Reviewerly/exHarmony huggingface dataset:
MSA

Twitter is a popular social media platform for information sharing and dissemination. Many Twitter users post tweets to share their experiences about drugs and adverse drug reactions. Automatic detection of tweets mentioning drug names and adverse drug reactions at a large scale has important applications such as pharmacovigilance. However, detecting drug name and adverse drug reaction mentioning tweets is very challenging, because tweets are usually very noisy and informal, and there are massive misspellings and user-created abbreviations for these mentions. In addition, these mentions are usually context dependent. In this paper, we propose a neural approach with hierarchical tweet representation and multi-head self-attention mechanism to jointly detect tweets mentioning drug names and adverse drug reactions. In order to alleviate the influence of massive misspellings and user-created abbreviations in tweets, we propose to use a hierarchical tweet representation model to first learn word representations from characters and then learn tweet representations from words. In addition, we propose to use multi-head self-attention mechanism to capture the interactions between words to fully model the contexts of tweets. Besides, we use additive attention mechanism to select the informative words to learn more informative tweet representations. Experimental results validate the effectiveness of our approach.
Policy and planning of research data management in university libraries of Pakistan

Purpose This paper aims to assess the policy framework and planning regarding research data management (RDM) in university libraries of Pakistan. Design/methodology/approach Data were collected from 30 Higher Education Commission high ranking university libraries by using mixed method explanatory sequential design. Findings The results indicate that library heads just heard about RDM, but there was lack of knowledge and awareness. Few libraries were at the planning stage. Other major challenges including lack of willingness, motivation and coordination with researchers, non-availability of skillful professional and support staff, poor infrastructure and networking were found in this regard. Originality/value This is the first study of its kind that explores the planning and policy development regarding RDM in university libraries of Pakistan.
An efficient pixel clustering-based method for mining spatial sequential patterns from serial remote sensing images

The accumulation of serial remote sensing images provides plentiful data for discovering sequential spatial patterns in various fields such as agricultural monitoring, urban development, and vegetation cover. Otherwise, traditional sequential pattern-mining algorithms cannot be directly or efficiently applied to remote sensing images. In this study, we propose a pixel clustering-based method to improve the efficiency of mining spatial sequential patterns from raster serial remote sensing images (SRSI). Firstly, the images are compressed by using the Run-Length coding schema. Then, pixels with identical sequences are clustered by means of the Run-length code-based spatial overlay operation. Finally, a pruning strategy is proposed, to extend the prefixSpan algorithm to skip unnecessary database scanning when mining from pixel groups. The experimental results indicate that the method presented in this paper could extract spatial sequential patterns from SRSI efficiently. Although accurate support rates for the patterns may not be obtained, our method could ensure that all patterns are extracted with a lower time cost.
Decision-Making Approaches for Performance QoS in Distributed Storage Systems: A Survey

Distributed storage systems designed to offer explicit performance quality-of-service (QoS) guarantees must regulate the allocation and use of resources to achieve a user-specified level of service. QoS-driven systems employ decision-making techniques to decide on appropriate actions to take during initial deployment or under variations in workload and/or system configuration. In this survey we cover both traditional approaches to decision-making for explicit performance QoS (control theory, multi-dimensional constrained optimization, policy-based techniques) as well as more recent approaches based on machine-learning, offering a broad perspective to the state-of-the-art in the field. As performance prediction is a central concept in decision-making, we also summarize research on performance prediction techniques used in this context.
ConfLLVM

We present a compiler-based scheme to protect the confidentiality of sensitive data in low-level applications (e.g. those written in C) in the presence of an active adversary. In our scheme, the programmer marks sensitive data by lightweight annotations on the top-level definitions in the source code. The compiler then uses a combination of static dataflow analysis, runtime instrumentation, and a novel taint-aware form of control-flow integrity to prevent data leaks even in the presence of low-level attacks. To reduce runtime overheads, the compiler uses a novel memory layout.
Exposing Video Compression History by Detecting Transcoded HEVC Videos from AVC Coding

