Reviewerly/exHarmony huggingface dataset:
A Novel Multiview Predictive Local Adversarial Network for Partial Transfer Learning in Cross-Domain Fault Diagnostics

In recent years, transfer learning technology has been widely developed in the field of fault diagnosis and detection. At present, most transfer learning methods are based on the assumption that the source domain and the target domain share the same label space. However, in the industrial production process, it is more common that the label space of the target domain is a subset of the label space of the source domain, which is usually called partial transfer learning. In this article, a new multiview predictive local adversarial network (MPLAN) is proposed to solve this problem. The proposed method aligns the source domain and the target domain by local adversarial training where the features of normal states in the source domain and target domain are used to implement adversarial training to learn domain-invariant feature representations and avoid negative transfer caused by outlier classes. Moreover, two classifiers, which have different viewpoints, are used to predict the same sample in order to map the raw data to a better feature space. Weights of two classifiers are made a constraint in order to guarantee that two classifiers own different viewpoints. In order to verify the effectiveness of the proposed method, two datasets, which contain one 1-D sample dataset and one multidimensional difficult dataset, are used to design experiments. Based on the experimental results on two datasets, the proposed MPLAN method can improve the baseline by about 10%.
Geometrically Robust Digital Image Watermarking Based on Zernike Moments and FAST Technique

Geometrically robust digital image watermarking scheme based on Zernike moments and Features from Accelerated Segment Test (FAST) technique is presented in this paper. FAST technique detect the geometrically robust feature points from the image and circular patches centered around these detected features are used to embed the given watermark data sequence. Zernike transform calculate the Zernike moment magnitudes (ZMMs) of extracted circular patches and these are modified by embedding watermark sequence. Circular patches are reconstructed by utilizing modified ZMMs and these replaces the original patches. Robustness of proposed scheme is tested by performing various operations and attacks on watermarked image. Experimental results show the proposed method performs well under various common signal processing geometrical attacks.
Systematic Prevention of On-Core Timing Channels by Full Temporal Partitioning

Microarchitectural timing channels enable unwanted information flow across security boundaries, violating fundamental security assumptions. They leverage timing variations of several state-holding microarchitectural components and have been demonstrated across instruction set architectures and hardware implementations. Analogously to memory protection, (Ge et al. 2019) have proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time protection</i> for preventing information leakage via timing channels. They also showed that time protection calls for hardware support. This work leverages the open and extensible RISC-V instruction set architecture (ISA) to introduce the temporal fence instruction <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fence.t</monospace> , which provides the required mechanisms by clearing vulnerable microarchitectural state and guaranteeing a history-independent context-switch latency. We propose and discuss three different implementations of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fence.t</monospace> and implement them on an experimental version of the seL4 microkernel (Klein et al. 2014) and CVA6, an open-source, in-order, application class, 64-bit RISC-V core (Zaruba and Benini 2019). We find that a complete, systematic, ISA-supported erasure of all non-architectural core components is the most effective implementation while featuring a low implementation effort, a minimal performance overhead of less than 1%, and negligible hardware costs.
Competency Assessment for Autonomous Agents using Deep Generative Models

For autonomous agents to act as trustworthy partners to human users, they must be able to reliably communicate their competency for the tasks they are asked to perform. Towards this objective, we develop probabilistic world models based on deep generative modelling that allow for the simulation of agent trajectories and accurate calculation of tasking outcome probabilities. By combining the strengths of conditional variational autoencoders with recurrent neural networks, the deep generative world model can probabilistically forecast trajectories over long horizons to task completion. We show how these forecasted trajectories can be used to calculate outcome probability distributions, which enable the precise assessment of agent competency for specific tasks and initial settings.
Generator Knows What Discriminator Should Learn in Unconditional GANs

Recent methods for conditional image generation benefit from dense supervision such as segmentation label maps to achieve high-fidelity. However, it is rarely explored to employ dense supervision for unconditional image generation. Here we explore the efficacy of dense supervision in unconditional generation and find generator feature maps can be an alternative of cost-expensive semantic label maps. From our empirical evidences, we propose a new generator-guided discriminator regularization (GGDR) in which the generator feature maps supervise the discriminator to have rich semantic representations in unconditional generation. In specific, we employ an U-Net architecture for discriminator, which is trained to predict the generator feature maps given fake images as inputs. Extensive experiments on mulitple datasets show that our GGDR consistently improves the performance of baseline methods in terms of quantitative and qualitative aspects. Code is available at https://github.com/naver-ai/GGDR .
Chatbot for training and assisting operators in inspecting containers in seaports

