Reviewerly/exHarmony huggingface dataset:
Precise Detection of Content Reuse in the Web

With vast amount of content online, it is not surprising that unscrupulous entities "borrow" from the web to provide content for advertisements, link farms, and spam. Our insight is that cryptographic hashing and fingerprinting can efficiently identify content reuse for web-size corpora. We develop two related algorithms, one to automatically *discover* previously unknown duplicate content in the web, and the second to *precisely detect* copies of discovered or manually identified content. We show that *bad neighborhoods*, clusters of pages where copied content is frequent, help identify copying in the web. We verify our algorithm and its choices with controlled experiments over three web datasets: Common Crawl (2009/10), GeoCities (1990s–2000s), and a phishing corpus (2014). We show that our use of cryptographic hashing is much more precise than alternatives such as locality-sensitive hashing, avoiding the thousands of false-positives that would otherwise occur. We apply our approach in three systems: discovering and detecting duplicated content in the web, searching explicitly for copies of Wikipedia in the web, and detecting phishing sites in a web browser. We show that general copying in the web is often benign (for example, templates), but 6–11% are commercial or possibly commercial. Most copies of Wikipedia (86%) are commercialized (link farming or advertisements). For phishing, we focus on PayPal, detecting 59% of PayPal-phish even without taking on intentional cloaking.
Using structured knowledge and traditional word embeddings to generate concept representations in the educational domain

To capitalize on the benefits associated with word embeddings, researchers working with data from domains such as medicine, sentiment analysis, or finance, have dedicated efforts to either taking advantage of popular, general-purpose embedding-learning strategies, such as Word2Vec, or developing new ones that explicitly consider domain knowledge in order to generate new domain-specific embeddings. In this manuscript, we instead propose a mixed strategy to generate enriched embeddings specifically designed for the educational domain. We do so by leveraging FastText embeddings pre-trained using Wikipedia, in addition to established educational standards that serve as structured knowledge sources to identify terms, topics, and subjects for each school grade. The results of an initial empirical analysis reveal that the proposed embedding-learning strategy, which infuses limited structured knowledge currently available for education into pre-trained embeddings, can better capture relationships and proximity among education-related terminology. Further, these results demonstrate the advantages of using domain-specific embeddings over general-purpose counterparts for capturing information that pertains to the educational area, along with potential applicability implications when it comes to text processing and analysis for K–12 curriculum-related tasks.
SmartLock: Access Control Through Smart Contracts and Smart Property

This paper focuses on the viability of using smart contract controlled electronic locks to give access to people by the lock’s owner in locations like hotels, coworking spaces or even their homes. The goal is that the guest rents a property using the lock and receives a temporary digital key according to his reservation which he uses to unlock the property’s door using an app on his smartphone. The lock was designed using a Raspberry Pi 3 B+ controlling a 12v solenoid lock through a python script and running the chirp.io audio communication protocol for both microcomputer and smartphone. The smart contracts were developed using Solidity from the Ethereum platform. The paper will cover the designed architecture and implementation details of the project.
Improving Students Conceptual Understanding Of Calculus Based Physics Using Problem Based Learning Approach On An E-Learning Platform Applied To Engineering Education

Successful engineers are self-directed critical thinkers who are creative and analytical in their approach. They are good problem solvers and possess lifelong learning skills. Many students spend time to study but not the quality time in studying. One of the subject, which is most affected by this, is Physics as it involves more cognitive exercise. This is evident in areas related to calculus based problem solving. Whatever be the reason, in classrooms many physics students seem unprepared and unmotivated in areas related to problem solving in Physics. Especially in calculus based physics problems, students find difficulty with both mathematical aspect and concepts in physics. This experimental study focuses on how student's problem solving skills in calculus-based physics in a digital platform relates to their achievement. The result showed students motivation and collaboration were positively affected by this method. In addition, the student's performance in the final exam showed a significant increase. The result is in line with the shift in engineering education from an instructive to constructivist approach over the years.
Capture and visualisation of text understanding through semantic annotations and semantic networks for teaching and learning

