Reviewerly/exHarmony huggingface dataset:
Continuous Leakage-Resilient Certificate-Based Encryption Scheme Without Bilinear Pairings

Abstract Recently, much attention has been focused on designing provably secure cryptographic primitives in the presence of key leakage, even the continuous leakage attacks. However, several constructions on the (continuous) leakage-resilient certificate-based encryption (CBE) scheme were proposed based on the bilinear pairings, and the corresponding computational efficiency is lower. Also, the leakage on the master secret key is omitted in the previous constructions. In this paper, to further achieve the better performance, a new construction method of continuous leakage-resilient CBE scheme without bilinear pairings is proposed, and the chosen-ciphertext attacks security of designed scheme is proved based on the hardness of the classic decisional Diffie–Hellman assumption. The performance analysis shows that our method not only can obtain higher computational efficiency but also enjoys better security performances, such as the leakage parameter of secret key of user has the constant size, and an adversary cannot obtain any leakage on the secret key of user from the corresponding given ciphertext etc. The advantage is that our proposal allows leakage attacks of multiple keys, i.e. continuous leakage resilience of the secret key of user and bounded leakage resilience of the master secret key. Additionally, to provide the leakage resilience for the cloud computing, a novel data access control scheme for cloud storage service is proposed from our continuous leakage-resilient CBE scheme, which can keep its claimed security in the leakage seting.
A Structural Analysis of Bitcoin Cash's Emergency Difficulty Adjustment Algorithm

In this paper, we analyze the equilibrium behavior of cryptocurrency miners in the wake of a major cryptocurrency fork. Miners are responsible for validating transactions and ensuring smooth functioning of the cryptocurrency platform. Specifically, we are interested in their strategic interactions with the incentives provided by the developers of the newly born minority chain to prioritize its transactions over that of the dominant parent currency. We focus our attention on the Bitcoin (BTC) fork of August 2017 that resulted in the birth of Bitcoin Cash (BCH). BCH's developers introduced emergency difficulty adjustment (EDA) algorithm to incentivize miners to process BCH's transactions over that of BTC's. Based on the strategic actions of the miners, EDA dynamically altered the difficulty adjusted profitability of BCH, which resulted in miners exhibiting waiting (waiting for the EDA to trigger) and switching (switching to mining BCH after EDA activation) intents on the BCH chain despite BTC's much higher market price. We use a two-step structural approach to estimate the dynamic mining competition among miners for BTC and BCH blocks to uncover their profit motivations. Policy and transition functions governing equilibrium behavior are estimated in the first stage. Using a forward simulation procedure, we estimate the payoff parameters in the second stage. With the help of publicly available chain data, we find competition among miners for BCH blocks was a major contributor to profits during EDA-active periods. We also find that waiting was beneficial. Contrary to available online evidence, switching had a negative effect on miners' profits. Through our counterfactual simulations, we demonstrate BTC's robust stability and find that the implemented design of EDA resulted in maximal miners' profits when compared with other plausible designs. We also provide a comparative analysis of different EDA designs with respect to developers' tolerance for changes in the rewards schedule.
Mix Networks: Existing Scenarios and Future Directions on Security and Privacy

Background: Privacy-enhancing techniques are developed in order to provide strong protection to cyberspace. These techniques aim to allow users to keep their identities hidden during the communication when they are sending an email, making payments online, browsing the Web, or posting to newsgroups. MixNet is the most practical solution for concealing identities of message and sender’s identities. Objective: It provides the sender and receiver anonymity as well as message security. The main goal of MixNet is to overcome vulnerability and enhance the performance of communication. It can efficiently handle the messages of various lengths and produce desirable results with privacy. The goal of this patent is to acquire information and concepts regarding MixNet. We also grant hints for future lookups and references. Methods : The design of MixNet depends on what the cryptosystem method is used. Symmetric and Asymmetric both are used. Other methods could be also used such as PIR, CSP and FDR model, RPC, Token-based approach, or others. Result: In this patent, we provided an overview of MixNet approaches and presented a survey on MixNet based techniques and different models. We also constructed the comparison tables as per our requirements for better understanding. For this purpose, we found a total of 120 articles related to the MixNet published between 1990 and 2018 from the most relevant scientific resources. However, only 86 papers were analyzed due to comprehensiveness and relevancy in this article. Conclusion: Focused on the security and privacy of communication and how it can be achieved. This patent also reveals research progress and research gap on MixNet.
Photograph Ownership and Authorization using Blockchain