The paper presents the chatbot applicability for the health and safety of workers in the container transportation context. Starting from a literature review of risks and hazardous activities in sea container terminals, the paper underlines the need of innovative systems to ensure the lowest level of risks for labours. An analysis of the 4.0 technologies solutions in sea container terminals shows the lack of empirical application of chatbots in such a context. Focus is given to the current chatbot applications, and on the conceptual methodology for the chatbot design, defining five models and presenting a taxonomy for the chatbot feature definition. A case study shows the possible application of the conceptual methodology and the taxonomy, introducing the Popeye chatbot, consisting of a voice service, spoken language understanding component and an image processing app, to cope with the hazards in the process of examining freight and containers in dock areas. The main application of Popeye is the training of new employees involved in container safety-critical quality inspection and controls operations.
AutoLoss-Zero: Searching Loss Functions from Scratch for Generic Tasks

Significant progress has been achieved in automating the design of various components in deep networks. However, the automatic design of loss functions for generic tasks with various evaluation metrics remains under-investigated. Previous works on handcrafting loss functions heavily rely on human expertise, which limits their extensibility. Meanwhile, searching for loss functions is nontrivial due to the vast search space. Existing efforts mainly tackle the issue by employing task-specific heuristics on specific tasks and particular metrics. Such work cannot be extended to other tasks without arduous human effort. In this paper, we propose AutoLoss-Zero, which is a general framework for searching loss functions from scratch for generic tasks. Specifically, we design an elementary search space composed only of primitive mathematical operators to accommodate the heterogeneous tasks and evaluation metrics. A variant of the evolutionary algorithm is employed to discover loss functions in the elementary search space. A loss-rejection protocol and a gradient-equivalence-check strategy are developed so as to improve the search efficiency, which are applicable to generic tasks. Extensive experiments on various computer vision tasks demonstrate that our searched loss functions are on par with or superior to existing loss functions, which generalize well to different datasets and networks. Code shall be released.
Multimodal Sentiment Recognition With Multi-Task Learning

Sentiment recognition in social network aims at recognizing the underlying affective states of user-generated content. The research center of sentiment recognition is moving from pure texts to multimodal contents, with the explosive growth of social platforms. Different from the image-text contents on blog and review platforms, multimodal sequences play a dominant role in streaming media, e.g. YouTube and TikTok. Sentiment recognition for multimodal sequences needs to extract the common and specific information of modalities. But current studies only focus on learning the cross-modal fusion representation to explore the inter-modal interaction, while neglecting the interactions and characteristics within each modality. We propose a novel cascade and specific scoring model, which aims at learning better cross-modal and unimodal representations to capture both the inter- and intra-modal interactions for sentiment recognition. Qualitative and quantitative experiments on two benchmarks have demonstrated the competitive performances of the proposed methods.
Multimodal Private Signatures

We introduce Multimodal Private Signature (MPS) - an anonymous signature system that offers a novel accountability feature: it allows a designated opening authority to learn some partial information $$\textsf{op}$$ about the signer's identity $$\textsf{id}$$ , and nothing beyond. Such partial information can flexibly be defined as $$\textsf{op} = \textsf{id}$$ (as in group signatures), or as $$\textsf{op} = \textbf{0}$$ (like in ring signatures), or more generally, as $$\textsf{op} = G_j(\textsf{id})$$ , where $$G_j(\cdot )$$ is a certain disclosing function. Importantly, the value of $$\textsf{op}$$ is known in advance by the signer, and hence, the latter can decide whether she/he wants to disclose that piece of information. The concept of MPS significantly generalizes the notion of tracing in traditional anonymity-oriented signature primitives, and can enable various new and appealing privacy-preserving applications. We formalize the definitions and security requirements for MPS. We next present a generic construction to demonstrate the feasibility of designing MPS in a modular manner and from commonly used cryptographic building blocks (ordinary signatures, public-key encryption and NIZKs). We also provide an efficient construction in the standard model based on pairings, and a lattice-based construction in the random oracle model.
Robust Chaos With Novel 4-Transistor Maps

This brief presents four discrete-time chaotic map circuits, including three new map circuits and one that has been previously reported. The designs are hardware efficient as they each contain only four Metal Oxide Semiconductor (MOS) transistors. They offer robust chaotic performance with wide chaotic space and uniform entropic properties. The wide chaotic region is essential for applications where parametric perturbation can push the system out of the desirable chaotic region. The chaotic performance is analyzed using a bifurcation plot, Lyapunov exponent, correlation coefficient, sample entropy, and Shannon entropy, and improvement over previous transistor-level designs is demonstrated. The proposed method can be useful in applications such as random number generation, reconfigurable and flexible computing, side-channel attack mitigation, logic obfuscation, and chaos-based cryptography. A chaotic oscillator design is proposed and its application in a chaos-based reconfigurable logic gate is presented, as well.
Pointly-Supervised Panoptic Segmentation

