Reviewerly/exHarmony huggingface dataset:
Preparing for National Cyber Crises Using Non-linear Cyber Exercises

Cyber exercises are a well-received and established means to strengthen the problem-solving skills of personnel and to prepare staff for future cyber incidents. While this concept seems to work for the majority of expected issues, where practicing the application of specific processes, tools and methods to mitigate the effects of large-scale cyber attacks is key, existing cyber exercise approaches are just of limited use for crises management. The reason for this lies in the very nature of a crisis. While ‘common’ incidents appear to be more predictable and can usually be dealt with thoroughly prepared standard procedures and well-rehearsed responses, crises however, are inherently uncertain, and off-the-shelf solutions may even be counterproductive. Complex decisions are to be made in short time-frames, influenced by a lot more stakeholders compared to internal incidents, including regulators, the media, and even the general public. These decisions can barely be guided by prepared plans or checklists, thus new forms of preparation are required, which challenge the participants to practice decision making under pressure, but further give them the opportunity to re-consider choices, walk alternative paths and enable them to find the best possible solution for a given situation. For this purpose, this paper discusses a new approach for non-linear cyber exercises, which allow branching points to develop a storyline, and employ new techniques, such as ‘Fast Forward’ to quickly progress to the critical stages of long-lasting crises, ‘Playback’ to consolidate gained skills, and ‘Pause-Adapt-Repeat’ to play through alternative paths. In this paper, we discuss limiting factors of today’s cyber exercises for large-scale cyber crises preparation, and introduce concepts for non-linear exercises to compensate these issues.
Features Extraction for Robust Face Recognition Using GLCM and CS-LBP

For fast and efficient automatic identity verification, biometric technology has developed rapidly. As one of the useful biometric identification technologies is face recognition. The applications of the face identification system-wide such as criminal detection, security verification, credit card verification, medicine, video conference, and other occasions. The main elements of the pattern recognition system include preprocessing, feature extraction, and classification. The feature extraction stage is concerned in this paper in order to suggest a robust algorithm useful in the classification stage. Many types of research try to recognize the face utilizing various algorithms, comparison among these algorithms is presented in this paper to show the effect of the feature extraction algorithm on human face classification. The main goal of this research is to combine Gray Level Co-Occurrence Matrix (GLCM) and Center-Symmetric Local Binary Pattern (CS-LBP) in a manner way to be used for extracting the texture feature of the human face classification. The experimental result explains that the proposed method given high accuracy in recognizing the human face where the proposed algorithm capable of recognizing 28 humans under different poses and illumination conditions.
QKD BB84. A Taxonomy

Quantum key distribution has been the focus of an important number of studies due to benefits that a widespread adoption would bring to the ever-changing technological world. For this reason, an effective way to organize the available literature on the topic so as to identify key components as well as open challenges, is needed. The present work, upon analyzing the literature, proposes a taxonomy which tackles two different approaches, one being theory-focused whereas the other considers a practical scenario with the purpose of providing an overview of the structure followed. Furthermore, after a description of the well-known BB84 protocol, a brief review of some advancements is provided. Finally, the proposed taxonomy is utilized to classify related works, bringing to light some of the open challenges that are yet to be fully developed.
A Deep Convolutional LSTM for ADLs Classification of the Elderly

Activity analysis systems or activity recognition systems for the elderly is recently a part of the smart home systems design. This assisted system normally helps the elderly to live alone in a house, safely and improve a quality of life. Therefore, learning to recognize which activities are safe is necessary for classifying the activity of the elderly. Furthermore, this information will give us some insights to understand the basic daily lives of the elderly and it also helps us to monitor activity and health information of the elderly. In this paper, we collected activities data using the multi-sensor motion sensors embedded inside the smartwatch (Fitbit). We also present the novel method for detecting and recognizing the activity using Deep Convolutional LSTM. In brief, this paper shows that the proposed method yields 88.425% of accuracy for activity classification. The paper also compares the results with our previous work which used Backpropagation Neural Networks as a classifier (78% of accuracy).
AFES: An Advanced Forensic Evidence System

News media often contain reports that raise doubt related to policing operations. We examine the question of how to improve policing integrity during the execution of search warrants and provide an outline for law enforcement search warrants and digital forensic analysis procedures. Existing techniques for improving the integrity of search warrants are reviewed, limitations are noted, and we propose an Advanced Forensic Evidence System (AFES) to address these limitations.AFES provides an immutable record and biometric authentication of the officers present during the execution of a search warrant, time and location, video recording, seizure record, contemporaneous notes, and photographs. AFES records digital evidence items, imaging details, evidence hashes, provides an access control system, and an immutable record of access to all stored items. AFES uses a permissioned distributed ledger prototype, called Scrybe, developed under NSF aegis, to ensure evidence seizure integrity. Scrybe is run as multiple blockchain instances at law enforcement, prosecution, judicial, and defence organisations to ensure that an immutable record is maintained.
Improving Anticompression Robustness of JPEG Adaptive Steganography Based on Robustness Measurement and DCT Block Selection