During various learning activities, teachers and students need to clarify, explore and share their understanding of reading materials. For doing this, they must make explicit the mental representation constructed during the reading process. In this article, we propose an approach combining semantic annotations and semantic networks as formal means for elicitation, structuring, formalisation, analysis and sharing of teachers’ and students’ understanding of textual materials that they are asked to read in learning tasks. In the proposed approach, teachers can create learning tasks, in which students are asked to semantically annotate a text by associating portions of it to resources described in a Knowledge Base (KB) in accordance with a provided ontology. New instances can be created by students or teachers in the KB during the annotation process. We show how semantic networks can be used to visualise extracts of the resulting KB, and to help people organise their comprehension of texts. In addition, teachers can assess student evolution by analysing the semantic networks that each one produces during the reading and annotation process. This approach is implemented by using our annotation tool, integrated with a digital repository and a virtual learning environment. An empirical evaluation of the benefits of the proposed approach in a literature case study confirms that it facilitates information extraction, sharing and analysis, contributing to leverage teaching and learning.
A smart dialogue-competent monitoring framework supporting people in rehabilitation

In this paper, we present work in progress on the development of a smart monitoring framework to support people with motor disabilities and their caregivers in clinical and non-clinical rehabilitation and care environments. The innovation of the platforms lies in the combination of smart monitoring solutions, such as activity recognition and lifestyle tracking, with an intelligent virtual agent that aims to empower and motivate people in need through personalized feedback and responses, as well as to assist caregivers and clinicians to easily collect information about the patients. The proposed system exploits and combines state-of-the-art technologies in speech recognition and synthesis, knowledge representation and reasoning, dialogue management and sensor data analysis, infusing clinical knowledge and patient history. Aiming for a practical, acceptable solution, the proposed system takes into account aspects of integration, security and privacy.
Crowdsourced measurements for device fingerprinting

Physical layer identification allows verifying a user's identity based on their transmitter hardware. In contrast with digital identifiers at higher protocol layers, physical layer identification or device fingerprinting can identify unique signal characteristics at the physical layer introduced by manufacturing variability specific to each device. Recently, dynamic spectrum access has been proposed to allow a larger number of devices to efficiently access wireless spectrum. In such a system many low-cost devices may be distributed over a large area with spectrum allocated and managed by a central authority. Traditional authentication methods may not be secure, or adequate to identify existing users in a backwards compatible way: Identifiers such as MAC addresses can be impersonated, and the number of devices and their distributed nature may make key distribution and revocation difficult. Consequently, physical layer identification can be used to augment other security measures.
Simple Contrapositive Assumption-Based Frameworks

Assumption-based argumentation is one of the most prominent formalisms for logical (or structured) argumentation. It has been shown useful for representing defeasible reasoning and has tight links to logic programming. In this paper we study the Dung semantics for extended forms of assumption-based argumentation frameworks (ABFs), based on any contrapositive propositional logic, and whose defeasible rules are expressed by arbitrary formulas in that logic. In particular, new results on the well-founded semantics for such ABFs are reported, the redundancy of the closure condition is shown, and the use of disjunctive attacks is investigated. Finally, some useful properties of the generalized frameworks are considered.
Waveform Analysis and Feature Extraction from Speech Data of Dysarthric Persons

Speech recognition systems provide a natural way of interacting with computers and serve as an alternative to the more popular but less intuitive peripherals (input / output devices). Tools employing the techniques of Automatic Speech Recognition (ASR) can be extended to serve people with speech disabilities so that they can overcome the difficulties faced in their interaction with general public. An attempt is made here to achieve this goal by mapping the distorted speech signals of people with severe levels of dysarthria to that of a normal speech and/or less severe dysarthric speech. The analysis is carried out by comparing the speech waveforms of the people with and without communication disorders and then extracting the features from the audio files. The differences in time, duration, frequency and PSD are used to facilitate the mapping of unintelligible speech data to intelligible ones. When reasonable accuracy levels are achieved in this mapping, the normal voice can be used as the substitute / surrogate of the original distorted voice.
Attribute-Based Information Flow Control

