Reviewerly/exHarmony huggingface dataset:
Resolving the Multiple Withdrawal Attack on ERC20 Tokens

Custom tokens are an integral component of decentralized applications (dapps) deployed on Ethereum and other blockchain platforms. For Ethereum, the ERC20 standard is a widely used token interface and is interoperable with many existing dapps, user interface platforms, and popular web applications (e.g., exchange services). An ERC20 security issue, known as the multiple withdrawal attack, was raised on GitHub and has been open since October 2017. The issue concerns ERC20's defined method approve() which was envisioned as a way for token holders to give permission for other users and dapps to withdraw a capped number of tokens. The security issue arises when a token holder wants to adjust the amount of approved tokens from N to M (this could be an increase or decrease). If malicious, a user or dapp who is approved for N tokens can front-run the adjustment transaction to first withdraw N tokens, then allow the approval to be confirmed, and withdraw an additional M tokens. In this paper, we evaluate 10 proposed mitigations for this issues and find that no solution is fully satisfactory. We then propose 2 new solutions that mitigate the attack, one of which fully fulfills constraints of the standard, and the second one shows a general limitation in addressing this issue from ERC20's approve method.
Explore instance similarity: An instance correlation based hashing method for multi-label cross-model retrieval

With the rapid growth of multimedia data, cross-media hashing has gained more and more attention. However, most existing cross-modal hashing methods ignore the multi-label correlation and only apply binary similarity to measure the correlation between two instances. Most existing methods perform poorly in capturing the relevance between retrieval results and queries since binary similarity measurement has limited abilities to discriminate minor differences among different instances. In order to overcome the mentioned shortcoming, we introduce a novel notion of instance similarity method, which is used to evaluate the semantic correlation between two specific instances in training data. Base on the instance similarity, we also propose a novel deep instance hashing network, which utilizes instance similarity and binary similarity simultaneously for multi-label cross-model retrieval. The experiment results on two real datasets show the superiority of our proposed method, compared with a series of state-of-the-art cross-modal hashing methods in terms of several metric evaluations.
Exploring Unlabeled Faces for Novel Attribute Discovery

Despite remarkable success in unpaired image-to-image translation, existing systems still require a large amount of labeled images. This is a bottleneck for their real-world applications; in practice, a model trained on labeled CelebA dataset does not work well for test images from a different distribution -- greatly limiting their application to unlabeled images of a much larger quantity. In this paper, we attempt to alleviate this necessity for labeled data in the facial image translation domain. We aim to explore the degree to which you can discover novel attributes from unlabeled faces and perform high-quality translation. To this end, we use prior knowledge about the visual world as guidance to discover novel attributes and transfer them via a novel normalization method. Experiments show that our method trained on unlabeled data produces high-quality translations, preserves identity, and be perceptually realistic, as good as, or better than, state-of-the-art methods trained on labeled data.
Inpainting micro-CT images of fibrous materials using deep learning

Micro-computed tomography (CT) is an irreplaceable tool to characterize the three-dimensional microstructure of fiber-reinforced composites and other fibrous materials. However, this technique has limitations due to the small specimen size, image artifacts, and shaded regions of the image. With the development of the inpainting and deep learning techniques, especially with generative adversarial networks, it has become possible to effectively reconstruct missing or damaged parts of images, or to create a new (part of) image, which would twin the studied material. The literature, however, lacks methods that work with three-dimensional (3D) micro-CT images. In this work, we propose the deep learning-based methodology for the inpainting of 3D images of fibrous materials using a limited number of micro-CT scans. Three different architectures of 3D encoder-decoder generative adversarial network were designed and analyzed for 3D micro-CT images of random fiber composites to inpaint the artificially missing data. The developed algorithms are validated using images of compression molded short glass fiber reinforced thermoplastic composite. Inpainting results show good performance of the methods using the both image-related and physical quality metrics.
Real Time Machine Translation System for English to Indian language

