaver/QANet</uri> .
Reliable Architectures for Finite Field Multipliers Using Cyclic Codes on FPGA Utilized in Classic and Post-Quantum Cryptography

Fault detection is becoming greatly important in protecting cryptographic designs that can suffer from both natural or malicious faults. Finite fields over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {GF}(2^{m})$ </tex-math></inline-formula> are widely used in such designs, since their data are coded in binary form for practical reasons. Among the different finite field arithmetic, multiplication is the bottleneck operation for many cryptosystems due to its complexity. Therefore, in this work, fault detection schemes based on cyclic codes for finite field multipliers using different fields found in traditional and post-quantum cryptography are derived. Moreover, we implement such schemes by embedding them into the original architectures to perform an exhaustive study, benchmark the different overheads obtained, and prove their suitability for deeply constrained embedded systems. These implementations are performed on advanced micro devices (AMD)/Xilinx field-programmable gate array (FPGA) and provide a very high error coverage with acceptable overhead.
Hyperspectral Image Denoising With Weighted Nonlocal Low-Rank Model and Adaptive Total Variation Regularization

Hyperspectral image (HSI) is always corrupted by various types of noise during image capturing, such as Gaussian noise, stripe noise, deadline noise, impulse noise, and more. Such complicated noise significantly degrades imaging quality and thus limits the performance of downstream vision tasks. Current HSI denoising methods tackle this problem by modeling either the spectral-spatial prior of HSI or the noise characteristic of HSI, and few work consider the two aspects simultaneously. In this paper, we propose a new HSI denoising method by simultaneously modeling the HSI prior and the HSI noise characteristic. Specifically, we firstly utilize the non independent and identically distributed (non i.i.d.) mixture of Gaussian (MoG) assumption to characterize the complex noise, which corresponds to optimize a weighted fidelity function. Secondly, we exploit HSI’s non-local similarity and spatial-spectral correlation priors by applying non-local low rank model. Thirdly, we design an adaptive edge preserving total variation regularization term to characterize the non-local smooth property of HSI. Finally, we propose a new denoising model and develop effective ADMM algorithm to solve it. Extensive experiments on simulated data and real data substantiate the superiority of the proposed method beyond state-of-the-arts.
RA-Depth: Resolution Adaptive Self-supervised Monocular Depth Estimation

Existing self-supervised monocular depth estimation methods can get rid of expensive annotations and achieve promising results. However, these methods suffer from severe performance degradation when directly adopting a model trained on a fixed resolution to evaluate at other different resolutions. In this paper, we propose a resolution adaptive self-supervised monocular depth estimation method (RA-Depth) by learning the scale invariance of the scene depth. Specifically, we propose a simple yet efficient data augmentation method to generate images with arbitrary scales for the same scene. Then, we develop a dual high-resolution network that uses the multi-path encoder and decoder with dense interactions to aggregate multi-scale features for accurate depth inference. Finally, to explicitly learn the scale invariance of the scene depth, we formulate a cross-scale depth consistency loss on depth predictions with different scales. Extensive experiments on the KITTI, Make3D and NYU-V2 datasets demonstrate that RA-Depth not only achieves state-of-the-art performance, but also exhibits a good ability of resolution adaptation. Source code is available at https://github.com/hmhemu/RA-Depth .
DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision

Following the success of supervised learning, semi-supervised learning (SSL) is now becoming increasingly popular. SSL is a family of methods, which in addition to a labeled training set, also use a sizable collection of unlabeled data for fitting a model. Most of the recent successful SSL methods are based on pseudo-labeling approaches: letting confident model predictions act as training labels. While these methods have shown impressive results on many benchmark datasets, a drawback of this approach is that not all unlabeled data are used during training. We propose a new SSL algorithm, DoubleMatch, which combines the pseudo-labeling technique with a self-supervised loss, enabling the model to utilize all unlabeled data in the training process. We show that this method achieves state-of-the-art accuracies on multiple benchmark datasets while also reducing training times compared to existing SSL methods. Code is available at https://github.com/walline/doublematch.
ComforTable User Interfaces: Surfaces Reduce Input Error, Time, and Exertion for Tabletop and Mid-air User Interfaces

Real-world work-spaces typically revolve around tables, which enable knowledge workers to comfortably perform tasks over an extended period of time during productivity tasks. Tables afford more ergonomic postures and provide opportunities for rest, which raises the question of whether they may also benefit prolonged interaction in Virtual Reality (VR). In this paper, we investigate the effects of tabletop surface presence in situated VR settings on task performance, behavior, and subjective experience. In an empirical study, 24 participants performed two tasks (selection, docking) on virtual interfaces placed at two distances and two orientations. Our results show that a physical tabletop inside VR improves comfort, agency, and task performance while decreasing physical exertion and strain of the neck, shoulder, elbow, and wrist, assessed through objective metrics and subjective reporting. Notably, we found that these benefits apply when the UI is placed on and aligned with the table itself as well as when it is positioned vertically in mid-air above it. Our experiment therefore provides empirical evidence for integrating physical table surfaces into VR scenarios to enable and support prolonged interaction. We conclude by discussing the effective usage of surfaces in situated VR experiences and provide initial guidelines.
Skellam mixture mechanism

Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern. An effective solution to this problem is to train models with differential privacy ( DP ), which provides rigorous privacy guarantees by injecting random noise to the gradients. This paper focuses on the scenario where sensitive data are distributed among multiple participants, who jointly train a model through federated learning , using both secure multiparty computation ( MPC ) to ensure the confidentiality of each gradient update, and differential privacy to avoid data leakage in the resulting model. A major challenge in this setting is that common mechanisms for enforcing DP in deep learning, which inject real-valued noise , are fundamentally incompatible with MPC, which exchanges finite-field integers among the participants. Consequently, most existing DP mechanisms require rather high noise levels, leading to poor model utility. Motivated by this, we propose Skellam mixture mechanism (SMM), a novel approach to enforcing DP on models built via federated learning. Compared to existing methods, SMM eliminates the assumption that the input gradients must be integer-valued, and, thus, reduces the amount of noise injected to preserve DP. The theoretical analysis of SMM is highly non-trivial, especially considering (i) the complicated math of DP deep learning in general and (ii) the fact that the mixture of two Skellam distributions is rather complex. Extensive experiments on various practical settings demonstrate that SMM consistently and significantly outperforms existing solutions in terms of the utility of the resulting model.
A Gravitational Search Algorithm Study on Text Summarization Using NLP

Over the last decade, the amount of data available on the internet has grown exponentially. As a result, there is a need for a resolution that converts this massive atomic data into valuable information that a brain could comprehend arises. Text Summarization (TS) is the procedure of generating a synopsis of a particular document that comprises only the most critical info from the novel; the objective is to obtain a concise synopsis of the important points in the document. Text summarization is one such technique that is frequently used in research to aid in the management of massive amounts of data. Automated summarization is a well-known technique for distilling the main points of a document. It works by preserving significant information in the text by creating a condensed version of it. Extractive and Abstractive methods of text summarization are available. Extractive summarization methods alleviate some of the concern of summarization by extracting a subset of relevant sentences from the original text. The objective of abstractive method of multiple records is to create condensed form of the record while retaining essential info. While there are numerous methods available, investigators studying in NLP using Gravitational Search Algorithm are predominantly focus on extractive methods. The inferences of sentences are determined in terms of linguistic and statistical characteristics. Finally, this paper compiles the new and pertinent study in the area of TS for further research and examination. It will be important because it will provide a new path for forthcoming scholars concerned in this domain.
Autonomous Navigation of an AMR using Deep Reinforcement Learning in a Warehouse Environment

Mobile robots have been used in warehouses worldwide as a means for distribution of goods and gained demand after the Covid19 labor issue. This paper proposes an Autonomous Mobile Robot (AMR) to navigate in a warehouse environment to its target location using LIDAR. The method used to solve this problem is a deep reinforcement learning algorithm called deep Q-network (DQN) to detect and avoid obstacles and reach the target location. DQN is used as it is desired for solving complex tasks. Training of the DQN algorithm is carried out in ROS Gazebo environment using LIDAR-based robot model. The LIDAR sensor detects the obstacles and the odometer sensor helps to find the distance between the target location are used as inputs for training the algorithm and optimal actions are taken based on the two inputs. A reward policy is awarded when an obstacle is avoided and reaches the target location. The results show that mobile robot can successfully navigate in an unknown environment through simulation and real life.
Input-level Inductive Biases for 3D Reconstruction

Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases. In this paper we tackle 3D reconstruction using a domain agnostic architecture and study how to inject the same type of inductive biases directly as extra inputs to the model. This approach makes it possible to apply existing general models, such as Perceivers, on this rich domain, without the need for architectural changes, while simultaneously maintaining data efficiency of bespoke models. In particular we study how to encode cameras, projective ray incidence and epipolar geometry as model inputs, and demonstrate competitive multi-view depth estimation performance on multiple benchmarks.
MGCNet: Multilevel Gated Collaborative Network for RGB-D Semantic Segmentation of Indoor Scene

RGB-D semantic segmentation of indoor scenes has long been an enduring research topic. However, because of the intrinsic differences in modal information and large gaps in multi-level feature cues, adopting the traditional U-Net framework provides suboptimal indoor scene segmentation. In this paper, we consider an effective feature exploration approach to achieve accurate segmentation. Specifically, it consists of three steps. First, in the encoder, we design a difference-exploration fusion module, which extracts the difference weights of the two modalities to guide them for fusion, so as to achieve intrinsically consistent feature fusion. The gated decoder module relates to the remaining two steps. Second, we use a gating unit for each level of fusion information to reduce the difference between layers, which also increases the unique distinction of a specific layer while avoiding the exclusion between layers of information. Finally, we use a serial-parallel alternation strategy to increase the ability to capture contextual knowledge. Considering the above three steps, we construct the multilevel gated collaborative network (MGCNet). Extensive experiments indicate the performance of the proposed MGCNet can compete favorably against state-of-the-art models under three standard metrics.
Statistically Sender-Private OT from LPN and Derandomization

