Reviewerly/exHarmony huggingface dataset:
Cancelling with the world’s largest scholarly publisher: lessons from the Swedish experience of having no access to Elsevier

This article covers the consequences of the decision of the Bibsam consortium to cancel its journal licence agreement with Elsevier, the world’s largest scholarly publisher, in 2018. First, we report on how the cancellation affected Swedish researchers. Second, we describe other consequences of the cancellation. Finally, we report on lessons for the future. In short, there was no consensus among researchers on how the cancellation affected them or whether the cancellation was positive or negative for them. Just over half (54%) of the 4,221 researchers who responded to a survey indicated that the cancellation had harmed their work, whereas 37% indicated that it had not. Almost half (48%) of the researchers had a negative view of the cancellation, whereas 38% had a positive view. The cancellation highlighted the ongoing work at research libraries to facilitate the transition to an open access publishing system to more stakeholders in academia than before. It also showed that Swedish vice-chancellors were prepared to suspend subscriptions with a publisher that could not accommodate the needs and requirements of open science. Finally, the cancellation resulted in the signing of a transformative agreement which started on 1 January 2020. If it had not been for the cancellation, the reaching of such an agreement would have been unlikely.
Paraphrase Bidirectional Transformer with Multi-task Learning

It is very important to analyze the semantic similarity of two sentences for Natural Language Processing (NLP). This paper proposes a Paraphrase-BERT to perform Paraphrase Identification task. We first fine-tune the pre-trained BERT with MRPC data and add a Whole Word Masking, which is pre-training method recently announced by Google, to the BERT. Finally, we perform Multi-Task Learning (MLT) to improve performance. Specifically, the Question Answering task and the Paraphrase Identification (PI) task are learned sequentially to improve performance of PI task. As a result, it has shown that MLT affect a performance improvement of downstream task (11.11% point absolute accuracy improvement, 7.88% point absolute F1 improvement).
Official Document Management for Government Service in Indonesia using Smart Contract

In general, the electronic official documents system is designed with a centralized system and managed by each government agency, so that data and records of official documents process activities are stored in a centralized database and managed by a single organizational unit. However, there are some disadvantages of this centralized system, especially in the workflow of official documents that involve more than one organizational unit. i.e. dependence on third parties which is the sole authority of the official documents data management. If there is a system failure there is a certain period of time, it will increase the activity cycle time and decrease activity time efficiency in the official documents business process. The blockchain-based solution described in this paper is to avoid the occurrence of delay times during the activity cycle time in business processes when a system failure occurs which can cause a decrease in activity time efficiency. Blockchain makes the official documents system become “Trusless Operation” with each participant of different organizational unit having a copy of transaction data through a consensus mechanism. The simulation results show that our system can reduce time delay in activity cycle time during a system failure as long as the number of faulty peers within the tolerance limit so that it can increase the activity time efficiency.
Evaluation of Global Solar Irradiance Estimates from GL1.2 Satellite-Based Model over Brazil Using an Extended Radiometric Network

The GL (GLobal radiation) physical model was developed to compute global solar irradiance at ground level from (VIS) visible channel imagery of geostationary satellites. Currently, its version 1.2 (GL1.2) runs at Brazilian Center for Weather Forecast and Climate Studies/National Institute for Space Research (CPTEC/INPE) based on GOES-East VIS imagery. This study presents an extensive validation of GL1.2 global solar irradiance estimates using ground-based measurements from 409 stations belonging to the Brazilian National Institute of Meteorology (INMET) over Brazil for the year 2016. The INMET reasonably dense network allows characterizing the spatial distribution of GL1.2 data uncertainties. It is found that the GL1.2 estimates have a tendency to overestimate the ground data, but the magnitude varies according to region. On a daily basis, the best performances are observed for the Northeast, Southeast, and South regions, with a mean bias error (MBE) between 2.5 and 4.9 W m−2 (1.2% and 2.1%) and a root mean square error (RMSE) between 21.1 and 26.7 W m−2 (10.8% and 11.8%). However, larger differences occur in the North and Midwest regions, with MBE between 12.7 and 23.5 W m−2 (5.9% and 11.7%) and RMSE between 27 and 33.4 W m−2 (12.7% and 16.7%). These errors are most likely due to the simplified assumptions adopted by the GL1.2 algorithm for clear sky reflectance (Rmin) and aerosols as well as the uncertainty of the water vapor data. Further improvements in determining these parameters are needed. Additionally, the results also indicate that the GL1.2 operational product can help to improve the quality control of radiometric data from a large network, such as INMET's. Overall, the GL1.2 data are suitable for use in various regional applications.
A Privacy-Preserving Trajectory Publication Method Based on Secure Start-Points and End-Points

