Reviewerly/exHarmony huggingface dataset:
NFC Key Exchange - A light-weight approach to authentic Public Key Exchange for IoT devices

The Near Field Communication (NFC) technology has experienced a steep rise in popularity due to new advances in contactless payment or virtual public transport tickets on mobile devices. Though, NFC can also be used to exchange arbitrary data between two devices within close distance. This aspect is inherently useful to prove physical access, e.g. during authentication. Modern wireless technologies such as Wi-Fi or Bluetooth 5.0 also use NFC for their pairing schemes. However, there does not exist any approach towards an NFC supported authentication scheme for digital signatures. This paper proposes a novel approach to authentically exchange public keys with the aid of NFC. Using said technique allows the key exchanging parties to prove their authenticity to each other, by exploiting the close and limited wireless communication distance of NFC. Using the proposed algorithm scalable, authentic and cost-effective sensor networks can be built, without compromising the security of the exchanged keys. With the proposed NFC challenge-response scheme, the public key of the sender can be transferred without any third party being able to smuggle in their own public key. Following the proposed scheme, any attempts to exchange unauthentic keys can be directly identified and consequently rejected. The proof-of-concept example shows, that the algorithm allows for dynamically adding of new sensors as well as an authentic communication between the gateway and the sensor devices.
ALiSE: Through the mirrored space, and what user interacts with avatars naturally

Augmented Reality (AR) and Virtual Reality (VR) interfaces, such as a conventional head-mounted display (HMD), have the problem of being unable to share the content experience with people who are not wearing the device. To solve this problem, we focus on AR mirrors and propose ALiSE (Augmented Layer Interweaved Semi-Reflecting Existence)—a display that displays images in a mirrored space using half-mirrors and gaps. We compared the service quality of this device with that of an ordinary display and an HMD. As a result, we were able to confirm the superiority of the ALiSE over conventional displays in several items. The results suggest that the connection generated between the service provider and the user using ALiSE is equivalent to the experience in VR. In other words, our proposed display method can provide the same level of satisfaction as the services provided in the conventional VR space. In addition, it is possible to share the content experience with accessibility equivalent to observing digital signage without wearing an HMD.
Automated data function extraction from textual requirements by leveraging semi-supervised CRF and language model

Function Point Analysis (FPA) provides an objective, comparative measure for size estimation in the early stage of software development. When practicing FPA, analysts typically abide by the following steps: data function (DF) extraction, transactional function extraction, function type classification and adjustment factor determination. However, due to lack of approach and tool support, these steps are usually conduct by human efforts in practice. Related approaches can hardly be applied in the FPA due to the following three challenges, i.e., FPA rule-driven extraction, domain-specific parsing, and expensive labeled resources. In this paper, we aim to automate the extraction of DFs, which is the starting and fundamental step in FPA. We propose an automated approach named DEX to extract data functions from textual requirements. Specifically, DEX introduces the popularly-used conditional random field (CRF) model to predict the boundary of a data function. Besides, DEX employs the bootstrapping-based algorithm and DF-oriented language model to further boost the performance. We evaluate DEX from two aspects: evaluation on a real industrial dataset and a manual review by domain experts. The evaluation on the real industrial dataset shows that DEX could achieve 80% precision, 84% recall, and 82% F1, and outperforms three state-of-the-art baselines. The expert review suggests that DEX could increase 16% precision and 13% recall, compared with those produced by engineers. DEX could achieve promising results under a small number of labeled requirements and outperform the state-of-the-art approaches. Moreover, DEX could help engineers produce more accurate and complete DFs in the industrial environment.
A Secure Blockchain based Finance Application