With the development of the Internet, social network platforms (SNPs) have become the most common channel for image sharing. As a result, transmitting stego images in the public channels gives steganographers the best opportunity to transmit secret messages with behavioral security preserved. However, the SNPs typically compress uploaded images and damage the weak signal of steganography. In this study, a robust JPEG steganographic scheme based on robustness measurement and cover block selection (CBSRS) is proposed. We first design a deep learning-based model to fit the blockwise change rate of coefficients after JPEG recompression. Then, a cover block selection strategy is proposed to improve the robustness by optimizing the joint distortion function of transmission costs and classic costs. Moreover, by embedding indicator of cover block selection in chrominance channels of JPEG images, a shareable cover construction scheme is designed to solve the problem of auxiliary information transmission. The experimental results show that our proposed framework improves robustness while maintaining statistical security. Comparing with state-of-the-art methods, the framework achieves better performance under given recompression channels.
Analysis and Recognition of Emotions from Voice Samples Using Ant Colony Optimization Algorithm

In this work, the authors attempt to an improved speech emotion recognition (SER) using ant colony optimization (ACO) algorithm. Observation shows mostly discussed spectral features consider the entire frequency range hence containing irrelevant information. The desired modeling requires a larger memory, reduces the system response, and decreases the accuracy. Thus, the authors focus only on the spectral roll-off (SR), spectral centroid (SC), and spectral flux (SF), log energy, and formants at a few chosen sub-bands for the intended analysis. The emotional voice samples have been collected from the surrey audio-visual expressive emotion (SAVEE) dataset which is easily accessible and is in the English language. The ACO algorithm is further explored to develop a more discriminative and relevant feature set of the baseline techniques. Finally, the individual optimized feature sets are concatenated to develop suitable identification system models. The K-nearest neighbor (KNN) classifier has been chosen for the proposed investigation due to its simplicity and suitability in the reduced feature domain. Results show the hybridized optimized feature set using the ACO technique has indeed improved the SER accuracy as compared to the baseline feature sets.
UAV Detection Using Split-Parallel CNN For Surveillance Systems

Commercial drones have become available to everyone with different sizes and shapes. Many are equipped with cameras and some with signal sabotage devices, the scariest scenario is that there are websites that offers weapons which can be attached to the drone. All those security threats either for privacy matters or people's safety, encouraged the researchers to find an intelligent system that can be implemented into the surveillance systems to classify unauthorized UAVs that are flying in a restricted area. This paper proposes a system that detects UAVs by acquiring RGB images via sensor then apply them to a convolutional neural network (CNN) that behave as an object classifier. Proposing Split-Parallel Cross Stage Partial DenseNet (PCSPDensenet) that is built from a modified CSPDenseNet. By splitting the feature map in two parts. Then, make each part flow in different side of the parallel network. The proposed network shows simulation results of an increment in the precision and showed higher <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AP_{50}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AP_{75}$</tex> at higher frame rate on the UAV dataset With lower computational complexity.
A collaborative game theory approach for determining the feasibility of a shared AS blockchain infrastructure

The paper studies the feasibility of building a shared AS blockchain infrastructure employing a collaborative game theory approach. Game theory is used due to its versatility on analyzing situation where the outcome depends on the actions of several actors (AS) and on the estimated payoff when an actor makes a decision. The games chosen are in the category of Cooperative Game type, where multiple actors cooperate and form a coalition which they join or leave based upon their payoffs/reward. PFG is the basis of the algorithm implemented to demonstrate the feasibility of integrating a blockchain solution in an AS federation and to validate the reason for this integration. A mechanism of incentives is proposed to stimulate the cooperation of multiple AS.
MQDS: An energy saving scheduling strategy with diverse QoS constraints towards reconfigurable cloud storage systems

