Reviewerly/exHarmony huggingface dataset:
SITM: See-In-The-Middle Side-Channel Assisted Middle Round Differential Cryptanalysis on SPN Block Ciphers

Side-channel analysis constitutes a powerful attack vector against cryptographic implementations. Techniques such as power and electromagnetic side-channel analysis have been extensively studied to provide an efficient way to recover the secret key used in cryptographic algorithms. To protect against such attacks, countermeasure designers have developed protection methods, such as masking and hiding, to make the attacks harder. However, due to significant overheads, these protections are sometimes deployed only at the beginning and the end of encryption, which are the main targets for side-channel attacks.In this paper, we present a methodology for side-channel assisted differential cryptanalysis attack to target middle rounds of block cipher implementations. Such method presents a powerful attack vector against designs that normally only protect the beginning and end rounds of ciphers. We generalize the attack to SPN based ciphers and calculate the effort the attacker needs to recover the secret key. We provide experimental results on 8-bit and 32-bit microcontrollers. We provide case studies on state-of-the-art symmetric block ciphers, such as AES, SKINNY, and PRESENT. Furthermore, we show how to attack shuffling-protected implementations.
A Systematic Literature Review on DevOps Capabilities and Areas

Businesses today need to respond to customer needs at an unprecedented speed. Driven by this need for speed, many companies are rushing to the DevOps movement. DevOps, the combination of development and operations, is a new way of thinking in the software engineering domain that recently received much attention. Since DevOps has recently been introduced as a new term and novel concept, no common understanding of what it means has yet been achieved. Therefore, the definitions of DevOps often are only a part relevant to the concept. This research presents a systematic literature review to identify the determining factors contributing to the implementation of DevOps, including the main capabilities and areas with which it evolves.
Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function

Magnetic resonance imaging (MRI) images synthesized from computed tomography (CT) data can provide more detailed information on pathological structures than that of CT data alone; thus, the synthesis of MRI has received increased attention especially in medical scenarios where only CT images are available. A novel convolutional neural network (CNN) combined with a contextual loss function was proposed for synthesis of T1- and T2-weighted images (T1WI and T2WI) from CT data.A total of 5,053 and 5,081 slices of T1WI and T2WI, respectively were selected for the dataset of CT and MRI image pairs. Affine registration, image denoising, and contrast enhancement were done on the aforementioned multi-modality medical image dataset comprising T1WI, T2WI, and CT images of the brain. A deep CNN was then proposed by modifying the ResNet structure to constitute the encoder and decoder of U-Net, called double ResNet-U-Net (DRUNet). Three different loss functions were utilized to optimize the parameters of the proposed models: mean squared error (MSE) loss, binary crossentropy (BCE) loss, and contextual loss. Statistical analysis of the independent-sample t-test was conducted by comparing DRUNets with different loss functions and different network layers.DRUNet-101 with contextual loss yielded higher values of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Tenengrad function (i.e., 34.25±2.06, 0.97±0.03, and 17.03±2.75 for T1WI and 33.50±1.08, 0.98±0.05, and 19.76±3.54 for T2WI respectively). The results were statistically significant at P<0.001 with a narrow confidence interval of difference, indicating the superiority of DRUNet-101 with contextual loss. In addition, both image zooming and difference maps presented for the final synthetic MR images visually reflected the robustness of DRUNet-101 with contextual loss. The visualization of convolution filters and feature maps showed that the proposed model can generate synthetic MR images with high-frequency information.The results demonstrated that DRUNet-101 with contextual loss function provided better high-frequency information in synthetic MR images compared with the other two functions. The proposed DRUNet model has a distinct advantage over previous models in terms of PSNR, SSIM, and Tenengrad score. Overall, DRUNet-101 with contextual loss is recommended for synthesizing MR images from CT scans.
An improved distributed storage model of remote sensing images based on the HDFS and pyramid structure

With the rapidly growing amount of remote sensing data, data management needs to adopt a new architecture. Some improvements have been made in some areas. For example, the Hadoop distributed file system (HDFS) can be utilised for large files. However, small files will be produced when storing remote sensing images in the pyramid-based structure. In this paper, we propose a method based on the Hadoop system with MapFile. The method is an improved storage model for efficient storage and allows for access of the small files on the HDFS. The proposed method combines small files into the MapFile serialised container, and it reduces the number of small files. The metadata sensor information is stored in the index to improve the search speed. Results demonstrate the efficiency of remote sensing image processing using the pyramid model and the reduced execution time compared with the HDFS, HDWebGIS and Hadoop archives.
Exceeding 95% system efficiency within the telecom C-band in superconducting nanowire single photon detectors