By judging whether the start-point and end-point of a trajectory conform to the user’s behavioral habits, an attacker who possesses background knowledge can breach the anonymous trajectory. Traditional trajectory privacy preservation schemes often generate an anonymous set of trajectories without considering the security of the trajectory start- and end-points. To address this problem, this paper proposes a privacy-preserving trajectory publication method based on generating secure start- and end-points. First, a candidate set containing a secure start-point and end-point is generated according to the user’s habits. Second, k −1 anonymous trajectories are generated bidirectionally according to that secure candidate set. Finally, accessibility corrections are made for each anonymous trajectory. This method integrates features such as local geographic reachability and trajectory similarity when generating an anonymized set of trajectories. This provides users with privacy preservation at the k -anonymity level, without relying on the trusted third parties and with low algorithm complexity. Compared with existing methods such as trajectory rotation and unidirectional generation, theoretical analysis and experimental results on the datasets of real trajectories show that the anonymous trajectories generated by the proposed method can ensure the security of trajectory privacy while maintaining a higher trajectory similarity.
A Comparison of Machine Learning Algorithms in Keystroke Dynamics

Usernames and passwords have become weaker as the methods used to bypass this system have advanced. Today, secondary biometric authentication systems can be used to mitigate these vulnerabilities. Biometrics are unique traits that distinguish every individual from each other. One biometric technique is keystroke dynamics which is an authentication method based on a user's typing rhythm on a keyboard. These rhythm patterns are based on digraphs or the timing between two successive key presses. Our goal was to record the keystroke patterns of each user to determine if the users can be authenticated accurately by this method using machine learning. We recorded the latencies between keystrokes and taught machine learning algorithms the datasets to determine if the pattern can be learnable by the machine. We tested four different machine learning algorithms: Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, to determine which is most effective on accuracy. We also tested two text sizes to compare each algorithm's prediction rate based on input size.
Fast Texture Synthesis for Discrete Example-Based Elements

Considering the problem of discrete texture synthesis and the time for texturing, this paper proposes a novel framework for synthesizing texture images based on discrete example-based elements. We start with extracting texture feature distribution from exemplars and then produce discrete elements based on the cluster algorithm. After initializing a texture image, we propose a texture optimization algorithm based on heuristic searching to improve the quality of the texture image. Final, we use a texture transfer method based on Convolutional Neural Network (CNN) to stylize the optimized texture image. Our results show that the proposed texture synthesis method can significantly improve the quality of discrete texture synthesis and effectively shorten the time for texture generation.
Recognition of Drone Formation Intentions Using Supervised Machine Learning

Drones are becoming a major element in defense applications, geographic surveillance, delivery of packages and their uses are expanding. Drone activity detection and identification have become an important research subject. An even more challenging problem is recognizing the intentions of a group of drones. Their intention may not be obvious, which might impose a security threat in several instances. Recognizing the targeted plan of a group of drones is the subject of study in this paper. We focus on identifying the formation a group of drones is trying to achieve. We predict the formation during the transition phase from one formation to another using softmax regression. We test several feature vector designs and present our results.
Database SQL Injection Security Problem Handling with Examples

Database management system have been in existence for over fifty years and they are used to store private and sensitive data. DBMS must ensure the data stored is safe from malicious hackers' attacks. In this paper, we summarize methods for detecting and preventing malicious SQL Injection in a standalone DBMS or Cloud space DBMS.
Predicting the Helpfulness Score of Product Reviews Using an Evidential Score Fusion Method

Everyday many online product sales websites and specialized reviewing forums publish a massive volume of human-generated product reviews. People use these reviews as valuable free source of knowledge when decide to buy products. Therefore, an accurate automated system for distinguishing useful reviews from non-useful ones is of great importance. This article presents a new model for specifying the usefulness of comments using the textual features extracted from the reviews. Various types of features including emotion-related, linguistic and text-related features, valence, arousal, and dominance (VAD) values, review-length and polarity of comments are exploited in this study. Moreover, two new algorithms are presented: an improved evidential algorithm for emotion recognition, and an algorithm for extracting VAD values for each review. Finally, the usefulness of reviews is predicted using the mentioned features and an improved Dempster–Shafer score fusion algorithm. The proposed method is applied to review datasets of Books and Video Games of Amazon. The results show that combining the features associated with emotions, features of VAD, and text-related features improves the accuracy of predicting the usefulness of reviews. Also, in comparison with the original Dempster–Shafer method, the precision of the improved Dempster–Shafer algorithm for both datasets is 15% and 11% higher, respectively.
Deep multilevel similarity hashing with fine-grained features for multi-label image retrieval

