Reviewerly/exHarmony huggingface dataset:
New Results on Machine Learning-Based Distinguishers

Machine Learning (ML) is almost ubiquitously used in multiple disciplines nowadays.Recently, we have seen its usage in the realm of differential distinguishers for symmetric key ciphers.It has been shown that ML-based differential distinguishers can be easily extended to break round-reduced versions of ciphers.In this paper, we show new distinguishers on the unkeyed and round-reduced versions of SPECK-32, SPECK-128, ASCON, SIMECK-32, SIMECK-64, and SKINNY-128.We explore multiple avenues in the process.In summary, we use neural networks and support vector machines in various settings (such as varying the activation function), apart from experimenting with a number of input difference tuples.Among other results, we show a distinguisher of 8-round SPECK-32 that works with low data complexity.
Regression to Classification: Ordinal Prediction of Calcified Vessels Using Customized ResNet50

A substantial percentage of women die from cardiovascular disease (CVD). Computed tomography (CT) scan helps predict/monitor CVD-related diseases. However, previous studies found relation to assessing the risk of CVD by estimating severity of breast arterial calcification (BACs). In many countries, women undergo breast cancer screening using mammograms regularly, so it is easier to access vast datasets compared to CT-scan without additional cost and absorbing radiation. Earlier, measuring BACs severity was largely depends on the performance of segmentation of calcified vessel. Nevertheless, dataset maintenance for segmentation is expensive and tedious. As pixel-wise manual ground-truth (GT) marking can only be performed by multiple experienced experts. First time ever, to the best of our knowledge, a direct ordinal measurement of BACs severity is proposed without prior segmentation. Therefore, due to the complexity of calcified vessels, cascade classification performs better than end-to-end classification. In this proposed method, first, we imply Frangi filter to deduct background automatically without any prior ground-truth marking. Then, unlike other methods, interpolation is implied to shrink cropped images rather than splitting images. Also, left view images have flipped to reduce the complexity of different directions of breasts. In addition, among various residual network (ResNet) architecture, proposed method customized ResNet50 to adjust large image size and vessel complexity. The proposed method reduces cost, time, and tedious job of researchers as this does not require segmenting calcified vessels and provides efficient prediction of BACs severity which is helpful to assess the risk of CVD in long run.
A Novel Digital Forensic Framework for Data Breach Investigation

Data breaches are becoming an increasingly prevalent and global concern due to their massive impact. One of the primary challenges in investigating data breach incidents is the unavailability of a specific framework that acknowledges the characteristics of a data breach incident and provides clear steps on how the investigative framework can comprehensively answer what, who, when, where, why, and how (5WH) questions. This paper aims to develop a novel digital forensic investigation framework that can overcome these data breach investigation challenges. The proposed framework utilizes the data breach breakdown phases to analyze data breach incidents according to their characteristics. The main contribution of our work is a novel digital forensic framework for data breach investigation that enhances the 5WH analysis depth by utilizing evidence classification and artifact visualization based on data breach breakdown phases. Furthermore, we design the framework components to provide comprehensive analysis results that make it easier for investigators to summarize the answers to the 5WH questions. To validate the framework, we apply it to a case study of enterprise-level data breach incidents. Based on the case study analysis, the proposed investigation framework successfully provides all the answers to the 5WH questions. This comprehensive answering ability is the study’s fundamental strength compared to other digital forensic investigation frameworks.
An edge guided coarse-to-fine generative network for image outpainting

Deep-learning based generative models have achieved outstanding performance in various image processing tasks. This paper introduces a method to address the problem of extrapolating or outpainting visual context. When the input size is a small proportion of the output size, a limited amount of information is present to regenerate a semantically coherent image. This task is challenging because the missing region of the original image may include crucial semantic and spatial structural information, which is difficult to predict from the input. We propose a three-stage edge-guided coarse-to-fine generative network model, consisting of a contextual inference network, structural edge map generator and edge enhanced network, to synthesise semantically consistent output from small picture inputs. Our model adopts a gradual growth inference strategy in the contextual inference network so that the generated image can present a more coherent structure, and this result can support the structural edge map generator to generate a reasonable edge map in a large missing area. Combining the contextual inference network and structural edge map generator outputs enables the edge enhanced network to generate more convincing images. We evaluate our model using four public datasets: CelebA, Places2, Oxford Flower102and CUB200. Our experimental results demonstrate that the proposed image outpainting network can successfully regenerate high-quality images with a large missing region even when some structural features are lost in the input images.
Futuristic study of a criminal facial recognition: A open-source face image dataset

