Reviewerly/exHarmony huggingface dataset:
ATSSIA: Asynchronous Truly-Threshold Schnorr Signing for Inconsistent Availability

Threshold signature schemes allow any qualified subset of participants (t-out-of-n) to combine its shares and generate a signature that can be verified using a single threshold public key. While there are several existing threshold signature schemes, most are either n-out-of-n and/or require consistent availability of the exact same set of participants through several rounds. This can result in signer availability becoming a bottleneck in the signing process. Our threshold signature scheme removes this dependence by introducing truly threshold asynchronous signatures, i.e., once the message to be signed has been revealed, the signers simply sign and broadcast their signature. Our scheme also uses misbehaviour detection to impose accountability for invalid signing. We prove that our scheme is safe against known distributed attacks and is $$EUF-CMA$$ secure in the Random Oracle Model for up to $$t-1$$ malicious participants.
New General Framework for Algebraic Degree Evaluation of NFSR-Based Cryptosystems

At CRYPTO 2017, Liu presented a general framework of iterative estimation of algebraic degree for NFSR-based cryptosystems, by exploiting a technique, called numeric mapping, and gave distinguishing attacks on Trivium-like ciphers, including Trivium, Kreyvium and TriviA-SC. This paper aims at further investigating algebraic degree estimation of NFSR-based cryptosystems from a new perspective. A new general framework for algebraic degree estimation of NFSR-based cryptosystems is formalized to exploit a new way of constructing distinguishing attacks. This illustrates that our new framework is more accurate than Liu’s when estimating the upper bound on algebraic degree of NFSR-based cryptosystems. As result, the best known attack on the full simplified variant of TriviA-SC v2 is presented.
IBM Watson Studio for Building an Automated Essay Grading System

Abstract: Essays are one of the most important methods for assessing learning and intelligence of a student. Manual essay grading is a time-consuming process for the evaluator, a solution to such problem is to make evaluation through computers. Aim of this paper is to understand and analyze current essay grading systems and compare them primarily focusing on technique used, performance and focused attributes.
An Hour-Glass CNN for Language Identification of Indic Texts in Digital Images

Understanding multi-lingual texts in any digital image calls for identifying the corresponding languages of the localized texts. India houses a multi-lingual ambience which necessitates the pursuit of an efficient model that is robust against various complexities and successfully identifies the language of Indic texts. This paper presents a deep learning based Convolutional Neural Network (CNN) model having an hour-glass like structure, for classifying texts in popular Indic languages like Bangla, English and Hindi. A new dataset, called Indic Texts in Digital Images (ITDI), is also presented which is a collection of text images, both scene and born-digital, written in Bangla, English and Hindi. The performance of the hour-glass CNN is evaluated upon standard Indic dataset like AUTNT giving an accuracy of 90.93% which is higher than most state-of-the-art models. The proposed model is also used to benchmark the performance on ITDI dataset with a reasonable accuracy of 85.18%. Sample instances of the proposed ITDI dataset can be found at: https://github.com/NCJUCSE/ITDI
Exploring the Role of Adversarial Attacks in Image Anti-forensics

Deep learning (DL) has grown significantly in the field of image forensics. A lot of research has been going on to develop deep learning based image manipulation detection techniques. On the contrary, researchers are also challenging the robustness of these DL-based image forensic techniques by developing efficient anti-forensic schemes. This paper reveals the role of adversarial attacks in image anti-forensics with better human imperceptibility against recent general-purpose image forensic techniques. We propose an image anti-forensic framework by using recent adversarial attacks, i.e., Fast Gradient Sign Method (FGSM), Carlini and Wagner (C &W), and Projected Gradient Descent (PGD). Firstly, we have trained recent image forensic models on the BOSSBase dataset. Then, we generate adversarial noise by using the gradient of these image forensic models corresponding to each adversarial attack. Afterward, the obtained noise is added to the input image, resulting in the adversarial image corresponding to the particular attack. These adversarial images are generated by using the BOSSBase dataset and tested on the recent image forensic models. The experimental results show that the performance of the recent forensic models has decreased rapidly in the range from 50–75% against the different adversarial attacks, i.e., FGSM, C &W, and PGD. Furthermore, the high human imperceptibility of generated adversarial images is confirmed from the PSNR values.
Detecting Document Forgery Using Hyperspectral Imaging and Machine Learning