Abstract Information flow control (IFC) regulates where information is permitted to travel within information systems. To enforce IFC, access control encryption (ACE) was proposed to support both the no read-up rule and the no write-down rule. There are some problems in existing schemes. First, the communication cost is linear with the number of receivers. Second, senders are not authenticated, namely an unauthorized sender can send a message to a receiver. To reduce communication cost and implement sender authentication, we propose an attribute-based IFC (ABIFC) scheme by introducing attribute-based systems into IFC. Our ABIFC scheme captures the following features: (i) flexible IFC policies are defined over a universal set of descriptive attributes; (ii) both the no read-up rule and the no write-down rule are supported; (iii) the communication cost is linear with the number of required attributes, instead of receivers; (iv) receivers can outsource heavy computation to a server without compromising data confidentiality; (v) authorized senders can control release their attributes when sending messages to receivers. To the best of our knowledge, it is the first IFC scheme where flexible policies are defined over descriptive attributes and outsourced computation is supported.
Identifying maritime vessels at multiple levels of descriptions using deep features

Varying resolution quality of operational data, size of targets, view occlusions, and large variation in sensors due to nature of overhead systems as compared to consumer devices contribute to degradation of the maritime vessel identification. We exploit the maritime domain characteristics to optimize and refine the deep learning Mask-RCNN framework for training generic maritime vessel classes. Maritime domain, compared to consumer domain, lack alternative targets that would be incorrectly associated as maritime vehicles: this allows us to relax the parameter constraints learned on urban natural scenes in consumer photos, adjust parameters of the model inference, and achieve robust performance and high AP measure for transfer learning scenarios. In this paper, we build upon this robust localization work, and extend our transfer learning work to new domains and datasets. We propose new approach for identifying specific category of maritime vessels and build a refined multi-label classifier that is based on deep Mask-RCNN features. The classifier is designed to be robust to domain transfer (e.g. different overhead maritime video feed), and to the noise in the data annotation (e.g. vessel is not correctly marked or label is ambiguous). We demonstrate superior category classification results of this low shot learning approach on publicly available MarDCT dataset.
A Preliminary Approach to Using PRNU Based Transfer Learning for Camera Identification

In this paper, a preliminary method of source camera identification using transfer learning methods is proposed. Every image taken by a camera has a unique artefact, known as photo response non-uniformity (PRNU) noise that is manifested in the image, because of irregularities during the manufacturing process of the image sensor present in that camera. A convolutional neural network (CNN) is used here to extract the features pertaining to the noise residual of a specific camera, which can be later passed to a classifier. In this paper, we use a CNN architecture that has weights that have been pre-trained to extract the residual features. This method is called transfer learning and is used after denoising the images in the dataset. The noise residual features are extracted by the transfer learning model and are then passed to a Support Vector Machine (SVM) classifier. Despite the simplicity of this approach, the results obtained are favorable compared to the existing methods, especially considering the small dataset used.
The future of internet pragmatics

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Defects Detection Technique of Use Case Views during Requirements Engineering

In the past decade, object-oriented software engineering (OOSE) has gained popularity from many software developers, especially OOSE with a unified modeling language (UML). Use case views are often used in most systems during an analysis phase. These views show the system functionality related to the system stakeholders. Hence, use case views seem to be a corner stone for a software system. The defects occurring in use case views will affect the later designs. If these defects can be found early, it would save time and cost in software development. Therefore, this research proposes a technique for detecting defects in use case views during an analysis phase or requirements engineering process. Correct users' requirements were created as requirements specifications in a traditional form-based style for testing comparison. The algorithms for generating decision table from form-based requirements and UML use case specification as well as the algorithm for use case view validation were invented. Two simple case studies were investigated and used as the preliminary evaluation. The nineteen fourth-year students were selected as the subjects for the preliminary investigation in order to compare between manual fault detection and our automated proposed system. They were asked to perform four tasks for each case study. The results show that the efficiency of manual fault detection is less than the proposed technique at 47% and 44% or overall average as 45.5% for the two case studies. Currently, we apply this proposed technique to more complex industrial setting and familiar software systems to software engineers.
MODEM: an UML profile for MODEling and Predicting software Maintenance before implementation