We present a single-photon detector with system detection efficiencies exceeding 95% over the wavelength range 1520-1550 nm, with polarization sensitivities in the range of 1.02-1.08. The wavelength range is tunable over 200 nm via variation of the top-two dielectric layer thicknesses.
Design and Implementation of Chinese Common Braille Translation System Integrating Braille Word Segmentation and Concatenation Rules

An important sign of the accessibility of Braille information is the realization of the mutual translation between Chinese and the Braille. Due to the irregularity and uncertainty of the Prevailing Mandarin Braille, coupled with the lack of a large-scale Braille corpus, the quality of Chinese-Braille translation seems to be poor. In July 2018, the National Language Commission released the “Chinese Common Braille Scheme” and advocated replacing the “Prevailing Mandarin Braille.” Aimed at improving translation accuracy, this research, which is based on the self-built Chinese Common Braille corpus and combined with the HanLP (Han Language Processing) dictionary and the Chinese-Braille word corpus (a Braille word segmentation and concatenation dictionary for generating a unigram language model), uses the n-gram language model to design and implement a Chinese-Braille intertranslation system that integrates Chinese and Braille Word Segmentation and Concatenation Rules. More importantly, this research proposes an experimental plan for improving the Braille Word Segmentation and Concatenation Rules using a Chinese-Braille word corpus. Experiments show that in the field of educational literature, the accuracy rate of translation from Chinese to Chinese Common Braille has reached 95.01%, and the accuracy of Chinese Common Braille to Chinese translation has reached 90.15%.
Breast Cancer Detection Using Machine Learning Techniques

Abstract: Cancer is one of the most prominent cause of fatalities around the world, accounting over 1 crore deaths in past year out of which 22.6% deaths were due to Breast cancer (BC). BC is the most common type of cancer among women, it accounts for 14.7 % of cancer cases in India. Multiple pieces of research have been conducted over the early detection of BC that can help begin treatment on time thus decreasing the mortality rate. Out of the total diagnosed, only about 86% are diagnosed correctly. Biopsy images of cells have a risk of false detection which may endanger the life of a person. There is a dire need of discovering new alternative methods that have an easy implementation with different data sets, are cost-effective, reliable, and safer, that can give an accurate prediction. This paper proposes a model combined with several Machine Learning algorithms(ML) that are Decision Trees, Artificial Neural Network, K-Nearest Neighbour, Support Vector Machine for an effective and accurate breast cancer diagnosis. Index Terms - Breast Cancer; Breast Cancer Detection; Machine Learning Key Words: Breast Cancer (BC); Artificial neural networks (ANN); Wisconsin breast cancer data set.
In Search of the Most Attractive Lip Proportions and Lip Volume: An Eye Tracking– and Survey-Based Investigation

Despite various aesthetic trends, the ideal lip proportion and lip volume remains elusive. Thus, the aim of this study was to investigate the aesthetic perception of various lip shapes to identify the most attractive lips.Fifty-nine White study participants with a mean age of 32.73 ± 9.4 years were asked to assess lips of various proportions and of various volumes. Gaze assessment and aesthetic rating of the same set of modified lip images were performed.The results revealed that the lip proportion that was rated to be most attractive was the 1:1.6 ratio (upper-to-lower lip, 4.21 of a possible 5). This lip proportion, however, was not the first one to be viewed on initial image display (1.20 sec) and had the shortest duration of a stable eye fixation within the 6-second image display interval (2.18 of 6 seconds). The lip volume that received the highest aesthetic rating was the 100 percent volume (original lip size), with 4.56 of a possible 5. This lip volume, however, had the third longest interval between initial image exposure and the first stable eye fixation (0.81 second) and had the shortest duration of stable eye fixations during the 6 seconds of image display (2.22 of 6 seconds).The results presented could be potentially reflective of the internal cognitive processes during the involuntary and voluntary assessment of lip attractiveness.
Low Dimensional Trajectory Hypothesis is True: DNNs can be Trained in Tiny Subspaces

Deep neural networks (DNNs) usually contain massive parameters, but there is redundancy such that it is guessed that they could be trained in low-dimensional subspaces. In this paper, we propose a Dynamic Linear Dimensionality Reduction (DLDR) based on the low-dimensional properties of the training trajectory. The reduction method is efficient, supported by comprehensive experiments: optimizing DNNs in 40-dimensional spaces can achieve comparable performance as regular training over thousands or even millions of parameters. Since there are only a few variables to optimize, we develop an efficient quasi-Newton-based algorithm, obtain robustness to label noise, and improve the performance of well-trained models, which are three follow-up experiments that can show the advantages of finding such low-dimensional subspaces. The code is released (Pytorch: https://github.com/nblt/DLDR and Mindspore: https://gitee.com/mindspore/docs/tree/r1.6/docs/sample_code/dimension_reduce_training).
Task Scheduling Techniques for Energy Efficiency in the Cloud