For humans it is difficult to understand different languages. As in India there are more than 22 official languages it becomes extremely difficult for a human to know all the languages, in this type of cases communication becomes a hindrance, this is where human interpreters might be required. It cannot be assured that human interpreters would be available every time. Due to this Machine Translation (MT) system comes into picture. MT performs replacement of words from one language to another. The idea behind this project is to achieve the real-time translation of languages with respect to real-time audio as an input by using NLP. It is done by the computers to learn the languages. The proposed system will take real-time English audio as an input and will translate English into Indian language and will produce audio output in the Indian language. The translation system will make use of OpenNMT. OpenNMT is an open source initiative for NMT and neural sequence modeling. NMT is used for automated translation which helps in learning process for both ends which is used to overcome many weaknesses of previously based translation systems.
Signature-Based Abduction for Expressive Description Logics

Signature-based abduction aims at building hypotheses over a specified set of names, the signature, that explain an observation relative to some background knowledge. This type of abduction is useful for tasks such as diagnosis, where the vocab- ulary used for observed symptoms differs from the vocabulary expected to explain those symptoms. We present the first complete method solving signature-based abduction for observations expressed in the expressive description logic ALC, which can include TBox and ABox axioms. The method is guaranteed to compute a finite and complete set of hypotheses, and is evaluated on a set of realistic knowledge bases.
Neural Markov Logic Networks.

We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules. Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure. Similarly to many neural symbolic methods, NMLNs can exploit embeddings of constants but, unlike them, NMLNs work well also in their absence. This is extremely important for predicting in settings other than the transductive one. We showcase the potential of NMLNs on knowledge-base completion, triple classification and on generation of molecular (graph) data.
Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose

Real-world talking faces often accompany with natural head movement. However, most existing talking face video generation methods only consider facial animation with fixed head pose. In this paper, we address this problem by proposing a deep neural network model that takes an audio signal A of a source person and a very short video V of a target person as input, and outputs a synthesized high-quality talking face video with personalized head pose (making use of the visual information in V), expression and lip synchronization (by considering both A and V). The most challenging issue in our work is that natural poses often cause in-plane and out-of-plane head rotations, which makes synthesized talking face video far from realistic. To address this challenge, we reconstruct 3D face animation and re-render it into synthesized frames. To fine tune these frames into realistic ones with smooth background transition, we propose a novel memory-augmented GAN module. By first training a general mapping based on a publicly available dataset and fine-tuning the mapping using the input short video of target person, we develop an effective strategy that only requires a small number of frames (about 300 frames) to learn personalized talking behavior including head pose. Extensive experiments and two user studies show that our method can generate high-quality (i.e., personalized head movements, expressions and good lip synchronization) talking face videos, which are naturally looking with more distinguishing head movement effects than the state-of-the-art methods.
Rapido: Scaling blockchain with multi-path payment channels

One of the fundamental problems that hinders the current blockchain systems for industrial use is scalability. Lightning Network has been proposed to address this challenge, which executes massive amount of transactions off-chain via pre-constructed payment channels, and only records the final state when a payment channel is closed. However, current Lightning Network heavily rely on the single path payment. When the payment value exceeds the deposit of each individual path, the customer has to close all the existing payment channels and construct a new one from scratch, which we term as the overload issue. In addition, the single path payment bears the inherent risk of sensitive information (e.g., payment value) leaking. In this paper, we propose a multi-path off-chain payment mechanism, termed Rapido, which explicitly addresses the overload and privacy leaking issues by splitting and distributing payment to multiple paths. Specifically, a new smart contract, D-HTLC, is equipped with Rapido to encode multi-path payment protocol. We also theoretically prove that the VDP (Value Distributing Problem) is NP-hard. Finally, extensive experiments show that our approach not only resolves the overload issue but also mitigates the skewness of payment channels. Quantitative results further reveal that with the restriction of no adding new payment channels, the success rate of Rapido is more than 3 times higher than that of the Lightning Network. The implementation will be released upon acceptance.
On the power of template attacks in highly multivariate context

