Reviewerly/exHarmony huggingface dataset:
Image segmentation based on scanned document and hand script counterfeit detection using neural network

Biometrics could be considered as couple of behavioral instances signature verification, keystroke activity and physiological activities such as iris, fingerprint. In the past, before utilization of computers, hand script signature is popular as biometric authentication and it has become a common identity for personal verification in all sectors and especially instant verification required to permit bank cheques. Among different sectors most of them uses offline verification. Taking this as an advantage the signatures are being forged for document authentication. Due to this, there is a need for automation to verification of offline signatures. In this work developed a model for signature verification using image segmentation technique for image processing using Convolutional neural networks. This model is utilized to classify the signature as genuine which is leads to the claimed personal or forgery which is created by someone else. For evaluation of the performance of the system, here considered a test set which consists of genuine signatures and forgeries. And hence the results are declared whether the considered signatures are genuine or forged signatures.
AutoML-Emo: Automatic Knowledge Selection Using Congruent Effect for Emotion Identification in Conversations

Emotion recognition in conversations (ERC) has wide applications in medical care, human-computer interaction, and other fields. Unlike the general task of emotion analysis, humans usually rely on context and commonsense knowledge to convey emotions in conversations. Only when the model can connect and fully utilize a large-scale commonsense knowledge base, it can better understand latent contents in conversations. Unfortunately, there is no available knowledge selection mechanism to address such knowledge needs and to make sure the system is not flooded with irrelevant commonsense knowledge. Therefore, we propose an AutoML strategy based on emotion congruent effect to select suitable knowledge and models, called AutoML-Emo. Global exploration and local exploitation-based selection mechanisms (G&LESM) are used for automatic knowledge selection. The transformer-based architecture search (TAS) is applied to model selection, the selected transformer-based model is employed to incorporate knowledge and capture context information in conversations. The experimental results show that AutoML-Emo can effectively enhance external knowledge in different sizes and domain datasets. Moreover, the selected transformer-based model derived from TAS is superior to the most advanced models.
Semi-supervised Anomaly Detection with Reinforcement Learning

Reconstruction-based anomaly detections with convolutional autoencoders (CAEs) have been commonly used for unsupervised anomaly detection. The task of anomaly classification and segmentation is carried out by calculating the error between the reconstructed output and its original input with a pre-determined threshold. However, the process of determining a suitable threshold is a timely process that requires an extra search process with a pre-defined minimum defect area that must be optimized for each type in various classes. Consequently, the resulting detection performance becomes highly biased on the threshold. Therefore, the underlying principle of using a fixed threshold value for a per-pixel anomalous region decision is questionable. To address this issue, we propose a deep reinforcement learning approach to learn an optimal policy that can differentiate between anomalous and normal samples from the given residual map. Empirical experiments on the MVTec anomaly detection dataset demonstrate that the proposed method significantly improves detection performance without changing the residual map and can be further enhanced depending on the input to the policy network model.
NeFSAC: Neurally Filtered Minimal Samples

Since RANSAC, a great deal of research has been devoted to improving both its accuracy and run-time. Still, only a few methods aim at recognizing invalid minimal samples early, before the often expensive model estimation and quality calculation are done. To this end, we propose NeFSAC, an efficient algorithm for neural filtering of motion-inconsistent and poorly-conditioned minimal samples. We train NeFSAC to predict the probability of a minimal sample leading to an accurate relative pose, only based on the pixel coordinates of the image correspondences. Our neural filtering model learns typical motion patterns of samples which lead to unstable poses, and regularities in the possible motions to favour well-conditioned and likely-correct samples. The novel lightweight architecture implements the main invariants of minimal samples for pose estimation, and a novel training scheme addresses the problem of extreme class imbalance. NeFSAC can be plugged into any existing RANSAC-based pipeline. We integrate it into USAC and show that it consistently provides strong speed-ups even under extreme train-test domain gaps – for example, the model trained for the autonomous driving scenario works on PhotoTourism too. We tested NeFSAC on more than 100 k image pairs from three publicly available real-world datasets and found that it leads to one order of magnitude speed-up, while often finding more accurate results than USAC alone. The source code is available at https://github.com/cavalli1234/NeFSAC .
Securing Serverless Computing: Challenges, Solutions, and Opportunities

