Reviewerly/exHarmony huggingface dataset:
Towards a Novel Cryptanalysis Platform based Regions Of Interest Detection via Deep Learning models

Cryptanalysis is an audit step that leads designers to develop more robust cryptographic algorithms and assess algorithms’ overall performance. The fundamental problem is that the human evaluation of the cryptanalysis results is essential in this process. It optionally allows the remarkable convergence towards a promising result if it is based on better criteria, as it does not allow to find any solutions.To overcome the human intervention in this process we propose, in this work, a new cryptanalysis platform of image permutation-only cipher based on the detection of significant parts (ROIs) implementing the genetic algorithm and two models based on deep learning namely: Faster R-cnn for object detection and Mask R-cnn for segmentation.This is to automate the process of decryption keys evaluation and minimize the search space, which makes it possible to directly determine the permutation key or the most part of it. This work is applicable to color (RGB) images encrypted by pixel permutation techniques. It is independent of the permutation algorithm and it based on cipher text only attack by the advantages of those models exploitation to discover the correlation between adjacent pixels and to ameliorate this significant correlation by the genetic algorithm.
Feasibility of continuous-variable quantum key distribution through fog

Free-space quantum key distribution (QKD) can be very attractive due to the possibility of its flexible and rapid deployment. In spite of the advantages of free-space transmission, there is still a risk of encountering bad weather such as fog. Here we experimentally demonstrate free-space QKD using continuous variables under foggy conditions and estimate achievable transmission distances based on the experimental results. Pessimistically, a weather condition that has visibility of 1 km allows about 0.6-km transmission. An optimistic result, on the other hand, shows that a transmission distance of 1.8 km at visibility of 1.5 km can be achieved. The results suggest that free-space continuous-variable quantum communication systems are potentially applicable in the presence of fog.
Learning and Retrieval Operational Modes for Three-Layer Restricted Boltzmann Machines

We consider a three-layer restricted Boltzmann machine, where the two visible layers (encoding for input and output, respectively) are made of binary neurons while the hidden layer is made of Gaussian neurons, and we show a formal equivalence with a Hopfield model. The machine architecture allows for different learning and operational modes: when all neurons are free to evolve we recover a standard Hopfield model whose size corresponds to the overall size of visible neurons; when input neurons are clamped we recover a Hopfield model, whose size corresponds to the size of the output layer, endowed with an external field as well as additional slow noise. The former stems from the signal provided by the input layer and tends to favour retrieval, the latter can be related to the statistical properties of the training set and tends to impair the retrieval performance of the network. We address this model by rigorous techniques, finding an explicit expression for its free-energy, whence a phase-diagram showing the performance of the system as parameters are tuned.
Boosting Graph Alignment Algorithms

The problem of graph alignment is to find corresponding nodes between a pair of graphs. Past work has treated the problem in a monolithic fashion, with the graph as input and the alignment as output, offering limited opportunities to adapt the algorithm to task requirements or input graph characteristics. Recently, node embedding techniques are utilized for graph alignment. In this paper, we study two state-of-the-art graph alignment algorithms utilizing node representations, CONE-Align and GRASP, and describe them in terms of an overarching modular framework. In a targeted experimental study, we exploit this modularity to develop enhanced algorithm variants that are more effective in the alignment task.
Hardness Prediction for More Reliable Attribute-based Person Re-identification

Recognition of person attributes in surveillance camera imagery is often used as an auxiliary cue in person re-identification approaches. Additionally, increasingly more attention is being payed to the cross modal task of person re-identification based purely on attribute queries. In both of these settings, the reliability of attribute predictions is crucial for success. However, the task attribute recognition is affected by several non-trivial challenges. These include common aspects, such as degraded image quality through low resolution, motion blur, lighting conditions and similar factors. Another important factor in the context of attribute recognition is, however, the lack of visibility due to occlusion through scene objects, other persons or self-occlusion or simply due to mis-cropped person detections. All these factors make attribute prediction challenging and the resulting detections everything but reliable. In order to improve their applicability to person re-identification, we propose to apply hardness prediction models and provide an additional hardness score with each attribute that measures the likelihood of the actual prediction to be reliable. We investigate several key aspects of hardness prediction in the context of attribute recognition and compare our resulting hardness predictor to several alternatives. Finally, we include the hardness prediction into an attribute-based re-identification task and show improvements in the resulting accuracy. Our code is available at https://github.com/Lucas-Florin/hardness-predictor-for-par.
GIAD: Generative Inpainting-Based Anomaly Detection via Self-Supervised Learning for Human Monitoring