When implemented in software (or hardware), a cryptographic protocol can leak sensitive information during its execution. Side-channel attacks can use those leakages in order to reveal some information about the secret used by the algorithm. The leaking side-channel information can take place in many time samples. Measurement appliances can cope with the acquisition of multiple samples. From an adversarial point of view, it is therefore beneficial to attempt to make the most of highly multivariate traces. On the one hand, template attacks have been introduced to deal with multivariate leakages, with as few assumptions as possible on the leakage model. On the other hand, many works have underlined the need for dimensionality reduction. In this paper, we clarify the relationship between template attacks in full space and in linear subspaces, in terms of success rate. In particular, we exhibit a clear mathematical expression for template attacks, which enables an efficient computation even on large dimensions such as several hundred of samples. It is noteworthy that both of PoI-based and PCA-based template attacks can straightforwardly benefit from our approach. Furthermore, we extend the approach to the masking-based protected implementations. Our approach is validated both by simulated and real-world traces.
Image Classification of Solar Radio Spectrum based on Deep Learning

Aiming at the problems that traditional image denoising methods cannot well filter the background noise of solar radio spectrum images, and the training sample data is small and unbalanced, it is proposed to segment the features and background of the spectrum image through Gaussian filtering and image binarization. Then use the morphological closed operation to enhance the feature; through the combination of image transformation and random indexing, the problem of uneven distribution of various samples in the solar radio spectrum database is solved. By designing the structure of the convolutional neural network, the classification model can better extract image features and improve the classification accuracy. Experimental results show that the convolutional neural network combined with image preprocessing has achieved an average TPR value of 97.97% on the solar radio spectrum data set, which is better than the existing research results and has application value for the automatic classification of solar radio spectrum images.
CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders

This work presents CLIPDraw, an algorithm that synthesizes novel drawings based on natural language input. CLIPDraw does not require any training; rather a pre-trained CLIP language-image encoder is used as a metric for maximizing similarity between the given description and a generated drawing. Crucially, CLIPDraw operates over vector strokes rather than pixel images, a constraint that biases drawings towards simpler human-recognizable shapes. Results compare between CLIPDraw and other synthesis-through-optimization methods, as well as highlight various interesting behaviors of CLIPDraw, such as satisfying ambiguous text in multiple ways, reliably producing drawings in diverse artistic styles, and scaling from simple to complex visual representations as stroke count is increased. Code for experimenting with the method is available at: this https URL
Superconducting Coupler with Exponentially Large On:Off Ratio

Tunable two-qubit couplers offer an avenue to mitigate errors in multiqubit superconducting quantum processors. However, most couplers operate in a narrow frequency band and target specific couplings, such as the spurious $ZZ$ interaction. We introduce a superconducting coupler that alleviates these limitations by suppressing all two-qubit interactions with an exponentially large on-off ratio and without the need for fine-tuning. Our approach is based on a bus mode supplemented by an ancillary nonlinear resonator mode. Driving the ancillary mode leads to a coupler-state-dependent field displacement in the resonator which, in turn, results in an exponential suppression of real and virtual two-qubit interactions with respect to the drive power. A superconducting circuit implementation supporting the proposed mechanism is presented.
An end‐to‐end framework for the detection of mathematical expressions in scientific document images

Abstract The detection of mathematical expressions is a prerequisite step for the digitisation of scientific documents. Many different multistage approaches have been proposed for the detection of expressions in document images, that is, page segmentation and expression detection. However, the detection accuracy of such methods still needs improvement owing to errors in the page segmentation of complex documents. This paper presents an end‐to‐end framework for mathematical expression detection in scientific document images without requiring optical character recognition (OCR) or document analysis techniques applied in conventional methods. The novelty of this paper is twofold. First, because document images are usually in binary form, the direct use of these images, which lack texture information as input for detection networks, may lead to an incorrect detection. Therefore, we propose the application of a distance transform to obtain a discriminating and meaningful representation of mathematical expressions in document images. Second, the transformed images are fed into the faster region with a convolutional neural network (Faster R‐CNN) optimized to improve the accuracy of the detection. The proposed framework was tested on two benchmark data sets (Marmot and GTDB). Compared with the original Faster R‐CNN, the proposed network improves the accuracies of detection of isolated and inline expressions by 5.09% and 3.40%, respectfully, on the Marmot data set, whereas those on the GTDB data set are improved by 4.04% and 4.55%. A performance comparison with conventional methods shows the effectiveness of the proposed method.
PRNU registration under scale and rotation transform based on convolutional neural networks

