Reviewerly/exHarmony huggingface dataset:
Accelerating Image Encryption with AES Using GPU: A Quantitative Analysis

From military imaging to sharing private pictures, confidentiality, integrity and authentication of images play an important role in the Internet of modern world. AES is currently one of the most famous symmetric cryptographic algorithms. Performing encryption/decryption of high-resolution images with AES is highly computation-intensive and time-consuming due to their large sizes and the underlying complexity of AES algorithm. Earlier increasing cryptographic complexity meant an increase in the processing time for encryption as well as decryption. Now, with the rise of the powerful GPUs containing thousands of high-performance and efficient cores and with the evolvement of GPU computing, the processing time has been reduced to a fraction of the time it used to take earlier. This paper presents a parallel implementation of AES using NVIDIA CUDA and OpenCV to encrypt images rapidly. We achieved an average speed up of four times on GPU as compared to CPU-only.
Door Recognition Principles in GITA’s Machine Vision

Abstract Nowadays, in the traditional door or object recognition system, people use GluonCV and Tensorflow to help teach machines millions of pictures, and then base on the information in the database collected from the pictures to do the door recognition. While in the new proposal, a new door recognition system based on a machine carrier called GITA is proposed. In the proposal, the recognition elements comprise motionless elements, including door boundary, handle/bar, and ratio of body width and door width, and loco-motor elements, which include people’s gesture, change of door depth and change of illumination. In order to prove it, several different tests based on seven doors of different types are done.
Case-Based Reasoning for Context-Aware Solutions Supporting Personalised Asthma Management

Context-aware solutions have the potential to address the personalisation required for implementing asthma management plans. However, they have limitations to aid people with asthma when their triggers and symptoms are poorly known or changing. Case-Based Reasoning can address these limitations as it can effectively deal with personal constraints in problems that involve evolving context adaptation. This research work proposes to use Case-Based Reasoning together with Context-Aware Reasoning to aid the personalisation of asthma management plans at specific stages of the condition when the triggers and symptoms are not completely known or evolving. The proposal was implemented and evaluated using historical weather and air pollution data, and two control cases that were defined based on a set of interviews. Finally, the benefits and challenges of the proposal are presented and analysed based on the results of the evaluation.
RDFM: Resilient Distributed Factorization Machines

Factorization Machines algorithms have been successfully applied to recommender systems due to their ability to handle data sparsity and the cold-start problem. Their scalability makes it suitable to produce evergrowing complex predictive models, which are based on Big Data without performance degradation. The algorithm has been scaled to contexts of distributed and parallel computation, but in general with the strong assumption that those environments are safe and are not subject to arbitrary errors, malicious attacks, and hardware failures. In this work, we show that a distributed average consensus strategy is capable to deal with unsafe and dynamic learning environments.
Cross-View Action Recognition Using View-Invariant Pose Feature Learned from Synthetic Data with Domain Adaptation

Recognizing human activities from unknown views is a challenging problem since human shapes appear quite differently from different viewpoints. In this paper, we learn a View-Invariant Pose (VIP) feature for depth-based cross-view action recognition. The proposed VIP feature encoder is a deep convolutional neural network that transfers human poses from multiple viewpoints to a shared high-level feature space. Learning such a deep model requires a large corpus of multi-view paired data which is very expensive to collect. Therefore, we generate a synthetic dataset by fitting human physical models with real motion capture data in the simulators and rendering depth images from various viewpoints. The VIP feature is learned from the synthetic data in an unsupervised way. To ensure the transferability from synthetic data to real data, domain adaptation is employed to minimize the domain difference. Moreover, an action can be considered as a sequence of poses and the temporal progress is modeled by recurrent neural network. In the experiments, our method is applied on two benchmark datasets of multi-view 3D human action and has been shown to achieve promising results when compared with the state-of-the-arts.
An Adaptive Denoising Method for Industrial Big Data with Multi-indicator Fusion

With the development of information technology and network technology, as well as sensing technology, more and more data are collected from the processing of producing and machining in industrial sites. Industrial data are usually with noises and errors because the interference of ambient and the fault of sensors. Data preprocessing is necessary for analyzing further. The local polynomial regression is usually used to data denoising because of its characters of simplicity and flexibility, but the smoothing parameter of this algorithm need to be manually set by trial and error. In this paper, an adaptive parameter optimization method is used to calibrate its parameter, that is, the multi-indicator fusion method is used to fuse the root mean square error (RMSE), signal-to-noise ratio (SNR) and smoothness (r) of the denoised signal into a composite evaluation indicator. The smoothing parameter corresponding to the minimum value of the indicator is the optimal smoothing parameter of the denoising algorithm. A data set which is collected from monitoring the temperature field and thermal error of a heavy-duty CNC machine tool is used to validate the method.
Image Registration using Fireworks Algorithm and Chaotic Sequences