The analysis of video compression history is one of the important issues in video forensics. It can assist forensics analysts in many ways, e.g., to determine whether a video is original or potentially tampered with, or to evaluate the real quality of a re-encoded video, etc. In the existing literature, however, there are very few works targeting videos in HEVC format (the most recent standard), especially for the issue of the detection of transcoded videos. In this paper, we propose a novel method based on the statistics of Prediction Units (PUs) to detect transcoded HEVC videos from AVC format. According to the analysis of the footprints of HEVC videos, the frequencies of PUs (whether in symmetric patterns or not) are distinguishable between original HEVC videos and transcoded ones. The reason is that previous AVC encoding disturbs the PU partition scheme of HEVC. Based on this observation, a 5D and a 25D feature set are extracted from I frames and P frames, respectively, and are combined to form the proposed 30D feature set, which is finally fed to an SVM classifier. To validate the proposed method, extensive experiments are conducted on a dataset consisting of CIF ( 352 × 288 ) and HD 720p videos with a diversity of bitrates and different encoding parameters. Experimental results show that the proposed method is very effective at detecting transcoded HEVC videos and outperforms the most recent work.
Separable Data-Hiding Scheme for Encrypted Image to Protect Privacy of User in Cloud

As cloud computing becomes popular, the security of users’ data is faced with a great threat, i.e., how to protect users’ privacy has become a pressing research topic. The combination of data hiding and encryption can provide dual protection for private data during cloud computing. In this paper, we propose a new separable data-hiding scheme for encrypted images based on block compressive sensing. First, the original uncompressed image is compressed and encrypted by block compressive sensing (BCS) using a measurement matrix, which is known as an encryption key. Then, some additional data can be hidden into the four least significant bits of measurement using the data-hiding key during the process of encoding. With an encrypted image that contains hidden data, the receiver can extract the hidden data or decrypt/reconstruct the protected private image, according to the key he/she possesses. This scheme has important features of flexible compression and anti-data-loss. The image reconstruction and data extraction are separate processes. Experimental results have proven the expected merits of the proposed scheme. Compared with the previous work, our proposed scheme reduces the complexity of the scheme and also achieves better performance in compression, anti-data-loss, and hiding capacity.
Finding ‘anonymous’ in the digital archives: The problem of Arden of Faversham

Abstract This investigation re-examines debates about the authorship of the play Arden of Faversham, first published (anonymously) in 1592, and sometimes attributed to Shakespeare, Kyd, or Marlowe. More generally, it seeks to explain why modern data-driven attribution methods, which have created consensus about the authorship of The Revenger's Tragedy and other seventeenth-century plays, have failed to produce consistent results for plays written for the London commercial theaters in the years up to 1594. It proposes that attribution problems in that period can be better understood if plays are tested against authorial canons that include non-dramatic as well as dramatic works, using algorithms based on the evidence of n-grams and collocations, which seem not to be genre-dependent. It tests a sample passage from Scene 10 of Arden against the digital canons of fifteen writers known or suspected to have been writing for the commercial theater in the period 1585–92, using primarily EEBO-TCP. All tests identify the author as the poet, translator, and playwright Thomas Watson (1555–92). These data do not establish Watson's authorship of the entire play but open several new lines of enquiry for Arden and other anonymous and collaborative early plays.
Geometric Relation Distribution for Place Recognition

In this letter, we illustrate geometric relation distribution (GRD), a novel signature for place recognition and loop closure with landmark maps. GRD encodes geometric pairwise relations between landmark points into a continuous probability density function. The pairwise angles are represented by von Mises distribution whereas two alternative distributions, Erlang or biased Rayleigh, are proposed for distances. The GRD function is represented through its expansion into Fourier series and Laguerre polynomial basis. Such orthogonal basis representation enables efficient computation of the translation and rotation invariant metric used to compare signatures and find potential loop closure candidates. The effectiveness of the proposed method is assessed through experiments with standard datasets.
Hybrid 3D Shape Measurement Using the MEMS Scanning Micromirror

A surface with large reflection variations represents one of the biggest challenges for optical 3D shape measurement. In this work, we propose an alternative hybrid 3D shape measurement approach, which combines the high accuracy of fringe projection profilometry (FPP) with the robustness of laser stripe scanning (LSS). To integrate these two technologies into one system, first, we developed a biaxial Microelectromechanical Systems (MEMS) scanning micromirror projection system. In this system, a shaped laser beam serves as a light source. The MEMS micromirror projects the laser beam onto the object surface. Different patterns are produced by controlling the laser source and micromirror jointly. Second, a quality wised algorithm is delivered to develop a hybrid measurement scheme. FPP is applied to obtain the main 3D information. Then, LSS helps to reconstruct the missing depth guided by the quality map. After this, the data fusion algorithm is used to merge and output complete measurement results. Finally, our experiments show significant improvement in the accuracy and robustness of measuring a surface with large reflection variations. In the experimental instance, the accuracy of the proposed method is improved by 0.0278 mm and the integrity is improved by 83.55%.
Seeing power with a flashlight: DIY thermal sensing technology in the classroom