We construct a two-message oblivious transfer protocol with statistical sender privacy (SSP OT) based on the Learning Parity with Noise (LPN) Assumption and a standard Nisan-Wigderson style derandomization assumption. Beyond being of interest on their own, SSP OT protocols have proven to be a powerful tool toward minimizing the round complexity in a wide array of cryptographic applications from proofs systems, through secure computation protocols, to hard problems in statistical zero knowledge (SZK). The protocol is plausibly post-quantum secure. The only other constructions with plausible post quantum security are based on the Learning with Errors (LWE) Assumption. Lacking the geometric structure of LWE, our construction and analysis rely on a different set of techniques. Technically, we first construct an SSP OT protocol in the common random string model from LPN alone, and then derandomize the common random string. Most of the technical difficulty lies in the first step. Here we prove a robustness property of the inner product randomness extractor to a certain type of linear splitting attacks. A caveat of our construction is that it relies on the so called low noise regime of LPN. This aligns with our current complexity-theoretic understanding of LPN, which only in the low noise regime is known to imply hardness in SZK.
Learning Multiple Dense Prediction Tasks from Partially Annotated Data

Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient approach and look at jointly learning of multiple dense prediction tasks on partially annotated data (i.e. not all the task labels are available for each image), which we call multi-task partially-supervised learning. We propose a multi-task training procedure that successfully leverages task relations to supervise its multi-task learning when data is partially annotated. In particular, we learn to map each task pair to a joint pairwise task-space which enables sharing information between them in a computationally efficient way through another network conditioned on task pairs, and avoids learning trivial cross-task relations by retaining high-level information about the input image. We rigorously demonstrate that our proposed method effectively exploits the images with unlabelled tasks and outperforms existing semi-supervised learning approaches and related methods on three standard benchmarks.
Exploring the Effect of Virtual Hand Realism on Embodiment by Subjective Ratings and Eye Tracking

Previous research has emphasized the significance of virtual hand representations to induce users’ embodiment, including three subcomponents: body ownership, agency and self-location. However, there are insufficient evidence and conflicting conclusions about agency and self-location with various hand representations in literature. This study aims to explore the influence of hand realism on three subcomponents of users’ embodiment in virtual reality. An experiment was conducted with three hand realism levels to collect participants’ perception data. The results revealed the lower embodiment in abstract hand from the self-report and eye behavior, and the correlation between agency and eye behavior from integrating the subjective and objective data. This study brings new insights to users’ embodiment with virtual hand by subjective measure through self-report and objective method of eye tracking.
Comparative approach for VM Scheduling using Modified Particle Swarm Optimization and Genetic Algorithm in Cloud Computing

The Users can access Cloud services anytime and from any location, depending on their needs. In a cloud platform, data of a vast amount is transferred from the user to the server and vice-versa. Whenever the VM Scheduling takes longer than expected, or the selected VM does not exist in the datacenter may utilize more Energy consumption and SLA (Service Level Agreement) violations with more VM Migrations. Because the VM is the primary element in the Cloud Environment, the VM’s assignment must be done correctly; resources must be utilized effectively, and no violations must occur with less VM Migrations. Two approaches are implemented for the comparison i.e., Modified Particle Swarm optimization (MPSO), and Genetic Algorithm (GA). The MPSO resulted better than GA by 6.0S%, LR-MMT by 32.2%, and GA at 27.81% compared to Local Regression-Minimum Migration Time (LR-MMT) in energy consumption. The MPSO resulted better than GA by 48.39%, LR-MMT by 91.6%, and GA by S3.73% compared to LR-MMT in VM migrations. The MPSO resulted better than GA by 5%, Local RegressionRandom Selection (LR-RS) by 71.21%, and GA resulted in 67.21% compared to Local Regression-Maximum Correlation (LR-MC) in SLA Violation. Therefore, the acquired results indicated that the suggested approach converges to optimal solutions with higher quality than existing algorithms compared to the QoS parameters.
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation

Deep convolutional neural networks have considerably improved state-of-the-art results for semantic segmentation. Nevertheless, even modern architectures lack the ability to generalize well to a test dataset that originates from a different domain. To avoid the costly annotation of training data for unseen domains, unsupervised domain adaptation (UDA) attempts to provide efficient knowledge transfer from a labeled source domain to an unlabeled target domain. Previous work has mainly focused on minimizing the discrepancy between the two domains by using adversarial training or self-training. While adversarial training may fail to align the correct semantic categories as it minimizes the discrepancy between the global distributions, self-training raises the question of how to provide reliable pseudolabels. To align the correct semantic categories across domains, we propose a contrastive learning approach that adapts category-wise centroids across domains. Furthermore, we extend our method with self-training, where we use a memory-efficient temporal ensemble to generate consistent and reliable pseudo-labels. Although both contrastive learning and self-training (CLST) through temporal ensembling enable knowledge transfer between two domains, it is their combination that leads to a symbiotic structure. We validate our approach on two domain adaptation benchmarks: GTA5 → Cityscapes and SYNTHIA → Cityscapes. Our method achieves better results than the state-of-the-art.