Reviewerly/exHarmony huggingface dataset:
Performance of New Hope and CRYSTALS-Dilithium Postquantum Schemes in the Transport Layer Security Protocol

In recent years, there has been a notable amount of research on developing cryptographic schemes that are secure against both quantum and classical computers.In 2016, the National Institute of Standards and Technology (NIST) initiated a process to solicit, evaluate, and standardize one or more quantum-resistant public key cryptographic schemes.This process originated because quantum computers can exploit quantum mechanical phenomena and solve mathematical problems that are difficult or intractable for classical computers.This kind of mathematical problem is the basis of secure public key cryptography.As a consequence, in a near future quantum computers will be able to break many of the public key schemes currently in use.However, the challenge is especially acute for devices with different architectures.They might not be well equipped to run the new standards and interoperate with existing communication protocols and networks.In this work, we analyze the performance of postquantum schemes in the transport layer security (TLS) protocol considering x86 as the server architecture and x86/ARM architectures as clients.All of them lack cloud computing or virtualized environments.Our analysis considers integrating the implementation of two cryptographic schemes that were successful in the second round of the postquantum standardization process, namely, Dilithium and New Hope.The performance of postquantum schemes in the TLS protocol is statistically analyzed in x86 and ARM architectures, giving the relationships, the effects and the survival of the analysis.
Comparing factual recall of tapped vs. handwritten text

As a result of the rushed transition to remote teaching because of the COVID-19 pandemic, teachers have suddenly been forced to design mobile-assisted language learning (MALL; m-learning) activities, mostly for the first time in their careers. However, it is imperative that instructors realize that the challenge is greater than simply converting paper-based assignments into a digital format. There has been very little research done examining the cognitive effects of writing on a smartphone, compared to writing on paper. The current study therefore sought to examine how recollection of content differed depending on the medium used for notetaking. A population of 138 Japanese university students of English-as-a-Foreign-Language (EFL) were asked to transcribe a short text, either by paper or by smartphone, and then quizzed on the content immediately afterwards. Students who wrote the text by hand were found to have significantly greater recollection of the content compared to those who had ‘tapped’ on smartphones. These results follow precursor research which indicated that Japanese students compose significantly slower on their phones in English (Lee, 2020b), and wrote significantly less in writing tasks (Lee, 2019, Lee, 2020) than handwriting counterparts.
NLtoPDDL : One-Shot Learning of PDDL Models from Natural Language Process Manuals

Existing automated domain acquisition approaches require large amounts of structured data in the form of plans or plan traces to converge. Further, automatically-generated domain models can be incomplete, error-prone, and hard to understand or modify. To mitigate these issues, we take advantage of readily-available natural language data: existing process manuals. We present a domain-authoring pipeline called NLtoPDDL, which takes as input a plan written in natural language and outputs a corresponding PDDL model. We employ a two-stage approach: stage one advances the state-of-the-art in action sequence extraction by utilizing transfer learning via pre-trained contextual language models (BERT and ELMo). Stage two employs an interactive modification of an object-centric algorithm which keeps human-in-the-loop to one-shot learn a PDDL model from the extracted plan. We show that NLtoPDDL is an effective and flexible domain-authoring tool by using it to learn five real-world planning domains of varying complexities and evaluating them for their completeness, soundness and quality.
Pavan: A privacy-preserving system for DB-as-a-Service

There is a significant security challenge when database systems are deployed over the cloud and offered as DataBaseas-a-Service. The current encryption schemes are only partially homomorphic, which means they are designed to enable only one particular type of computation to be carried out on the encrypted data. To address these challenges, we introduce Pavan, a system that securely stores data in a cloud database while still enabling encrypted data to be processed. Pavan enables query processing with an order-preserving partially homomorphic encryption scheme. For data stored in the cloud, we show that Pavan is a practical system that preserves data privacy with reasonable overhead at the same time enable data processing and sharing with multiple users when it is outsourced on the cloud.
Robust and Blind Audio Watermarking Algorithm in Dual Domain for Overcoming Synchronization Attacks