In this paper, we propose a new approach to applying point-level annotations for weakly-supervised panoptic segmentation. Instead of the dense pixel-level labels used by fully supervised methods, point-level labels only provide a single point for each target as supervision, significantly reducing the annotation burden. We formulate the problem in an end-to-end framework by simultaneously generating panoptic pseudo-masks from point-level labels and learning from them. To tackle the core challenge, i.e., panoptic pseudo-mask generation, we propose a principled approach to parsing pixels by minimizing pixel-to-point traversing costs, which model semantic similarity, low-level texture cues, and high-level manifold knowledge to discriminate panoptic targets. We conduct experiments on the Pascal VOC and the MS COCO datasets to demonstrate the approach’s effectiveness and show state-of-the-art performance in the weakly-supervised panoptic segmentation problem. Codes are available at https://github.com/BraveGroup/PSPS.git .
2D vs. 3D LiDAR-based Person Detection on Mobile Robots

Person detection is a crucial task for mobile robots navigating in human-populated environments. LiDAR sensors are promising for this task, thanks to their accurate depth measurements and large field of view. Two types of LiDAR sensors exist: the 2D LiDAR sensors, which scan a single plane, and the 3D LiDAR sensors, which scan multiple planes, thus forming a volume. How do they compare for the task of person detection? To answer this, we conduct a series of exper-iments, using the public, large-scale JackRabbot dataset and the state-of-the-art 2D and 3D LiDAR-based person detectors (DR-SPAAM and CenterPoint respectively). Our experiments include multiple aspects, ranging from the basic performance and speed comparison, to more detailed analysis on localization accuracy and robustness against distance and scene clutter. The insights from these experiments highlight the strengths and weaknesses of 2D and 3D LiDAR sensors as sources for person detection, and are especially valuable for designing mobile robots that will operate in close proximity to surrounding humans (e.g. service or social robot).
A Novel Approach to Generate the Captions for Images with Deep Learning using CNN and LSTM Model

Everyday millions of images are circulated in internet, news, articles, documents, and many of those images are used for advertisement. End users are not interested in every image they come across. We also use internet to find the images but when we search, we will get lots of irrelevant images it will be difficult for us to categorise the images. E-commerce business is the one, which will analyse the images and generates the appropriate attributes for online catalogs. We could save time and cost if we can enable automatic product tagging. In this proposed model we can accomplish this job of image captioning. We do find CCTV everywhere today, It help us to see the world, but along with this if we are able to generate a caption for the images captured in CCTV, We can sense the dangerous activity and can raise the alarm. It will be very helpful if we have a way to automatically create captions for photos. Our proposed method will generate visual captions using the convolutional neural network (CNN) as well as the Long-Term Short-Term Memory (LSTM) model for In-depth Reading method.
A Novel Approach for Predictable Governance of Decentralized Autonomous Organizations Based on Parallel Intelligence

Decentralized autonomous organizations (DAOs) have become an indispensable part of digital infrastructure in recent years. The unique organizational characteristics and functional structure empower them to become an effective tool for solving corporate governance issues, including contract risks, principal-agent dilemmas, etc. However, DAOs themselves also face a variety of governance issues. On one hand, as a new economic organization model, the existing corporate governance theories and methods are no longer fully applicable to DAOs. On the other hand, unpredictable logic vulnerabilities and code loopholes in the governance mechanism might cause devastating damage to DAOs. The parallel intelligence theory based on the ACP method (i.e., artificial systems, computational experiments, and parallel execution) is an elegant research paradigm and a practical approach tailored to solving these challenges. As such, we propose a novel parallel governance framework for DAOs based on the parallel intelligence theory and further discuss its technical methodology and implementation model. Furthermore, we construct a parallel governance system for GnosisDAO and conduct computational experiments to validate the effectiveness of its governance mechanism. The experimental results not only confirm the defects of the GnosisDAO governance mechanism but also illustrate parallel governance as a useful research direction to solve existing governance problems of DAOs.
Custom Structure Preservation in Face Aging

