Reviewerly/exHarmony huggingface dataset:
Spanish corpora for sentiment analysis: a survey

Corpora play an important role when training machine learning systems for sentiment analysis. However, Spanish is underrepresented in these corpora, as most primarily include English texts. This paper describes 20 Spanish-language text corpora—collected to support different tasks related to sentiment analysis, ranging from polarity to emotion categorization. We present a brand-new framework for the characterization of corpora. This includes a number of features to help analyze resources at both corpus level and document level. This survey—besides depicting the overall landscape of corpora in Spanish—supports sentiment analysis practitioners with the task of selecting the most suitable resources.
Security Vulnerability Information Service with Natural Language Query Support

The huge data breaches and attacks reported in the past years (e.g., the cases of Yahoo and Equifax) have significantly raised the concerns on the security of software used and developed by companies for their day-to-day operations. In this context, becoming aware about existing security vulnerabilities and taking preventive actions is of paramount importance for security professionals to help keep software secure. The increasingly large number of vulnerabilities discovered every year and the scattered and heterogeneous nature of vulnerability-related information make this, however, a non-trivial task. This paper aims at mitigating this problem by making security vulnerability information timely available and easily searchable. We propose to enrich and index security vulnerability information collected from publicly available sources on the Web. To make this information easily queryable we propose a natural language interface that allows users to query this index using plain English. The evaluation results of our proposal demonstrate that our solution can effectively answer questions typically asked in the security vulnerability domain.
Single‐Atom Entanglement for a System of Directly Linked Two Cavities in the Presence of an External Classical Field: Effect of Atomic Coherence

Abstract The Hamiltonian of a system composed of two standard Jaynes‐Cummings cells connected by the overlap of evanescent cavity fields, which allows photons to hop from the first cavity to the second and vice versa, and interacting with an external classical field, is simplified in the framework of unitary canonical transformations. James's method of the effective Hamiltonian is used in the derivation of two effective Hamiltonians with two completely dispersive regimes. Analytical expressions for the time dependent wavefunction and the elements of the reduced atomic operators for the different Hamiltonians are derived. The temporal evolution of atomic quantum entropy is calculated when the fields are initially in their vacuum states with various forms of the atomic initial states. Effects of the photon hopping strength, detuning and the various forms of the atomic initial states on the evolution of the von Neumann entropies are analyzed. The impact of the time‐average technique on the features resulting due to involving the controlling parameters and their influences on the considered functions is discussed. Damping is taken into account through a non‐Hermitian Hamiltonian and results are discussed . General conclusions reached are illustrated by numerical results.
An Implementation and Evaluation of Pairing Library ELiPS for BLS Curve with Several Techniques

In recent years, pairing encryption is receiving a lot of attention since it enables many innovative and multi-functional cryptographic applications based on pairing e.g. searchable encryption, broadcast encryption and so on. Pairing is a map from two additive rational point groups G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> to a multiplicative group G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> , however, it requires complexity computation. Therefore, the author tries to develop a 128-bit security level pairing library ELiPS (Efficient Library for Pairing Systems) that incorporates various high-speed methods using BLS curves. As a result, scalar multiplications on G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , exponentiation on G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> , and optimal-ate pairing can be computed in 0.42[ms], 0.79[ms], 1.13[ms], and 3.12[ms], respectively.
Capturing the spatio‐temporal continuity for video semantic segmentation

In recent years, image semantic segmentation based on a convolutional neural network has achieved many advances. However, the development of video semantic segmentation is relatively slow. Directly applying the image segmentation algorithms to each video frame separately may ignore the temporal region continuity inherent in videos. In this study, the authors propose a novel deep neural network architecture with a newly devised spatio-temporal continuity (STC) module for video semantic segmentation. Particularly, the architecture includes an encoding network, an STC module, and a decoding network. The encoding network is used to extract a high-level feature map. The STC module then uses the high-level feature map as input to extract the STC feature map. For decoding, they use four dilated convolutional layers to obtain more abstract representation and a deconvolutional layer to increase the size of the representation. Finally, they fuse the current feature representation and the previous feature representation and get the class probabilities. Thus, this architecture receives a sequence of consecutive video frames and outputs the segmentation result of the current frame. They extensively evaluate the proposed approach on the CamVid and KITTI datasets. Compared with other methods, the authors’ approach not only achieves competitive performance but also has lower complexity.
Accelerating Monte Carlo Bayesian Prediction via Approximating Predictive Uncertainty Over the Simplex

