Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Support Vector Machine, K-Nearest-Neighbor, Artificial Neural Networks, and Recurrent Neural networks in Gender Recognition from Voice Signals

Nowadays, biometric data is the most common used data in the field of security. Audio signals are one of these biometric data. Voice signals have used frequently in cases such as identification, banking systems, and forensic cases solution. The aim of this study is to determine the gender of voice signals. In the study, many different methods were used to determine the gender of voice signals. Firstly, Mel Frequency kepstrum coefficients were used to extract the feature from the audio signal. Subsequently, these attributes were classified with support vector machines, k-nearest neighborhood method and artificial neural networks. At the other stage of the study, it is aimed to determine gender from audio signals without using feature extraction method. For this, recurrent neural networks (RNN) was used. The performance analyzes of the methods used were made and the results were given. The best accuracy, precision, recall, f-score in the study has found to be 87.04%, 86.32%, 88.58%, 87.43% using K-Nearest-Neighbor algorithm.
High-Resolution and Accurate Topography Reconstruction of Mount Etna from Pleiades Satellite Data

The areas characterized by dynamic and rapid morphological changes need accurate topography information with frequent updates, especially if these are populated and involve infrastructures. This is particularly true in active volcanic areas such as Mount (Mt.) Etna, located in the northeastern portion of Sicily, Italy. The Mt. Etna volcano is periodically characterized by explosive and effusive eruptions and represents a potential hazard for several thousands of local people and hundreds of tourists present on the volcano itself. In this work, a high-resolution, high vertical accuracy digital surface model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set. We believe that this is the first time that the ASP using Pleiades imagery has been applied to Mt. Etna with sub-meter vertical root mean square error (RMSE) results. The model covers an area of about 400 km2 with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical RMSE of 0.78 m. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short amount of processing time, making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions.
Deep eigen-filters for face recognition: Feature representation via unsupervised multi-structure filter learning

Training deep convolutional neural networks (CNNs) often requires high computational cost and a large number of learnable parameters. To overcome this limitation, one solution is computing predefined convolution kernels from training data. In this paper, we propose a novel three-stage approach for filter learning alternatively. It learns filters in multiple structures including standard filters, channel-wise filters and point-wise filters which are inspired from variations of CNNs' convolution operations. By analyzing the linear combination between learned filters and original convolution kernels in pre-trained CNNs, the reconstruction error is minimized to determine the most representative filters from the filter bank. These filters are used to build a network followed by HOG-based feature extraction for feature representation. The proposed approach shows competitive performance on color face recognition compared with other deep CNNs-based methods. Besides, it provides a perspective of interpreting CNNs by introducing the concepts of advanced convolutional layers to unsupervised filter learning.
Augmented Reality for Education; AR Children's Book

By augmenting the real world with virtual information, Augmented Reality provides new possibilities for education. Education process should be all about creativity & interaction. A child learns the most when the lesson is interactive. So, it's necessary to provide them with a platform where they can interact with the subject, for their mental growth. This app is an approach to fulfill that goal.
Implementation of Haversine formula for school location tracking

Abstract The School Location Information Application is an application developed to produce various information and it can be seen and distributed in order to support school tracking and their locations. School Location Information Application is done by collecting data analysis, design, and implementation stages. This application is made by Android base for clients using Java programming language and the server base on web to process data using HTML and PHP accompanied by SQL as the database manager. However, The Haversine Formula method is used to determine distance between user’s location and school location that they want. The result of this application development is expected to be able to solve some problems related to the school tracking and to support the users to easily find the closest school with available information.
A Security-Enhanced Monitoring System for Northbound Interface in SDN using Blockchain

In Software-Defined Networking (SDN), Northbound Interface provides APIs, which allow network applications to communicate with SDN controllers. However, a malicious application can access to SDN controller and perform illegal activities via these APIs. Although some studies proposed AAA (Authentication, Authorization, Accounting) systems to protect SDN controllers from malicious applications, their proposed systems also exist several limitations. Attackers can compromise a system, then modify its database or files to gain higher privileges. This system can be taken down because of Single Point of Failure threat. To enhance security for the Northbound interface, we propose a novel system using blockchain, namely BlockAS. It is used to authenticate, authorize and monitor accessing critical controller resources from applications. Specifically, BlockAS leverages blockchain features to maintain the immutability and decentralization of credential data. Our proposed system has five key properties: immutability of database, decentralization, authentication, authorization, and accounting to enhance security for SDN controller and its offered services.
Multiview 3D Markerless Human Pose Estimation from OpenPose Skeletons