Open face recognition provides an uncertain challenge to criminal face identification. Thus, this recognition has many uses, from security to entertainment. Even if other biometric characteristics like iris and fingerprints are reliable, a person must be present to use them. Therefore, there are better tools for thieves to use for authentication. Anyone can recognize a criminal in an image using face recognition technology in conjunction with a criminal database containing the individual's personal information. In addition to needing great accuracy, a criminal recognition system also needs to be flexible to numerous variations in illumination, occlusion, maturity level, postures, etc. We use the Haar cascade and Histograms of oriented gradients (HOG) recognition learning systems discussed and contrasted in this study. We assessed models, and HOG outperformed the criminal recognition system. We also go through several practical uses for this criminal recognition.
Data Protection of Accounting Information Based on Big Data and Cloud Computing

With the rapid development of Internet technology, mankind has entered the era of big data. The Internet records all kinds of information, and the amount of information generated in the future is in a state of explosive growth. With the development of enterprises, the data of accounting information have grown, and its security has also been threatened. Once the accounting information is leaked, it will have a great impact on the company. Therefore, it is very necessary to protect accounting information data. This paper aims to study how to protect accounting information data based on big data and cloud computing. Based on this, this paper proposes an attribute-based encryption method based on big data and cloud computing and analyzes the ABE scheme of a single authority and multiple authorities. The multiauthority ABE scheme can effectively solve the problems of low computational efficiency, poor security performance, and high time overhead in the single-authority ABE scheme. The experimental results of this paper show that, under the single-authority ABE scheme in the information data protection of enterprise A, the confidentiality of data is 54% at the highest and 46% at the lowest, which is generally not very high. In the information data protection of the multiauthority ABE scheme in enterprise A, the highest data confidentiality is 86% and the lowest is 79%. The highest is 32% more than the single-authority ABE scheme. The lowest is also 33% more than that. Generally speaking, the confidentiality of its data is very high. In terms of reliability, integrity, and effectiveness, the single-authority ABE scheme is not as good as the multiauthority ABE scheme. It shows that the multiauthority ABE scheme proposed in this paper has a powerful information data security protection function and can be effectively applied to accounting information data protection.
RCMR 280k: Refined Corpus for Move Recognition Based on PubMed Abstracts

ABSTRACT Existing datasets for move recognition, such as PubMed 200k RCT, exhibit several problems that significantly impact recognition performance, especially for Background and Objective labels. In order to improve the move recognition performance, we introduce a method and construct a refined corpus based on PubMed, named RCMR 280k. This corpus comprises approximately 280,000 structured abstracts, totaling 3,386,008 sentences, each sentence is labeled with one of five categories: Background, Objective, Method, Result, or Conclusion. We also construct a subset of RCMR, named RCMR_RCT, corresponding to medical subdomain of RCTs. We conduct comparison experiments using our RCMR, RCMR_RCT with PubMed 380k and PubMed 200k RCT, respectively. The best results, obtained using the MSMBERT model, show that: (1) our RCMR outperforms PubMed 380k by 0.82%, while our RCMR_RCT outperforms PubMed 200k RCT by 9.35%; (2) compared with PubMed 380k, our corpus achieve better improvement on the Results and Conclusions categories, with average F1 performance improves 1% and 0.82%, respectively; (3) compared with PubMed 200k RCT, our corpus significantly improves the performance in the Background and Objective categories, with average F1 scores improves 28.31% and 37.22%, respectively. To the best of our knowledge, our RCMR is among the rarely high-quality, resource-rich refined PubMed corpora available. Our work in this paper has been applied in the SciAIEngine, which is openly accessible for researchers to conduct move recognition task.
Learning photons go backward

Efficient learning algorithms are implemented in a silicon photonic neural network chip.
Measuring Human Perception to Improve Open Set Recognition

The human ability to recognize when an object belongs or does not belong to a particular vision task outperforms all open set recognition algorithms. Human perception as measured by the methods and procedures of visual psychophysics from psychology provides an additional data stream for algorithms that need to manage novelty. For instance, measured reaction time from human subjects can offer insight as to whether a class sample is prone to be confused with a different class - known or novel. In this work, we designed and performed a large-scale behavioral experiment that collected over 200,000 human reaction time measurements associated with object recognition. The data collected indicated reaction time varies meaningfully across objects at the sample-level. We therefore designed a new psychophysical loss function that enforces consistency with human behavior in deep networks which exhibit variable reaction time for different images. As in biological vision, this approach allows us to achieve good open set recognition performance in regimes with limited labeled training data. Through experiments using data from ImageNet, significant improvement is observed when training Multi-Scale DenseNets with this new formulation: it significantly improved top-1 validation accuracy by 6.02%, top-1 test accuracy on known samples by 9.81%, and top-1 test accuracy on unknown samples by 33.18%. We compared our method to 10 open set recognition methods from the literature, which were all outperformed on multiple metrics.
Obtaining a single-photon weak value from experiments using a strong (many-photon) coherent state

