Reviewerly/exHarmony huggingface dataset:
Securing GOOSE: The Return of One-Time Pads

IEC 61850 is an international standard that is widely used in substation automation systems (SAS) in smart grids. During its development, security was not considered thus leaving SAS vulnerable to attacks from adversaries. IEC 62351 was developed to provide security recommendations for SAS against (distributed) denial-of-service, replay, alteration, spoofing and detection of devices attacks. However, real-time communications, which require protocols such as Generic Object-Oriented Substation Event (GOOSE) to function efficiently, cannot implement these recommendations due to latency constraints. There has been researching that sought to improve the security of GOOSE messages, however, some cannot be practically implemented due to hardware requirements while others are theoretical, even though latency requirements were met. This research investigates the possibility of encrypting GOOSE messages with One- Time Pads (OTP), leveraging the fact that encryption/decryption processes require the random generation of OTPs and modulo addition (XOR), which could be a realistic approach to secure GOOSE while maintaining latency requirements. Results show that GOOSE messages can be encrypted with some future work required.
Requirements for IoT Forensics

The main objective of this paper is to determine the requirements for IoT forensics. The development of requirements for Internet of Things (IoT) forensics will make a groundbreaking contribution to the current body of knowledge. The secondary objective of the paper is the formulation of a Digital Forensic Readiness (DFR) model for the implementation of IoT forensics. The model aims to address the shortfalls of the requirements, as well as incorporate various Digital Forensic (DF) techniques that can be used in IoT environments. The model is presented in theoretical as well as graphical form. The model is then discussed to highlight various areas of possible new research. The model is furthermore evaluated to determine the level of contribution it makes to the current body of research.
Quantum generative model for sampling many-body spectral functions

Quantum phase estimation is at the heart of most quantum algorithms with exponential speedup. In this paper we demonstrate how to utilize it to compute the dynamical response functions of many-body quantum systems. Specifically, we design a circuit that acts as an efficient quantum generative model, providing samples out of the spectral function of high rank observables in polynomial time. This includes many experimentally relevant spectra such as the dynamic structure factor, the optical conductivity, or the NMR spectrum. Experimental realization of the algorithm, apart from logarithmic overhead, requires doubling the number of qubits as compared to a simple analog simulator.
High-fidelity Trotter formulas for digital quantum simulation

Quantum simulation promises to address many challenges in fields ranging from quantum chemistry to material science and high-energy physics, and could be implemented in noisy intermediate-scale quantum devices. A challenge in building good digital quantum simulators is the fidelity of the engineered dynamics given a finite set of elementary operations. Here we present a framework for optimizing the order of operations based on a geometric picture, thus abstracting from the operation details and achieving computational efficiency. Based on this geometric framework, we provide two alternative second-order Trotter expansions: one with optimal fidelity at a short timescale, and the second robust at a long timescale. Thanks to the improved fidelity at different timescales, the two expansions we introduce can form the basis for experimental-constrained digital quantum simulation.
Privacy-Preserving Outsourcing Computation of QR Decomposition in the Encrypted Domain

Signal processing in encrypted domain has become an important mean to protect privacy in an untrusted network environment. Due to the limitations of the underlying encryption methods, many useful algorithms that are sophisticated are not well implemented. Considering that QR decomposition is widely used in many fields, in this paper, we propose to implement QR decomposition in homomorphic encrypted domain. We firstly realize some necessary primitive operations in homomorphic encrypted domain, including division and open square operation. Gram-Schmidt process is then studied in the encrypted domain. We propose the implementation of QR decomposition in the encrypted domain by using the secure implementation of Gram-Schmidt process. We conduct experiments to demonstrate the effectiveness and analyze the performance of the proposed outsourced QR decomposition.
Bis: A Novel Blockchain Based Bank-Tax Interaction System in Smart City

When banks loan to small and micro enterprises, they are faced with higher credit costs and difficulties in controlling risks. Blockchain provides a distributed infrastructure for business connectivity across organizations. Blockchain is a consensus, traceable, tamper-proof, final shared data ledger. Blockchain not only improves the efficiency of data connections, but also promotes the credible flow of data. In this background, we propose Bis: a Novel Blockchain based Bank-tax interaction system in smart city. We have designed the total scheme, and the tax bureau can use blockchain to build bank-tax interactive system. Most commercial banks can obtain the enterprises tax information in an efficient and safe way through multi-channels technology of Blockchain. We carried out system development and experiments to prove that our Bis system can solve enterprise tax data sharing and business connection problems across enterprises, tax bureau and banks.
Densely connected convolutional network block based autoencoder for panorama map compression