Detecting anomalies in human monitoring is an important task in many real-world applications. In addition to typical anomalies of objects or actions which have never been observed before, there are also anomalous combinations where we need to consider the relations of items. Existing methods for visual anomaly detection predominantly rely on global level comparisons for computing anomaly scores without focusing on local differences or relations. Some anomalous combination detection methods usually need labels to obtain good context models, which is not suitable for human monitoring task as being aware of all possible anomalies beforehand is usually impossible. As one of our baselines, there is also a clustering-based method which can detect anomalous combinations of two overlapping regions. However, we consider a more general situation that the regions do not necessarily overlap and propose a different way to detect anomalies. Specifically, we propose a self-supervised learning method, Generative Inpainting-based Anomaly Detection (GIAD), to detect not only typical anomalies but also anomalous combinations. The proposed method employs unmasked areas in the salient regions and the information around them (contextual information) with a designed local and global inpainting loss for recovering masked areas in the regions so as to detect anomalies. We also propose a novel attention-based Gaussian weighting anomaly score by considering the importance of each salient region. Experimental evaluations on two real-world datasets demonstrate that our method outperforms the baselines by \(6.07\%\)–\(21.46\%\) on AUC scores.
Implementation and Comparative Analysis of Symmetric Encryption Model Based on Substitution Cipher Techniques

nowadays, the need to protect information from various attackers is growing. There are asymmetric cryptographic algorithms that offer a good security rate, but at the same time, they consume a lot of system resources and need more encryption time. It is very important to create an algorithm that will have a high level of security and which can be easily implemented in the system. The purpose of this paper is to describe a novel cryptosystem. The encryption algorithm is evaluated according to speed, entropy level, security, and encrypted text size. To evaluate the proposed algorithm, the comparative analysis was done. For comparison, the AES and RSA cryptosystems were used. Experimental research has shown that the proposed cryptosystem has good results in the following criteria: encryption speed, flexibility, needs low system requirements, the ciphertext size. According to the given algorithm, using modern programming technologies we have developed the web application. This software product is easy to implement and fast for use. Experimental research results showed that the proposed encryption method can highly improve transferred information security and has good encryption speed than AES and RSA. To evaluate security and resistance, frequency analysis and entropy research was conducted. Analysis showed that the proposed cryptosystem has a good security level. Security analysis demonstrates that our scheme is secure.
Image Captioning for Near-Future Events from Vehicle Camera Images and Motion Information

Image captioning is a task to generate a sentence explaining an input image. In autonomous driving, image captioning is expected for providing linguistic explanations of autonomous driving control decision-making because it can reduce the psychological burden on passengers and prevent accidents. Current image-captioning methods are limited to generating a caption for an input image and not generating captions for events in the near future. It is important to generate captions for any event that will happen in the near future to prevent accidents and alert passengers. Therefore, we created a task to generate an explanatory sentence of near-future events using images observed from past to present. For this task, we propose a near-future image-captioning method suitable for in-vehicle camera images. Our experiments using the Berkeley Deep Drive eXplanation Dataset showed that the proposed method can appropriately generate captions for near-future events.
A Cost-Efficient Framework for Scene Text Detection in the Wild

Scene text detection in the wild is a hot research area in the field of computer vision, which has achieved great progress with the aid of deep learning. However, training deep text detection models needs large amounts of annotations such as bounding boxes and quadrangles, which is laborious and expensive. Although synthetic data is easier to acquire, the model trained on this data has large performance gap with that trained on real data because of domain shift. To address this problem, we propose a novel two-stage framework for cost-efficient scene text detection. Specifically, in order to unleash the power of synthetic data, we design an unsupervised domain adaptation scheme consisting of Entropy-aware Global Transfer (EGT) and Text Region Transfer (TRT) to pre-train the model. Furthermore, we utilize minimal actively annotated and enhanced pseudo labeled real samples to fine-tune the model, aiming at saving the annotation cost. In this framework, both the diversity of the synthetic data and the reality of the unlabeled real data are fully exploited. Extensive experiments on various benchmarks show that the proposed framework significantly outperforms the baseline, and achieves desirable performance with even a few labeled real datasets.
Hardware-supported remote persistence for distributed persistent memory

The advent of Persistent Memory (PM) necessitates an evolution of Remote Direct Memory Access (RDMA) technologies for supporting remote data persistence. Previous software-based solutions require remote CPU intervention and postpone the visibility of remote persistence. In this paper, we design several hardware-supported RDMA primitives to flush data from the volatile cache of RDMA Network Interface Cards (RNICs) to the PM. We also propose durable RPCs based on the proposed RDMA Flush primitives to support remote data persistence and fast failure recovery. We emulate the performance of RDMA Flush primitives through other RDMA primitives, and compare our proposals with several state-of-the-art RPCs in a real testbed equipped with PM and InfiniBand networks. Experimental results show that our proposals can improve the throughput of RPCs by up to 90%, and reduce the 99th percentile latency by up to 49%. The experimental studies also provide instructive guidelines for designing RDMA-based distributed PM systems.
Using Machine Learning for Image Recommendation in News Articles

