Reviewerly/exHarmony huggingface dataset:
<i>How Suboptimal is Work-From-Home Security in IT/ICS Enterprises?</i> A Strategic Organizational Theory for Managers

The COVID-19 pandemic (e.g., especially the first and second COVID waves) had forced firms (organizations) to radically shift a considerable (if not all) proportion of their employees to serve in a work-from-home (WFH) mode. Industry statistics showcase that despite ushering in significant work-flexibility (and other) benefits, the WFH mode has also expanded an organization’s cyber-vulnerability space, and increased the number of cyber-breaches in IT and IT-OT systems (e.g., ICSs). This leads us to an important fundamental question: is the WFH paradigm detrimental to IT and IoT-driven ICS security in general? While vulnerability reasoning and empirical statistics might qualitatively support an affirmative answer to this question, a rigorous, practically motivated, and strategic cost-benefit analysis is yet to be conducted to establish in principle whether and to what degree WFH-induced cyber-security in an IT/ICS system is sub-optimal when compared to that in the non-WFH work mode. We propose a novel and rigorous strategic method to dynamically quantify the degree of sub-optimal cyber-security in an IT/ICS organization of employees, all of whom work in heterogeneous WFH “siloes”. We first derive as benchmark for a WFH setting - the centrally-planned socially optimal aggregate employee effort in cyber-security best practices at any given time instant. We then derive and compute (using Breton’s Nash equilibrium computation algorithm for stochastic dynamic games) for for the same setting - the distributed time-varying strategic Nash equilibrium amount of aggregate employee effort in cyber-security. The time-varying ratios of these centralized and distributed estimates quantify the free riding dynamics, i.e., a proxy concept for security sub-optimality, within an IT/ICS organization for the WFH setting. We finally compare the free-riding ratio between WFH and non-WFH work modes to gauge the (possible) extent of the increase (lower bound) in security sub-optimality when the organization operates in a WFH mode. We counter-intuitively observe through extensive real-world-trace-driven Monte Carlo simulations that the maximum of the time-dependent median increase in the related security sub-optimality ranges around 25% but decreases fast with time to near 0% (implying security sub-optimality in the WFH mode equals that in the non-WFH mode) if the impact of employee security effort is time-accumulative (sustainable) even for short time intervals.
A high-efficiency, reliable multilevel hardware-accelerated annealer with in-memory spin coupling and complementary read algorithm

Abstract We proposed an in-memory spin coupler based on the 55 nm NOR flash technology to tackle the combinatorial optimization problems. The high-density and cost-effective floating-gate (FG) devices can overcome the capacity limitation in the conventional annealing machines based on static random access memory. In addition, the FG devices featuring high endurance and excellent data retention provide more robust annealing computation as compared to resistive random access memory. A novel complementary read algorithm is further developed to increase the tolerance on threshold voltage ( V th ) variation by 60%. Demonstrations show that the proposed in-memory spin coupling architecture with high efficiency and scalability has great potential for solving the combinatorial optimizations regardless of the problem size.
Algorithm-hardware Co-optimization for Energy-efficient Drone Detection on Resource-constrained FPGA

Convolutional neural network (CNN)-based object detection has achieved very high accuracy; e.g., single-shot multi-box detectors (SSDs) can efficiently detect and localize various objects in an input image. However, they require a high amount of computation and memory storage, which makes it difficult to perform efficient inference on resource-constrained hardware devices such as drones or unmanned aerial vehicles (UAVs). Drone/UAV detection is an important task for applications including surveillance, defense, and multi-drone self-localization and formation control. In this article, we designed and co-optimized an algorithm and hardware for energy-efficient drone detection on resource-constrained FPGA devices. We trained an SSD object detection algorithm with a custom drone dataset. For inference, we employed low-precision quantization and adapted the width of the SSD CNN model. To improve throughput, we use dual-data rate operations for DSPs to effectively double the throughput with limited DSP counts. For different SSD algorithm models, we analyze accuracy or mean average precision (mAP) and evaluate the corresponding FPGA hardware utilization, DRAM communication, and throughput optimization. We evaluated the FPGA hardware for a custom drone dataset, Pascal VOC, and COCO2017. Our proposed design achieves a high mAP of 88.42% on the multi-drone dataset, with a high energy efficiency of 79 GOPS/W and throughput of 158 GOPS using the Xilinx Zynq ZU3EG FPGA device on the Open Vision Computer version 3 (OVC3) platform. Our design achieves 1.1 to 8.7× higher energy efficiency than prior works that used the same Pascal VOC dataset, using the same FPGA device, but at a low-power consumption of 2.54 W. For the COCO dataset, our MobileNet-V1 implementation achieved an mAP of 16.8, and 4.9 FPS/W for energy-efficiency, which is ∼ 1.9× higher than prior FPGA works or other commercial hardware platforms.
Evaluating Image Super-Resolution Performance on Mobile Devices: An Online Benchmark