Serverless computing is a new cloud service model that reduces both cloud providers' and consumers' costs through agile development, operation, and charging mechanisms. It has been widely applied since its emergence. Nevertheless, some characteristics of serverless computing, such as fragmented application boundaries, have raised new security challenges. Considerable literature has been committed to addressing these challenges. Commercial and open-source serverless platforms implement many security measures to enhance serverless environments. This article presents the first survey of serverless security that considers both the literature and industrial security measures. We summarize the primary security challenges, analyze corresponding solutions from the literature and industry, and identify potential research opportunities. Then, we conduct a gap analysis of the academic and industrial solutions, as well as commercial and open-source serverless platforms' security capabilities. Finally, we present a complete picture of current serverless security research.
New Ontology structure for intelligent controlling of traffic signals

This article proposes a novel ontology design for intelligent controlling of traffic signals, considering the investigated factors, crowded factors, road factors, visibility conditions, and emergency situations. Essentially, the proposed method uses video-based knowledge and key feature from a monocular video camera only, capturing footage from either a traffic signals perspective or the top of the road lane. The key factors and entities in the traffic scene are formed into an ontology, which has been evaluated using synthetic datasets to interpret challenging cases. Semantic features related to the key factors in the scene are obtained and fed to the ontology. The experimental results indicate that the proposed method is capable of controlling traffic signals more efficiently than the fixed intervals protocol.
Asymptotically Free Broadcast in Constant Expected Time via Packed VSS

Broadcast is an essential primitive for secure computation. We focus in this paper on optimal resilience (i.e., when the number of corrupted parties t is less than a third of the computing parties n), and with no setup or cryptographic assumptions. While broadcast with worst case t rounds is impossible, it has been shown [Feldman and Micali STOC’88, Katz and Koo CRYPTO’06] how to construct protocols with expected constant number of rounds in the private channel model. However, those constructions have large communication complexity, specifically $${\mathcal {O}}(n^2L+n^6\log n)$$ expected number of bits transmitted for broadcasting a message of length L. This leads to a significant communication blowup in secure computation protocols in this setting. In this paper, we substantially improve the communication complexity of broadcast in constant expected time. Specifically, the expected communication complexity of our protocol is $${\mathcal {O}}(nL+n^4\log n)$$ . For messages of length $$L=\varOmega (n^3 \log n)$$ , our broadcast has no asymptotic overhead (up to expectation), as each party has to send or receive $${\mathcal {O}}(n^3 \log n)$$ bits. We also consider parallel broadcast, where n parties wish to broadcast L bit messages in parallel. Our protocol has no asymptotic overhead for $$L=\varOmega (n^2\log n)$$ , which is a common communication pattern in perfectly secure MPC protocols. For instance, it is common that all parties share their inputs simultaneously at the same round, and verifiable secret sharing protocols require the dealer to broadcast a total of $${\mathcal {O}}(n^2\log n)$$ bits. As an independent interest, our broadcast is achieved by a packed verifiable secret sharing, a new notion that we introduce. We show a protocol that verifies $${\mathcal {O}}(n)$$ secrets simultaneously with the same cost of verifying just a single secret. This improves by a factor of n the state-of-the-art.
Exploring and Evaluating Image Restoration Potential in Dynamic Scenes

In dynamic scenes, images often suffer from dynamic blur due to superposition of motions or low signal-noise ratio resulted from quick shutter speed when avoiding motions. Recovering sharp and clean results from the captured images heavily depends on the ability of restoration methods and the quality of the input. Although existing research on image restoration focuses on developing models for obtaining better restored results, fewer have studied to evaluate how and which input image leads to superior restored quality. In this paper, to better study an image's potential value that can be explored for restoration, we propose a novel concept, referring to image restoration potential (IRP). Specifically, We first establish a dynamic scene imaging dataset containing composite distortions and applied image restoration processes to validate the rationality of the existence to IRP. Based on this dataset, we investigate several properties of IRP and propose a novel deep model to accurately predict IRP values. By gradually distilling and selective fusing the degradation features, the proposed model shows its superiority in IRP prediction. Thanks to the proposed model, we are then able to validate how various image restoration related applications are benefited from IRP prediction. We show the potential usages of IRP as a filtering principle to select valuable frames, an auxiliary guidance to improve restoration models, and also an indicator to optimize camera settings for capturing better images under dynamic scenarios.
Certificate-Based Signcryption Scheme for Securing Wireless Communication in Industrial Internet of Things

