Reviewerly/exHarmony huggingface dataset:
Orchestrating Live Immersive Media Services Over Cloud Native Edge Infrastructure

Live streaming is becoming an essential for the entertainment and media industry. Quality of Service (QoS) and reliability is a major factor in live streaming, whereas availability and interaction between users are considered very important. Furthermore, the amount of data those services produce is huge, while the data production rate has dramatically increased the last years. Thus, the use of 5G networking technologies, such as Network Function Virtualization (NFV), appears to be a promising solution for addressing the above-mentioned challenges. To this end, in this paper, we describe how an existing infrastructure of live streaming and social media services can take advantage of the NFV-enabled 5GTANGO Service Platform (SP). Also, in this paper we describe the capabilities of the SP and how it can support such services. To be more specific, an efficient monitoring framework, that supports the monitoring of Service Level Agreements (SLAs) is introduced. It aims to guarantee the QoS that is agreed in a high-level objective in the SLA, while this scenario is applied on an immersive media NS. Apart from the SP architecture and specific components description, the paper also provides explicit implementation requirements, needed for deployment of the presented media service and discusses promising results in terms of service management, monitoring flexibility and SLA assurance.
Blockchain Value Networks

This paper presents my ideas on how blockchain technology and distributed ledger technology in general can facilitate the establishment and management of open value networks and value networks in general. The paper defines value networks, open value networks, business-ecosystems and collaboration spaces. The paper also argues that the Resource-Event-Agent (REA) ontology can offer valuable support for describing the collaboration spaces of which value networks are composed. The paper also discusses the nature, relevance, potential impact and issues of the blockchain technology. Finally, the paper proposes an MDA approach for both smart-contract and blockchain development to address the listed blockchain technology issues.
Innovations in Higher Education in India

The present century is an age of knowledge-based economy. Though India's achievement in the field of higher education in the post-independence period is remarkable, gross enrollment ratio in higher education and the quality of higher education in India is not encouraging. ICT infrastructure is one way of stimulating growth in national innovation and economic productivity. According to NSSO survey in India, in higher education, 85.4% of students are able to operate computers, 81.6% of students are able to search the internet, and 78.2% of students are able to e-mail. The Government of India has taken various innovative strategies in higher education including online courses and facilities. The chapter has also highlighted different digital initiatives in higher education in India. In India MOOCs are offered by SWAYAM, IITBX, mooKIT, and NPTEL. Other digital innovation includes E-PG Pathshala, SWAYAM Prabha, e-Shodh Sindhu, Shodh Gangotri, Shodhganga, AISHE, National Academic Depository (NAD), etc.
Mining Strategies for Completing the Longest Blockchain

Bitcoin is an innovative decentralized cryptocurrency that records transactions in a public log, termed the blockchain.Due to delays in the Bitcoin network, the Bitcoin blockchain has the potential for forming forks.In this paper, we consider a mining strategy in which the player who created the fork can attract other miners by posting rewards and the third party (other miners) has the ability to choose the fork based on their interests.We provide a model to formalize the considered case and analyze its feasibility.Based on certain game theoretical models, the best policies for two players, termed the creator of the fork and the third party, are presented.Finally, some cases of the considered model are simulated, and the average revenue of each player is compared with the theoretical revenue to verify the correctness of our strategy.When the delay varies from 0 to 400 milliseconds, adopting our strategy can acquire 23% more revenue than adopting the default strategy.The result shows that the default strategy of a miner is not always a good choice.
A Cloud Image Data Protection Algorithm with Multilevel Encryption Scheme and Automated- Selection Mechanism

In this paper, we present a cloud image data protection algorithm with a multilevel encryption scheme and automated-selection mechanism to maintain the privacy of cloud data contents. This algorithm is also useful for the protection of personal or commercial data uploaded to the cloud server for real-time applications, monitoring, and transmission. Fundamental and well-known in cryptography, the confusion–diffusion scheme, as well as an automated-selection mechanism (sliding pixel window) were selected as the main motor of the proposed algorithm to cipher images. First, a sliding pixel window is selected to expedite a two-stepped process, whether in small or big images. The confusion stage was designed to drastically change data from plain image to cipher image. The conversion of pixels from decimal to binary and their vertical and horizontal relocation were performed to help in this stage, not only by randomly moving bits, but also by changing the pixel values when they returned to their corresponding decimal values. Meanwhile, the diffusion stage was designed to destroy all possible existing patterns in the sliding pixel window after the confusion stage. Two hyperchaotic systems, together with a logistic map (multilevel scheme), produce pseudorandom numbers to separately conceal the original data of each subplain image through first- and second-level encryption processes. The two-stepped algorithm was designed to be easily implemented by practitioners. Furthermore, the experimental analysis demonstrates the effectiveness and feasibility of the proposed encryption algorithm after being tested using the benchmark “Lena” image, as well as the “Bruce Lee” image, the latter of which is completely different to the first one, statistically speaking.
Permuted Puzzles and Cryptographic Hardness