Deep learning-based image super-resolution (SR) has shown its strong capability in recovering high-resolution image details from low-resolution inputs. With the ubiquitous use of AI-accelerators on mobile devices (e.g., smartphones), increasing attention has been received to develop mobile-friendly SR models. Because of the complicated and tedious routines to deploy SR models on mobile devices, researchers have to use indirect indices, such as FLOPs, number of parameters, and activations, to evaluate and compare the efficiency of SR models. However, these indices cannot faithfully reflect the real performance of SR models on mobile devices. To mitigate this gap, we develop an online benchmark to automatically evaluate the performance of SR models on mobile devices. With a simple model definition file as input, e.g., PyTorch or ONNX file, our benchmark can generate the on-device evaluation indices and relevant statistics, including latency, memory, and energy consumption within 15 min, freeing the researchers from labor-intensive SR model deployment works. We further comprehensively study current SR models on mobile devices equipped with typical AI accelerators, such as Qualcomm, MediaTek, Hisilicon, and Samsung. Our benchmark provides a common platform for researchers to easily evaluate and compare the practical performance of their SR models on mobile devices. More details can be found at https://github.com/xindongzhang/MobileSR-Benchmark .
Unsupervised Domain Adaptive Object Detection with Class Label Shift Weighted Local Features

Due to the high transferability of features extracted from early layers (called local features), aligning marginal distributions of local features has achieved compelling results in unsupervised domain adaptive object detection. However, such marginal feature alignment suffers from the class label shift between source and target domains. Existing class label shift correction methods focus on image classification, and cannot be directly applied to object detection due to objects' co-occurrence. Meanwhile, one property of local features is that they have small receptive fields and can be easily mapped back to specific areas of input images. Therefore, to handle object co-occurrence scenarios, we propose to leverage this property to decompose the source feature maps and compute the source domain class distribution at the pixel level. The decomposition is based on each feature pixel's receptive field overlap with ground- truth bounding boxes. In the target domain, where no labels are available, we estimate this distribution using predicted bounding boxes and thus get the estimated class label shift between domains. This estimated shift is further used to re-weight source local features during the feature alignment. To the best of our knowledge, this is the first work trying to explicitly correct class label shift in unsupervised domain adaptive object detection. Experimental results demonstrate that this approach can systematically improve several recent domain adaptive object detectors, such as SW and HTCN on benchmark datasets with different degrees of class label shift.
SW-VAE: Weakly Supervised Learn Disentangled Representation via Latent Factor Swapping

Representation disentanglement is an important goal of the representation learning that benefits various of downstream tasks. To achieve this goal, many unsupervised learning representation disentanglement approaches have been developed. However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning. Therefore, we propose a novel weakly-supervised training approach, named as SW-VAE, which incorporates pairs of input observations as supervision signal by using the generative factors of datasets. Furthermore, we introduce strategies to gradually increase the learning difficulty during training to smooth the training process. As shown on several datasets, our model shows significant improvement over state-of-the-art (SOTA) methods on representation disentanglement tasks.
SITTA: Single Image Texture Translation for Data Augmentation

Recent advances in data augmentation enable one to translate images by learning the mapping between a source domain and a target domain. Existing methods tend to learn the distributions by training a model on a variety of datasets, with results evaluated largely in a subjective manner. Relatively few works in this area, however, study the potential use of image synthesis methods for recognition tasks. In this paper, we propose and explore the problem of image translation for data augmentation. We first propose a lightweight yet efficient model for translating texture to augment images based on a single input of source texture, allowing for fast training and testing, referred to as Single Image Texture Translation for data Augmentation (SITTA). Then we explore the use of augmented data in long-tailed and few-shot image classification tasks. We find the proposed augmentation method and workflow is capable of translating the texture of input data into a target domain, leading to consistently improved image recognition performance. Finally, we examine how SITTA and related image translation methods can provide a basis for a data-efficient, "augmentation engineering" approach to model training.
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-label Image Classification

Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expensive in practice to annotate all labels in every training image. Existing works on partial-label learning focus on the case where each training image is annotated with only a subset of its labels. A special case is to annotate only one positive label in each training image. To further relieve the annotation burden and enhance the performance of the classifier, this paper proposes a new partial-label setting in which only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled. To handle this new setting, we propose an end-to-end deep network, PLMCL (Partial-Label Momentum Curriculum Learning), that can learn to produce confident pseudo labels for both partially-labeled and unlabeled training images. The novel momentum-based law updates soft pseudo labels on each training image with the consideration of the updating velocity of pseudo labels, which help avoid trapping to low-confidence local minimum, especially at the early stage of training in lack of both observed labels and confidence on pseudo labels. In addition, we present a confidence-aware scheduler to adaptively perform easy-to-hard learning for different labels. Extensive experiments demonstrate that our proposed PLMCL outperforms many state-of-the-art multi-label classification methods under various partial-label settings on three different datasets.
Fool me once: A systematic review of techniques to authenticate digital artefacts

When conducting digital forensic investigations, practitioners are concerned with understanding whether the digital artefacts they encounter are authentic and have not been the subject of tampering activity. This is one factor of investigations which could potentially impact of the reliability of any subsequent findings. Some research into this problem has already been undertaken, however there is currently very little understanding of how effective current technique are. In this paper, a Systematic Review (SR) of existing literature will be undertaken to identify the techniques that currently exist to authenticate digital artefacts. Furthermore, consideration will be given to understanding whether existing techniques are effective in solving the problem of digital artefact authentication and whether they are accessible by the practitioner community. The results of the SR will show that while research effort has been devoted to this problem, there are relatively few techniques which can be generally applied. Additionally, very little effort has been devoted to understanding the effectiveness of these techniques. Furthermore, the lack of standardised datasets for evaluation makes comparison between techniques impossible and none of the identified papers provided publicly available implementations. The shortcomings identified in this SR show that further research effort in this area could benefit the community in its aim to produce more reliable findings in forensic investigations.
Optical-cryptographic scheme based on an image self-disordering algorithm

This paper demonstrates a novel optical-cryptographic system based on a new image self-disordering algorithm (ISDA). The cryptographic stage is based on an iterative procedure using an ordering sequence from the input data to produce diffusion and confusion keys. Our system uses this approach over plaintext and optical cipher from a 2f-coherent processor working with two random phase masks. Since the keys used for encryption depend on the initial input information, the system is resistant to common attacks such as the chosen-plaintext attack (CPA) and the known-plaintext attack (KPA). In addition, since the ISDA operates the optical cipher, the 2f processor linearity is destroyed, producing an enhanced ciphertext in phase and amplitude, improving optical encryption protection. This new approach offers higher security and efficiency than other reported systems. We perform security analyses and validate the feasibility of this proposal by synthesizing an experimental keystream and performing color image encryption.
Overexposure Mask Fusion: Generalizable Reverse ISP Multi-step Refinement

With the advent of deep learning methods replacing the ISP in transforming sensor RAW readings into RGB images, numerous methodologies solidified into real-life applications. Equally potent is the task of inverting this process which will have applications in enhancing computational photography tasks that are conducted in the RAW domain, addressing lack of available RAW data while reaping from the benefits of performing tasks directly on sensor readings. This paper's proposed methodology is a state-of-the-art solution to the task of RAW reconstruction, and the multi-step refinement process integrating an overexposure mask is novel in three ways: instead of from RGB to bayer, the pipeline trains from RGB to demosaiced RAW allowing use of perceptual loss functions; the multi-step processes has greatly enhanced the performance of the baseline U-Net from start to end; the pipeline is a generalizable process of refinement that can enhance other high performance methodologies that support end-to-end learning.
Sliding Window Recurrent Network for Efficient Video Super-Resolution

Video super-resolution (VSR) is the task of restoring high-resolution frames from a sequence of low-resolution inputs. Different from single image super-resolution, VSR can utilize inter-frames' temporal information to reconstruct results with more details. Recently, with the rapid development of convolution neural networks (CNN), the VSR task has drawn increasing attention and many CNN-based methods have achieved remarkable results. However, only a few VSR approaches can be applied to real-world mobile devices due to the computational resources and runtime limitations. In this paper, we propose a Sliding Window based Recurrent Network (SWRN) which can be real-time inference while still achieving superior performance. Specifically, we notice that video frames should have both spatial and temporal relations that can help to recover details, and the key point is how to extract and aggregate these information together. Address it, we input three neighboring frames and utilize a hidden state to recurrently store and update the important temporal information. Our experiment on REDS dataset shows that the proposed method can be well adapted to mobile devices and produce visually pleasant results.
Unifying Conditional and Unconditional Semantic Image Synthesis with OCO-GAN