Forgery is the process of fabricating, transforming or imitating writings, objects, or documents. It is a white-collar crime. Investigating forged cheques, wills or modified documents frequently involves analysing the inks used in these write-ups. Hyperspectral imaging can be used to identify various types of materials. This technology paired with powerful classifiers can be implemented to identify the various types of inks used in a document. This study leveraged the UWA Writing Ink Hyperspectral Images database (WIHSI) to carry forth ink detection by applying three different dimension reduction algorithms namely: Principal Component Analysis, Factor Analysis, and Independent Component Analysis. After which, a comparative study was carried forth between different processes applied in this study and existing methods. In essence, this work aims to integrate the use of hyperspectral imagery with machine learning and dimension reduction to detect document forgery.
Human Action Recognition in Still Images

In this research work, we are addressing the problem of action recognition in still images, in which the model focuses on recognizing the person's action from a single image. We are using the dataset published by V. Jacquot, Z. Ying, and G. Kreiman in CVPR 2020. The dataset consists of the 3 action classes: Drinking, Reading, and Sitting. The images are not classified into these 3 classes. Instead, binary image classification is used on each class i.e., whether the person is performing that particular action or not. To classify the images, we started with the Detectron2 Object detection model for detecting the person performing the activity and the object related to it (foreground) and then we remove everything else (background) from the image. And then, these images without the background are used for the classification task. The classification is done by using various deep learning models with the help of transfer learning. And as a result, the classification accuracy of HAR in still images increases by 10% on VGG16, 7% on InceptionV3, 1% on Xception, and 4% on the Inception-Resnet model.
Pose Guided Controllable Gesture to Gesture Translation

Gesture-to-gesture (G2G) translation is a very difficult task due to various poses, size of hands, location of fingers and other important factors. This task serves various applications, such as gesture synthesis, gesture recognition, etc. We propose a pose guided controllable Generative Adversarial Network (GAN). We have taken into consideration the structural similarity based gesture synthesis for conditional image to image translation. Along with this, controllable structures are also used to generate image. The proposed model learns the translational mapping from a combination of losses which are used for training the proposed GAN. The generated images are evaluated based on Peak Signal-to-Noise Ratio (PSNR), Inception Score (IS) and Frechet Inception Distance (FID) on NTU hand digit dataset and Senz3D dataset.
Introspecting Local Binary Feature Vectors for Classification of Radiographic Weld Images

With the advent of technology and industrialization, welding has emerged as a potential tool. The process of welding gives a permanent joint to the material which is joined together, but it also affects the properties of the constituents. Any kind of deficiency in the welding process gives rise to weld defects. There should be a mechanism for accurate inspection of welded materials in order to maintain the superiority of design of the welded material and smooth operation. A proper welded material assures proper protection and trustworthiness. Non – Destructive Inspection is one of the significant features for accurate identification of the weld defects. Nowadays, this technique is extensively used for weld flaw detection as it doesn’t alter with the property of the welded objects. As computer technology has paved its way in all domain of engineering, the current research work focusses on discovery of a scientific solution for exact identification and classification of imperfections in welding. In the present work, the image database has been created from Welding research laboratory, Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee and consists of 79 radiographic weld images with 8 types of flaws and one without flaws. The present work explores the various local binary pattern feature vectors for the classification of flaws in radiographic weld images. It gives a concise description of the various existing feature extraction techniques for the proposed database.
Efficient Approximation of Curve-Shaped Objects in $${\mathbb Z}^2$$ Based on the Maximum Difference Between Discrete Curvature Values

In this paper, we propose a novel algorithm for the cubic approximation of digital curves and curve-shaped objects in $$\mathbb {Z}^{2}$$ . At first, the discrete curvature value is computed for each point of the given digital curve, C, using the improved k-curvature estimation technique. Based on the estimated k-curvature value, the points are selected from C to obtain the resultant set of reduced points, $$C^{'}$$ . We use a set of cubic B-splines for the approximation of the given digital curve C. For the selection of control points, our algorithm works on a new parameter, threshold, defined as the maximum difference between discrete curvature values, based on which the control points are selected from the given digital curve, C, such that the maximum discrete curvature difference from the last selected point and the next point to be selected do not exceed the threshold. Further adjustments are made in the selection of control points based on the principle that high curvature areas of a digital curve represent more information whereas low curvature areas represent less information. Experimental results and comparisons with the existing algorithm on various digital objects demonstrate our approach’s effectiveness. It has been observed that our algorithm generates better output for approximating real-world curves in which there are large number of control points, and the rate of curvature change is fast. Our algorithm also takes less computational time since it selects the control points of a digital curve in a single iteration.
Deep Two-Stage LiDAR Depth Completion