The Fireworks algorithm is a relatively new optimization meta-heuristic which has good performances for global optimization problems. For some resources consuming problems, as image registration is, the optimization algorithm's performances are important, especially for real time computing. In this paper, the usage of chaos enhanced Fireworks algorithm for features based image registration is proposed. During evolution, the chaotic sequences are used to compute the position of the newly generated regular sparks. The proposed procedure is applied for biomedical image registration and the performances obtained by using ten different chaotic sequences are compared to those obtained by using the standard algorithm.
The Secret to Popular Chinese Web Novels: A Corpus-Driven Study

What is the secret to writing popular novels? The issue is an intriguing one among researchers from various fields. The goal of this study is to identify the linguistic features of several popular web novels as well as how the textual features found within and the overall tone interact with the genre and themes of each novel. Apart from writing style, non-textual information may also reveal details behind the success of web novels. Since web fiction has become a major industry with top writers making millions of dollars and their stories adapted into published books, determining essential elements of publishable novels is of importance. The present study further examines how non-textual information, namely, the number of hits, shares, favorites, and comments, may contribute to several features of the most popular published and unpublished web novels. Findings reveal that keywords, function words, and lexical diversity of a novel are highly related to its genres and writing style while dialogue proportion shows the narration voice of the story. In addition, relatively shorter sentences are found in these novels. The data also reveal that the number of favorites and comments serve as significant predictors for the number of shares and hits of unpublished web novels, respectively; however, the number of hits and shares of published web novels is more unpredictable.
An Analysis Model of Buffer Overflow Vulnerability Based on FSM

Buffer overflow vulnerabilities have been the most common form of software vulnerabilities. It is very difficult and time consuming to detect possible types of vulnerabilities from a program. This paper proposes an analysis model of buffer overflow vulnerability based on finite state machine (FSM). The model conducts static analysis on source code. And then it analyzes the formation of buffer overflow vulnerabilities and process of data overflow. For the two types of buffer overflow vulnerabilities caused by function call errors and loop copy errors, the corresponding vulnerability analysis model is designed. The vulnerability analysis model proposed in this paper is verified by two scenarios. The experimental results show that the model can detect buffer overflow vulnerability automatically and effectively.
A Hybrid Evolutionary Algorithm for Evolving a Conscious Machine

The paper discloses a novel concept of developing a conscious machine. Human consciousness is a driving factor behind the presented concept. ‘Integrated Information Theory (IIT)’ is applied to the hardware circuits in order to make the circuit(s) with a certain configuration active/alive. We have used an evolutionary algorithm that combines ‘Evolvable Hardware’ with ‘Integrated Information Theory of Consciousness’ to develop a conscious set of machines. Evolvable hardware is simulated by using Darwin’s evolution theory that is related to Genetic Algorithms (GA). Further, IIT is integrated into the results of first GA so as to harness the consciousness factor in circuits with a certain circuit configuration. The results of the evolutionary algorithm are evaluated to validate the proposed concept.
An overview on handwritten documents word spotting

The current paper gives an overview on the technologies used in word spotting especially for handwritten documents. We present recent interesting published researches in this domain. We firstly give a general overview on the steps of the word spotting system, and then we describe a framework of techniques and methods used in each step, at the end we present a summary of these research in a table.
The Concept of the ALMM Solver Knowledge Base Retrieval Using Protégé Environment

The paper presents the concept of using a Protégé environment to extract knowledge about the properties and features of discrete problems described by ALMM technology. The concept of an Intelligent ALMM system was proposed firstly by Dudek-Dyduch E. The main purpose of the ALMM system is not only to solve problems with discreet optimization, but also to help the user in choosing the right method and algorithm for solving them. The authors, based on earlier work connected with component structure based solvers using ALMM technology, proposed the exploitation of description of the properties of individual problems in order to create the corresponding knowledge base. This paper presents an example of a knowledge base structure for the problem of scheduling tasks on a single machine that can be used to solve various optimization tasks. Using the OWL language convention, the architecture of classes and related properties of solved problems: axioms and assertions were proposed. An on-line knowledge base representing specific classes of problems together with its own query language can be a scenario for pre-modeling the architecture of the final application. For the ontological architecture, the examples of knowledge extraction scenarios to solve the optimization problem of the tasks scheduling on a single machine are presented too.
Image Classification Using Deep Learning and Fuzzy Systems