Generative image models have been extensively studied in recent years. In the unconditional setting, they model the marginal distribution from unlabelled images. To allow for more control, image synthesis can be conditioned on semantic segmentation maps that instruct the generator the position of objects in the image. While these two tasks are intimately related, they are generally studied in isolation. We propose OCO-GAN, for Optionally COnditioned GAN, which addresses both tasks in a unified manner, with a shared image synthesis network that can be conditioned either on semantic maps or directly on latents. Trained adversarially in an end-to-end approach with a shared discriminator, we are able to leverage the synergy between both tasks. We experiment with Cityscapes, COCO-Stuff, ADE20K datasets in a limited data, semi-supervised and full data regime and obtain excellent performance, improving over existing hybrid models that can generate both with and without conditioning in all settings. Moreover, our results are competitive or better than state-of-the art specialised unconditional and conditional models.
DSR: Towards Drone Image Super-Resolution

Despite achieving remarkable progress in recent years, single-image super-resolution methods are developed with several limitations. Specifically, they are trained on fixed content domains with certain degradations (whether synthetic or real). The priors they learn are prone to overfitting the training configuration. Therefore, the generalization to novel domains such as drone top view data, and across altitudes, is currently unknown. Nonetheless, pairing drones with proper image super-resolution is of great value. It would enable drones to fly higher covering larger fields of view, while maintaining a high image quality. To answer these questions and pave the way towards drone image super-resolution, we explore this application with particular focus on the single-image case. We propose a novel drone image dataset, with scenes captured at low and high resolutions, and across a span of altitudes. Our results show that off-the-shelf state-of-the-art networks witness a significant drop in performance on this different domain. We additionally show that simple fine-tuning, and incorporating altitude awareness into the network's architecture, both improve the reconstruction performance (Our code and data are available at https://github.com/IVRL/DSR ).
CEN-HDR: Computationally Efficient Neural Network for Real-Time High Dynamic Range Imaging

High dynamic range (HDR) imaging is still a challenging task in modern digital photography. Recent research proposes solutions that provide high-quality acquisition but at the cost of a very large number of operations and a slow inference time that prevent the implementation of these solutions on lightweight real-time systems. In this paper, we propose CEN-HDR, a new computationally efficient neural network by providing a novel architecture based on a light attention mechanism and sub-pixel convolution operations for real-time HDR imaging. We also provide an efficient training scheme by applying network compression using knowledge distillation. We performed extensive qualitative and quantitative comparisons to show that our approach produces competitive results in image quality while being faster than state-of-the-art solutions, allowing it to be practically deployed under real-time constraints. Experimental results show our method obtains a score of 43.04 $$\mu $$ -PSNR on the Kalantari2017 dataset with a framerate of 33 FPS using a Macbook M1 NPU. The proposed network will be available at https://github.com/steven-tel/CEN-HDR
RCBSR: Re-parameterization Convolution Block for Super-Resolution

Super resolution(SR) with high efficiency and low power consumption is highly demanded in the actual application scenes. In this paper, We designed a super light-weight SR network with strong feature expression. The network we proposed is named RCBSR. Based on the novel technique of re-parameterization, we adopt a block with multiple paths structure in the training stage and merge multiple paths structure into one single 3 $$\times $$ 3 convolution in the inference stage. And then the neural architecture search(NAS) method is adopted to determine amounts of block M and amounts of channel C. Finally, the proposed SR network achieves a fairly good result of PSNR(27.52 dB) with power consumption(0.1 W@30 fps) on the MediaTek Dimensity 9000 platform in the challenge testing stage.
Light Field Angular Super-Resolution via Dense Correspondence Field Reconstruction

Light field (LF) angular super-resolution (SR) aims at reconstructing a densely sampled LF from a sparsely sampled one. To achieve accurate angular SR, it is important but challenging to incorporate the complementary information among input views, especially when dealing with large disparities. In this paper, we propose to reconstruct dense correspondence field among different views for LF angular SR. According to the LF geometry structure, we first capture correspondences along the horizontal and vertical axes of input views with a global receptive field. We then incorporate the linear structure prior among angular viewpoints to reconstruct a dense correspondence field. With the reconstructed dense correspondence field, the relationship between each target view and the input views is constructed. Next, we develop a view projection approach to project input views to the target positions. Moreover, a projection loss is introduced to preserve the LF parallax structure. Extensive experiments demonstrate that our proposed network can recover accurate details and preserve LF parallax structure. Comparative results show the advantage of our method over state-of-the-art methods on synthetic and real-world datasets.