The recent developments in the machine learning domain have changed the journalism workflow significantly. A growing interest is being observed in adapting to technologies that can scan, process, filter, and even suggest photos for news articles. This research paper focuses on image selection by harnessing keyword extraction. The terms present in the article are first converted to a machine-readable format and then ranked based on three characteristics, namely first occurrence, term frequency, and entity category. Four variations of machine learning approach and simple statistical calculations are applied for the image selection process followed by that. Then, the highest-ranking terms are used for search query generation that involves a heuristic method predicting threshold probability values for the terms.
Latency and Energy-Aware Load Balancing in Cloud Data Centers: A Bargaining Game Based Approach

With the rapid surge in cloud services, cloud load balancing has become a paramount research issue. The major part of a cloud computing system's operational costs is attributed to energy consumption. Therefore, to provide better QoS, considering the energy minimization factor in load balancing is essential. This paper addresses the latency and energy-aware load balancing problem in a cloud computing system. Specifically, two fundamental performance criteria–response time and energy–for the load balancing problem are considered. To solve this problem, first, the load balancing problem is formulated as an optimization problem. Then it is modeled as a cooperative game so that the solution of the game, called the Nash bargaining solution (NBS), can simultaneously optimize both criteria. The existence and computation of NBS are analyzed theoretically, and an efficient algorithm, called <inline-formula><tex-math notation="LaTeX">${{\sf L}}$</tex-math></inline-formula> atency and <inline-formula><tex-math notation="LaTeX">${{\sf E}}$</tex-math></inline-formula> nergy a <inline-formula><tex-math notation="LaTeX">${{\sf W}}$</tex-math></inline-formula> are load balancIng <inline-formula><tex-math notation="LaTeX">${{\sf S}}$</tex-math></inline-formula> cheme ( <inline-formula><tex-math notation="LaTeX">${{\sf LEWIS}}$</tex-math></inline-formula> ), is proposed to compute the NBS. Further, to assess the efficacy of <inline-formula><tex-math notation="LaTeX">${{\sf LEWIS}}$</tex-math></inline-formula> , it is compared with three other approaches, i.e., <inline-formula><tex-math notation="LaTeX">${\mathsf {Coop\_{RT}}}$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">${\mathsf {Coop\_{EN}}}$</tex-math></inline-formula> , and <inline-formula><tex-math notation="LaTeX">${\mathsf {NCG}}$</tex-math></inline-formula> , on problem instances of various settings. The experimental results show that <inline-formula><tex-math notation="LaTeX">${{\sf LEWIS}}$</tex-math></inline-formula> not only provides less response time while consuming less energy but also gauntness fairness to the end-users.
Fuzzy Logic in Control Theory

A control system regulates the operation of a device or another system with the help of a set of IF–THEN logical rules. In particular, a fuzzy control system analyzes the input data in terms of variables taking continuous values in the interval [0, 1]. Fuzzy logic has the advantage of expressing the solution of a problem in the natural language, which enables the mechanization of tasks that have been already performed successfully by humans. The present paper studies the operation of a fuzzy control system and illustrates it by an application to a building’s central heating boiler.
Analysis of the Desynchronization Attack Impact on the E2EA Scheme

The healthcare IoT system is considered to be a significant and modern medical system. There is broad consensus that these systems will play a vital role in the achievement of economic growth in numerous growth countries. Among the major challenges preventing the fast and widespread adoption of such systems is the failure to maintain the data privacy of patients and the integrity of remote clinical diagnostics. Recently, the author proposed an end-to-end authentication scheme for healthcare IoT systems (E2EA), to provide a mutual authentication with a high data rate between the communication nodes of the healthcare IoT systems. Although the E2EA authentication scheme supports numerous attractive security services to resist various types of attack, there is an ambiguous view of the impact of the desynchronization attack on the E2EA authentication scheme. In general, the performance of the authentication scheme is considered a critical issue when evaluating the applicability of such schemes, along with the security services that can be achieved. Therefore, this paper discusses how the E2EA authentication scheme can resist the desynchronization attack through all possible attack scenarios. Additionally, the effect of the desynchronization attack on the E2EA scheme performance is analyzed in terms of its computation and communication costs, based on a comparison with the recently related authentication schemes that can prevent such attack. Moreover, this research paper finds that the E2EA authentication scheme can not only prevent the desynchronization attack, but also offers a low cost in terms of computations and communications, and can maintain consistency and synchronization between the communication nodes of the healthcare IoT systems during the next authentication sessions.
Unsupervised feature disentanglement for video retrieval in minimally invasive surgery

