Reviewerly/exHarmony huggingface dataset:
TRIDEnT

Sophisticated mass attacks, especially when exploiting zero-day vulnerabilities, have the potential to cause destructive damage to organizations and critical infrastructure. To timely detect and contain such attacks, collaboration among the defenders is critical. By correlating real-time detection information (threat indicators) from multiple sources, defenders can detect attacks and take the appropriate measures in time. However, although the technical tools to facilitate collaboration exist, real-world adoption of such collaborative security mechanisms is still underwhelming. This is largely due to a lack of trust and participation incentives for companies and organizations. This paper proposes TRIDEnT, a novel collaborative platform that aims to enable parties to exchange network threat indicators, thus increasing their overall detection capabilities. TRIDEnT allows parties that may be in a competitive relationship, to selectively advertise, sell and acquire threat indicators in the form of (near) real-time peer-to-peer streams. To demonstrate the feasibility of our approach, we instantiate our design in a decentralized manner using Ethereum smart contracts and provide a fully functional prototype.
The ARK Identifier Scheme

The ARK (Archival Resource Key) naming scheme is designed tofacilitate the high-quality and persistent identification ofinformation objects. A founding principle of the ARK is thatpersistence is purely a matter of service and is neither inherent inan object nor conferred on it by a particular naming syntax. The bestthat an identifier can do is to lead users to the services thatsupport robust reference. The term ARK itself refers both to thescheme and to any single identifier that conforms to it. An ARK hasfive components: [http://NMAH/]ark:[/]NAAN/Name[Qualifier] an optionaland mutable Name Mapping Authority Hostport (usually a hostname), theark: label, the Name Assigning Authority Number (NAAN), the assignedName, and an optional and possibly mutable Qualifier supported by theNMA. The NAAN and Name together form the immutable persistentidentifier for the object independent of the URL hostname. An ARK is aspecial kind of URL that connects users to three things: the namedobject, its metadata, and the provider's promise about itspersistence. When entered into the location field of a Web browser,the ARK leads the user to the named object. That same ARK, inflectedby appending a single question mark (`?'), returns a brief metadatarecord that is both human- and machine- readable. When the ARK isinflected by appending dual question marks (`??'), the returnedmetadata contains a commitment statement from the current provider.Tools exist for minting, binding, and resolving ARKs.
One Step Beyond: Lecturing With A Tablet Pc

In the Fall 2005 semester, the authors used a Tablet PC during lectures as a replacement for the chalkboard.The Tablet PC allows the instructor to write on the PC screen while facing the students, similar to an overhead projector.The advantage is that the entire presentation can be saved in electronic format and uploaded to the course website, where it is available to both the students and instructor immediately after the session ends.Students and the instructor can focus on concept learning rather than transcription.With wireless capability the instructor would be able to walk around the room while projecting his solutions in real time.This paper reports on two specific case experiences with this tool, including student data on usability and satisfaction.
Integrating Morphological Edge Detection and Mutual Information for Nonrigid Registration of Medical Images

Background: Medical images are widely used within healthcare and medical research. There is an increased interest in precisely correlating information in these images through registration techniques for investigative and therapeutic purposes. This work proposes and evaluates an improved measure function for registration of carotid ultrasound and magnetic resonance images (MRI) taken at different times. Methods: To achieve this, a morphological edge detection operator has been designed to extract the vital edge information from images which is integrated with the Mutual Information (MI) to carry out the registration process. The improved performance of proposed registration measure function is demonstrated using four quality metrics: Correlation Coefficient (CC), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF) and Gradient Magnitude Similarity Deviation (GMSD). The qualitative validation has also been done through visual inspection of the registered image pairs by clinical radiologists. Results: The experimental results showed that the proposed method outperformed the existing method (based on integrated MI and standard edge detection) for both ultrasound and MR images in terms of CC by about 4.67%, SSIM by 3.21%, VIF by 18.5%, and decreased GMSD by 37.01%. Whereas, in comparison to the standard MI based method, the proposed method has increased CC by 16.29%, SSIM by 16.13%, VIF by 52.56% and decreased GMSD by 66.06%, approximately. Conclusion: Thus, the proposed method improves the registration accuracy when the original images are corrupted by noise, have low intensity values or missing data.
Can We Generate Emotional Pronunciations for Expressive Speech Synthesis?