This paper contributes to the growing literature on 'making and doing' in Science and Technology Studies (STS) by describing and theorizing the teaching of making and doing. We describe a collaborative do-it-yourself (DIY) technology project taught simultaneously in Canada and the United States, in sociology and public health, to undergraduates with no prior electronics experience. Students built thermal flashlights - low cost digital tools for making thermal images - and employed them to research their surrounding environments. By making and using the thermal flashlights, learners investigated power in two senses: identifying social power relationships embedded within normally unquestioned infrastructures, and exploring these infrastructures' connection to industrial forms of power, such as heat and electricity. Students and instructors came to understand how the control of power, light and temperature is vital to human-made infrastructure and environmental health threats that characterize the 21st century. Through this project, students went from being passive consumers of such power to become active investigators of their socio-technical systems by producing unique knowledge that enabled them to imagine how they might make and inhabit their environments differently. Breaking down the distinction between teaching and research, this article explores the promise of 'making and doing' in university courses to create new collaborative research platforms that could spread laterally and scale to transform social and technical infrastructures.
Feature Subset Selection using Adaptive Differential Evolution

In this paper, we developed a feature subset selection method by employing Adaptive Differential Evolution as a wrapper. The proposed wrapper utilizes four independent classifiers namely Logistic Regression, Probabilistic Neural Network, Naive Bayes and Support Vector Machine. We employed the Matthews Correlation Coefficient (MCC) as the fitness function or evaluation measure. In order to demonstrate the efficacy of the proposed method, we tested on three datasets, of which two are related to credit scoring and one is related to financial statement fraud. Our proposed method yielded better results than other standard methods in the literature as well as Differential Evolution. We also performed a statistical significance test i.e. t-test at 1% level of significance, which infers that some of the proposed wrappers are statistically one and the same.
Trust-Based Video Management Framework for Social Multimedia Networks

Social multimedia networks (SMNs) have attracted much attention from both academia and industry due to their impact on our daily lives. The requirements of SMN users are increasing along with time, which make the satisfaction of those requirements a very challenging process. One important challenge facing SMNs consists of their internal users that can upload and manipulate insecure, untrusted, and unauthorized contents. For this purpose, controlling and verifying content delivered to end users is becoming a highly challenging process. So far, many researchers have investigated the possibilities of implementing a trustworthy SMN. In this vein, the aim of this paper is to propose a framework that allows collaboration between humans and machines to ensure secure delivery of trusted video content over SMNs while ensuring an optimal deployment cost in the form of CPU, RAM, and storage. The key concepts beneath the proposed framework consist in assigning to each user a level of trust based on his/her history, creating an intelligent agent that decides which content can be automatically published on the network and which content should be reviewed or rejected, and checking the videos' integrity and delivery during the streaming process. Accordingly, we ensure that the trust level of the SMNs increases. Simultaneously, efficient capital expenditure and operational expenditures can be achieved.
Encryption techniques for H.264/AVC videos: A literature review

There are tremendous impacts of advancement in information and telecommunication technology over the use of multimedia data. Multimedia data especially videos are stored and transmitted through the wired and wireless network, where they can be easily accessed. Thus, safeguarding the video data from unauthorized access and protecting the integrity and authenticity has become mandatory for transmission as well as storage. Encryption is one of the best technologies in Digital Rights Management (DRM) scheme which is used to protect copyright works. This paper reviews some previous and recent works for encryption techniques of H.264/AVC video encoders. The reviewed work has been categorized based on the area where the encryption scheme is applied.
A Framework for Teaching Security Design Analysis Using Case Studies and the Hybrid Flipped Classroom

With ever-greater reliance of the developed world on information and communication technologies, constructing secure software has become a top priority. To produce secure software, security activities need to be integrated throughout the software development lifecycle. One such activity is security design analysis (SDA), which identifies security requirements as early as the software design phase. While considered an important step in software development, the general opinion of information security subject matter experts and researchers is that SDA is challenging to learn and teach. Experimental evidence provided in literature confirms this claim. To help solve this, we have developed a framework for teaching SDA by utilizing case study analysis and the hybrid flipped classroom approach. We evaluate our framework by performing a comparative analysis between a group of students who attended labs generated using our framework and a group that participated in traditional labs. Our results show that labs created using our framework achieve better learning outcomes for SDA, as opposed to the traditional labs. Secondary contributions of our article include teaching materials, such as lab descriptions and a case study of a hospital information system to be used for SDA. We outline instructions for using our framework in different contexts, including university courses and corporate training programs. By using our proposed teaching framework, with our or any other case study, we believe that both students and employees can learn the craft of SDA more effectively.
Collusion By Blockchain And Smart Contracts