Abstract As a novel virtual reality (VR) format, panorama maps are attracting increasing attention, while the compression of panorama images is still a concern. In this paper, a densely connected convolutional network block (dense block) based autoencoder is proposed to compress panorama maps. In the proposed autoencoder, dense blocks are specially designed to reuse feature maps and reduce redundancy of features. Meanwhile, a loss function, which imports a position-dependent weight item for each pixel, is proposed to train and adjust network parameters, in order to make the autoencoder fit to properties of panorama maps. Based on the proposed autoencoder and the weighted loss function, a greedy block-wise training scheme is also designed to avoid gradient vanishing problem and speed up training. During training process, the autoencoder is divided into several sub-nets. After each sub-net is trained separately, the whole network is fine-tuned to achieve the best performance. Experimental results demonstrate that the proposed autoencoder, compared with JPEG, saves up to 79.69 % bit rates, and obtains 7.27dB gain in BD-WS-PSNR or 0.0789 gain in BD-WS-SSIM. The proposed autoencoder also outperforms JPEG 2000, HEVC and VVC in both BD-WS-PSNR and BD-WS-SSIM. Meanwhile, subjective results show that the proposed autoencoder can recover details of panorama images, and reconstruct maps with high visual quality.
Human Fall Detection during Activities of Daily Living using Extended CORE9

There is an ever-increasing need for automated monitoring systems to enable independent living in today's elderly population. Human fall detection is widely researched within the field of assistive technologies. However, fall detection systems based on wearable sensors is often incapacitated by inadvertent neglect to wear the sensors. Computer vision based approaches to detect a human fall in video holds promise. It has been noted that an informative yet compact representation schema can significantly improve the performance of video understanding. For human activity recognition, Extended CORE9 has been used for obtaining a qualitative spatial description of the video activity. The spatial description of an activity obtained using Extended CORE9 along with the temporal information can be encoded within a graph structure. Extended CORE9 has been applied for human-human and human-object interactions. In this paper, we show how Extended CORE9 can be applied for single-person activities. We evaluate our approach for detecting fall in an assisted living environment. Experiments performed on the UR Fall Detection dataset shows promising results.
Using Deep Convolutional Neural Network to Recognize LTE Uplink Interference

In this paper, we provide a novel and field-proven approach to recognize Long Term Evolution (LTE) uplink interference. We first pre-process the time domain signal into spectral waterfall so that we can formulate the LTE uplink interference recognition problem as an image classification task. We design a convolutional neural network (CNN) based learning system to solve the problem. We then show by experimental studies that the proposed CNN model achieves a 95% accuracy on average even with imbalanced and limited training data. Furthermore, we evaluate the proposed CNN model by visualizing the learned parameters of the model. We find that local patterns of the uplink interference can be well learned through the first layer convolution filters of the model. To the best knowledge of the authors, this paper represents the first application of CNN to LTE uplink interference recognitions.
Project Management Model Based on Consistency Strategy for Blockchain Platform

In Korea, a budget of tens of trillion won is put into the national R & D project every year [1]. Thanks to continuous technology and industrial development, Korea's ICT industry is gaining global attention. National R & D projects manage R & D projects in order of Planning - Evaluation - Management - Project results. In Korea, the R & D cycle process is diverse from the planning stage to the research results manager stage. In other words, we manage planning and evaluation by different departments and institutions. This type of management leads to problems such as redundant planning, insufficient sharing of research results, and duplicate submission. Currently, NTIS (National Technology Information Service) provides to address issues of redundancy and sharing. However, the NTIS's review of project redundancy is centered on keywords and does not provide information on the planning stage. This situation leads to redundant planning by ministries and closed management of research results. Therefore, we propose a platform that can easily share project planning information, review project redundancy, share research results, and check duplication of research results using block chain technology, which many experts and entrepreneurs refer to as future innovative technologies. In this paper, we propose a platform called Perfect Sharing Project Platform (PSPP). We describe that PSPP can achieve excellent research results through information sharing of project process. To support the perfect sharing, this platform uses a new notion of consensus algorithm, called POA (Proof of Atomicity). This platform is suitable for sharing information.
OOC-O: A Reference Ontology on Object-Oriented Code

With the rise of polyglot programming, different programming languages with different constructs have been combined in the same software development projects. However, to our knowledge, no axiomatization demonstrating the existential commitments of a language have been presented, nor is there effort to adopt a consensual conceptualization between languages, in particular object-oriented ones. In this paper, we propose OOC-O, a reference ontology on Object-Oriented Code whose purpose is to identify and represent the fundamental concepts present in OO source code. The ontology is based on UFO, was developed according to the SABiO method, verified according to its competency questions and validated by instantiation of concepts in OO code form and a process of harmonization among popular object-oriented languages.
Intelligent Interface for Fake Product Review Monitoring and Removal