The Industrial Internet of Things (IIoT) community is concerned about the security of wireless communications between interconnected industries and autonomous systems.Providing a cyber-security framework for the IIoT offers a thorough comprehension of the whole spectrum of securing interconnected industries, from the edge to the cloud.Several signcryption schemes based on either identity-based or certificateless configurations are available in the literature to address the IIoT's security concerns.Due to the identity-based/certificateless nature of the available signcryption schemes, however, issues such as key escrow and partial private key distribution occur.To address these difficulties, we propose a Certificate-Based Signcryption (CBS) solution for IIoT in this article.Hyperelliptic Curve Cryptosystem (HECC), a light-weight version of Elliptic Curve Cryptosystem (ECC), was employed to construct the proposed scheme, which offers security and cost-efficiency.The HECC utilizes 80-bit keys with fewer parameters than the ECC and Bilinear Pairing (BP).The comparison of performance in terms of computation and communication costs reveals that the proposed scheme provides robust security with minimal communication and communication costs.Moreover, we used Automated Validation of Internet Security Protocols and Applications (AVISPA) to assess the security toughness, and the results show that the proposed scheme is secure.
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images

Detecting and segmenting objects within whole slide images is essential in computational pathology workflow. Self-supervised learning (SSL) is appealing to such annotation-heavy tasks. Despite the extensive benchmarks in natural images for dense tasks, such studies are, unfortunately, absent in current works for pathology. Our paper intends to narrow this gap. We first benchmark representative SSL methods for dense prediction tasks in pathology images. Then, we propose concept contrastive learning (ConCL), an SSL framework for dense pre-training. We explore how ConCL performs with concepts provided by different sources and end up with proposing a simple dependency-free concept generating method that does not rely on external segmentation algorithms or saliency detection models. Extensive experiments demonstrate the superiority of ConCL over previous state-of-the-art SSL methods across different settings. Along our exploration, we distill several important and intriguing components contributing to the success of dense pre-training for pathology images. We hope this work could provide useful data points and encourage the community to conduct ConCL pre-training for problems of interest. Code is available at https://github.com/TencentAILabHealthcare/ConCL .
Developing Programming Assignments for Teaching Quantum Computing and Quantum Programming

This report describes a variety of programming assignments that can be used to teach quantum computing in a practical manner. These assignments let the learners get hands-on experience with all stages of quantum software development process, from solving quantum computing problems and implementing the solutions to debugging the programs, performing resource estimation, and running the code on quantum hardware.
TruVR: Trustworthy Cybersickness Detection using Explainable Machine Learning

Cybersickness can be characterized by nausea, vertigo, headache, eye strain, and other discomforts when using virtual reality (VR) systems. The previously reported machine learning (ML) and deep learning (DL) algorithms for detecting (classification) and predicting (regression) VR cybersickness use black-box models; thus, they lack explainability. Moreover, VR sensors generate a massive amount of data, resulting in complex and large models. Therefore, having inherent explainability in cybersickness detection models can significantly improve the model’s trustworthiness and provide insight into why and how the ML/DL model amved at a specific decision. To address this issue, we present three explainable machine learning (xML) models to detect and predict cybersickness: 1) explainable boosting machine (EBM), 2) decision tree (DT), and 3) logistic regression (LR). We evaluate xML-based models with publicly available physiological and gameplay datasets for cybersickness. The results show that the EBM can detect cybersickness with an accuracy of 99.75% and 94.10% for the physiological and gameplay datasets, respectively. On the other hand, while predicting the cybersickness, EBM resulted in a Root Mean Square Error (RMSE) of 0.071 for the physiological dataset and 0.27 for the gameplay dataset. Furthermore, the EBM-based global explanation reveals exposure length, rotation, and acceleration as key features causing cybersickness in the gameplay dataset. In contrast, galvanic skin responses and heart rate are most significant in the physiological dataset. Our results also suggest that EBM-based local explanation can identify cybersickness-causing factors for individual samples. We believe the proposed xML-based cybersickness detection method can help future researchers understand, analyze, and design simpler cybersickness detection and reduction models.
Information Security using Chaotic Encryption and Decryption of Digital Images

Interestingly, with text-information, images are generally bigger in size and have high relationship among pixels which grant information misfortune. Because of this particular nature of images customary codes, for example, Data Encryption Standard and Advanced Encryption Standard are inadequately appropriate for picture encryption. The turbulent dynamical frameworks hold amazing properties like ergodicity and awareness towards starting condition. Cell automata show captivating properties like complex arbitrary way of behaving and unconventionality from basic guidelines. Henceforth, the association of cell automata and bedlam based picture encryption configuration is a decent answer for picture security issues. The subsequent exploration work presents an effective novel numerous variety picture encryption calculation in view of rudimentary cell automata rule-30 and two-layered Logistic guide. Right off the bat, the information images are joined into an enormous picture and afterward sectioned into numerous little and equivalent estimated unadulterated picture components. Besides, utilizing the rudimentary cell automata rule-30, these unadulterated picture components are permuted to get blended picture components. Thirdly, second-level stage is attempted on the blended picture components by applying crisscross example filtering. Fourthly, pixel values are subbed by utilizing the roundabout shift technique; consequently, second-level pixel replacement is acknowledged by utilizing turbulent arbitrary successions from the two-layered Logistic guide. At long last, the enormous scrambled picture is divided into more modest encoded images.
Detection of River Floating Debris in UAV Images Based on Improved YOLOv5