LiDAR depth completion aims at accurately estimating dense depth maps from sparse and noisy LiDAR depth scans, often with the aid of the color image. However, most of the existing deep learning-based LiDAR depth completion approaches focus on learning one-stage networks with computationally intensive RGB-D fusion strategies to compensate for the prediction errors. To eliminate such drawbacks, we have explored a simple yet effective two-stage learning framework where the former stage generates a coarse dense output which is processed in the latter stage to produce a fine dense depth map. The refined dense depth map is obtained at the output of the second stage by employing iterative feedback mechanism that removes any ambiguity associated with a single feed-forward network. Our two-stage learning mechanism allows for simple RGB-D fusion operations devoid of high computational overload. Experiments conducted on the KITTI depth completion benchmark validate the efficacy of our proposed method.
Elderly Patient Fall Detection Using Video Surveillance

The important goal of this work is to design a system for elderly people who are at higher risk of falling due to some illness. We aim to do this without burdening them with the different number of devices around them including wear-able devices. We aim to continuously monitor them in a closed environment for any kind of falls or injuries that might occur to them. This paper aims to achieve this with the help of only video surveillance [13, 16]. We also aim to propose a method where the model is very cost-friendly and easy to implement in any closed environment. Our model removes any use of wearable sensors and proposes an approach where we use inputs from only RGB camera-based sensors and then uses different computer vision approaches [2, 6] to decide if the person has fallen or not. We are using a two-stream network to attain more accuracy. Estimating the motion of a person on regular basis is the main step in the proposed method. There are two most popular publically available methods for motion detection namely Optical Flow (OF) [26] and Motion History Image (MHI) [17]. In the proposed method, we are using OF for the motion estimation. With the help of OF [26] and VGG16 [25] we have proposed a method for elderly patient monitoring system using live camera to detect a falling person. Despite not using multiple wearable and non-wearable sensors our proposed method outperforms the existing fall detection models. We have worked on the publically available UR-Fall Dataset [10].
(MS)2EDNet: Multiscale Motion Saliency Deep Network for Moving Object Detection

Foreground segmentation in videos is a perplexing task. Infrequent motion of objects, illumination, shadow, camouflage, etc. are major factors which degrades the quality of segmentation. Usage of visual features like color, texture or shape, deficiencies the acquaintance of semantic evidence for foreground segmentation. In this paper, a novel compact multiscale motion saliency encoder-decoder learning network, ((MS)2EDNet) is presented for moving object detection (MOD). Initially, the lengthy streaming video is split into several small video streams (SVS). The background for each SVS is estimated using proposed network. Further, the saliency map is estimated via the input frames and estimated background for each SVS. Further, a compact multiscale encoder–decoder network (MSEDNet) is presented to extract the multiscale foregrounds from saliency maps. The extracted multiscale foregrounds are integrated to estimate the final foreground of the video frame. The effectiveness of the proposed (MS)2EDNet is estimated on three standard datasets (CDnet-2014 [1], and Wallflower [3]) for MOD. The compactness of the (MS)2EDNet is analyzed based on computational complexity and compared with the present approaches. Experimental study shows that proposed network outpaces the present state-of-the-art approaches on three standard datasets for MOD in terms of both detection accuracy and computational complexity.
Towards Intelligent Security- and Process-Aware Information Systems

Abstract This chapter summarizes the work and explains its significance by considering the contributions and its applicability. Because of its practical setting, the approach is applicable to a range of practical applications. For example, it could recommend who shall perform which tasks in a so-called break-glass situation, or act as a delegation assistant to suggest potential best delegates (with fewest violations) to the delegator. A corresponding process-aware information system could automate these delegations and provide additional mitigating techniques to prioritize audits of affected cases. Moreover, the graphical view of obstruction analysis could help policy designers to deepen their understanding of security policies and to improve their own security policies. The chapter concludes with extensions that could be envisaged.
EDR: Enriched Deep Residual Framework with Image Reconstruction for Medical Image Retrieval

In recent times, the advancement of Artificial Intelligence (AI) attracted many researchers in medical image analysis. Analyzing the vast medical data through traditional approaches is a bit tedious and time-consuming in designing feature descriptors. Therefore, we presented an EDR: Enriched Deep Residual Framework for robust medical image retrieval in this paper. The proposed EDR framework consists of an image reconstruction module using a residual encoder and sequential decoder. Also, the image matching module is followed by retrieval to retrieve similar images from the database. The encoder module of the EDR framework consists of series of residual connections that encode the features from a given image and are forwarded to the reconstruction decoder module. The extracted encoded features provide the latent representation for the robust reconstruction of the input image. Further, this latent information is used in the image matching and retrieve similar images from the database. The performance of the proposed EDR framework is analyzed on benchmark medical image databases such as VIA/ELCAP-CT, ILD for image retrieval tasks. The proposed EDR framework is compared with the state-of-the-art approaches for average precision and recall over two datasets. The experiments and results show that the proposed framework outperformed existing works in medical image retrieval.
Automatic Double Contact Fault Detection in Outdoor Volleyball Videos