Nowadays, cloud storage systems are usually constructed by heterogeneous disks with reconfigurable speed for energy efficiency. Scheduling tasks with diverse QoS (Quality of Service) requirements to disks with different speeds is seldom considered in state-of-the-art mainstream disk scheduling algorithms. This paper proposes a multi-QoS disk scheduling strategy (MQDS), aiming to support energy-saving and diverse QoS constraints towards reconfigurable heterogeneous cloud storage systems. Three algorithms are designed in MQDS: Time Prior Disks Algorithm (TPDS) for time-sensitive tasks, Cost Prior Disks Algorithm (CPDS) for cost-sensitive tasks, and Benefit Function-based Disks Algorithm (BFDS) for tasks with time-varying QoS requirements. CloudSimDisk simulator is extended to evaluate the response time, energy consumption and cost expenditure performance of the proposed algorithms, for comparison with the extended Robin Round Disk scheduling algorithm (E-RRDS). Extensive experimental results demonstrate that the three proposed algorithms outperform E-RRDS. TPDS consumes shortest response time and CPDS has the least cost; and BFDS achieves balance between TPDS and CPDS, which provide more options to users. It is worth mentioning that all the algorithms in MQDS are more energy efficient than E-RRDS. As a whole, the proposed MQDS is energy efficient and able to accommodate diverse QoS constraints well.
Helmet Wearing Detection Based on YOLOv4-MT

Current networks based on methods such as YOLO, SSD and R-CNN are more complex and large, making it difficult to use on embedded devices without high-performance Gpu support. Aiming at the application of the model in embedded devices, an improved YOLOv4-MT helmet wearing detection model is proposed to keep the detection speed and accuracy basically unchanged while reducing the model volume. YOLOv4-MT combines the advantages of MobileNet and YOLOv4, and uses K-means algorithm to cluster the prior box to improve the network output scale. The backbone module of YOLOv4 is replaced by the deep separable convolution module of MobileNet. The head part of YOLOv4 is retained and improved, and a new residual module is built in the output part. The CIOU loss function is used to evaluate the loss function. The pre-training model is loaded, and the freezing training is used to freeze part of the weights and train the network parameters of the latter part. Self-made data set based on construction site background, including a variety of complex construction environment. In order to verify the detection effect and performance of the model, several models are compared in NVIDIA GeForce GTX 1650 experimental environment. The new model YOLOv4-MT improved by 7% compared with YOLOv4-Tiny mAP, and reduced by 7 times compared with YOLOv4 model size. Compared with MobileNet-SSD, the detection speed is 4.6 times higher.
Gravity Alignment for Single Panorama Depth Inference

Monocular depth inference methods based on 360° images allow 3D reconstruction of entire rooms with a single capture. However, most state-of-the-art approaches assume gravity-aligned images and are highly sensitive to camera rotations. Such limitations result in poor depth estimates, which may jeopardize further 3D-based applications. Here, we present a pipeline for spherical single-image depth inference supplied by a novel rotation correction module. We show that our gravity alignment module can improve existing single-image depth estimation methods, being also useful for aligning color and depth to the horizon, which is highly desirable in many applications.
Breast Cancer Data Analysis using Machine Learning Approaches

One of the leading cause of death is cancer. Lung cancer is the most common cancer and breast cancer is the second common cancer found in women. Thus sophisticated techniques must be designed to deal with these patients or the data generated from these patients. This system focuses on prediction of breast cancer where it categorizes the tumor as malignant or benign. Specialized machine learning algorithms have been used for creating models like decision trees, logistic regression, random forest, naive Bayes, Support vector machine along with Artificial neural networks which are applied on preprocessed data. Preprocessing of the data was done to check for inadequacies such as missing or null data points, categorical data for variables to contain label value rather than numeric, splitting of data set so as to have training and testing set and feature scaling to put our data set in range. Furthermore, dimensionality reduction methods were used in some datasets to improve the accuracy of the models. Artificial neural networks were used with different optimizers to check for the best performance.
Insulator defect detection in power inspection image using focal loss based on YOLO v4

With the continuous expansion of the power industry, insulators, as one of the important power transmission equipment, have been exposed for a long time, causing wear, string loss and other problems, which threaten the safe operation of the power system. In this paper,yolov4 target detection algorithm based on deep learning is used. Based on CSPDarknet-53 and adding SPP Net, the receptive field is greatly increased. PANet is introduced to strengthen the feature extraction network，and focal loss is used as a function to calculate the loss.Then the models trained using RetinaNet and yolov4 are compared.The experimental data show that the defect detection accuracy can reach 96.2% and the recall rate can reach 97.7%.
A Framework for Real-time Sentiment Analysis of Big Data Generated by Social Media Platforms

Sentiment and Opinion analysis have been of significant interest with the possibilities of creating more meaningful business analytics from using data sources such as social media creating a large-scale implementation using Big Data. There has been a range of implementation, typically focusing on one social media platform and user entered text as input. Recently, efforts have been made to make a real-time implementation of such a sentiment system using API and data streams from social media platforms. There exists a need to create a system that uses multiple input sources from social media in real-time. We present an architecture using existing Big Data technologies to implement a real-time multi-social media input source with a central sentiment extraction and analysis component. The proposal uses Apache Kafka for the ingestion layer, lexicon-based classifier and Spark for the analytical layer, YARN clusters for the tasks execution management, and MongoDB database for the storage layer. The performance of the proposed framework is measured based on different quality metrics.
Studying Comments on Russian Patriotic Actions: Sentiment Analysis Using NLP Techniques and ML Approaches