For multi-label image retrieval based on deep hashing, the ultimate challenge is to map from the original image to binary space while preserving high-level semantic similarity. Recently, many supervised deep hashing approaches for multi-label image retrieval have been proposed to generate high-quality binary codes. However, most such methods are only introduced to learn simple similarity based on these image characteristics, therein ignoring complex multilevel semantic similarity with fine-grained features. In this paper, we propose a framework named deep hashing with fine-grained feature learning (DH-FFL) to preserve complex multilevel semantic similarity between multi-label image pairs. In this proposed model, compact bilinear pooling convolutional neural networks (CNNs) with normalization are adopted to extract fine-grained feature descriptors. In addition, a novel multilevel contrastive loss is designed to preserve multilevel semantic similarity by introducing a zero-loss parameter. Moreover, a multi-label classification loss is used to maintain the unique semantic structure of each image and maximize the distinguishing ability of binary codes. Comprehensive experiments on three benchmark datasets show that the proposed DH-FFL achieves promising performance compared with other state-of-the-art multi-label image retrieval applications.
MPSign: A Signature from Small-Secret Middle-Product Learning with Errors

We describe a digital signature scheme $$\mathsf {MPSign}$$ , whose security relies on the conjectured hardness of the Polynomial Learning With Errors problem ( $$\mathsf {PLWE}$$ ) for at least one defining polynomial within an exponential-size family (as a function of the security parameter). The proposed signature scheme follows the Fiat-Shamir framework and can be viewed as the Learning With Errors counterpart of the signature scheme described by Lyubashevsky at Asiacrypt 2016, whose security relies on the conjectured hardness of the Polynomial Short Integer Solution ( $$\mathsf {PSIS}$$ ) problem for at least one defining polynomial within an exponential-size family. As opposed to the latter, $$\mathsf {MPSign}$$ enjoys a security proof from $$\mathsf {PLWE}$$ that is tight in the quantum-access random oracle model. The main ingredient is a reduction from $$\mathsf {PLWE}$$ for an arbitrary defining polynomial among exponentially many, to a variant of the Middle-Product Learning with Errors problem ( $$\mathsf {MPLWE}$$ ) that allows for secrets that are small compared to the working modulus. We present concrete parameters for $$\mathsf {MPSign}$$ using such small secrets, and show that they lead to significant savings in signature length over Lyubashevsky’s Asiacrypt 2016 scheme (which uses larger secrets) at typical security levels. As an additional small contribution, and in contrast to $$\mathsf {MPSign}$$ (or $$\mathsf {MPLWE}$$ ), we present an efficient key-recovery attack against Lyubashevsky’s scheme (or the inhomogeneous $$\mathsf {PSIS}$$ problem), when it is used with sufficiently small secrets, showing the necessity of a lower bound on secret size for the security of that scheme.
Global and Local Tensor Factorization for Multi-criteria Recommender System

In multi-criteria recommender systems, matrix factorization characterizes users and items via latent factor vectors inferred from user-item rating patterns. However, two-dimensional matrix factorization models may not be able to cope with the recommendation problem that involves additional criterion-specific rating data. This study introduces a tensor factorization method to handle three-dimensional user-item-criterion rating data. Moreover, we observe that using single global tensor factorization alone may not be sufficient to characterize diverse preferences among different groups of users, and a combined global and local tensor factorization method (GLTF) for multi-criteria recommendation is thus proposed. One key benefit of the GLTF is that it can leverage global user-item-criterion rating patterns while also exploiting local user-subset specific rating behaviors to jointly infer the latent factor representations for users, items, and specific item criteria. Experimental results, which used real-life data available to the public, demonstrated that the GLTF is superior to well-established baseline methods.
An Automated Corpus Annotation Experiment in Brazilian Portuguese for Sentiment Analysis in Public Security

This paper aims to present an experiment developed in order to produce a corpus with automated annotation, using pre-existing annotated corpus and machine learning classification methods. A search for pre-existing annotated corpora in Brazilian Portuguese was applied, founding six corpora of which one has been selected as the training dataset. A set of tweets was collected in a specific area of Recife (Pernambuco-Brazil) using some keywords related to kinds of crimes and reinforcing some places in that area. Preprocessing tasks were applied over the pre-existing corpus and the tweets' set collected. Latent Dirichlet Allocation was applied for topic modeling followed by Multinomial Naïve Bayes, Linear Support Vector Machines, and Logistic Regression for the sentiment polarity classification. The results of the cross-validation of the experiment indicated Linear Support Vector Machines as the most accurate classification method among the three considering the specific training set used, and by this method, the new annotated corpus about the selected topic related to public security was created.
Global Research Trends of Public Libraries from 1968 to 2017: A Bibliometric and Visualization Analysis