How to effectively resist synchronization attacks is the most challenging topic in the research of robust watermarking algorithms. A robust and blind audio watermarking algorithm for overcoming synchronization attacks is proposed in dual domain by considering time domain and transform domain. Based on analysing the characteristics of synchronization attacks, an implicit synchronization mechanism (ISM) is developed in the time domain, which can effectively track the appropriate region for embedding and extracting watermarks. The data in this region will be subjected to discrete cosine transform (DCT) and singular value decomposition (SVD) in turn to obtain the eigenvalue that can be utilized to carry watermarks. In order to extract the watermark blindly, the eigenvalue will be quantized. Genetic algorithm (GA) is utilized to optimize the quantization step to balance both transparency and robustness. The experimental results confirm that the proposed algorithm not only withstands various conventional signal processing operations but also resists malicious synchronization attacks, such as time scale modification (TSM), pitch-shifting modification (PSM), jittering, and random cropping. Especially, it can overcome TSM with strength from −30% to +30%, which is much higher than the standard of the International Federation of the Phonographic Industry (IFPI) and far superior to the other algorithms in related papers.
Secure Data Sharing With Lightweight Computation in E-Health

Edge computing provides users with nearby services, promotes the development of the Internet of Things and transforms the mode of traditional electronic medical system that improves the speed and accuracy of medical diagnosis. However, edge computing has introduced new security challenges, such as the difficulty in applying standard encryption algorithms because of the limited computing capability of devices on the edge and concerns of privacy leakage. In this article, we propose a data access scheme by employing a lightweight attribute-based encryption (ABE) to address the security weaknesses of edge computing for e-health. Our novel modular exponential outsourcing algorithm outsources the shared data to the untrusted edge server and verifies the correctness of the returned results while achieving a lightweight computational load. The proposed Mask Algorithm along with blind pairs conceals the bases and exponents of the encrypted data, thus preserving the privacy of sensitive data in the process of outsourced computing. Security analysis and performance evaluation confirmed the security and efficiency of the proposed method.
Celestial: A Smart Contracts Verification Framework

We present Celestial, a framework for formally verifying smart contracts written in the Solidity language for the Ethereum blockchain.  Celestial allows programmers to write expressive functional specifications for their contracts. It translates the contracts and the specifications to F* to formally verify, against an F* model of the blockchain semantics, that the contracts meet their specifications. Once the verification succeeds, Celestial performs an erasure of the specifications to generate Solidity code for execution on the Ethereum blockchain. We use Celestial to verify several real-world smart contracts from different application domains such as tokens, digital wallets, and governance. Our experience shows that Celestial is a valuable tool for writing high-assurance smart contracts.
Hybridization of Improved Binary Bat Algorithm for Optimizing Targeted Offers Problem in Direct Marketing Campaigns

One of the biggest business problems of marketers, is to optimize the return on investments of direct Marketing campaign.This main purpose which can only be ensured by targeting the appropriate customer.The main challenge faced by companies when advertising, is to configure properly a campaign, by choosing the right target, so A high user acceptance rate is ensured to advertisements.However, when dealing with an important size of data, the important specification to consider is the combinatorial aspect of the problem and the limitation of the approach based on mathematical programming methods.In this work, and considering the optimization of targeting offers as an of NP-hard problems, we concluded that the use of a meta heuristic algorithm is more suitable to use a classical (exact) method.We choice to use an improved bat Algorithm hybridized with Genetic Algorithm.The results of computational experiments confirmed that the proposed algorithm gives competitive results.
Learning Multi-Level Features to Improve Crowd Counting

Crowd counting is a task that aims to estimate the number of people in an image. Recent crowd counting methods make significant progress by employing convolutional neural networks to regress crowd density maps. One of the most challenging problems in this task is the drastic scale variation of the region of interest in images. In this paper, a Feature Fusion Attention Network (FFANet) is proposed for crowd counting. Firstly, the VGG16 network is adapted as the backbone of the FFANet to extract the features of crowd images. Then, the extracted features are fused by the subsequent two stages. Specifically, the information enhancement operations on the multi-levels features are conducted by Feature Fusion Attention Module (FFAM), which are further refined by the Residual Block (RB). Finally, the features are processed by the Compression Module (CM) to generate a density map. To demonstrate the effectiveness, the proposed algorithm is verified on three benchmark datasets. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates the proposed FFANet outperforms the existing methods.
Meet my artificially-intelligent virtual self: creative avatars, machine learning, smart contracts and the copyright conundrum

Meet my artificially-intelligent virtual self: creative avatars, machine learning, smart contracts and the copyright conundrum Eugene C Lim Eugene C Lim Email: ec.lim@utoronto.ca Search for other works by this author on: Oxford Academic Google Scholar Journal of Intellectual Property Law & Practice, Volume 16, Issue 1, January 2021, Pages 66–78, https://doi.org/10.1093/jiplp/jpaa192 Published: 29 November 2020
Hidden Service Website Response Fingerprinting Attacks Based on Response Time Feature