Due to the increasing number of online social media resources and social networks the problem of natural language processing (NLP) applying machine learning (ML) approaches is becoming an important concept in monitoring non-formalised text data and catching information in real time. Studies in historical memory and appropriate patriotic actions have to be based on detailed people’s moods examination. The presented study contains approaches to analyse the polarity of comments on patriotic actions in Russia. Described scheme of NLP text processing and ML classifier were used in the analysis of two regions. There were used standard approaches to convert natural texts into numerical representation. To identify the polarity of the text several models including linear and deep neural networks were examined. Long-short-term-memory network has demonstrated the best result on validation set and was used to the solve the stated problem.
Robust Training of Optical Neural Network with Practical Errors using Genetic Algorithm: A Case Study in Silicon-on-Insulator-Based Photonic Integrated Chips

Optical neural network (ONN) utilizes light to process a mass amount of information in parallel using photonic integrated chips. It has great potential to bypass the limitation of Moore’s law and overcome the inherent bandwidth bottleneck in electronics enabled by the >10 THz wide optical telecommunications band. One of the main challenges for the realization of ONNs is how to avoid practical errors, including various device parameter errors during fabrication and the limited phase shifter control precision. Characterization of each individual chip is possible but time-consuming. To address this issue, in this paper, we propose a robust method to train a series of ONN chips with practical errors using the genetic algorithm (GA). The effect of different parameter errors on the data classification accuracy is analyzed, including the errors in phase shifters, coupling coefficient or extinction ratio, optical absorption loss, and photodetection noise. As a proof-of-concept demonstration, a simulated feedforward ONN is implemented to identify a customized dataset with four classes and four uncorrelated features. The simulation results show that our proposed method could increase the average classification accuracy from 86% to 96% for 50 erroneous ONN chips, approaching the ideal ONN accuracy of 99.69% and demonstrating the effectiveness for significant enhancement in training robustness against practical errors.
Research on Image Classification Algorithm Based on Convolutional Neural Network

Abstract Nowadays, we are in the information age. Pictures carry a lot of information and play an indispensable role. For a large number of images, it is very important to find useful image information within the effective time. Therefore, the excellent performance of the image classification algorithm has certain influence factors on the result of image classification. Image classification is to input an image, and then use a certain classification algorithm to determine the category of the image. The main process of image classification: image preprocessing, image feature extraction and classifier design. Compared with the manual feature extraction of traditional machine learning, the convolutional neural network under the deep learning model can automatically extract local features and share weights. Compared with traditional machine learning algorithms, the image classification effect is better. This paper focuses on the study of image classification algorithms based on convolutional neural networks, and at the same time compares and analyzes deep belief network algorithms, and summarizes the application characteristics of different algorithms.
X3D Audio Graph for the consistent declarative representation of the W3C Audio API

Modeling of sound propagation is very important for virtual acoustics and virtual reality applications. For that reason, highly advanced computer models for the prediction of sound fields in rooms have already been proposed. However, most of them are complex and require a skilled acoustician to use them effectively. In other words, there is a need for simpler models that are accurate and quick to use, but most importantly should provide a simpler syntax with a hierarchical structure that makes it easy to map user actions to audio parameters. This paper aims to integrating acoustic properties associated with geometric shapes together with 3D spatial sound in the X3D Graphics standard, by exploiting the structure and functionality of Web Audio API, using the audio graph model in order to simplify the development of audio applications. Examples for evaluation lead to useful conclusions and areas for future model development.
A Method to Construct Guidelines for Spanish Comments Annotation for Sentiment Analysis

The application of sentiment analysis in social networks supports the understanding of complaints and claims of users' comments. To train the models that automate this analysis, it is important to construct guidelines that generate a more robust corpus. As far as we know, no related work of guidelines for spanish comments annotation has been found. We propose a method to construct guidelines to annotators reach a consensus in the entire annotation process of spanish comments from social networks. We annotated 3259 spanish comments using our guidelines, where the concordance analysis from our annotators was 84%. We employed our corpus and eight baseline classifiers for sentiment analysis detection, achieving 78.63% as the highest F1-Score with Multilayer Perceptron. Our method is useful to tackle labeling spanish comments which can be used in NLP tasks such as sentiment analysis.