As the trend to shop online is increasing day by day and more people are interested in buying the products of their need from the online stores. This type of shopping does not take a lot of time of a customer. Customer goes to online store, search the item of his/her need and place the order. But, the thing by which people face difficulty in buying the products from online store is the bad quality of the product. Customer place the order only by looking at the rating and by reading the reviews related to the particular product. Such comments of other people are the source of satisfaction for the new product buyer. Here, it may be possible that the single negative review changes the angle of the customer not to buy that product. In this situation, it might possible that this one review is fake. So, in order to remove this type of fake reviews and provide the users with the original reviews and rating related to the products, we proposed a Fake Product Review Monitoring and Removal System (FaRMS) which is an Intelligent Interface and takes the Uniform Resource Locator (URL) related to products of Amazon, Flipkart and Daraz and analyzes the reviews, and provides the customer with the original rating. It is a unique quality of the proposed system that it works with the three e-commerce Websites and not only analyze the reviews in English but also the reviews written in Urdu and Roman Urdu. Previous work on fake reviews does not support feature to analyze the reviews written in languages like Urdu and Roman Urdu and cannot handle the reviews of multiple e-commerce Websites. The proposed work achieved the accuracy of 87% in detecting fake reviews of written in English by using intelligent learning techniques which is greater than the accuracy of the previous systems.
Towards Verifying Ethereum Smart Contracts at Intermediate Language Level

Smart contracts have exhibited great potential in a spectrum of applications, ranging from digital currency to online gaming. Yet smart contracts are known to be prone to errors and vulnerable to attacks. The validation of smart contracts before their deployment is an indispensable step for their correctness and security, and the highest level of guarantee can be provided using formal verification. The level of difficulty, reliability, etc., of the formal verification of a smart contract is deeply affected by the programming language in which the contract is implemented. In this paper, we discuss the benefits of verifying smart contracts at the level of intermediate languages, in comparison with machine-level languages and user-level languages. We augment the existing formalization of Yul – the intermediate language of Ethereum, realize an ERC20 token contract in this language, and verify the guarantees of all the functions provided by this contract. All this development has been performed in the proof assistant Isabelle/HOL. It demonstrates the feasibility and some of the most important advantages of mechanized verification for smart contracts at the intermediate-language level, such as a balance between the intuitiveness of the verification target and the ability to validate lower-level mechanisms like the function dispatcher.
Noise level policy advising system for mine workers

A noise level policy advising system to be used by mine administrators in assigning tasks to new employees at the mine is proposed. The presented novel system uses machine learning techniques which includes clustering and classification of new employee. The mine workers are clustered using K-means to determine their properties. By comparatively using Logistic regression, support vector machines, decision trees and random forests classification techniques, the mine workers are classified. Depending on the classification, which is based on the mine workers baseline and future threshold shift, recommendations to suitable mining tasks are made. The decision tree is the best performing model with the highest accuracy. It has an average testing accuracy of 91.25% and average training accuracy of 99.79%. However, logistic regression provides the best generalisation results on the testing set. Future work would include development of a friendly Graphical User Interface to facilitate easy use of the system.
Predicting Movie Popularity and Ratings with Visual Features

In Movie Recommender Systems, when a new user registers to the system and she has not yet provided any information about her, the system may not be able to generate personalized recommendations for that user. In such a Cold Start situation, many real-world recommender systems suggest popular movies to the new user. Such movies are very likely to be interesting to the new users. A very common approach for measuring the movie popularity is based on counting the number of ratings (as user votes) provided by a community of the existing users. However, in certain cases, we cannot properly measure the popularity of the movies with this common approach. This paper proposes a novel method for predicting the popularity of movies. The method is based on hybrid visually-driven features, representative of the movie content, which can be used to effectively predict not only the movie popularity but also the average rating of the movie. Our extensive experiments on a large dataset of more than 13'000 movies trailers show that the proposed hybrid approach achieves promising results by exploiting visual Attractiveness features of movies in comparison to the other baseline features.
Quantum Logic Synthesis with Formal Verification