Classification of images is a significant step in pattern recognition and digital image processing. It is applied in various domains for authentication, identification, defense, medical diagnosis and so on. Feature extraction is an important step in image processing which decides the quality of the model to be built for image classification. With the abundant increase in data now-a-days, the traditional feature extraction algorithms are finding difficulty in coping up with extracting quality features in finite time. Also the learning models developed from the extracted features are not so easily interpretable by the humans. So, considering the above mentioned arguments, a novel image classification framework has been proposed. The framework employs a pre-trained convolution neural network for feature extraction. Brain Storm Optimization algorithm is designed to learn the classification rules from the extracted features. Fuzzy rules based classifier is used for classification. The proposed framework is applied on Caltech 101 dataset and evaluated using accuracy of the classifier as the performance metric. The results demonstrate that the proposed framework outperforms the traditional feature extraction based classification techniques by achieving better accuracy of classification.
Teleporting an unknown state using quantum Fisher information parameters

The main aim of this work is to teleporte a two qubit state via quantum teleportation protocol, where a single atomic-field entangled state is considered as a quantum channel. By controlling the relevant parameters encoded in the teleported state we investigate the quantum Fisher information evolution. Our results show that, it is possible to estimate in terms of quantum Fisher information the weight and the phase parameters encoded in the initial teleported state. It shown that, the maximum amount Fisher information appears periodically. Interestingly, heigh estimation is investigated for the maximum amount Fisher information bounds and vice versa.
Elastic-Tweak: A Framework for Short Tweak Tweakable Block Cipher

Tweakable block cipher (TBC), a stronger notion than standard block ciphers, has wide-scale applications in symmetric-key schemes. At a high level, it provides flexibility in design and (possibly) better security bounds. In multi-keyed applications, a TBC with short tweak values can be used to replace multiple keys. However, the existing TBC construction frameworks, including TWEAKEY and XEX, are designed for general purpose tweak sizes. Specifically, they are not optimized for short tweaks, which might render them inefficient for certain resource constrained applications. So a dedicated paradigm to construct short-tweak TBCs (tBC) is highly desirable. In this paper, as a first contribution, we present a dedicated framework, called the Elastic-Tweak framework (ET in short), to convert any reasonably secure SPN block cipher into a secure tBC. We apply the ET framework on GIFT and AES to construct efficient tBCs, named TweGIFT and TweAES. These short-tweak TBCs have already been employed in recent NIST lightweight competition candidates, LOTUS-LOCUS and ESTATE. As our second contribution, we show some concrete applications of ET-based tBCs, which are better than their block cipher counterparts in terms of key size, state size, number of block cipher calls, and short message processing. Some notable applications include, Twe-FCBC (reduces the key size of FCBC and gives better security than CMAC), Twe-LightMAC_Plus (better rate than LightMAC_Plus), Twe-CLOC, and Twe-SILC (reduces the number of block cipher calls and simplifies the design of CLOC and SILC).
Variants of Simulated Annealing for Strip Constrained Binary Tomography

We consider the problem of reconstructing binary images from their row and column sums with prescribed number of strips in each row and column. In a previous paper we compared an exact deterministic and an approximate stochastic method (Simulated Annealing – SA) to solve the problem. We found that the latter one is much more suitable for practical purposes. Since SA is sensitive to the choice of the initial state, in this paper we present different strategies for choosing a starting image, and thus we develop variants of the SA method for strip constrained binary tomography. We evaluate the different approaches on images with varying densities of object pixels.
Album to Family Tree: A Graph Based Method for Family Relationship Recognition

Image-based kinship recognition is an important research area in the social network. Beyond kinship verification, family relationship recognition is a challenging task with little existing literature. In this paper, we propose a pictorial structure based method for automatic family tree construction. Unlike traditional work based on a single image, an album-oriented method can leverage the cooperation of multiple instances associated with the same identity. In this way, the reliability of kinship similarity can be improved. Besides, the family tree structure is used to describe kinship relations among each pair of members. The experimental result shows that the proposed method can construct a family tree effectively and improve the overall accuracy of kinship identification.
Freeform Surface Measurement with Experimental Ray Tracing – Refraction vs. Reflection

Experimental Ray Tracing is the equivalent of Ray Tracing in simulations, but in reality. We have developed and compared two experimental measurement setups to measure freeform surfaces, one based on refraction, one based on reflection.
Correction: MergedTrie: Efficient textual indexing