Blockchain is undoubtedly one of the most potentially attractive technologies for the organization of financial services. The significance of the Blockchain technology in a banking system can be determined by three main principles that is; Disintermediation, Transparency and Provenance. The DLT (distributed ledger technology) eliminates the need for trust enforcer in the banking management system. This is made possible by the tamper- proof data in the distributed data records and all the participants in the network have the same version of data record with them at all times and it is unalterable. Blockchain promotes a public database record of all the customer transactions and other data, ensuring a transparent and trustworthy system as the customers have a 24x7 real-time access to it. Blockchain keeps an immutable distributed data ledger of transactions and this result in maintaining a lifecycle of the assets and its ownership from its first transaction to the most recent one. It reduces the chances of occurrence of theft, insurance fraud and misselling of the high valued assets or intellectual properties. Blockchain can be implemented in the banking sector for various applications like maintenance of data confidentiality, cheque clearing system, real time money transfer system (digital currency), supply chain finance, insurance, trade finance, KYC (know your customer), cross-border payments/transactions system, monitoring of consortium accounts, auction system and digital asset management. Blockchain technology can hence enable a decentralized solution for the banking ecosystem. The proposed system enables one platform for both the lenders and the borrowers to initiate their money transaction for lending, borrowing and repayment of the loan money. The transactions made throughout, that is, from the start to the last repayment can be viewed easily on a blockchain network thus increasing the transparency and trust of the customers and it resolves any disputes that can arise in the loan system. Along with this, a KYC registry based on blockchain is also proposed which will help in maintaining the KYC details in one platform which can be accessed by various banks for identification and verification of the customer in an easy and faster manner. It will increase the customer satisfaction as there will be no need for the customer to perform a KYC check every time he/she creates a bank account in any bank as the bank can easily access their KYC details from this system.
An Efficient and Secure Public Key Authenticated Encryption With Keyword Search in the Logarithmic Time

Searchable encryption is an important cryptographic technique that achieves data security and keyword retrieval over encrypted data in cloud. In 2004, Boneh et.al proposed the first searchable encryption scheme based on asymmetric cryptography. Since then, many variants of public key searchable encryption were proposed. However, most previous works are vulnerable to keyword guessing attack, and they can not provide trapdoor indistinguishability. Another problem is how to improve the searching efficiency. To deal with two issues, we construct a fast and secure public key authenticated searchable encryption scheme with designed server. Our scheme can resist keyword guessing attacks, chosen multi-keyword attacks and multi-trapdoor attacks. The search function in our scheme achieves the logarithmic search time in number of keywords while most existing schemes required the linear time. By comparison with previous schemes in computational complexity, our scheme is very fast in keyword search.
Continuous Rationale Visualization

Continuous software engineering (CSE) is characterized by frequent changes. It is challenging for developers to change software while sustaining its high quality so that the software is always deployable to the users. Rationale management provides an opportunity to support the change process during CSE. However, rationale management is not well integrated into CSE. Even if the rationale is captured, e. g. in the issue tracking system, it is difficult to access in the context of requirements, code, and other software artifacts. In this paper, we present tool support called ConDec to make rationale explicit during CSE and to integrate rationale into a knowledge graph data structure consisting of requirements, code, and other software artifacts. The knowledge graph is visualized in various ways and the developers can access the knowledge views from software artifacts such as requirements and code. They can interact with and filter the knowledge views. In particular, they can use transitive links, for example, to access all the decisions made in the context of a particular requirement or code file. We demonstrate the usefulness of the knowledge views using a case study project.
Cofee: A Comprehensive Ontology for Event Extraction from Text

Data is published on the web over time in great volumes, but majority of the data is unstructured, making it hard to understand and difficult to interpret. Information Extraction (IE) methods obtain structured information from unstructured data. One of the challenging IE tasks is Event Extraction (EE) which seeks to derive information about specific incidents and their actors from the text. EE is useful in many domains such as building a knowledge base, information retrieval, summarization and online monitoring systems. In the past decades, some event ontologies like ACE, CAMEO and ICEWS were developed to define event forms, actors and dimensions of events observed in the text. These event ontologies still have some shortcomings such as covering only a few topics like political events, having inflexible structure in defining argument roles, lack of analytical dimensions, and insufficient gold-standard data especially in low-resource language such as Persian. To address these concerns, we propose an event ontology, namely COfEE, that incorporates both expert domain knowledge, previous ontologies and a data-driven approach for identifying events from text. COfEE consists of two hierarchy levels (event types and event sub-types) that include new categories relating to environmental issues, cyberspace, criminal activity and natural disasters which need to be monitored instantly. Also, dynamic roles according to each event sub-type are defined to capture various dimensions of events. In a follow-up experiment, the proposed ontology is evaluated on Wikipedia events, and it is shown to be general and comprehensive. Moreover, in order to facilitate the preparation of gold-standard data for event extraction, a language-independent online tool is presented based on COfEE. A gold-standard dataset annotated by 10 human experts is also prepared consisting 24K news articles in Persian language according to the COfEE ontology. In order to diversify the data, news articles from the Wikipedia event portal and 100 most popular Persian news agencies between the years 2008 and 2021 is collected. Finally, we present a supervised method based on deep learning techniques to automatically extract relevant events and corresponding actors.
Future Visions for Higher Education: An Investigation of the Benefits of Virtual Reality for Teaching University Students