Quantum computers capable of practical information processing are emerging rapidly. As these devices become more advanced, tools will be needed for converting generalized quantum algorithms into formally-verified forms that are executable on real quantum machines. In this work, a prototype tool is presented that transforms quantum algorithms into executable specifications where optimization procedures yield 9-24 % cost improvement on a range of benchmarks. Additionally, the tool incorporates formal verification internally with Quantum Multiple-valued Decision Diagrams to confirm that the generated technology-dependent executable is functionally equivalent to the original, technology-independent algorithm. Experimental results are provided that target the Rigetti family of quantum processing units although the tool may also target other architectures.
Arithmetic Circuit Homomorphic Encryption and Multiprocessing Enhancements

This is a feasibility study on homomorphic encryption using the TFHE library [1] in daily computing using cloud services. A basic set of arithmetic operations namely - addition, subtraction, multiplication and division were created from the logic gates provide. This research peeks into the impact of logic gates on these operations such as latency of the gates and the operation itself. Multiprocessing enhancement were done for multiplication operation using MPI and OpenMP to reduce latency.
Attainable Hacks on Keystore Files in Ethereum Wallets—A Systematic Analysis

Ethereum is a popular Blockchain platform that allows users to manage their cryptocurrency transactions through the wallets. Ethereum wallet helps interact with the blockchain network easily, and it keeps Ethereum cryptocurrency (Ether) transaction data of its users. The use of Ethereum and wallets grows rapidly. Since they handle huge value of crypto assets, attackers are keen to hack and steal Ethers from Ethereum wallets. But there lacks comprehensive security analysis, especially on keystore files in Ethereum wallets. There were a few incidents occurred with huge loss of Ethers in Etheruem wallets within the last five years. In this paper, we conducted a systematic analysis on hacking methods from the existing literature and conducted experiments to find how the Ethereum wallet's keystore file is vulnerable to the adversaries. Since the keystore file is secured with a password, we have used the brute-force and the dictionary attack to crack the password of the keystore file in Ethereum wallets. Our results showed that the dictionary attack is more efficient to hack the keystore file than the brute-force attack. Further, the keystore file is less vulnerable, if it is used complex password credentials.
Intelligent classification technique for breast cancer classification using digital image processing approach

Breast cancer is typical viewed as the second dangerous infection wherever all through the world as a result of its passing rate. Impacted patient will liveonly when the sickness diagnosedprior to the most physical variations in the body. By and by, mammographic (X-ray emission region) picture was commonly used in revealing the breast cancer. The aim for the system is to design a Computer Aided Diagnosis systematictoolfor perceivingnon cancerous and perilous (cancer causing) mammogram. CAD tool helped radiologist in expanding his assurance accuracy. Thetextual features were estimatedby Grey Level Co occasion Matrix (GLCM) at 0°. Incorporate best featuresof the find outthat has significant responsibility in achieving the perfect turnoutwhich are then designated and associated with ANN to train and classify as ANN is extensively used in various field, for instance, pattern affirmation, therapeutic diagnose, machine learning, etc.,In thispapermini MIAS data setwas used and with the system the general sensitivity, expressness and precisionof 99.3%, 100% and 99.4% achieved separately.
Maximizing receiver operating characteristics convex hull via dynamic reference point-based multi-objective evolutionary algorithm

The receiver operating characteristic convex hull (ROCCH) is a popular technique for analyzing the performance of classifiers, which is particularly effective for the tasks with unbalanced data distribution. Although maximization of ROCCH can be tackled as a bi-objective optimization problem, existing multi-objective evolutionary algorithms (MOEAs) encounter difficulties in obtaining an ROCCH, since ROCCH is always convex but the Pareto front obtained by MOEAs may be concave. To address the issue, in this paper, a dynamic reference point-based MOEA, namely DR-MOEA is proposed for maximizing ROCCH performance. Specifically, in DR-MOEA, a reference point-based sorting is suggested, where the solutions are sorted by their distances to the reference points instead of Pareto dominance. Hence an ROCCH rather than a Pareto front is expected to be obtained. In addition, a reference point adaptation strategy is also designed, with which the reference points are dynamically adjusted during the evolutionary process, and the performance of DR-MOEA is further enhanced. Empirical studies are conducted by comparing the proposed algorithm with several state-of-the-arts on different data sets. Experimental results demonstrate the superiority of DR-MOEA over the comparison methods in solving the ROCCH maximization problem.
Analyzing Different Mobile Applications in Time and Space: a City-Wide Scenario

We analyze a city-wide dataset of 4G mobile network traffic obtained directly from user-side logs, allowing fine-grained analyses of different application services over time and space. We group applications in classes and analyze their traffic patterns: the analysis reveals great heterogeneity in the usage of different applications and in their space/time correlations, with important implications for future networking services such as network slicing and resource allocations.