Despite the fact that marker-based systems for human motion estimation provide very accurate tracking of the human body joints (at mm precision), these systems are often intrusive or even impossible to use depending on the circumstances, e.g. markers cannot be put on an athlete during competition. Instrumenting an athlete with the appropriate number of markers requires a lot of time and these markers may fall off during the analysis, which leads to incomplete data and requires new data capturing sessions and hence a waste of time and effort. Therefore, we present a novel multiview video-based markerless system that uses 2D joint detections per view (from OpenPose) to estimate their corresponding 3D positions while tackling the people association problem in the process to allow the tracking of multiple persons at the same time. Our proposed system can perform the tracking in real-time at 20–25 fps. Our results show a standard deviation between 9.6 and 23.7 mm for the lower body joints based on the raw measurements only. After filtering the data, the standard deviation drops to a range between 6.6 and 21.3 mm. Our proposed solution can be applied to a large number of applications, ranging from sports analysis to virtual classrooms where submillimeter precision is not necessarily required, but where the use of markers is impractical.
Risk Management Framework for Distributed Software Team: A Case Study of Telecommunication Company

Distributed software development (DSD) has grown rapidly over the last few years and present definite risk to the software industry which need to be carefully analyzed and managed. The risks relating to DSD include lack of trust, ineffective communication, time zone difference, cultural differences such as language and corporate culture along with knowledge sharing challenges. Software risk management approach in DSD, however, is still inadequate and needed further attention. The aim of this paper is to identify the components involved in risk management process related to DSD, and finally to enhance existing risk management framework used in the organization to accommodate the distributed nature of the team. The quantitative approach which is survey method has been chosen as an appropriate research method to achieve the objectives of this paper. The results show that communication is the most prevalent risk faced by the team members in the current risk management practice. The data from the literature and survey were used to expand the list of identified risk in the organization and integrates communication element into the existing framework. The communication element emphasized on the collaboration and commitment of the stakeholders from every site so as to improve risk management in a distributed context.
Reliable and Dynamic Appearance Modeling and Label Consistency Enforcing for Fast and Coherent Video Object Segmentation With the Bilateral Grid

We propose a novel optimization framework for video object segmentation, given the initial annotations of objects in the keyframes of an input video sequence. In this work, video data is represented by a Markov Random Field model, and segmentation is achieved by finding the minimum graph cut label assignment. More specifically, we first create a bilateral representation of the input video sequence which reduces the size of the graph that the min-cut must operate on. We then introduce dynamic appearance models to learn the segmentation likelihoods, and the reliability of likelihoods is measured to identify false likelihoods that may cause segmentation errors. Thus, the model accurately describes changes in the object's appearance that have evolved over time. Furthermore, we augment spatial and temporal connections using a soft higher-order potential, ensuring long-range label consistency in the segmentation. We provide extensive analysis and evaluation with respect to the influence of each component of the framework through the ablation study. Experiments on three benchmark datasets (DAVIS 2016, YouTube-Objects and SegTrack v2) show that our method achieves competitive performance compared to state-of-the-art while having the order of magnitude faster runtime.
“Passwords protect my stuff”—a study of children’s password practices

Abstract Children use technology from a very young age and often have to authenticate. The goal of this study is to explore children’s practices, perceptions, and knowledge regarding passwords. Given the limited work to date and that the world’s cyber posture and culture will be dependent on today’s youth, it is imperative to conduct cyber-security research with children. We conducted surveys of 189 3rd to 8th graders from two Midwest schools in the USA. We found that children have on average two passwords for school and three to four passwords for home. They kept their passwords private and did not share with others. They created passwords with an average length of 7 (3rd to 5th graders) and 10 (6–8th graders). But, only about 13% of the children created very strong passwords. Generating strong passwords requires mature cognitive and linguistic capabilities which children at this developmental stage have not yet mastered. They believed that passwords provide access control, protect their privacy and keep their “stuff” safe. Overall, children had appropriate mental models of passwords and demonstrated good password practices. Cyber-security education should strive to reinforce these positive practices while continuing to provide and promote age-appropriate developmental security skills. Given the study’s sample size and limited generalizability, we are expanding our research to include children from 3rd to 12th graders across multiple US school districts.
Intel Distribution of OpenVINO Toolkit: A Case Study of Semantic Segmentation