This study sought to understand the social value to University students of engaging universally in a virtual environment throughout a taught module. In a series of weekly seminars throughout September-December 2020, 49 undergraduate and postgraduate Engineering students interacted with module convenors and each other within a virtual teaching island (termed ‘Nottopia’) to consolidate learning from pre-recorded lectures. Questionnaire results highlighted the positive impact this innovative form of teaching had on the cohort, as the overwhelming majority felt highly motivated to participate in class activities and felt connected to their peers and lecturers. Objective data backed up these findings as attendance at the optional seminars was 90-100% throughout the semester. Follow-up interviews with students and behavioural observations identified five themes elucidating why such benefits accrue from the use of VR in this educational context: 1) A shared, student-owned and purposive space was provided; 2) content was inherently highly spatial/3D; 3) natural group dynamics arose in activities; 4) students could express their identity and/or hide using their chosen/designed avatar; 5) many magical and informative interactions were possible. These themes are realized in an initial framework which can aid educators in considering the different ways in which VR can affect the socially oriented experience of their students.
IU-SmartCert: A Blockchain-Based System for Academic Credentials with Selective Disclosure

Blockchain-based systems for academic credentials have shown the potential to overcome existing limitations of paper-based credentials. These systems define procedures to issue and manage credentials using blockchain technology to enhance security and reduce the administrative cost of both universities and employers. However, more effort is needed to provide features that empower the flexibility and privacy of learners. In this paper, we present IU-SmartCert, a blockchain-based system and procedures for issuing, verifying and exchanging academic credentials with selective disclosure feature. The security analysis of the proposed system shows that while learners can selectively present their credentials, the validity and integrity of the credentials are verifiable. Also, the proposed system is general and can be extended for other domains in the future.
Effects of Energy Dissipation and Deformation Function on the Entanglement, Photon Statistics and Quantum Fisher Information of Three-Level Atom in Photon-Added Coherent States for Morse Potential

The present research paper considers a three-level atom (3LA) that interacts with a field mode primarily in a photon-added coherent state of Morse potential (PACSMP). The dynamics of entanglement, the photon statistics, and the quantum Fisher information are investigated. The statistics of field photons are discussed by evaluating the Mandel parameter. We check the influence of the energy dissipation and intensity-dependent function. Finally, we detect the relationship between the entanglement, the field’s nonclassical characteristics, and atomic quantum Fisher information throughout the evolution of time. The findings illustrate the important role of the number of added photons and CSMP in affecting the entanglement’s time evolution, the quantum Fisher information, and the Mandel parameter. Based on the obtained results, we reached significant physical phenomena, including the sudden birth and death of the nonlocal correlation between atom-Morse potential field structures.
Sealed-Bid auction scheme based on Blockchain and Secure Multi-Party Computation

With the rapid development of Internet technology, traditional offline auction is gradually transforming to electronic auction, in which the privacy of bidders is becoming more and more concerned. Most of the existing sealed electronic auction schemes guarantee the fairness of the auction through the third party auction house, but the third party’s distrust also brings security risks to the privacy disclosure of the bidder’s identity, price tag and so on. In view of the above problems, this paper proposes an auction scheme based on blockchain and secure multiparty computing. The scheme takes advantage of the characteristics of decentralized and tamper proof of blockchain to build an auction environment without third party. The idea of deposit on blockchain restricts the behavior of bidders and improves the security of sealed auction scheme. Secure multi-party computation and ring signature technology are used to protect the bidders’ privacy from disclosure and public verifiability. Through security analysis, the proposed auction scheme meets the security requirements of electronic auction, and is more fair and reliable than the traditional auction scheme.
Crawl-shing: A focused crawler for fetching phishing contents based on graph isomorphism

The Web is fed with experiences about phishing, which aim at sharing phisher techniques and behaviours. We argue that this textual information can be turned into knowledge, exploitable to prevent such attacks. Unlike anti-phishing works that aim at detecting phishing traces, this work is the first attempt to design a tool to retrieve web pages which have phishing contents. The expected crawler is dedicated to extract phishing feeds. Existing crawlers mainly rely on building vector space models (VSM) from pages while exploiting Term frequency inverse - document frequency (Tf-idf) and cosine similarity to compute Web page similarities related to a given query. Considering the fact that vector modelling ignores the order of appearance of terms in the document as well as proximity and the connections between terms, we introduce Crawl-shing, an improved search model based on isomorphic graphs, which given two documents evaluate their similarity degree by seeking the largest common subgraph. Experimental results with phishing Web pages show that Crawl-shing presents a harvest rate better than the Breadth First Search (BFS) approach. Crawl-shing has been found more precise during exploration compared to the approaches based on vector modelling.
TRIPTOUR:A Multi-itinerary Tourist Recommendation Engine Based on POI Visits Interval