It has been shown that website fingerprinting attacks are capable of destroying the anonymity of the communicator at the traffic level. This enables local attackers to infer the website contents of the encrypted traffic by using packet statistics. Previous researches on hidden service attacks tend to focus on active attacks; therefore, the reliability of attack conditions and validity of test results cannot be fully verified. Hence, it is necessary to reexamine hidden service attacks from the perspective of fingerprinting attacks. In this paper, we propose a novel Website Response Fingerprinting (WRFP) Attack based on response time feature and extremely randomized tree algorithm to analyze the hidden information of the response fingerprint. The objective is to monitor hidden service website pages, service types, and mounted servers. WRFP relies on the hidden service response fingerprinting dataset. In addition to simulated website mirroring, two different mounting modes are taken into account, the same-source server and multisource server. A total of 300,000 page instances within 30,000 domain sites are collected, and we comprehensively evaluate the classification performance of the proposed WRFP. Our results show that the TPR of webpages and server classification remain greater than 93% in the small-scale closed-world performance test, and it is capable of tolerating up to 10% fluctuations in response time. WRFP also provides a higher accuracy and computational efficiency than traditional website fingerprinting classifiers in the challenging open-world performance test. This also indicates the importance of response time feature. Our results also suggest that monitoring website types improves the judgment effect of the classifier on subpages.
Image Restoration under Cauchy Noise with Sparse Representation Prior and Total Generalized Variation

This article introduces a novel variational model for restoring images degraded by Cauchy noise and/or blurring.The model integrates a nonconvex data-fidelity term with two regularization terms, a sparse representation prior over dictionary learning and total generalized variation (TGV) regularization.The sparse representation prior exploiting patch information enables the preservation of fine features and textural patterns, while adequately denoising in homogeneous regions and contributing natural visual quality.TGV regularization further assists in effectively denoising in smooth regions while retaining edges.By adopting the penalty method and an alternating minimization approach, we present an efficient iterative algorithm to solve the proposed model.Numerical results establish the superiority of the proposed model over other existing models in regard to visual quality and certain image quality assessments.
Automatic Exploitation of YouTube Data: A Study of Videos Published by a French YouTuber During COVID-19 Quarantine in France

The objective of this study was to test the feasibility of automatically extracting and exploiting data from the YouTube platform, with a focus on the videos produced by the French YouTuber HugoDécrypte during COVID-19 quarantine in France. For this, we used the YouTube API, which allows the automatic collection of data and meta-data of videos. We have identified the main topics addressed in the comments of the videos and assessed their polarity. Our results provide insights on topics trends over the course of the quarantine and highlight users sentiment towards on-going events. The method can be expanded to large video sets to automatically analyse high amount of user-produced data.
A metamodel-based adaptive sampling approach for efficient failure region characterization of integrated circuits

Adaptive verification appears to be an e client solution to overcome the coverage problem and to accurately characterize the failure region of high dimensional spaces at integrated circuits' verification. Its main task is to gather more samples in the regions of interest based on the information learnt from previous samples. This helps engineers understand and interpret the behavior of the system under study with a reduced number of simulations/measurements compared to classical verification methods. To this end, we propose an adaptive sampling approach for the failure region characterization using the concept of metamodeling. Compared to other sampling methods for the failure region characterization, it has the advantage that it can detect and sample more in the near-failure region in the absence of a fail region. The concept has been applied on several synthetic test functions and on lab measurements of an analog integrated circuit. Results reveal that this adaptive sampling approach is very promising for failure region characterization.
An exploratory analysis of data noisy scenarios in a Pareto-front based dynamic feature selection method

Feature selection has become a mandatory step in several data exploration and Machine Learning applications since data quality can have a strong impact in the performance of machine learning models. Many feature selection strategies have been developed in the past decades, using different criteria to select the most relevant features. The use of dynamic feature selection, however, has showed that the use of multiple simultaneously criteria to determine the best attribute subset for similar instances can deliver encouraging results. In this context, this paper proposes to analyze the performance of a pareto-front based dynamic feature selection (PF-DFS) method under data noise scenarios. In order to do this, we intentionally added noise in 15 datasets and evaluated the PF-DFS performance in order to measure its stability under two different data noise scenarios. The obtained results are compared to some state-of-the-art algorithms and show that, in terms of accuracy, the PF-DFS method is more robust to the other methods for the majority of the analyzed scenarios.
How Do Students Behave When Using A Tutoring System? Employing Data Mining to Identify Behavioral Patterns Associated to The Learning of Mathematics