Estimating the predictive uncertainty of a Bayesian learning model is critical in various decision-making problems, e.g., reinforcement learning, detecting the adversarial attack, self-driving car. As the model posterior is almost always intractable, most efforts were made on finding an accurate approximation to the true posterior. Even though a decent estimation of the model posterior is obtained, another approximation is required to compute the predictive distribution over the desired output. A common accurate solution is to use Monte Carlo (MC) integration. However, it needs to maintain a large number of samples, and evaluate the model repeatedly, and average multiple model outputs. In many real-world cases, this is computationally prohibitive. In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by the model posterior with a parameterized model and in an amortized fashion. The aim is to approximate the predictive uncertainty of a specific Bayesian model, meanwhile alleviating the heavy workload of MC integration at testing time. The proposed method is universally applicable to Bayesian classification models that allow for posterior sampling. Theoretically, we show that the idea of amortization incurs no additional costs on approximation performance. Empirical results validate the strong practical performance of our approach.
Technology to Empower Relationships, Interactions and Emotions in the Classroom

Technology is often used as a tool for teachers to `entertain' their students, attracting all the attention and loosing focus on the real issue in education: relationships. The teacher-student relationship is formed by different interactions both in the classroom and outside the classroom. Teachers have to make their subjects attractive, so that students find lessons compelling and exciting, reaching their emotions. Active methodologies and gamification are two strategies to achieve this, motivating students through emotions. Therefore, a greater effort should be made in humanizing educational technology, promoting and not getting in the way of relationships, interactions and emotions in class. In this paper some example experiences implemented in Universidad Rey Juan Carlos are described.
Direct and Reverse Rewriting in Data Interoperability

Data interoperability refers to the issue of accessing and processing data from multiple sources in order to create more holistic and contextual information for improving data analysis, for better decision-making, and for accountability purposes. In the era towards a data-driven society, the notion of data interoperability is of paramount importance. Looking at the research work in the last decades, several types of data interoperability scenarios emerged, including the following.
The design and application of a web-based teacher evaluation system for STEM education

The quality of a teacher is the most important factor that affects students’ learning and achievement. The weak or incompetent teachers are a threat to the reputation of the profession and the quality of education that students receive. Recently, educators and policymakers are concerned on how to improve teacher quality. Many types of research have demonstrated that teacher evaluation could promote the teaching efficiency and teacher quality. In order to avoid fearing from the instructors, good teacher evaluation should have the following factors. (1) Authenticity: Participants in the teacher evaluation system cannot be impersonated and signatures cannot be forged. (2) Integrity: The results of teacher evaluation cannot be altered. (3) Security: The details of the process of teacher evaluation and information of participants should be kept as secure as possible. (4) Privacy: Sensitive information such as the participant's name must be protected. Based on the characteristics mentioned above, to design a high-quality evaluation system, this work proposes the teacher evaluation system by applying the cryptography, to develop a web-based system for STEM education. All the participants were those with the ability to operate the online evaluation system. Finally, the system could really provide a fair evaluation.
Virtual-Time-Accelerated Emulation for Blockchain Network and Application Evaluation

Blockchain technologies are in the ascendant of transforming the ways we manage contracts, make transactions, and manifest own- ership of property. The trend calls for a realistic testing and evalua- tion platform for blockchain applications and systems. We present Minichain, a container-based emulator that allows testing proof- of-work-based blockchains on a commodity computer. Minichain contains a realistic and configurable network environment, which is missing in today's blockchain testbeds. This unique feature enables us to evaluate the impact of network events (e.g., cyber-attacks) and conditions (e.g., congested or failed links) on blockchain appli- cations. Meanwhile, Minichain allows the direct execution of un- modified application code in the containers for fidelity, and utilizes the virtual time technique to speed up experiments and improve the system scale that one can accurately emulate. In particular, we mathematically analyze the convergence of the proof-of-work- based consensus algorithm to show the effectiveness of virtual time. We evaluate the performance of Minichain across both net- work layer and application layer, and demonstrate its usability by emulating a selfish mining attack initiated from the network layer
Heavy metals and rare earth elements distribution in the brine fields of awe, keana and giza, central benue trough, Nigeria