A software is developed to satisfy some requirements that could change with respect to technology progress or new clients needs. That is why a software evolves continually in order to fit with occurring requirements. Software evolution or maintenance represents a non-trivial issue that should be treated seriously. In fact, according to some studies software maintenance could consume about 80% of software development total cost. In most of works, such issue was treated in a source code level by proposing new techniques and tools that could optimize maintenance cost. In this paper, we treat the same issue but in an earlier phase of software development and exactly in software design. We propose here an UML profile that allows to software developers and designers to carry about maintenance tasks at the design phase in order to predict handling maintenance tasks before happening, to get an historical trace of these tasks and to express explicitly on models some design patterns helping to resolve software maintenance problems in order to make maintenance easier and cheaper.
Personality Classification based on Facebook status text using Multinomial Naïve Bayes method

Psychological research shows that the way people write is influenced by their personalities. Words that are often used can describe someone's personality. Facebook is one of the social networking sites for users to express themselves. User status posts can be used to identify the user's personality. To find out the personality of a person based on the statuses they write on Facebook use text classification techniques using Multinomial Naive Bayes method. The language is English. The test result shows that accuracy increases after reducing the number of word variations and variations of prior probability values. The accuracy obtained by using stemming in the preprocessing process is 59.9% and using uniform prior increases 0.3%.
Design Principles for True Random Number Generators for Security Applications

The generation of high quality true random numbers is essential in security applications. For secure communication, we also require high quality true random number generators (TRNGs) in embedded and IoT devices. This paper provides insights into modern TRNG design principles and their evaluation, based on standard's requirements and design experience. We illustrate our approach with a case study of a recently proposed delay chain based TRNG.
A Comparative Study of the Learner Model in Adaptive Mobile Learning Systems

Currently, scientific researches in the field of e-learning in general and m-learning in particular are oriented towards learning platforms where learner's expectations, motivation, learning styles, habits and needs are increasingly taken into consideration. This research direction enables developers to build a model of goals, preferences and knowledge of each individual user in order to adapt the learning to his/her needs and characteristics. For this purpose, we present in this paper a comparative study of the existing learner models in adaptive m-learning systems that consider a combination of learner's characteristics and context in order to provide each learner with the most suitable learning content and format of presentation. We end our paper by suggesting a future proposal after criticizing the related works.
Modeling Local Field Potentials with Regularized Matrix Data Clustering

In this paper, we propose a novel regularized mixture model for clustering matrix-valued image data. The new framework introduces a sparsity structure (e.g., low rank, spatial sparsity) and separable covariance structure motivated by scientific interpretability. We formulate the problem as a finite mixture model of matrix-normal distributions with regularization terms, and then develop an Expectation-Maximization-type of algorithm for efficient computation. Simulation results and analysis on brain signals show the excellent performance of the proposed method in terms of a better prediction accuracy than the competitors and the scientific interpretability of the solution.
Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classification

Michael Roth, Shyam Upadhyay. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
An Enhanced Key Security of Playfair Cipher Algorithm

Cryptography is one of the best methods to keep the information safe from security attack by unauthorized people. At present, many studies had been conducted to generate a more robust cryptographic algorithm to provide high security for data communication. In this study, the authors proposed an enhanced key security of Playfair cipher algorithm using Playfair cipher 16x16 matrix, XOR, two's complement, and bit swapping. The result of the study shows that the processing time for encryption and decryption processes are linearly proportional. It means the longer characters of the key; the more significant times needed to encrypt and decrypt the key. Also, the proposed method is faster than the existing key security of Playfair cipher algorithm. Experimental results also show that the proposed key security algorithm exhibits strong avalanche effect. It was also found that the key cannot be easily cracked by an attacker performing brute-force attack.
Hate Speech and Self-Restraint

In this article, my aim is to consider under what circumstances, and for what reasons, individuals may freely choose not to speak hatefully about others. Even if not threatened with legal sanction, why might they decide not to say something which they think they have good reason to say? My suggestion will be that there are various pro tanto reasons for individuals to restrain themselves from saying what they wanted to say. To be specific, I shall argue that such reasons fall into three analytically distinct categories, which I shall describe as normative codes of civility, ethics and morality. Thus each of these codes may provide different sorts of reasons for not engaging in hate speech. My hope is that the results of this investigation may usefully inform a strategy which aims to combat such speech without resource to the law.