This study aimed to highlight the articles published on public libraries over a 50-year period from 1968 to 2017.This bibliometric study used Scopus citation database to collect articles.All public libraries articles indexed in Scopus were included.The downloaded records were analyzed by using Excel and VOS viewer.The results of the study revealed that the process of publishing public libraries articles was ascending and the number of documents reached from 4 in 1968 to 97 in 2017.The paper of Audunson R. ( 2005) entitled "The public library as a meeting-place in a multicultural and digital context: The necessity of low-intensive meeting-places" was the most highly-cited article.Bertot (28), United States (935), and Loughborough University (44) were the most productive author, country and university, respectively.Visualization showed that "Public libraries", "libraries", and "public library" were the most commonly used keywords in public library articles.In addition, the "Public Library Quarterly" was the most important source of articles publication."Public Library Quarterly", "Library Trends", and "Library Journal" were the top journals, and "Bertot", "Jaeger" and "McClure" were the top authors cited in articles.This was the first study on the bibliometric analysis and visualization of public libraries articles.This study can help better perceive intellectual trends and dominant discourses in this area by providing researchers with a roadmap.
Perception of Walking Self-body Avatar Enhances Virtual-walking Sensation

Various virtual walking systems have been developed using treadmill or leg-support devices to move users’ legs. We proposed and evaluated a virtual walking system for sitting observers using only passive sensations such as optic flow and foot vibrations. In this study, we examined whether the virtual body representing observer’s walking could facilitate the sensations of virtual walking in a virtual environment. We found that the virtual body enhanced sensations of walking, leg action and presence except for self-motion. These results suggest that perception of walking self-body avatar would facilitate sensations of walking and presence independently from foot vibrations.
Contemporary issues in forensic handwriting examination. A discussion of key issues in the wake of the starzecpyzel decision

A considerable amount of attention has been focused on the field of forensic handwriting examination as a result of a recent Daubert hearing regarding the admissibility of forensic handwriting testimony (United States of America v. Roberta and Eileen Starzecpyzel, 1995). The findings of the hearing provide us with an opportunity to reflect on some of the basic postulates and practices associated with the field, particularly as they are perceived by individuals working within mainstream scientific paradigms. It appears that there are some postulates that are still mounted as underpinning forensic handwriting examination that defy even basic logic when seen in the environment of normal behavioural sciences. Rather than dwell on the possible reasons for this phenomenon, a few basic alternatives to the current explanation of theory and practice will be overviewed. Although what is presented here is largely ‘theoretical’ in nature, it does provide a framework which currently forms the focus of our research. Ultimately, the question as to whether what we do can be regarded as science or a practical skill falls within the frame of reference of those who choose to define those terms. What is important is not that we waste time and effort arguing over the details of which group we belong to, but rather that we concentrate on improving the paradigm within which we all work. The first step in this process is defining what the paradigm is.Purchase Article - $10
QDetect: Time Series Querying Based Road Anomaly Detection

Road anomaly detection has attracting increasing attention in recent years due to its significant role in the public transportation of modern cities. A few methods has been proposed to detect road anomaly with inertial sensors (e.g., accelerometer and gyroscope), which usually utilize classification techniques by extracting time and frequency domain features from inertial sensor data. However, existing methods are time consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the self-similarity of the data when vehicle passes over the road anomalies. In this paper, we propose QDetect, a road anomaly detection system with less data-dependency via querying and re-comparing. Specifically, QDetect consists of two phases: 1) Query filter. This phase is designed to roughly extract road anomaly segments by matching existing labelled anomalies; 2) Re-comparison on suspicious anomalies to identify their anomaly types. We have conducted comprehensive experiments on two real-world data sets, and the results show that our method outperforms some existing methods in both detection performance and running time. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work.
Behavioral Analysis of Trickbot Banking Trojan with its New Tricks

This study aims to carry out a behavioral analysis of Trickbot and propose a machine learning technique for Trickbot detection.In the current study, an analysis is conducted to reveal Trickbot behavior while its code is injected into official banking websites.Trickbot is a banking trojan designed to steal users private information.Static and dynamic analyses using different tools were performed to identify TrickBot-associated streams and detect TrickBot infection.As a result of the analysis, it is found out that its authors use web injections to banking websites to steal and access login information.In addition, the analysis revealed that Trickbot targets many international banks in many countries via web injections.It is discovered that Trickbot uses different interfaces and files to replicate itself.The findings of this study could be used to deal with these attacks efficiently and prevent them in the future.