The number of photographs taken is increasing exponentially each year. This includes the photographs captured by professional as well as non-professional personnel. The ease in accessibility of these work of art over the internet has increased unauthorized use of owner's property without handing them their deserved credit. In addition, the process to take the authorization over the use of the photograph from the respective owner takes a large amount of time as well as money. One of the solutions to this problem is the use of distributed ledger technology known as ‘Blockchain’ in order to keep the decentralized record of ownership. In addition, storing the photograph itself on the decentralized platform can make it more secure. Furthermore, designing a smart contract in which the user and owner can agree on the use of the owner's property can address the problem of unauthorized use. This paper describes the integration of blockchain, distributed storage, and peer-to-peer communication in order to solve the current problem of handling photograph ownership and authorization. Ethereum blockchain is implemented to achieve secure transaction and handling functionality regarding authorization with the help of smart contracts that runs on the blockchain. As the storage layer, the paper discusses the InterPlanetary File System (IPFS) as a distributed database with which the photographs can be stored such that decentralization can be achieved. Only after the verifying the similar image does not exist on the system the image is stored in IPFS. The existence of a duplicate image is done with the implementation of dHash algorithm and hamming distance. Whisper protocol allows the owner of the photograph and user on the Ethereum blockchain to leverage peer-to-peer communication to reach a consensus.
Genetic constraints and possible mechanism of gold mineralization within the carbonaceous metasedimentary units of the Dalma volcano‐sedimentary belt, North Singhbhum Mobile Belt, eastern India: Implications from pyrite geochemistry and carbon and sulfur isotope studies

In the present work, gold‐bearing carbonaceous phyllites in the Dalma volcano‐sedimentary belt (DVSB), occupying the central region of the North Singhbhum Mobile Belt in eastern India were studied to understand the nature and mode of occurrence of gold mineralization. We propose that the protolith of the carbonaceous phyllites, that is, organic‐rich black shale, was the major source of Au in the phyllite‐hosted gold mineralization reported from the DVSB. LA‐ICP‐MS analysis of pyrite in this study reveals elevated Au contents in the early‐formed sedimentary pyrite (syngenetic and diagenetic pyrite). Trace element systematics confirms the presence of invisible Au with in the sedimentary pyrite as solid solution (invisible gold). Petrographical evidences suggest a close spatial relationship of Au‐enriched pyrite and carbonaceous material. Stable carbon isotope values confirm the organic nature of the carbonaceous matter. Further, the high organic carbon content of these samples falls well within the favourable range of an auriferous carbonaceous host rock. These evidences suggest that the organic carbon played a key role in fixation of gold in the carbonaceous shales. Subsequent diagenesis resulted in the release of Au from the organic matter and its incorporation onto the coeval sedimentary pyrite. Field observations and reports by previous workers indicate that these rocks were subjected to a late‐stage metamorphic/hydrothermal event. Paragenesis of pyrite coupled with its trace element systematics reveals that this late‐stage event resulted in the formation of younger euhedral pyrite and pyrrhotite and released invisible gold from the pre‐existing sedimentary pyrite followed by its precipitation in the quartz reefs. Based on these studies, we propose a two‐stage model of gold mineralization, wherein gold was initially incorporated in black shale‐hosted sedimentary pyrite and later released and precipitated in quartz veins.
Label Distribution Augmented Maximum Likelihood Estimation for Reading Comprehension

Reading comprehension (RC) aims to locate a text span from a context passage to answer the given question. Despite the effectiveness of modern neural RC models, most existing work relies on maximum likelihood estimation (MLE) and ignores the structure of the output space. That is during training, one treats all the text spans do not match the ground truth as equally poor, leading to overconfident predictions on ground truth labels and reduced generalization ability in test. One way to bridge the gap between training and test is to take into account the task reward of alternative outputs using the reinforcement learning (RL) algorithms, which is often deficient in optimization as compared with MLE. In this paper, we propose a new learning criterion for the RC task which combines the merits of both MLE and RL-based methods. Specifically, we show that we are able to derive the distribution of the outputs, i.e., label distribution, using their corresponding task rewards based on the decomposition property of the RC problem. We then optimize the RC model by directly learning towards the auxiliary label distribution, instead of the ground truth label, using the MLE framework. In this way, we can make use of the structure of the output space for better generalization (as RL) via efficient optimization (as MLE). We name our approach as Label Distribution augmented MLE (LD-MLE), which is a general learning criterion that could be adopted by almost all the existing RC models. Experiments on three representative benchmark datasets demonstrate that RC models learned with the LD-MLE criterion can achieve consistently improved results over those based on the traditional MLE and RL-based criteria.
Fuzzy Deep Neural Learning Based on Goodman and Kruskal's Gamma for Search Engine Optimization