Rivers are an essential part of the aquatic environment. The accurate detection and timely cleaning of river floating debris plays a vital role in the landscape and aquatic ecological environment. At present, the detection of river floating debris mainly relies on manually patrolling rivers and fixed camera monitoring, which has the problem of low efficiency and high cost. In this paper, we used Unmanned Aerial Vehicle (UAV) to photograph rivers and then utilized deep learning algorithms to detect floating debris in the images to improve the efficiency of river regulation. However, its application faces some problems, including the lack of datasets, the complex background of UAV images, and the small and sparsely distributed objects. To address the above issues, we used a UAV to capture river images and labeled the floating debris to construct a dataset. Moreover, to enhance the detection capability of river floating debris, we proposed an improved algorithm based on YOLOv5s. Firstly, the algorithm adds a microscale detection layer in the detection phase to improve small targets' detection. Secondly, it introduces an improved CBAM in the feature fusion phase to suppress the effects of useless and complex background information. Finally, in the loss function, a weighting factor is added to objectness loss to raise the loss weight of positive samples to ameliorate where negative samples loss much more than positive samples. The experimental results illustrated that compared to the baseline, our method has superior precision, recall, and mAP@0.5, reaching 87.4%, 85.6%, and 91.8%, respectively. The proposed method can accurately detect river floating debris in UAV images and provide technical support for river regulation.
Blockchain-based Accountable Auditing with Multi-Ownership Transfer

Cloud auditing enables the integrity verification of cloud data without the necessity of data retrieval, which significantly promotes the storage service of cloud computing. Auditing with ownership transfer is a variation where both cloud data and the tags for integrity verification can be transferred. In some scenarios, like joint-stock enterprise acquisition and electronic medical records migration, we argue that auditing and transferring data belonging to multiple owners are significantly important. However, to the best of our knowledge, there exists no such protocol in multi-ownership scenarios in the literature. In this paper, we propose a blockchain-based accountable auditing protocol with multi-ownership transfer for the first time. One distinguishable property is the simultaneous achievement of verifiability, accountability and multi-ownership transferability, merely with very little extra cost. Specifically, we construct a novel tag structure based on homomorphic authenticators and compact multi-signatures, enabling integrity verification and multi-ownership transfer. Subsequently, we record the information concerning data generation and ownership transfer on immutable blockchains to make these procedures accountable. Furthermore, we present a comprehensive analysis and extensive experiments to demonstrate the security and efficiency of the proposed protocol.
“This Is My Unicorn, Fluffy”: Personalizing Frozen Vision-Language Representations

Large Vision & Language models pretrained on web-scale data provide representations that are invaluable for numerous V &L problems. However, it is unclear how they can be extended to reason about user-specific visual concepts in unstructured language. This problem arises in multiple domains, from personalized image retrieval to personalized interaction with smart devices. We introduce a new learning setup called Personalized Vision & Language (PerVL) with two new benchmark datasets for retrieving and segmenting user-specific (“personalized”) concepts “in the wild”. In PerVL, one should learn personalized concepts (1) independently of the downstream task (2) allowing a pretrained model to reason about them with free language, and (3) without providing personalized negative examples. We propose an architecture for solving PerVL that operates by expanding the input vocabulary of a pretrained model with new word embeddings for the personalized concepts. The model can then simply employ them as part of a sentence. We demonstrate that our approach learns personalized visual concepts from a few examples and effectively applies them in image retrieval and semantic segmentation using rich textual queries. For example the model improves MRR by 51.1% (28.4% vs 18.8%) compared to the strongest baseline. The code and benchmark are available on github under NVlabs/PALAVRA ( https://github.com/NVlabs/PALAVRA ) and NVlabs/PerVLBenchmark ( https://github.com/NVlabs/PerVLBenchmark ).
OPD: Single-View 3D Openable Part Detection

We address the task of predicting what parts of an object can open and how they move when they do so. The input is a single image of an object, and as output we detect what parts of the object can open, and the motion parameters describing the articulation of each openable part. To tackle this task, we create two datasets of 3D objects: OPDSynth based on synthetic objects, and OPDReal based on RGBD reconstructions of real objects. We then design OpdRcnn, a neural architecture that detects openable parts and predicts their motion parameters. Our experiments show that this is a challenging task especially when considering generalization across object categories, and the limited amount of information in a single image. Our architecture outperforms baselines and prior work especially for RGB image inputs.