The inclusion of technology in the academic processes has led to constant innovation and investment of resources to offer a first-level educational service, with international standards, methodologies, study plans, and last generation laboratories. This paper focuses on how teachers can make use of an educational technology tool that will allow them to identify patterns that are associated with learning based on human-computer interaction. We present evidence of learning outcomes based on the detection of behaviors in Intelligent Tutoring Systems. These patterns pave the way for the auto-matic identification of patterns in association to students’ learning while using an educational technological tool. The results suggest a model for student’s behavior identification when interacting with the technological tool “Scooter”. This model identifies students with prospective better learning outcomes as well as students with difficulties to solve math prob-lems. Work for the future will analyze data that comes from different set-tings apart from solving exercises in Scooter to prove the hypothesis that there are patterns of behavior associated to learning outcomes in different problem-solving situations presented by educational technology.
A New and Efficient Genetic Algorithm with Promotion Selection Operator

Genetic algorithm (GA) is a widely used probabilistic search optimization algorithm. In the GA, selection is an important operator to guarantee the quality of solution. Therefore, the behavior of selection operator makes a great effect on the performance of the algorithm. This paper designs a new and efficient selection operator for GA base on the idea of promotion competition. This operator simulates the rule and process of promotion competition to protect the well perform chromosomes and eliminates poor chromosomes. This is a fundamental but significant research issue in GA that may be adopted into any existing GA variants to replace any other selection operators. We design four types of experiments to comprehensively verify the behavior of the proposed promotion selection operator, by comparing it with five other existing and commonly used selection operators. The results show that promotion selection operator has a general good performance in enhancing GA in terms of solution quality, convergence speed, and running time.
Data Control in Public Cloud Computing: Issues and Challenges

Background: The Advancement in the Hardware and Progress in IoT based devices has led to significant transformation in digitalization and globalization of business models in the IT World. In fact Cloud Computing has attracted many companies to expand their business by providing IT infrastructure with very less budget in pay per use model. The Expansion and Migration of Companies to Cloud Computing facilities has really brought many pros and cons and opened new area of Research. The Management of IT infrastructure as per business requirement is a great challenge for the IT Infrastructure managers because of complex business models which needs to be updated with market trends and it requires huge and updated infrastructure to accelerate their business requirements. No doubt there are many benefits of moving to Cloud but several vulnerabilities and potential threats related to security is a major concern for any business sensitive data. These security challenges place restrictions on moving on-premises workloads to the Cloud. This paper has discussed key differences in cloud models and existing various Cloud Security Architectures and challenges in cloud computing related to Data Security at Rest and in Transit. Also data controlling mechanism need to be adopted by IT Industry along with end to end security mechanism has been explained. Objective: The main objective of this paper is to discuss about the prevailing issues in cloud in terms of data security which is discouraging the Industry and organizations to move their data into public cloud and also to discuss how to enhance security mechanism in cloud during data migration and multitenant environment. Methods: Based on different reports and analysis, it has been pointed that data breach and data security are most challenging and concerning factor for any customer when someone think to migrate the workloads from On-Premises datacenter to Cloud Computing. It needs more attention in every consideration. All criteria and considerations to secure and protect the customer’s information and data have been classified and discussed. Data-at-rest and Data-in-transit are transmission method for storing and moving the data from one source to destination. Different encryption methods for protecting and security data-at-rest and data-in-transit have been identity. However, there are still more areas need to work in for filling gaps for Cloud data control and security which is still a serious concern and on top of attackers every day. Results &amp; Conclusion: Since, cyber-attacks occurring very frequently and causing a huge amount of investment on re-establishing the environment, it needs more control with effective usages of technology. All those concerns related to security are very reasonable concerns that needs to be addressed.
Correction to: Single Image Super-Resolution via a Holistic Attention Network

In the originally published version of chapter 12, the first affiliation stated a wrong city and country. This has been corrected.
Product Line Architecture Design of Software-Intensive Physical Protection Systems

A physical protection system (PPS) integrates people, procedures, and equipment for the protection of assets or facilities against theft, sabotage, or other malevolent intruder attacks. Since PPSs are not radically different and share lots of commonalities, there is an important potential for reuse and herewith an opportunity to substantially reduce the cost and development time, and enhance the quality of the developed PPSs. In this paper, we report on the design of a product line architecture for a family of software-intensive PPSs. With this, we adopt a model-based systems engineering (MBSE) approach in which we focus on the architecture design of PPSs. We model the corresponding architecture view models for the PPS product line architecture and discuss the development of specific PPSs.