Blockchain may transform transactions the same way the Internet altered the dissemination and nature of information. If that were to be the case, all relationships between companies would change, including prohibited ones such as collusive agreements. For that reason, the stakes are crucial and the absence of academic studies entirely dedicated to this issue must be remedied.To this end, this article introduces the first taxonomy of collusion on blockchain. The discussion then moves on to explore their functioning, their robustness and their limits through the three fundamental stages of the existence of collusive agreements: their birth, life and death. The article further highlights how companies may use smart contracts and sophisticated algorithms to collude in the blockchain environment, thus contributing to the literature solely focused on algorithms.Using empirical studies, economic analyses and existing case law, we draw legal conclusions that we extend beyond the sole blockchain technology. Along the way, we propose methods of action for antitrust and competition agencies.
Topic features for machine learning-based sentiment analysis in Indonesian tweets

Purpose The purpose of this paper is to analyze topics as alternative features for sentiment analysis in Indonesian tweets. Design/methodology/approach Given Indonesian tweets, the processes of sentiment analysis start by extracting features from the tweets. The features are words or topics. The authors use non-negative matrix factorization to extract the topics and apply a support vector machine to classify the tweets into its sentiment class. Findings The authors analyze the accuracy using the two-class and three-class sentiment analysis data sets. Both data sets are about sentiments of candidates for Indonesian presidential election. The experiments show that the standard word features give better accuracies than the topics features for the two-class sentiment analysis. Moreover, the topic features can slightly improve the accuracy of the standard word features. The topic features can also improve the accuracy of the standard word features for the three-class sentiment analysis. Originality/value The standard textual data representation for sentiment analysis using machine learning is bag of word and its extensions mainly created by natural language processing. This paper applies topics as novel features for the machine learning-based sentiment analysis in Indonesian tweets.
Epsilon-smooth measure of coherence

In this paper, by minimizing the coherence quantifiers over all states in an $\ensuremath{\epsilon}$ ball around a given state, we define a generalized smooth quantifier, called the $\ensuremath{\epsilon}$-smooth measure of coherence. We use it to estimate the difference between the expected state and the actually prepared state and quantify quantum coherence contained in an actually prepared state, and it can been interpreted as the minimal coherence guaranteed to present in an $\ensuremath{\epsilon}$ ball around given quantum state. We find that the $\ensuremath{\epsilon}$-smooth measure of any coherence monotone is still a coherence monotone, but it does not satisfy monotonicity on average under incoherent operations. We show the $\ensuremath{\epsilon}$-smooth measure of coherence is continuous even if the original coherence quantifier is not. We also study the $\ensuremath{\epsilon}$-smooth measure of distance-based coherence quantifiers, and some interesting properties are given. Moreover, we discuss the dual form of the $\ensuremath{\epsilon}$-smooth measure of coherence by maximizing over all states in an $\ensuremath{\epsilon}$ ball around the given state and show that the dual $\ensuremath{\epsilon}$-smooth measure of coherence provides an upper bound of one-shot coherence distillation.
Semantic Data Integration of Big Biomedical Data for Supporting Personalised Medicine

Big biomedical data has grown exponentially during the last decades and a similar growth rate is expected in the next years. Likewise, semantic web technologies have also advanced during the last years, and a great variety of tools, e.g., ontologies and query languages, have been developed by different scientific communities and practitioners. Although a rich variety of tools and big data collections are available, many challenges need to be addressed in order to discover insights from which decisions can be taken. For instance, different interoperability conflicts can exist among data collections, data may be incomplete, and entities may be dispersed across different datasets. These issues hinder knowledge exploration and discovery, being thus required data integration in order to unveil meaningful outcomes. In this chapter, we address these challenges and devise a knowledge-driven framework that relies on semantic web technologies to enable knowledge exploration and discovery. The framework receives big data sources and integrates them into a knowledge graph. Semantic data integration methods are utilized for identifying equivalent entities, i.e., entities that correspond to the same real-world elements. Fusion policies enable the merging of equivalent entities inside the knowledge graph, as well as with entities in other knowledge graphs, e.g., DBpedia and Bio2RFD. Knowledge discovery allows for the exploration of knowledge graphs in order to uncover novel patterns and relations. As proof of concept, we report on the results of applying the knowledge-driven framework in the EU funded project iASiS ( http://project-iasis.eu/ ) in order to transform big data into actionable knowledge, paving thus the way for personalised medicine.