A permuted puzzle problem is defined by a pair of distributions $$\mathcal{D}_0,\mathcal{D}_1$$ over $$\varSigma ^n$$ . The problem is to distinguish samples from $$\mathcal{D}_0,\mathcal{D}_1$$ , where the symbols of each sample are permuted by a single secret permutation $$\pi $$ of [n]. The conjectured hardness of specific instances of permuted puzzle problems was recently used to obtain the first candidate constructions of Doubly Efficient Private Information Retrieval (DE-PIR) (Boyle et al. & Canetti et al., TCC’17). Roughly, in these works the distributions $$\mathcal{D}_0,\mathcal{D}_1$$ over $${\mathbb F}^n$$ are evaluations of either a moderately low-degree polynomial or a random function. This new conjecture seems to be quite powerful, and is the foundation for the first DE-PIR candidates, almost two decades after the question was first posed by Beimel et al. (CRYPTO’00). However, while permuted puzzles are a natural and general class of problems, their hardness is still poorly understood. We initiate a formal investigation of the cryptographic hardness of permuted puzzle problems. Our contributions lie in three main directions:
Detection of Trojaning Attack on Neural Networks via Cost of Sample Classification

To overcome huge resource consumption of neural networks training, MLaaS (Machine Learning as a Service) has become an irresistible trend, just like SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service) have been. But it comes with some security issues of untrustworthy third-party services. Especially machine learning providers may deploy trojan backdoors in provided models for the pursuit of extra profit or other illegal purposes. Against the redundant nodes-based trojaning attack on neural networks, we proposed a novel detecting method, which only requires the untrusted model to be tested and a small batch of legitimate dataset. By comparing different processes of neural networks training, we found that the embedding of malicious nodes will make their parameter configuration abnormal. Moreover, by analysing the cost distribution of test dataset on network nodes, we successfully detect the trojaned nodes in the neural networks. As far as we know, the research on the defence against trojaning attack on neural networks is still in its infancy, and our research may shed light on the security of MLaaS in real-life scenarios.
The Holy Grail of "Systems for Machine Learning"

Although there is a large corpus of research focused on using machine learning to detect cyber threats, the solutions presented are rarely actually adopted in the real world. In this paper, we discuss the challenges that currently limit the adoption of machine learning in security operations, with a special focus on label acquisition, model deployment, and the integration of model findings into existing investigation workflows. Moreover, we posit that the conventional approach to the development of machine learning models, whereby researchers work offline on representative datasets to develop accurate models, is not valid for many cybersecurity use cases. Instead, a different approach is needed: to integrate the creation and maintenance of machine learning models into security operations themselves.
A Polynomial Time Algorithm to Compute Geodesics in CAT(0) Cubical Complexes

This paper presents the first polynomial time algorithm to compute geodesics in a CAT(0) cubical complex in general dimension. The algorithm is a simple iterative method to update breakpoints of a path joining two points using Owen and Provan’s algorithm (IEEE/ACM Trans Comput Biol Bioinform 8(1):2–13, 2011) as a subroutine. Our algorithm is applicable to any path in any CAT(0) space in which geodesics between two close points can be computed, not limited to CAT(0) cubical complexes.
Privacy Preserving Approach in Dynamic Social Network Data Publishing

In recent years, social networks have gained special attention to share information and to maintain a relationship with other people. As the data produced from such platforms are being analyzed, the privacy preservation methods must be applied before making the data publicly available. The anonymization techniques consider one-time releases and do not re-publish the dynamic social network data. The relationship between individuals changes with time so it may breach user privacy in dynamic social networks. In this paper, we propose an anonymization approach to preserve the user identity from all the published time-series dataset of a social network. Multiple instances of the social network may allow the adversary to identify the user by joining the information together. The existing anonymization methods for a single instance of a social network are not enough to preserve user privacy across multiple instances. Moreover, it requires all instances together for the social graph anonymization process. We proposed a method that anonymizes the current instance of the social graph and publishes it as soon as the instance is available. The proposed anonymization technique modifies the current social graph irrespective of further instances. The average relative error calculates the deviation in query results for different privacy levels. The experimental results highlight that the proposed approach generates fewer dummy nodes.
Quantum algorithms for disordered physics