We provide an overview of the Intel Distribution of OpenVINO toolkit. The application of the OpenVINO toolkit is represented on the case study of semantic segmentation of on-road images. We provide a step-by-step tutorial for the problem solution based on the Dilation10 model, trained on the Cityscapes dataset. The Inference Engine component of the OpenVINO toolkit is used to implement inference of deep model. We focus on synchronous inference mode. Comparison of on-road semantic segmentation models supported by the OpenVINO toolkit is provided. Performance analysis of the Dilation10 inference implemented using various deep learning tools is carried out.
Web Application Security: An Investigation on Static Analysis with other Algorithms to Detect Cross Site Scripting

Among web application vulnerabilities, XSS is the most frequently occurring. Where a web application accepts a user-input, it is possible for such vulnerability to inject malicious scripts. The greater part of the literature concentrated on the application of static analysis in order to locate XSS vulnerabilities. The reason for this is its capability of achieving effectively a 100 percent code coverage and observing every path of the program. Nevertheless, the main restriction of static analysis, being the false positive rate shown in the results, continues. Consequently, researchers began to merge static analysis with other algorithms, such as genetic algorithm, machine learning and pattern matching. This is to improve the XSS detection results as well as the static analysis run time. This essay defines the algorithms which formerly improved the static analysis outcomes regarding XSS vulnerability detection. Furthermore, each method's restriction was mentioned in which the studies continue to lack an efficient detection of XSS vulnerability in PHP web application.
CAGE: Constrained deep Attributed Graph Embedding

In this paper we deal with complex attributed graphs which can exhibit rich connectivity patterns and whose nodes are often associated with attributes, such as text or images. In order to analyze these graphs, the primary challenge is to find an effective way to represent them by preserving both structural properties and node attribute information. To create low-dimensional and meaningful embedded representations of these complex graphs, we propose a fully unsupervised model based on Deep Learning architectures, called Constrained Attributed Graph Embedding model (CAGE). The main contribution of the proposed model is the definition of a novel two-phase optimization problem that explicitly models node attributes to obtain a higher representation expressiveness while preserving the local and the global structural properties of the graph. We validated our approach on two different benchmark datasets for node classification. Experimental results demonstrate that this novel representation provides significant improvements compared to state of the art approaches, also showing higher robustness with respect to the size of the training data.
Kalman-like filtering with intermittent observations and non-Gaussian noise

The paper concerns the sub-optimal filtering problem when the measurement signal is sent through an unreliable channel and the noise signals are not necessarily Gaussian. In particular, we assume that the measurement packet losses are modeled by an i.i.d. Bernoulli sequence with known probability mass function, and the moments of the (generally) non-Gaussian noise sequences up to the fourth order are known. By mean of a suitable rewriting of the system through an output injection term, and by considering an augmented system with the second-order Kronecker power of the measurements, an optimal solution among the quadratic transformations of the output is provided. Numerical simulations show the effectiveness of the proposed method.
Risk Assessment Using NIST SP 800-30 Revision 1 and ISO 27005 Combination Technique in Profit-Based Organization: Case Study of ZZZ Information System Application in ABC Agency

Risk management is a practical step in handling risk scenarios in an organization, including in the field of information security. There are many techniques used to carry out information security risk assessments. One of them is a combination technique using ISO 27005 and NIST SP 800-30 revision 1. Previous research proved that the combination technique could be implemented in a non-profit organization (government). However, the detailed risk assessment steps are not explained clearly yet. Thus, raising the question of whether this new approach can be utilized in a common organization or not (not only non-profit but also profit organization). This research focuses on information security risk assessment by implementing the combination technique in a profit organization using semi-quantitative methods. The result, the combination technique can be used in common organizations both profit and non-profit with clear step by step translation.
Appearance and shape based image synthesis by conditional variational generative adversarial network