In the field of expressive speech synthesis, a lot of work has been conducted on suprasegmental prosodic features while few has been done on pronunciation variants. However, prosody is highly related to the sequence of phonemes to be expressed. This article raises two issues in the generation of emotional pronunciations for TTS systems. The first issue consists in designing an automatic pronunciation generation method from text, while the second issue addresses the very existence of emotional pronunciations through experiments conducted on emotional speech. To do so, an innovative pronunciation adaptation method which automatically adapts canonical phonemes first to those labeled in the corpus used to create a synthetic voice, then to those labeled in an expressive corpus, is presented. This method consists in training conditional random fields pronunciation models with prosodic, linguistic, phonological and articulatory features. The analysis of emotional pronunciations reveals strong dependencies between prosody and phoneme assimilation or elisions. According to perceptual tests, the double adaptation allows to synthesize expressive speech samples of good quality, but emotion-specific pronunciations are too subtle to be perceived by testers.
Multiparty Classical Choreographies

We present Multiparty Classical Choreographies (MCC), a language model where global descriptions of communicating systems (choreographies) implement typed multiparty sessions. Typing is achieved by generalising classical linear logic to judgements that explicitly record parallelism by means of hypersequents. Our approach unifies different lines of work on choreographies and processes with multiparty sessions, as well as their connection to linear logic. Thus, results developed in one context are carried over to the others. Key novelties of MCC include support for server invocation in choreographies, as well as logic-driven compilation of choreographies with replicated processes.
Medical undergraduate students’ perception towards the use and non-use of e-books: a case from Pakistan

Purpose The purpose of this study is to explore the perception of medical undergraduate students regarding their behavior towards use and non-use of e-books. It also elucidates their preferences between print and electronic book format. Design/methodology/approach It is a quantitative study based on survey method. A semi-structured questionnaire was used to collect the data from final year medical students of three medical colleges of Lahore, Pakistan. Statistical package for social sciences (SPSS) was used to analyse 232 useable questionnaires. Findings The findings indicate that the use and non-use of e-books is almost equal among students. The students who use e-books mostly access them through Google books, and they used e-books for academic purpose. Users were convinced about the benefits of e-books such as easy access, 24/7 availability and up-to-date information. However, users and non-users of e-books preferred to use printed version of books. Non-users identified that e-books often distract them because of different links on the website, and they do not want to change their habit of reading printed books. Moreover, they are satisfied with printed books available in their libraries and hesitant to read on screen. Research limitations/implications The present study is limited to the medical sciences; its results cannot be generalizing to other academic disciplines. Practical implications The findings of the study will assist the medical libraries to improve their e-books collection and also negotiate the publishers for its licence issues. The study will also encourage the librarians to acquire handheld devices and load them with e-books for their users. Originality/value This is believed to be the first study on the use of e-books in the perspective of medical students in Pakistan.
Authentication and Authorization for Constrained Environments (ACE) using the OAuth 2.0 Framework (ACE-OAuth)

This specification defines a framework for authentication andauthorization in Internet of Things (IoT) environments called ACE-OAuth. The framework is based on a set of building blocks includingOAuth 2.0 and CoAP, thus making a well-known and widely usedauthorization solution suitable for IoT devices. Existingspecifications are used where possible, but where the constraints ofIoT devices require it, extensions are added and profiles are defined.
Enhanced Homomorphic Unsharp Masking method for intensity inhomogeneity correction in brain MR images

MRI is a powerful diagnostic tool providing excellent soft tissue contrast and high spatial resolution. However, imperfections arising in the radio frequency field and scanner-related intensity artefacts in MRI produce intensity inhomogeneity. These intensity variations pose major challenges for subsequent image processing and analysis techniques. Therefore, a robust means of correcting intensity inhomogeneity is essential for any accurate and efficient processing technique. Homomorphic filtering based techniques are commonly used for intensity correction in brain MR images, but the halo effect induced by these methods limit their performance in the presence of a tumour. To mitigate the halo effect, a novel Enhanced Homomorphic Unsharp Masking (EHUM) intensity correction algorithm is proposed in this paper. The proposed method first determines the ROI to overcome the halo effect between foreground and background regions. Then, the intensity correction is carried out using homomorphic filtering and linear grey scale mapping. The quantitative and qualitative evaluation results show the efficiency of the proposed method in overcoming the intensity inhomogeneity in both normal and abnormal brain MR images.
Updated Goals, Operators, Methods, and Selection Rules (GOMS) with Touch Screen Operations for Quantitative Analysis of User Interfaces