Energy efficiency is a key goal in cloud datacentre since it saves money and complies with green computing standards. When energy efficiency is taken into account, task scheduling becomes much more complicated and crucial. Execution overhead and scalability are major concerns in current research on energy-efficient task scheduling. Machine learning has been widely utilized to solve the problem of energy-efficient task scheduling, however, it is usually used to anticipate resource usage rather than selecting the schedule. The bulk of machine learning approaches are used to anticipate resource consumption, and heuristic or metaheuristic algorithms utilize these predictions to choose which computer resource should be assigned to a certain activity. As per the knowledge and research, none of the algorithms have independently used machine learning to make an energy-efficient scheduling decision. Heuristic or meta-heuristic approaches, as well as approximation algorithms, are frequently used to solve NP-complete problems. In this paper, we discuss various studies that have been used to solve the problem of task scheduling which belongs to a class of NP-hard. We have proposed a model to achieve the objective of reduced energy consumption and CO2 emission in a cloud environment. In the future, the model shall be implemented in MATLAB and would be assessed on various parameters like makespan, execution time, resource utilization, QoS, Energy utilization, etc.
Blockchain Based Non-repudiable IoT Data Trading: Simpler, Faster, and Cheaper

Next-generation wireless technology and machine-to-machine technology can provide the ability to connect and share data at any time among IoT smart devices. However, the traditional centralized data sharing/trading mechanism lacks trust guarantee and cannot satisfy the real-time requirement. Distributed systems, especially blockchain, provide us with promising solutions. In this paper, we propose a blockchain based non-repudiation scheme for IoT data trading to resolve the credibility and real-time limits. The proposed scheme has two parts, i.e., a trading scheme and an arbitration scheme. The trading scheme employs a divide-and-conquer method and two commitment methods to support efficient IoT data trading, which runs in a two-round manner. The arbitration scheme first leverages a smart contract to solve disputes on-chain in real time. In case of on-chain arbitration dissatisfaction, the arbitration scheme also employs an off-line arbitration to make a final resolution. Short-term and long-term analysis show that the proposed scheme enforces non-repudiation among the data trading parties and runs efficiently for rational data owners and buyers. We implemented the proposed scheme. Experimental results confirm that the proposed scheme has an orders-of-magnitude performance speedup than the state-of-the-art scheme.
Video Anomaly Detection Based on Convolutional Recurrent AutoEncoder

As an essential task in computer vision, video anomaly detection technology is used in video surveillance, scene understanding, road traffic analysis and other fields. However, the definition of anomaly, scene change and complex background present great challenges for video anomaly detection tasks. The insight that motivates this study is that the reconstruction error for normal samples would be lower since they are closer to the training data, while the anomalies could not be reconstructed well. In this paper, we proposed a Convolutional Recurrent AutoEncoder (CR-AE), which combines an attention-based Convolutional Long–Short-Term Memory (ConvLSTM) network and a Convolutional AutoEncoder. The ConvLSTM network and the Convolutional AutoEncoder could capture the irregularity of the temporal pattern and spatial irregularity, respectively. The attention mechanism was used to obtain the current output characteristics from the hidden state of each Covn-LSTM layer. Then, a convolutional decoder was utilized to reconstruct the input video clip and the testing video clip with higher reconstruction error, which were further judged to be anomalies. The proposed method was tested on two popular benchmarks (UCSD ped2 Dataset and Avenue Dataset), and the experimental results demonstrated that CR-AE achieved 95.6% and 73.1% frame-level AUC on two public datasets, respectively.
Optimised calibration of machine vision system for close range photogrammetry based on machine learning

Real-time inspection of large mechanical parts manufacturing using camera-based scanning systems are increasingly adopted in industry 4.0. It leads to take preventive actions during the manufacturing process and then to fabricate mechanical parts right-first-time with respect to specified tolerances. Therefore, the use of camera-based scanners requests a preliminary calibration process. It consists on estimating the intrinsic and extrinsic parameters required to relate the 3D world point to its projection on the image plane. Since selection of the calibration grid poses affect the calibration quality, one approach-based machine learning (ML-approach) is proposed including the polynomial approximation of the reprojection errors function of 6 degree of freedom (DoF) combined with particle swarm optimization (PSO). Synthetic and experimental evaluations have been performed while assessing the performance of the proposed ML-approach. The synthetic evaluation reveals a better convergence of the intrinsic and extrinsic parameters in comparison to recent published calibration methods by Wizard (CW-method) and Rojtberg (R-method). The experimental evaluation of the ML-approach shows an average error RE < 12 µm and a sub-micrometre repeatability, which confirm the benefit of using machine vision-based scanning systems for the inspection of large volume parts in real time.
Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