A common type of weak-value experiment prepares a single particle in one state, weakly measures the occupation number of another state, and post-selects on finding the particle in a third state (a “click”). Most weak-value experiments have been done with photons, but the heralded preparation of a single photon is difficult and slow of rate. Here, we show that the weak value mentioned above can be measured using strong (many-photon) coherent states, while still needing only a click detector such as an avalanche photodiode. One simply subtracts the no-click weak value from the click weak-value and scales the answer by a simple function of the click probability.
Effects of reservoir squeezing on trace-distance correlations and emergence of the pointer basis

We investigate theoretically the dynamics of 1-norm geometric quantum correlations and their classical counterparts in a two-qubit system. Both qubits are initially prepared in Bell-diagonal states and locally coupled to separated thermal squeezed baths or a common squeezed thermal bath via energy-preserving interactions. We then unveil the effects of reservoir squeezing on the abrupt changes in the evolution of geometric correlations. It is found that adequately tuning the squeezing phase can efficiently suppress the dephasing rate and delay the appearance of sudden transitions in geometric correlations. Further, we show that the squeezing phase of the bath renders a different avenue to enhance the finite time interval for frozen quantum correlation. On the other hand, in this context, we show that the squeezing strength of the reservoir exhibits a negative role. In addition, in the common bath case, we observe the steady-state correlations and decoherence-free subspace, which can be governed via squeezing parameters. Moreover, the abrupt change from a decaying regime to a constant nonzero value in classical correlation signals the emergence of a pointer-state basis. We show that the emergence of a pointer-state basis can be delayed by suitably adjusting the bath squeezing parameters. Remarkably, we find the optimal value of the squeezing phase, which introduces maximum retardation in the appearance of a pointer-state basis.
Physics simulation via quantum graph neural network

We develop and implement two realizations of quantum graph neural networks (QGNN), applied to the task of particle interaction simulation. The first QGNN is a speculative quantum-classical hybrid learning model that relies on the ability to directly utilize superposition states as classical information to propagate information between particles. The second is an implementable quantum-classical hybrid learning model that propagates particle information directly through the parameters of RX rotation gates. A classical graph neural network (CGNN) is also trained in the same task. Both the Speculative QGNN and CGNN act as controls against the Implementable QGNN. Comparison between classical and quantum models is based on the loss value and accuracy of each model. Overall, each model had a high learning efficiency, in which the loss value rapidly approached zero during training; however, each model was moderately inaccurate. Comparing performances, our results show that the Implementable QGNN has a potential advantage over the CGNN. Additionally, we show that a slight alteration in hyperparameters in the CGNN notably improves accuracy, suggesting that further fine tuning could mitigate the issue of moderate inaccuracy in each model.
TA-DNN—two stage attention-based deep neural network for single image rain removal

Computer Vision algorithms easily get affected by images or videos captured in outdoor environments due to various bad weather conditions such as rain, fog, snow and haze. Two-stage deep neural network based on attention learning is proposed for single image rain removal. Based on the fact that the implicit connection among rain lines within the image is higher than that between the rain line and the background image, it is easier to learn the rain lines in the rainy image using attention learning. The proposed Two-stage Attention-based Deep Neural Network (TA-DNN) for single image rain removal essentially consists of modules such as Inception, Sequential Dual Attention Block (SDAB), and Multi-Scale Feature Aggregation Module (MSFAM) for feature extraction, rain line detection, and transfiguration, respectively. The experimental results illustrate that the proposed method performs better when compared with the state-of-the-art methods both qualitatively and quantitatively.
How to Limit Label Dissipation in Neural-network Validation: Exploring Label-free Early-stopping Heuristics