We show how a quantum computer may efficiently simulate a disordered Hamiltonian, by incorporating a pseudo-random number generator directly into the time evolution circuit. This technique is applied to quantum simulation of few-body disordered systems in the large volume limit; in particular, Anderson localization. The method requires a number of (error corrected) qubits proportional to the logarithm of the volume of the system, and each time evolution step requires a number of gates polylogarithmic in the volume. We simulate the method to observe the metal-insulator transition on a three-dimensional lattice. Additionally, we demonstrate the algorithm on a one-dimensional lattice, using physical quantum processors.
Three-dimensional route planning for multiple unmanned aerial vehicles using Salp Swarm Algorithm

Route planning for multiple Unmanned Aerial Vehicles (UAVs) is a series of translation and rotational steps from a given start location to the destination goal location. The goal of the route planning problem is to determine the most optimal route avoiding any collisions with the obstacles present in the environment. Route planning is an NP-hard optimization problem. In this paper, a newly proposed Salp Swarm Algorithm (SSA) is used, and its performance is compared with deterministic and other Nature-Inspired Algorithms (NIAs). The results illustrate that SSA outperforms all the other meta-heuristic algorithms in route planning for multiple UAVs in a 3D environment. The proposed approach improves the average cost and overall time by 1.25% and 6.035% respectively when compared to recently reported data. Route planning is involved in many real-life applications like robot navigation, self-driving car, autonomous UAV for search and rescue operations in dangerous ground-zero situations, civilian surveillance, military combat and even commercial services like package delivery by drones.
A detection and identification method based on signal power for different types of Electronic Jamming attacks on GPS signals

In this work, a detection method is proposed for Global Positioning System (GPS) jamming attacks. GPS signals despite being an indispensable part of our life, come with a great weakness which is having a weak signal. This makes it vulnerable to external attacks like jamming and spoofing. The purpose of this study is to properly be able to identify the type of jammer affecting the GPS signal so that proper defensive measures can be applied to ensure the safety of the signal. This work is the second phase of a study based on the effect of Electronic Jammers (EJ) on GPS signals. Four different EJ are studied and the purpose is to be able to identify which one of them is affecting the signal in order to apply the appropriate defensive measure. In order to achieve this, the power of the signal is analyzed in order to achieve a threshold/interval to be able to identify the type of jammer. All of the simulations and measurements are carried on MatLAB.
A novel framework for joint sparse clustering and alignment of functional data

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Communication Patterns of Kanban Teams and Their Impact on Iteration Performance and Quality

Software development industry is growing rapidly and so are the time and budget constraints getting stringent. After Scrum, the widely adopted agile method, agile practitioners are now shifting towards Kanban due to its effective communication facilitation, transparency and limited work in progress traits. Since, the industry is in transition from scrum to Kanban therefore we don't find many empirical studies yielding results of adopting Kanban. Therefore, in this study we aim to explore more on Kanban teams. Mainly, we aim to find the impact of Kanban team's communication patterns on their iteration performance and quality. The findings revealed that the centralization communication patterns have negative impact on iteration performance and quality of a project. However, small world communication pattern has positive impact on iteration performance and quality of a project.
Real-Time Face Tracking Application with Embedded Facial Age Range Estimation Algorithm

Human facial age estimation has extensive range of real-world application in security control, law enforcement, and human computer interaction. However, despite advances in automatic age estimation, it remains a challenging problem. It is challenging due to process to make every single algorithm run automatically in a real-time and the age estimation features perform well with higher accuracy. In this paper, we present a design of a detail and reliable framework for automatic face tracking application with embedded age range estimation algorithm. It starts with face region detection algorithm, automated face features extraction and facial features landmark points localization process. Based on the selected points, the age range estimation algorithm will continue the process. In the last phase, the real-time application has been implemented in the whole framework. The results obtained from this work is the real-time age range estimation algorithm with face tracking application which was performed with higher accuracy which is up to 93%. It is also able to implement using live video input with partial occlusions, and facial small expression changes. Furthermore, the practicability of the proposed system and ideas was corroborated by satisfying the effectiveness of the proposed design.
Emerging Applications of 3D Integration and Approximate Computing in High-Performance Computing Systems: Unique Security Vulnerabilities