[This corrects the article DOI: 10.1371/journal.pone.0215288.].
Optimization Design of Internet Fraud Case Based on Knowledge Graph and Case Teaching

Compared with traditional fraud, Internet frauds have many characteristics, such as lots of methods, strong concealment, strong penetration and wide harm. In the recent years, more and more fraud cases occurred continuously, analysis of various fraud cases that have emerged is of great significance in preventing fraud. The fraud cases involve different crowds, platforms, software, purpose, etc. There are similarities and differences between cases. The optimization case is conducive to improving the anti-internet fraud education effect and improving anti-internet technology. Knowledge graph has advantages in reflecting the structure of knowledge relationship. This paper uses the knowledge graph to organize the correlation between internet fraud cases and the relevance of knowledge points within the case, propose a Knowledge Graph Case Teaching (KGCT) model to optimize the internet fraud case teaching, and select the most relevant cases for different crowd to strengthen crowd's awareness of anti-internet fraud.
Contrasting logical sequences in multi-relational learning

In this paper, we present the BeamSouL sequence miner that finds sequences of logical atoms. This algorithm uses a levelwise hybrid search strategy to find a subset of contrasting logical sequences available in a SeqLog database. The hybrid search strategy runs an exhaustive search, in the first phase, followed by a beam search strategy. In the beam search phase, the algorithm uses the confidence metric to select the top k sequential patterns that will be specialized in the next level. Moreover, we develop a first-order logic classification framework that uses predicate invention technique to include the BeamSouL findings in the learning process. We evaluate the performance of our proposals using four multi-relational databases. The results are promising, and the BeamSouL algorithm can be more than one order of magnitude faster than the baseline and can find long and highly discriminative contrasting sequences.
HS-SA-Based Precise Modeling of the Aircraft Fuel Center of Gravity Using Sensors Data

The traditional modeling methods of aircraft fuel center of gravity (CG) based on sensor data have some disadvantages, such as large data storage requirements and low computational efficiency. In this article, a novel hybrid heuristic search-simulated annealing (HS-SA) algorithm is used to reduce the data storage requirements and improve the efficiency of the established models based on sensor data. First, a fuel CG model is established based on the multidimensional interpolation of flight sensors and fuel tank data, which can accurately reflect the nonlinear characteristics of the problem and reduce the data storage needs. Then, the calculation nodes are reasonably selected and optimized based on the proposed HS-SA algorithm to improve the precision of the model of the aircraft fuel CG. The established model of the fuel CG has obvious advantages over traditional methods in improving the temporal efficiency and meeting the storage requirements for sensor data in actual flights. Finally, detailed simulations are conducted based on more than 16,000 sets of sensor data, and the results demonstrate the effectiveness of the proposed HS-SA algorithm.
CalliGAN: Unpaired Mutli-chirography Chinese Calligraphy Image Translation

The style translation of Chinese calligraphy image is a challenging problem: changing the style and layout of strokes while retaining the content attributes like overall structure and combination of radicals. The existing calligraphy character generation methods, such as brush modeling, skeleton rendering, strokes extracting and assembling, etc., are ineffective and difficult to apply to multi-chirography styles and numerous data. The recent neural style transfer methods for general image-to-image style translation tasks can not apply to our problem due to the different meanings of the "style". The GAN-based methods demonstrating good results require image-pairs for training, which is hard to collect in our task. Therefore, in this paper we propose a novel GAN-based model, called CalliGAN, for the mutli-chirography Chinese calligraphy image translation. In CalliGAN, We present a joint optimization method which only requires unpaired multiple chirography sets for training. In our experiment, we build a chirography style dataset called Chiro-4 and then compare our method with various general translation methods. The experiment results demonstrate the validity of our method on calligraphy style translation task.
A Comparative Study of Statistical and Neural Network Models for PLC Network Traffic Anomaly Detection

Protection of systems and computer networks against novel, unknown attacks is currently an intensively examined and developed domain. One of possible solutions to the problem is detection and classification of abnormal behaviors reflected in the analyzed network traffic. In the presented article we attempt to resolve the problem by anomaly detection in the analyzed network traffic described with the use of three different models. We tested two class of models which differed in prediction. The first sorts was composed of ARFIMA and Holt-Winters models which are characterized by statistical dependences. The second sorts, on the other hand, included neural network auto-regression model which are characterized by single hidden layer and lagged inputs for forecasting univariate time series. In order to detect anomalies in the network traffic we used differences between real network traffic and its estimated model. The experiment results confirmed efficiency and effectiveness of the presented method.