Abstract Person image synthesis based on shape and appearance using deep generative models opens the door in mickle applications, such as person re-identification (ReID) and movie industry. The methods of image synthesis are driven by producing the image of an object directly, which fail to recover spatial deformations when images are generated. In this paper, we present a conditional variational generative adversarial network (CVGAN) to synthesize desired images guided by target shape by modeling the inherent interplay between shape and appearance. Firstly, the shape and appearance of the given images are disentangled by adopting variational inference, which enables us to generate person images with arbitrary shapes. Secondly, to preserve the details and generate photo-realistic images, the Kullback–Leibler (KL) loss is adopted to reduce the gap between the condition image and generated image. Thirdly, to prevent partly gradient vanishing problem for training our framework stably, we propose combined general learning method, where the discriminative network leverages least squares loss. In addition, we experiment on COCO, DeepFashion and Market-1501 datasets, and results demonstrate that VGAN significantly improves the synthesis of images on discriminability, diversity and quality over the existing methods.
CryptSQLite: SQLite With High Data Security

SQLite, one of the most popular light-weighted database system, has been widely used in various systems. However, the compact design of SQLite did not make enough consideration on user data security. Specifically, anyone who has obtained the access to the database file will be able to read or tamper the data. Existing encryption-based solutions can only protect data on storage, while still exposing data when in computation. In this article, we combine the Trusted Execution Environment(TEE) technology and the authenticated encryption scheme, proposed and developed the CryptSQLite, a high security SQLite database system, which protects both the confidentiality and integrity of users' data. Our security analysis proves that CryptSQLite can protect data confidentiality and integrity. Our implementation and experiments indicate that CryptSQLite incurs an average of 21 percent of extra time for SQL statement executions, compared with traditional encryption-based solutions that failed to offer rigorous security guarantees.
Joint Learning of Local and Global Context for Temporal Action Proposal Generation

Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content. This problem requires methods not only generating proposals with precise temporal boundaries, but also retrieving proposals to cover ground truth action instances with high recall and high overlap using relatively fewer proposals. To address these difficulties, we introduce an effective and efficient proposal generation method, named Local-Global Network (LGN), by which local and global contexts are jointly learned to generate high quality proposals. Locally, LGN first locates temporal boundaries with high starting and ending probabilities separately, then directly combines these boundaries as proposals. Globally, LGN evaluates the actionness probability of multiple-durations temporal regions simultaneously using temporal convolutional layers and anchor mechanism. Finally, we combine the boundary probabilities of each proposal with actionness probability of matched temporal regions as the confidence score, which is used for retrieving proposals. We conduct experiments on two datasets: ActivityNet-1.3 and THUMOS-14, where LGN outperforms other state-of-the-art methods with both high recall and high temporal precision. Finally, further experiments demonstrate that by combining existing action classifiers, our method significantly improves the state-of-the-art temporal action detection performance.
Feature-specificity in visual statistical summary processing

Visual statistical summary processing enables people to extract the average feature of a set of items rapidly and accurately. Previous studies have demonstrated independent mechanisms for summarizing low (e.g. color, orientation) and high-level (facial identity, emotion) visual information. However, no study to date has conclusively determined whether there are feature-specific summarization mechanisms for low-level features or whether there are low-level, feature agnostic summarization mechanisms. To address this issue, we asked participants to report either the average orientation or the average size from a set of lines where both features varied. Participants completed these tasks either in single-task or mixed-task conditions; in the latter, successful performance required extraction of both summaries concurrently. If there were feature-specific summarization mechanisms that could operate in parallel, then errors in mean size and mean orientation tasks should be independent, in both single and mixed task conditions. On the other hand, a central domain-general mechanism for low-level summarization would imply a correlation between errors for both features and greater error in the mixed than single task trials. In Experiment 1, we found that there was no correlation between the mean size and mean orientation errors and performance was similar across single and mixed-task conditions, suggesting that there may be independent summarization mechanisms for size and orientation features. To further test the feature-specificity account, in Experiment 2 and 3 (with mask), we manipulated the display duration to determine whether there were any differences in the summarization of earlier (orientation) vs. later (size) features. While these experiments replicated the pattern of results observed in Experiment 1, at shorter display durations, no differences emerged across features. We argue that our data is consistent with independent, multi-level feature-specific statistical summary mechanisms for low-level visual features.