Search engine optimization (SEO) is a significant problem for enhancing a website&#x0027;s visibility with search engine results. SEO issues, such as Site Popularity, Content Quality, Keyword Density, and Publicity, were not considered during the search engine optimization process. Therefore, the retrieval rate of the existing techniques is inadequate. In this study, Triangular Fuzzy Deep Structured Learning-Based Predictive Page Ranking (TFDSL-PPR) technique is proposed to solve these limitations. First, the TFDSL-PPR technique takes a number of user queries as input in the input layer, and then it employs four hidden layers in order to deeply analyze the web pages based on an input query. The first hidden layer determines the keywords from the user query. The second hidden layer measures the site popularity, content quality, keyword density and publicity of all web pages in the search engine. It then accomplishes Goodman and Kruskal&#x0027;s Gamma Predictive Ranking process in the third hidden layer, where it ranks the web pages by considering their similarities. The proposed TFDSL-PPR technique is applied to the ClueWeb09 Dataset with respect to a variety of user queries. The results are benchmarked by existing methods based on several metrics such as retrieval rate, time, and false-positive rate.
Generating Mock Skeletons for Lightweight Web-Service Testing

Modern application development allows applications to be composed using lightweight HTTP services. Testing such an application requires the availability of services that the application makes requests to. However, access to dependent services during testing may be restrained. Simulating the behaviour of such services is, therefore, useful to address their absence and move on application testing. This paper examines the appropriateness of Symbolic Machine Learning algorithms to automatically synthesise HTTP services' mock skeletons from network traffic recordings. These skeletons can then be customised to create mocks that can generate service responses suitable for testing. The mock skeletons have human-readable logic for key aspects of service responses, such as headers and status codes, and are highly accurate.
The Lightweight System for UAV Cooperative Mission Planning Simulation

Multi-UAV cooperative mission planning is an important part of cooperative operation of UAV clusters. There are many simulation system for UAV algorithms now. However, they have some shortcomings, such as cross-perform problems and the scalability of the algorithm. The paper focus on solving these short-comings, and designs a simulation system for multi-UAV cooperative mission planning. The framework of UAV algorithm simulation system based on Web technology is designed. The framework is divided into four modules: user management, scheme configuration, algorithm simulation and algorithm management. This paper realizes an algorithm management system for UAV based on Docker technology. This method not only realizes the management of UAV algorithm, but also solves the problem of conflict of running environment of algorithm problem. The simulation system in this paper solves the cross-platform problem of the traditional UAV simulation system, and improves the algorithm scalability of the simulation system.
Feature Tracking Utilizing a Maximum-Weight Independent Set Problem

Tracking the temporal evolution of features in time-varying data remains a combinatorially challenging problem. A recent method models event detection as a maximum-weight independent set problem on a graph representation of all possible explanations [35]. However, optimally solving this problem is NP-hard in the general case. Following the approach by Schnorr et al., we propose a new algorithm for event detection. Our algorithm exploits the modelspecific structure of the independent set problem. Specifically, we show how to traverse potential explanations in such a way that a greedy assignment provides reliably good results. We demonstrate the effectiveness of our approach on synthetic and simulation data sets, the former of which include ground-truth tracking information which enable a quantitative evaluation. Our results are within 1% of the theoretical optimum and comparable to an approximate solution provided by a state-of-the-art optimization package. At the same time, our algorithm is significantly faster.
Deep Convolutional Neural Networks for Human Activity Classification

In this work, we investigate the potential of applying deep learning techniques in the field of Visual Lifelogging (VL), which means use a wearable camera to acquire information of an individual. Visual Lifelogging could be interpreted, as a complete and comprehensive black box of human's daily activities. This black box will offer great potential to extract accurate and opportune knowledge on how people live their lives. The sensing technology advent that allowing efficient sensing of personal activities, had led to huge collections of data that are available. The ability to process this data had increased as well. This is well seen in the popularity and growing interest given by the scientific community to the hot field of lifelogging. Using features that separate activities are vital for human behavior understanding and characterization. In this paper, we emphasize more particularly on human activity recognition (HAR) captured by a low temporal resolution wearable camera. To achieve this goal, we have used an already trained Deep Convolutional Neural Network (DCNN). The training is done on the large Dataset ImageNet, which contains millions of images and transfer this knowledge to recognize and classify automatically the daily human activities into one of the categorized activities. The numerical results of the proposed approach are very encouraging with an accuracy of 98.78%.
Left Ventricle Segmentation and Quantification with Attention-Enhanced Segmentation and Shape Correction