When developing applications, User Interfaces (UI) are considered as an important and integral component of any application. With badly designed UI, users are less likely to use the application, leading to low adoption rates and is not desirable in any application development setting. The process of developing good intuitive and stream-lined UI for users is complex, that requires many processes and experts from many fields to contribute. When evaluating potential UI designs, there are many attributes and features that could be examined either in a qualitative and/or quantitative standpoint. The Updated Goals, Operators, Methods, and Selection Rules (GOMS) model is an approach that has been used in the area. With the Keystroke Level (KLM) extension, it is possible to quantitatively estimate the time requirement or efficiency of UI for completing different tasks with minimal effort, and has been adopted in many GUI improvement projects. Due to the usefulness, extensions to the GOMS model had been proposed over the years including extensions to account for motion control interfaces. Though the GOMS model has been useful for quantitatively evaluating UI design, the main input device of modern smartphones and tablets are touch screens, which are different in nature when compared to traditional computer inputs. The differences leads to the point that GOMS model with KLM is ill-suited for touch screen based applications. This research paper addresses the issue by proposing extensions to the GOMS model to account for UI that are based on touch screen input devices.
Modified framework for sarcasm detection and classification in sentiment analysis

&lt;span&gt;Sentiment analysis is directed at identifying people's opinions, beliefs, views and emotions in the context of the entities and attributes that appear in text. The presence of sarcasm, however, can significantly hamper sentiment analysis. In this paper a sentiment classification framework is presented that incorporates sarcasm detection. The framework was evaluated using a non-linear Support Vector Machine and Malay social media data. The results obtained demonstrated that the proposed sarcasm detection process could successfully detect the presence of sarcasm in that better sentiment classification performance was recorded. A best average F-measure score of 0.905 was recorded using the framework; a significantly better result than when sentiment classification was performed without sarcasm detection.&lt;/span&gt;
Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks.

In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, the deep learning community adopted ordinal regression frameworks to take such ordering information into account. Neural networks were equipped with ordinal regression capabilities by transforming ordinal targets into binary classification subtasks. However, this method suffers from inconsistencies among the different binary classifiers. To resolve these inconsistencies, we propose the COnsistent RAnk Logits (CORAL) framework with strong theoretical guarantees for rank-monotonicity and consistent confidence scores. Moreover, the proposed method is architecture-agnostic and can extend arbitrary state-of-the-art deep neural network classifiers for ordinal regression tasks. The empirical evaluation of the proposed rank-consistent method on a range of face-image datasets for age prediction shows a substantial reduction of the prediction error compared to the reference ordinal regression network.
Aggressive Action Estimation: A Comprehensive Review on Neural Network Based Human Segmentation and Action Recognition

Human action recognition has been a talked topic since machine vision was coined.With the advent of neural networks and deep learning methods, various architectures were suggested to address the problems within a context.Convolutional neural network has been the primary go-to architecture for image segmentation, flow estimation and action recognition in recent days.As the problem itself is an extended version of various subproblems, such as frame segmentation, spatial and temporal feature extraction, motion modeling and action classification as a whole, some methods reviewed in this paper addressed sub-problems and some tried to address a single architecture to the action recognition problem.While being a success, convolution neural networks have drawbacks in its pooling methods.CapsNet, on the other hand, uses squashing function to determine the activation.Also it addresses spatiotemporal information with the normalized vector maps while CNN-based methods extracts feature map for spatial and temporal information and later augment them in a fusion layer for combining two separate feature maps.Critical review of papers provided in this work can contribute significantly in addressing human action recognition problem as a whole.
Hybrid M-Ary in Braided Single Stage Approach for Multiphoton Quantum Secure Direct Communication Protocol

Quantum secure direct communication (QSDC) is a branch of quantum cryptography which exploits laws of physics and was recently proposed for securing information transferred from sender to receiver without relying on computational complexity. Instead of an entangled photon, the quantum cryptography employs two other approaches, single photon and multiphoton for encoding the messages. The multiphoton was proposed to outperform the weaknesses of a single photon by introducing the multiplicity of a photon to perform data encryption tasks. Despite the advantages of multiphoton, the transmission time to transfer the encoded data is still regarded as a critical issue. Most of the multiphoton approaches require multiple photons to travel along a number of stages. Moreover, extra time is needed to change the polarization angle of the optical device for encoding purposes. These situations have resulted in an increase in source redundancy, which subsequently leads to an increase in the transmission time. In this paper, a compression message approach is proposed to mitigate the substantial increase in time used for the data transmission processes. The proposed compression approach, namely, hybrid M-ary in a braided single stage (HMBSS) is based on the lossless data encoding such that the number of photons is reduced in the data transmission stage. Extensive simulation experiments have been conducted to test the performance of the proposed approach against some selected multiphoton approaches. The results show that the proposed approach outperforms braided single-stage, M-ary three-stage and initialization vector in terms of reducing total average transmission time to encode the photon by about 75.9% and 91.7%, respectively. Furthermore, the HMBSS reduces the overhead on the transmission channel by minimizing the number of message's bits by about 45% in average even when the length of the message increases during the transmission as compared to others.
Multi Scrolls Chaotic Encryption for Physical Layer Security in CO-OFDM