In this work, we propose a novel architecture for face age editing that can produce structural modifications while maintaining relevant details present in the original image. We disentangle the style and content of the input image and propose a new decoder network that adopts a style-based strategy to combine the style and content representations of the input image while conditioning the output on the target age. We go beyond existing aging methods allowing users to adjust the degree of structure preservation in the input image during inference. To this purpose, we introduce a masking mechanism, the CUstom Structure Preservation module, that distinguishes relevant regions in the input image from those that should be discarded. CUSP requires no additional supervision. Finally, our quantitative and qualitative analysis which include a user study, show that our method outperforms prior art and demonstrates the effectiveness of our strategy regarding image editing and adjustable structure preservation.
Copyright Content Moderation in the EU: An Interdisciplinary Mapping Analysis

of the EU on Case C-401/19 -Poland v Parliament and Council (26 April 2022), national implementations of the Copyright in the Digital Single Market Directive (2019/790), as examined by our national experts in a second phase of national questionnaires we ran; the final approval of the Digital Services Act Regulation, resulting from a European Parliament legislative resolution of 5 July 2022.As regards the latter, since at the time of writing this Regulation has not yet been published in the Official Journal of the European Union, we make reference throughout this report to the numbering of provisions as listed in the European Parliament's legislative resolution.The nature of the research described in this report is that of a mapping exercise, on which the subsequent evaluative work in WP6 will be developed, in particular the deliverables D.6.3 (Final Evaluation and Measuring Report -impact of moderation practices and technologies on access and diversity) and D.6.4 (Best Practices and Policy Recommendations Brief).As such, we are cautious to delimit our normative analysis and recommendations to what follows clearly from our mapping analysis.The majority of the research for this report was carried between the 2020 and 2021, with the exception of the aforementioned extensions and updates, which were concluded by July 2022.The empirical research of the report, mostly reflected in Chapter 5, was concluded in 2021.The ideas and arguments that form the basis of this report have been iterated gradually by the authors over the course of several years of scholarly work, including during the period of this research project.Parts of the report are reproduced or adapted from previously published or forthcoming work.We identify these clearly with footnotes in the respective sections and subsections of this report.
L-Verse: Bidirectional Generation Between Image and Text

Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalability. Especially with cross-modal tasks between image and text, vector quantized variational autoencoders (VQ-VAEs) are widely used to make a raw RGB image into a sequence of feature vectors. To better leverage the correlation between image and text, we propose L-Verse, a novel architecture consisting of feature-augmented variational autoencoder (AugVAE) and bidirectional auto-regressive transformer (BiART) for image-to-text and text-to-image generation. Our AugVAE shows the state-of-the-art reconstruction performance on ImageNetlK validation set, along with the robustness to unseen images in the wild. Unlike other models, BiART can distinguish between image (or text) as a conditional reference and a generation target. L-Verse can be directly used for image-to-text or text-to-image generation without any finetuning or extra object detection framework. In quantitative and qualitative experiments, L-Verse shows impressive results against previous methods in both image-to-text and text-to-image generation on MS-COCO Captions. We furthermore assess the scalability of L-Verse architecture on Conceptual Captions and present the initial result of bidirectional vision-language representation learning on general domain.
A Comprehensive Review on Fake Images/Videos Detection Techniques

Now that image creation and manipulation have advanced so quickly, there are serious questions about how this may affect society. At best, this leads to loss of trust in digital content. There are many existing algorithms such as Naive Bayes, CNN, RNN, Robust Hashing, GANs, SVM etc. which are being used for the detection of fake videos. Making and classifying deep fakes using Deep Neural Networks (DNN) nowadays have increased the interest of researchers in this field. Deep Fake is the regenerated media that is attained by edging in or replacing some information within the DNN model. In this work, survey withdrawn by various research groups focused the feasibility loopholes that need to be recovered for deep fakes. The use of above-mentioned techniques has been increased by a significant percentage in video game industries and cinema like enhancing visual stuff in pictures. In this paper, different types of datasets used by authors and various contemporary techniques used for fake image/video detection are described. Finally, various research gaps and the possible future directions are highlighted.
A Flexible Calibration Algorithm for High-speed Bionic Vision System based on Galvanometer

Traditional gimbal-based bionic eye systems usually use a multi-degree-of-freedom mechanical platform to move the camera freely, which makes the structure complex and bulky. The galvanometer-based reflective bionic eye system uses a galvanometer to replace the traditional mechanical rotation structure, which separates the camera from the gimbal system, greatly simplifying the structure. However, there are currently few methods for calibrating such systems, mostly for object detection and tracking. In this paper, a flexible method for high-precision calibration of a galvanometer-based reflective bionic eye system is proposed. In this method, a planar target is used for the calibration of the bionic eye system. The effectiveness and accuracy of the method are evaluated by the reprojection error of the control voltage and the spatial localization of the binocular system. Experiments show that the error of the control voltage after calibration is less than 0.2%. At an indoor distance of about 7 m, the RMSE of spatial visual localization is less than 0.3 cm.