Abstract Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ( $${\textsc {TransMED}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>T</mml:mi> <mml:mstyle> <mml:mi>R</mml:mi> <mml:mi>A</mml:mi> <mml:mi>N</mml:mi> <mml:mi>S</mml:mi> <mml:mi>MED</mml:mi> </mml:mstyle> </mml:mrow> </mml:math> ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>T</mml:mi> <mml:mstyle> <mml:mi>R</mml:mi> <mml:mi>A</mml:mi> <mml:mi>N</mml:mi> <mml:mi>S</mml:mi> <mml:mi>MED</mml:mi> </mml:mstyle> </mml:mrow> </mml:math> ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>T</mml:mi> <mml:mstyle> <mml:mi>R</mml:mi> <mml:mi>A</mml:mi> <mml:mi>N</mml:mi> <mml:mi>S</mml:mi> <mml:mi>MED</mml:mi> </mml:mstyle> </mml:mrow> </mml:math> ’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.
Enriching Facial Anti-Spoofing Datasets via an Effective Face Swapping Framework

In the era of rapid development of the Internet of things, deep learning, and communication technologies, social media has become an indispensable element. However, while enjoying the convenience brought by technological innovation, people are also facing the negative impact brought by them. Taking the users’ portraits of multimedia systems as examples, with the maturity of deep facial forgery technologies, personal portraits are facing malicious tampering and forgery, which pose a potential threat to personal privacy security and social impact. At present, the deep forgery detection methods are learning-based methods, which depend on the data to a certain extent. Enriching facial anti-spoofing datasets is an effective method to solve the above problem. Therefore, we propose an effective face swapping framework based on StyleGAN. We utilize the feature pyramid network to extract facial features and map them to the latent space of StyleGAN. In order to realize the transformation of identity, we explore the representation of identity information and propose an adaptive identity editing module. We design a simple and effective post-processing process to improve the authenticity of the images. Experiments show that our proposed method can effectively complete face swapping and provide high-quality data for deep forgery detection to ensure the security of multimedia systems.
Robust Adversarial Reinforcement Learning with Dissipation Inequation Constraint

Robust adversarial reinforcement learning is an effective method to train agents to manage uncertain disturbance and modeling errors in real environments. However, for systems that are sensitive to disturbances or those that are difficult to stabilize, it is easier to learn a powerful adversary than establish a stable control policy. An improper strong adversary can destabilize the system, introduce biases in the sampling process, make the learning process unstable, and even reduce the robustness of the policy. In this study, we consider the problem of ensuring system stability during training in the adversarial reinforcement learning architecture. The dissipative principle of robust H-inﬁnity control is extended to the Markov Decision Process, and robust stability constraints are obtained based on L2 gain performance in the reinforcement learning system. Thus, we propose a dissipation-inequation-constraint-based adversarial reinforcement learning architecture. This architecture ensures the stability of the system during training by imposing constraints on the normal and adversarial agents. Theoretically, this architecture can be applied to a large family of deep reinforcement learning algorithms. Results of experiments in MuJoCo and GymFc environments show that our architecture effectively improves the robustness of the controller against environmental changes and adapts to more powerful adversaries. Results of the flight experiments on a real quadcopter indicate that our method can directly deploy the policy trained in the simulation environment to the real environment, and our controller outperforms the PID controller based on hardware-in-the-loop. Both our theoretical and empirical results provide new and critical outlooks on the adversarial reinforcement learning architecture from a rigorous robust control perspective.
A Causal Inference Look at Unsupervised Video Anomaly Detection

Unsupervised video anomaly detection, a task that requires no labeled normal/abnormal training data in any form, is challenging yet of great importance to both industrial applications and academic research. Existing methods typically follow an iterative pseudo label generation process. However, they lack a principled analysis of the impact of such pseudo label generation on training. Furthermore, the long-range temporal dependencies also has been overlooked, which is unreasonable since the definition of an abnormal event depends on the long-range temporal context. To this end, first, we propose a causal graph to analyze the confounding effect of the pseudo label generation process. Then, we introduce a simple yet effective causal inference based framework to disentangle the noisy pseudo label's impact. Finally, we perform counterfactual based model ensemble that blends long-range temporal context with local image context in inference to make final anomaly detection. Extensive experiments on six standard benchmark datasets show that our proposed method significantly outperforms previous state-of-the-art methods, demonstrating our framework's effectiveness.