In recent years, deep learning (DL) has achieved impressive successes in many application domains, including Handwritten-Text Recognition. However, DL methods demand a long training process and a huge amount of human-based labeled data. To address these issues, we explore several label-free heuristics for detecting the early-stopping point in training convolutional-neural networks: (1) Cumulative Distribution of the standard deviation of kernel weights (SKW) ; (2) the moving standard deviation of SKW, and (3) the standard deviation of the sum of weights over a window in the epoch series. We applied the proposed methods to the common RIMES and Bentham data sets as well as another highly challenging historical data set. In comparison with the usual stopping criterion which uses labels for validation, the label-free heuristics are at least 10 times faster per epoch when the same training set is used. The use of alternative stopping heuristics may require additional epochs, however, they never require the original computing time. The character error rate (%) on the test set of the label-free heuristics is about a percentage point less in comparison to the usual stopping criterion. The label-free early-stopping methods have two benefits: They do not require a computationally intensive evaluation of a validation set per epoch and all labels can be used for training, specifically benefitting the underrepresented word or letter classes.
Weight normalization optimization movie recommendation algorithm based on three-way neural interaction networks

Abstract Heterogeneous information networks are increasingly used in recommendation algorithms. However, they lack an explicit representation of meta-paths. In using bidirectional neural interaction models for recommendation models, interaction between users and items is often ignored, with an integral impact on the accuracy of the recommendations. To better apply the interaction information, this study proposes a weight-normalized movie recommendation model (SCLW_MCRec) based on a three-way neural interaction network. The model constructs a three-way neural interaction network $$\langle $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>⟨</mml:mo> </mml:math> user, meta-path, item $$\rangle $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>⟩</mml:mo> </mml:math> from meta-path contextual information, introducing meta-paths on top of the user-item representation to represent the user-item interaction information. Introduction of a two-layer, one-dimensional convolutional neural network helps capture higher-order interaction features between the user and the item, making the model more powerful in terms of interaction. Adding a dropout layer to the interaction model and using a two-layer convolutional neural network can prevent overfitting and discard irrelevant information features to improve the recommendation. In addition, an extreme cross-entropy loss (argmaxminloss) that incorporates the properties of the argmin and argmax functions is designed to reduce the model loss. A weight-normalization optimization approach is used to better optimize the model and accelerate convergence of the stochastic gradient descent optimization. Compared to current state-of-the-art recommendation models, the SCLW_MCRec model improves the Prec evaluation index by 2.94–35.8%, Recall by 1.15–53.51%, and NDCG by 6.7–49.37% on the MovieLens dataset. The framework provides a significant improvement in recommendation accuracy and also solves the cold-start problem with application of interaction information.
MT-GAN: toward realistic image composition based on spatial features

Abstract The purpose of image composition is to combine the visual elements of different natural images to produce a natural image. The performance of most existing image composition methods drops significantly when they solve multiple issues, such as image harmonization, image blending, shadow generation, object placement, and spatial transformation. To address this problem, we propose a multitask GAN for image compositing based on spatial features, aiming to simultaneously address the geometric and appearance inconsistency. We use three related learning objective functions to train the network. Moreover, a new dataset including 7756 images with RoI region annotations is contributed to help evaluate the multitask image compositing results. Extensive experiments demonstrate that our proposed method is effective on our dataset.
Employing FGP-3D, a Fully Gated and Anchored Methodology, to Identify Skeleton-Based Action Recognition

Recent years have seen an explosion in interest in and development of action recognition based on skeletal data. Contemporary methods using fully gated units can successfully extract characteristics from human skeletons by relying on the human topology that has been predefined. Despite advancements, fully gated unit-based techniques have trouble generalizing to other domains, particularly when dealing with various human topological structures. In this context, we introduce FGP-3D, a novel skeleton-based action recognition technique that can generalize across datasets while being effective at learning spatiotemporal features from human skeleton sequences. This is accomplished via a multi-head attention technique to learn an ideal dependence feature matrix from the uniform distribution. We next re-evaluate state-of-the-art techniques as well as the suggested novel descriptor FGP-3D in order to examine the cross-domain generalizability of skeleton-based action recognition in real-world video skeleton statistics. After being applied to commonly used action categorization datasets, experimental results demonstrate that the proposed FGP-3D, with pre-training, generalizes well and outperforms the state-of-the-art.
Longitudinal Data Analysis Based on Bayesian Semiparametric Method

A Bayesian semiparametric model framework is proposed to analyze multivariate longitudinal data. The new framework leads to simple explicit posterior distributions of model parameters. It results in easy implementation of the MCMC algorithm for estimation of model parameters and demonstrates fast convergence. The proposed model framework associated with the MCMC algorithm is validated by four covariance structures and a real-life dataset. A simple Monte Carlo study of the model under four covariance structures and an analysis of the real dataset show that the new model framework and its associated Bayesian posterior inferential method through the MCMC algorithm perform fairly well in the sense of easy implementation, fast convergence, and smaller root mean square errors compared with the same model without the specified autoregression structure.