One of the common faults in volleyball is double contact while setting the ball for a spike. It is hard to detect this fault by the players. Even the referees sometimes find it difficult to observe. In this work, we propose an automatic double contact fault detection approach using a single camera in outdoor volleyball video. The video is first analyzed to detect and track the ball; the bounding boxes are then processed to extract a deep Spatio-temporal representation using a state-of-the-art 3D-convolution-based neural network, which is finally fed to a multilayer perceptron for classification. To the best of our knowledge, this is the first work on volleyball double-contact detection. The proposed framework achieves an average accuracy of 77.16% on 5-fold cross-validation. The framework is useful for players during training and for referees as a decision-support tool.
MAG-Net: A Memory Augmented Generative Framework for Video Anomaly Detection Using Extrapolation

Anomaly detection is one of the popular problem in computer vision as it is elementary to several computer vision applications. However, robust detection of spatiotemporal anomalies with dependencies across multiple frames is equally challenging. Most reconstruction error based unsupervised anomaly detection approaches struggle in case of intraclass dissimilarities and interclass similarities. Robust reconstruction capability of CNN often results in misclassification. To this end, a memory augmented generative encoder-decoder network (MAG-Net) is proposed for anomaly detection. A memory element is inserted between encoder and decoder to store diverse representations of normal feature space. A query is compared with multiple memory items representing normality space to ensure low reconstruction loss for normal frames and vice versa. MAG-Net also employs a residual block in the encoder stream to preserve visual features by allowing feedback from a layer to its successors. Residual block contains channel and pixel attention blocks to further accentuate spatiotemporal representations. Evaluation is done by comparing MAG-Net with other methods on benchmark datasets like UCSD ped-2 and Avenue. Experimental results validate the robustness of the proposed framework .
Handwritten Text Retrieval from Unlabeled Collections

Handwritten documents from communities like cultural heritage, judiciary, and modern journals remain largely unexplored even today. To a great extent, this is due to the lack of retrieval tools for such unlabeled document collections. This work considers such collections and presents a simple, robust retrieval framework for easy information access. We achieve retrieval on unlabeled novel collections through invariant features learned for handwritten text. These feature representations enable zero-shot retrieval for novel queries on unlabeled collections. We improve the framework further by supporting search via text and exemplar queries. Four new collections written in English, Malayalam, and Bengali are used to evaluate our text retrieval framework. These collections comprise 2957 handwritten pages and over 300K words. We report promising results on these collections, despite the zero-shot constraint and huge collection size. Our framework allows the addition of new collections without any need for specific finetuning or labeling. Finally, we also present a demonstration of the retrieval framework. [Project Page] .
Residual Inception Cycle-Consistent Adversarial Networks

Unpaired Image-to-image translation is a problem formulation where our aim is to learn a function which can convert an image of one domain into another different domain without using a paired set of examples. One of the methods to tackle this problem is CycleGAN. Even though it had remarkable success in the recent years, it still have some issues Our method enhances CycleGAN formulation by replacing the Residual block with our proposed Residual-Inception module for multi-scale feature extraction and by adding a cyclic perceptual loss for improving the quality of texture in recovered image and generating visually better results. Qualitative results are presented on horse2zebra dataset and Quantitative results on I-Haze and Rain 1200 datasets. We show both quantitative and qualitative results on 3 datasets and show that our method improves the CycleGAN method.
The tragedy of otherness. Reflection on Lars von Trier’s film "Nymphomaniac"

The authors of the article, using a transdisciplinary method of research from the standpoint of cultural studies, art history, philology, philosophy, religious studies, psychology and social philosophy, try to comprehend and identify the main meanings and symbols of Lars von Trier's film "Nymphomaniac", to find the main purpose of this film. At the same time, M. Epstein draws attention, first of all, to the aspect of the pathological sensuality of the main character - Joe, her nymphomania and, appealing to a woman and a man before the fall in the Bible, whom God blessed to be fruitful and multiply, shows an unusual angle of sexual relations, gradually reveals stepping the way to love and holiness through debauchery and makes a shocking conclusion, which at the same time has the right to exist, about the purifying role of hellish debauchery for gaining holiness. N. Shelkovaya pays more attention to the disclosure of symbols in the film: trees, dry leaves, a mountain, Joe's climb up the mountain in search of his soul tree, the number 8, Seligman's yard as a crypt, Joe's son's exit to the balcony, somatic self-knowledge. As a result the authors come to the general conclusion that the film by Lars von Trier "Nymphomaniac" is not a pornographic, but a deeply philosophical and psychological film, showing a distorted path to love, God, Heaven, True Love in a distorted insensitive society, which only irrational love can save from destruction.