Assessing if an image comes from a specific device is fundamental in many application scenarios. The most promising techniques to solve this problem rely on the Photo Response Non Uniformity (PRNU), a unique trace left during image acquisition. A PRNU fingerprint is computed from several images of a given device, then it is compared with the probe residual noise by means of correlation. However, such a comparison requires that PRNUs are synchronized: even small image transformations can spoil this task. Most of the attempts to solve the registration problem rely on time consuming brute-force search, which is prone to missing detections and false positives. In this paper, the problem is addressed from a computer vision perspective, exploiting recent image registration techniques based on deep learning, and focusing on scaling and rotation transformations. Experiments show that the proposed method is both more accurate and faster than state-of-the-art approaches.
The Role of Military Cyber Exercises: A Case Study of Locked Shields

What are the opportunities and limits of conducting military cyber exercises? To answer this question, I present a case study analysis of Locked Shields, the largest international cyber defense exercise in the world. Relying on non-public After Action Reports, interviews, and publicly available material, this study highlights three main challenges: the teaching of more advanced technical skills in a virtual environment; the planning of long-term campaigns below the threshold of armed attack rather than sudden cyber attacks during a two-day event; and the promotion of NATO’s principles of collective defense as part of a competitive exercise. I also discuss three benefits: the ability to practice communication procedures, particularly across teams; the possibility for experimentation at the managerial level, such as assessments of team size; and the potential strategic signaling function of an exercise in a space that lacks many other opportunities for that.
PhyIR: Physics-based Inverse Rendering for Panoramic Indoor Images

Inverse rendering of complex material such as glossy, metal and mirror material is a long-standing ill-posed problem in this area, which has not been well solved. Previous approaches cannot tackle them well due to simplified BRDF and unsuitable illumination representations. In this paper, we present PhyIR, a neural inverse rendering method with a more completed SVBRDF representation and a physics-based in-network rendering layer, which can handle complex material and incorporate physical constraints by re-rendering realistic and detailed specular reflectance. Our framework estimates geometry, material and Spatially-Coherent (SC) illumination from a single indoor panorama. Due to the lack of panoramic datasets with completed SVBRDF and full-spherical light probes, we introduce an artist-designed dataset named FutureHouse with high-quality geometry, SVBRDF and per-pixel Spatially-Varying (SV) lighting. To ensure the coherence of SV lighting, a novel SC loss is proposed. Extensive experiments on both synthetic and real-world data show that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively, and is able to produce photorealistic results for a number of applications such as dynamic virtual object insertion.
Role of AI and Its Impact on the Development of Cyber Security Applications

With the Industry 4.0 revolution, modern organizations and their ecosystems are evolving rapidly. The cyberattack surface is growing simultaneously, thus enabling new cyberattacks that cannot be resolved by human intervention alone. Artificial intelligence (AI) has now become an essential tool that provides security as it has the potential to easily analyze billions of data and triangulate an array of possible cyberattacks that can range from active attacks like network exploitation or suspicious behavior such as eavesdropping, traffic analysis. An AI continues to learn from datasets, improve its models based on historic data and use those experiences to better recognize different types of attacks. Artificial intelligence has a great impact on the cyber security industry by predicting and preventing attacks before they even occur. In the Tokyo Olympics 2021, 450 million cyberattacks were attempted during the entirety of the events, but with the help of AI, none of the attacks affected the tournament because AI predicted attacks a week before it happened. AI has been helping with improved cyber security which would not have been achieved by human component alone. A recent survey done in IBM and Capgemini shows that 95% of security breaches arise due to human error alone. Forty-eight % executives in Capgemini use AI for threat detection, 18% say that it is used for response and 34% make use of AI for prediction. In the era of ever-evolving attacks and rapid increase in number of devices, AI and machine learning can aid in keeping track of attacks, help in threat detection automation, provide effective response to data exploitation, conduct network vulnerability surveillance, generate effective incident and response reports, form cyber threat intelligence reports, which are a few applications of AI in cyber security. As with every technology, AI comes with certain limitations too. Though it contributes to a high degree of accuracy and machine performance, there still exist certain levels of risk like producing false positives and negatives while detecting threats in a network. Using adversarial machine learning techniques to exploit the dataset of an AI and train it to mislead the application into believing threat activities are normal data is a possibility.