We propose a novel 3-dimension multi scrolls chaotic encryption for physical layer security in CO-OFDM. A key space of 10324 is created to enhance the security level of OFDM data encryption with lower encryption complexity.
Mining of Closed High Utility Itemsets: A Survey

Finding High Utility Itemsets (HUIs) is one of the major problems in the area of frequent itemsets mining. However, HUIs mine lots of redundant itemsets which degrade the performance and importance of high utility itemsets mining. For overcoming this limitation, closed HUIs mining has been proposed. Closed high utility itemsets mining finds complete and non-redundant itemsets. In this paper, we give recent studies on closed high utility itemsets mining algorithms. The main goal of this survey is to provide recent studies and future research opportunities. We give taxonomy of closed high utility itemsets mining algorithms. This paper provides a rough outline of the recent work and gives a general view of closed high utility itemsets mining field.
A secure and efficient remote data auditing scheme for cloud storage

Cloud Data storage services allows users to easily store and access their data with any type of connected devices. However, since the data is outsourced, the user needs an auditing mechanism that enables him to check the integrity of his data without recovering it. Furthermore, the auditing should not incur a large computational and communication cost, especially since users should not have any computational issues while checking the integrity of their data using a computationally challenged device (smart-phone, tablet…). In this paper we propose an auditing scheme that provides to users an efficient and secure process to audit their outsourced data. The proposed scheme has a low computation overhead on the users and enables them to delegate the data verification process to a third party auditor while maintaining a low computational and communication overhead without jeopardizing the confidentiality of the stored data.
Multiview Contouring for Breast Tumor on Magnetic Resonance Imaging

The shape and contour of the lesion are shown to be effective features for physicians to identify breast tumor as benign or malignant. The region of the lesion is usually manually created by the physician according to their clinical experience; therefore, contouring tumors on breast magnetic resonance imaging (MRI) is difficult and time-consuming. For this purpose, an automatic contouring method for breast tumors was developed for less burden in the analysis and to decrease the observed bias to help in making decisions clinically. In this study, a multiview segmentation method for detecting and contouring breast tumors in MRI was represented. The preprocessing of the proposed method reduces any amount of noises but preserves the shape and contrast of the breast tumor. The two-dimensional (2D) level-set segmentation method extracts contours of breast tumors from the transverse, coronal, and sagittal planes. The obtained contours are further utilized to generate appropriate three-dimensional (3D) contours. Twenty breast tumor cases were evaluated and the simulation results show that the proposed contouring method was an efficient method for delineating 3D contours of breast tumors in MRI.
A Novel <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mo stretchy="false">(</mml:mo><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> Secret Sharing Scheme Based upon Euler’s Theorem

The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mfenced separators="|"><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:math> secret sharing scheme is used to protect the privacy of information by distribution. More specifically, a dealer splits a secret into n shares and distributes them privately to n participants, in such a way that any t or more participants can reconstruct the secret, but no group of fewer than t participants who cooperate can determine it. Many schemes in literature are based on the polynomial interpolation or the Chinese remainder theorem. In this paper, we propose a new solution to the system of congruences different from Chinese remainder theorem and propose a new scheme for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mfenced separators="|"><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:math> secret sharing; its secret reconstruction is based upon Euler’s theorem. Furthermore, our generalized conclusion allows the dealer to refresh the shared secret without changing the original share of the participants.
Certificateless Sequential Aggregate Signature Scheme on NTRU Lattice

Having the advantages of certificateless signature and the aggregate signature at the same time, certificateless aggregate signature has been widely applied in e-business, e-government and software security since it was proposed in 2007. Although a number of certificateless aggregate signature schemes have been proposed, all of them are based on the classic number theory problem, which are no longer secure in the quantum era. In this paper, a certificateless sequential aggregate signature over number theory research unit lattice is proposed, which is proven to be secure in random oracle model. Moreover, we extend the new scheme into an efficient certificatebased sequential aggregate signature which is also secure in quantum era.