In this paper, we propose a novel method of Unsupervised Disentanglement of Scene and Motion (UDSM) representations for minimally invasive surgery video retrieval within large databases, which has the potential to advance intelligent and efficient surgical teaching systems. To extract more discriminative video representations, two designed encoders with a triplet ranking loss and an adversarial learning mechanism are established to respectively capture the spatial and temporal information for achieving disentangled features from each frame with promising interpretability. In addition, the long-range temporal dependencies are improved in an integrated video level using a temporal aggregation module and then a set of compact binary codes that carries representative features is yielded to realize fast retrieval. The entire framework is trained in an unsupervised scheme, i.e., purely learning from raw surgical videos without using any annotation. We construct two large-scale minimally invasive surgery video datasets based on the public dataset Cholec80 and our in-house dataset of laparoscopic hysterectomy, to establish the learning process and validate the effectiveness of our proposed method qualitatively and quantitatively on the surgical video retrieval task. Extensive experiments show that our approach significantly outperforms the state-of-the-art video retrieval methods on both datasets, revealing a promising future for injecting intelligence in the next generation of surgical teaching systems.
A secure model on Advanced Fake Image-Feature Network (AFIFN) based on deep learning for image forgery detection

Recent advancements in image editing tools made much impact on analysis of security issues with respect to digital media domain. In specific, the forged images been uploaded to create a panic situation for the users. Those synthesized images with fake content can be used in social media, which may cause several problems. Hence, it is important for the image forensics to detect the forged or manipulated images. With the motive to effectively detect the fake images and to provide security, the proposed work focused on developing an Advanced Fake Image-Feature Network (AFIFN) based on deep learning methods. In this model, Discrete Cosine Transformation (DCT) and Y Cr Cb based image pre-processing is employed. Further, the AFIFN is framed with two-layered network structure, obtaining the pair-wise data as input. The network is trained for differentiating the features between the forged and real images. Additionally, a classification layer is added with the framed AFIFN to detect the input image is forged or not. The results show that the proposed model significantly outperforms the results of other existing models in image forgery detection.
Detecting Potholes Using Image Processing Techniques and Real-World Footage

Roads are the main mode for transportation, nowadays. As we use roads heavily and frequently, it sometimes leads to potholes. Environmental factors also lead to potholes in several ways. These potholes are main reason for many accidents. A scheduled and proper maintenance is required where we need to monitor each and every road. The maintenance of all the roads at a time is not possible because it is not easy to monitor every single place. Moreover, it is due to people’s ignorance to check which causes the formation of potholes that causes unnecessary traffic and many of accidents. To monitor all the roads, this project for pothole detection using image processing techniques is implemented. To test the performance of the proposed system is going to be implemented in a Linux environment using Open CV library. Techniques of image processing detect the potholes on roads and save the data of pothole for road maintenance department. This helps in keeping manual labor to the minimum number. Canny edge detecting technique and along with the contour detecting technique are techniques of image processing which we use in the proposed system. Hough transformation technique in the end gives effective output of potholes, very accurately.
3D Interactive Segmentation With Semi-Implicit Representation and Active Learning

Segmenting complex 3D geometry is a challenging task due to rich structural details and complex appearance variations of target object. Shape representation and foreground-background delineation are two of the core components of segmentation. Explicit shape models, such as mesh based representations, suffer from poor handling of topological changes. On the other hand, implicit shape models, such as level-set based representations, have limited capacity for interactive manipulation. Fully automatic segmentation for separating foreground objects from background generally utilizes non-interoperable machine learning methods, which heavily rely on the off-line training dataset and are limited to the discrimination power of the chosen model. To address these issues, we propose a novel semi-implicit representation method, namely Non-Uniform Implicit B-spline Surface (NU-IBS), which adaptively distributes parametrically blended patches according to geometrical complexity. Then, a two-stage cascade classifier is introduced to carry out efficient foreground and background delineation, where a simplistic Naïve-Bayesian model is trained for fast background elimination, followed by a stronger pseudo-3D Convolutional Neural Network (CNN) multi-scale classifier to precisely identify the foreground objects. A localized interactive and adaptive segmentation scheme is incorporated to boost the delineation accuracy by utilizing the information iteratively gained from user intervention. The segmentation result is obtained via deforming an NU-IBS according to the probabilistic interpretation of delineated regions, which also imposes a homogeneity constrain for individual segments. The proposed method is evaluated on a 3D cardiovascular Computed Tomography Angiography (CTA) image dataset and Brain Tumor Image Segmentation Benchmark 2015 (BraTS2015) 3D Magnetic Resonance Imaging (MRI) dataset.