High-performance computing (HPC) systems rely on new technologies such as emerging devices, advanced integration techniques, and computing architecture to continue advancing performance. The adoption of new techniques could potentially leave high-performance computing systems vulnerable to new security threats. This work analyzes the security challenges in the HPC systems that employ three-dimensional integrated circuits and approximating computing. Case studies are provided to show the impact of new security threats on the system integrity and highlight the urgent need for new security measures.
Physical Models For Fast Estimation Of Guitar String, Fret And Plucking Position

In this paper, a novel method for analyzing guitar performances is proposed. It is both fast and effective at extracting the activated string, fret, and plucking position from guitar recordings. The method is derived from guitar-string physics and, unlike the state of the art, does not require audio recordings as training data. A maximum a posteriori classifier is proposed for estimating the string and fret based on a simulated model of feature vectors while the plucking position is estimated using estimated inharmonic partials. The method extracts features from audio with a pitch estimator that estimates also the inharmonicity of the string. The string and fret classifier is evaluated on recordings of an electric and acoustic guitar under noisy conditions. The performance is comparable to the state of the art, and the performance is shown to degrade at SNRs below 20 dB. The plucking position estimator is evaluated in a proof-of-concept experiment with sudden changes of string, fret and plucking positions, which shows that these can be estimated accurately. The proposed method operates on individual 40 ms segments and is thus suitable for high-tempo and real-time applications.
Statistical literacy for classification under risk: an educational perspective

Abstract After a brief description of the four components of risk literacy and the tools for analyzing risky situations, decision strategies are introduced, These rules, which satisfy tenets of Bounded Rationality, are called fast and frugal trees. Fast and frugal trees serve as efficient heuristics for decision under risk. We describe the construction of fast and frugal trees and compare their robustness for prediction under risk with that of Bayesian networks. In particular, we analyze situations of risky decisions in the medical domain. We show that the performance of fast and frugal trees does not fall too far behind that of the more complex Bayesian networks.
Sunshine Duration Prediction Using 1D Convolutional Neural Networks

The duration of solar radiation is one of the essential factors every day, such as agriculture or renewable energy. Forecasting the period of sunshine in various places like Indonesia is difficult. One method that can process sizable historical data for predictions is Deep Learning. This study proposed a 1D Convolutional Neural Network (1D CNN) method for predicting the duration of sunshine with climate features consisting of temperature, humidity, and length of solar radiation in 10 years. The training used 80% of historical data and tested with 20% rest. The results showed with Adam optimization models more stable and accurate so obtain 98.84% for the training data and by 54.55% for new data.
UHCTD: A Comprehensive Dataset for Camera Tampering Detection

An unauthorized or an accidental change in the view of a surveillance camera is called a tampering. Algorithms that detect tampering by analyzing the video are referred to as camera tampering detection algorithms. Most evaluations on camera tampering detection methods are presented based on individually collected datasets. One of the major challenges in the area of camera tamper detection is the absence of a public dataset with sufficient size and variations for an extensive performance evaluation. We propose a large scale synthetic dataset called University of Houston Camera Tampering Detection dataset (UHCTD) for development and testing of camera tampering detection methods. The dataset consists of a total 576 tampers with over 288 hours of video captured from two surveillance cameras. To establish an initial benchmark, we cast camera tampering detection as a classification problem. We train and evaluate three different deep architectures that have shown promise in scene classification, Alexnet, Resnet, and Densenet. Results are presented to show how the dataset can be used to train and classify images as normal, and tampered within and across cameras.
The Future of Authority Control: Issues and Trends in the Linked Data Environment

Within the past few years, the calls for transforming library data into linked data have become more and more frequent. The purpose of this study is to discuss the future of authority control in the linked data environment. Even though we may not have answers to all the questions, but one thing is clear – the authority control concept and process as we know it in the MARC-based legacy environment will not meet the requirements of linked data. The future of authority control lies in following closely the linked data principles.
Development of E-Recruitment as a Decision Support System for Employee Recruitment

Abstract This research aims to design and develop an employee recruitment system that can manage applicant data and provide recommendations for eligible applicants to be accepted or rejected by applying the Profile Matching method. Current problems in the employee selection process at PT. Duta Tata Echoindo is still done with a manual calculation system so that the recruitment process takes a long time and allows the company to get employees who are not in accordance with the desired competencies. The criteria used in the employee selection process are General Skills Assessment and Special Skills Assessment. The Profile Matching Method will calculate the gap between the criteria score and the applicant’s profile score. Applicants with the highest match score level have a great chance of being accepted. The result of this research is a web-based E-Recruitment system that can automatically calculate the match level of each applicant based on the Profile Matching method. The impact of this research is that the E-Recruitment system will speed up the recruitment process and make it easier for the HR division to make decisions to recruit employees.