Tourism is a vast industry in the world today and provides a country with a significant source of revenue and employment. Personalized multi-tour recommendations are challenging and time-consuming tasks. Recommended itineraries should fulfill tourists&#x0027; desires and must be arranged within a limited time span. In addition, current multi-itinerary systems do not include Point Of Interest (POI) visit time in the suggested tour. The POI visit time vary according to the duration of the POI visit, e.g. longer duration of visits owing to the buying of tickets on the sot. To address these challenges, we use an algorithm named TRIPTOUR for suggesting multiple itineraries based on POI visit time, tourist attractions, popularity of itineraries, and the costs involved. The algorithm was developed to recommend itineraries for the visitors who want to visit unfamiliar locations. We also extracted related interface functionality from the Flickr dataset for the selection of different itineraries. The results demonstrate that the TRIPToUR approach outperforms the benchmark methods based on parameters such as precision, Recall, Fl-Score and the number of suggested itineraries.
Motion-Based Multiple Object Detection and Tracking in Video

Detecting and tracking objects is recently very important problem in computer vision field. It is widely used in areas like surveillance, security, automated vehicle systems and many more. This application is capable of detecting and tracking multiple objects in video sequences captured by a static camera.
A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering

Feature selection methods are widely used to address the high computational overheads and curse of dimensionality in classifying high-dimensional data. Most conventional feature selection methods focus on handling homogeneous features, while real-world datasets usually have a mixture of continuous and discrete features. Some recent mixed-type feature selection studies only select features with high relevance to class labels and ignore the redundancy among features. The determination of an appropriate feature subset is also a challenge. In this paper, a supervised feature selection method using density-based feature clustering (SFSDFC) is proposed to obtain an appropriate final feature subset for mixed-type data. SFSDFC decomposes the feature space into a set of disjoint feature clusters using a novel density-based clustering method. Then, an effective feature selection strategy is employed to obtain a subset of important features with minimal redundancy from those feature clusters. Extensive experiments as well as comparison studies with five state-of-the-art methods are conducted on SFSDFC using thirteen real-world benchmark datasets and results justify the efficacy of the SFSDFC method.
Acme: A small 3D geometry library

<h2>Abstract</h2> Simulation of manned and unmanned vehicles has become more and more important in the last decades. High-performance real-time DIL (<i>Driver-In-The-Loop</i>) and HIL (<i>Hardware-In-The-Loop</i>) simulators require efficient, accurate, and fast algorithms to properly model how a vehicle moves in a virtual environment. Most of the time, the virtual world is made from a multitude of basic geometric entities that can collide and modify accordingly. The ability to quickly resolve basic geometric problems is one of the most important roles in this kind of simulation. Acme library is built to efficiently perform simple operations on a large number of basic geometric entities. In particular, the library is designed to address specific hard real-time tire–road contact geometry analysis. In this paper, we discuss the details about the Acme library implementation, data types, features, and an example application to demonstrate the capabilities of this newly born software.
Generating identities with mixture models for speaker anonymization

Speaker anonymization methods are a growing research area, due to the common use of voice interfaces coupled with growing privacy requirements. However, existing systems suffer from several issues, in particular a reduction in the entropy space of the newly created voices. This is problematic as it reduces the diversity of the produced anonymous voices, thus making distinguishing between anonymized voices more difficult, and limiting the number of anonymous voices that can be generated. In this work we propose a method for creating the new identity component for anonymized voices, termed an x-vector, which aims to better reflect the natural diversity of voices, in turn increasing the diversity of the voices of anonymized speakers. We combine this identity generation method with existing anonymization schemes, to produce an overall anonymization system, which we evaluate. Our results demonstrate that our scheme creates more diverse anonymized voices than the existing baseline method. Furthermore, our results show that the assumption of perfect de-coupling between identity and non-identity voice components used in existing speaker anonymization frameworks does not hold, highlighting a clear avenue for future work.