Quantification of the left ventricle (LV) is significant for cardiac disease diagnosis and progression assessment. Methods for LV quantification include segmentation based and regression learning method. However, the regression learning method cannot provide visual inspection, while the segmentation performance is still unsatisfactory, resulting in a large quantification error.
Reasoning about uncertain parameters and agent behaviors through encoded experiences and belief planning

Robots are expected to handle increasingly complex tasks. Such tasks often include interaction with objects or collaboration with other agents. One of the key challenges for reasoning in such situations is the lack of accurate models that hinders the effectiveness of planners. We present a system for online model adaptation that continuously validates and improves models while solving tasks with a belief space planner. We employ the well known online belief planner POMCP. Particles are used to represent hypotheses about the current state and about models of the world. They are sufficient to configure a simulator to provide transition and observation models. We propose an enhanced particle reinvigoration process that leverages prior experiences encoded in a recurrent neural network (RNN). The network is trained through interaction with a large variety of object and agent parametrizations. The RNN is combined with a mixture density network (MDN) to process the current history of observations in order to propose suitable particles and models parametrizations. The proposed method also ensures that newly generated particles are consistent with the current history. These enhancements to the particle reinvigoration process help alleviate problems arising from poor sampling quality in large state spaces and enable handling of dynamics with discontinuities. The proposed approach can be applied to a variety of domains depending on what uncertainty the decision maker needs to reason about. We evaluate the approach with experiments in several domains and compare against other state-of-the-art methods. Experiments are done in a collaborative multi-agent and a single agent object manipulation domain. The experiments are performed both in simulation and on a real robot. The framework handles reasoning with uncertain agent behaviors and with unknown object and environment parametrizations well. The results show good performance and indicate that the proposed approach can improve existing state-of-the-art methods.
Formalizing Architectural Rules with Ontologies - An Industrial Evaluation

Architecture conformance checking is an important means for quality control to assess that the system implementation adheres to its defined software architecture. Ideally, this process is automated to support continuous quality control. Many different approaches exist for automated conformance checking. However, these approaches are often limited in terms of supported concepts for describing and analyzing software architectures. We have developed an ontology-based approach that seeks to overcome the limited expressiveness of existing approaches. As a frontend of the formalism, we provide a Controlled Natural Language. In this paper, we present an industrial validation of the approach. For this, we collected architectural rules from three industrial projects. In total, we discovered 56 architectural rules in the projects. We successfully formalized 80% of those architectural rules. Additionally, we discussed the formalization with the corresponding software architect of each project. We found that the original intention of each architectural rule is properly reflected in the formalization. The results of the study show that projects could greatly benefit from applying an ontology-based approach, since it helps to precisely define and preserve concepts throughout the development process.
A New Method of Constructing Binary Quantum Codes From Arbitrary Quaternary Linear Codes

In this letter, a method of constructing Hermitian dual containing quaternary linear codes from general quaternary linear codes is proposed. Based on this result, a construction method of binary linear quantum codes is obtained. Using three cyclic codes and one linear code over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> that are not Hermitian dual containing, we construct four record breaking binary linear quantum codes. Furthermore, ten new record breaking binary additive quantum codes are derived from these four linear quantum codes by propagation rule.
Blind Motion Deblurring for Satellite Image using Convolutional Neural Network

KOMPSAT-3A was launched in March 2015, and has been performing normal operations. However, there is an inaccurate line-rate control in KOMPSAT-3A that causes motion blur. This induces an along-track modulation transfer function (MTF) value that is smaller than the across-track MTF value. The motion blur degrades image quality and tends to deteriorate the performance of various computer vision algorithms such as object detection, segmentation, feature detection, and matching. Thus, in order to enhance the visual quality of blurry images, a motion deblurring algorithm is necessary. However, there are few studies about solving motion blur of satellite images. Most studies have been focused on the motion blur of general images taken by smart phones, action cameras, and DSLRs. To solve the satellite image problem, we propose a model for motion deblurring of satellite images and generate training data using the proposed model and KOMPSAT-3A data. In addition, we trained a residual and dense block-based convolutional neural network (CNN). To verify the residual and dense block-based CNN, we utilized simulated blurred images and actual blurry KOMPSAT-3A images. As a result, we confirmed that the proposed method reliably removes more motion blur than do conventional CNN-based motion deblur methods. Additionally, our method made the along-track and across-track MTF values similar. In addition, overall the MTF values increased. The results show that effective learning is possible for use in reducing motion deblurring of satellite images effectively.