Groundwater in the brine fields of Awe, Keana and Giza, Central Benue Trough, Nigeria were investigated with the aim of determining the degree of its contamination which is vital for the proper planning and management of groundwater resource. Thirty (30) groundwater samples (10 from each location) from boreholes and dug wells were analyzed for physiochemical parameters (rare earth elements, heavy metals, major cations and anions) using enhanced ICP-OES/ICP-MS method. Results showed rare earth elements (RREs) such as Ce, Dy, Eu, Er, Gd, Hp, Nd, Sm, Tb and Yb are concentrated in the groundwater. Hydrochemical analysis revealed five (5) hydrochemical facies namely; Ca-HCO3, NaCl, NaHCO3, Ca-Na-HCO3 and Ca-Mg-Cl facies characterized the area indicating the groundwater quality situation. The weathering and leaching of mafic minerals from sandstone, shale, clay and barite mineralization control the concentration of calcium, sodium and magnesium ions. Total variance of 74.93% in the data set from principal component analysis (PCA) of the hydrochemicals revealed that the mineralization of the host rocks, weathering of galena, uranite, feldspathic minerals, dissolution of limestone and dolomite control the hydrogeochemistry of the area. The spatial distribution of the RREs in the study area showed that a strong positive correlation exists between brine and the RREs except Uranium which showed variation and is an indication of a distinct source from the RREs.
An Approach for Focused Crawler to Harvest Digital Academic Documents in Online Digital Libraries

With the rapid growth of digital information and user need, it becomes imperative to retrieve relevant and desired domain or topic specific documents as per the user query quickly. A focused crawler plays a vital role in digital libraries to crawl the web so that researchers can easily explore the domain specific search results list and find the desired content against the query. In this article, a focused crawler is being proposed for online digital library search engines, which considers meta-data of the query in order to retrieve the corresponding document or other relevant but missing information (e.g. paid publication from ACM, IEEE, etc.) against the user query. The different query strategies are made by using the meta-data and submitted to different search engines which aim to find more relevant information which is missing. The result comes out from these search engines are filtered and then used further for crawling the Web.
Deep dive

Immersion is an important factor in video experiences. Therefore, various methods and video viewing systems have been proposed. Head-mounted displays (HMDs) are home-friendly pervasive devices, which can provide an immersive video experience owing to their wide field-of-view (FoV) and separation of users from the outside environment. They are often used for viewing panoramic and stereoscopic recorded videos or virtually generated environments, but the demand for viewing standard plane videos with HMDs has increased. However, the theater mode, which restricts the FoV, is basically used for viewing plane videos. Thus, the advantages of HMDs are not fully utilized. Therefore, we explored a method for viewing plane videos by an HMD, in combination with view augmentation by LED implants to the HMD. We have constructed a system for viewing plane videos using an HMD with a deep neural network (DNN) model optimized for generating and extending images for peripheral vision and wide FoV customization. We found that enlarging the original video and extending the video with our DNN model can improve the user experience. However, our method provided more comfortable viewing by preventing motion sickness in a first-person-view video.
Detecting Helmet of Bike Riders in Outdoor Video Sequences for Road Traffic Accidental Avoidance

In metro cities of all over the world, the growing number personal vehicles and fast life style of the people frequently meet very serious accidents. Due to deeply regretted reports from the loss of manpower and economy, accidental avoidance becomes a hot challenging research topic. In this paper, we consider specifically the accidents that happen due to bike rider's involvement. Focusing on detecting helmet test, we proposed a computer vision based model that exploits HOOG descriptor with RBF kernel based SVM classification. Our experiments have two tier classifications, first is between bike riders and non-bike rider's detection and second is to determine whether the bike riders in the first phases wearing a helmet or not. The initial phase uses video surveillance for detecting the bike riders by using background modeling and bounding based object segmentation. The performance comparison of our model on three widely used kernels ensures the validation of the satisfactory results. We achieved helmet detection accuracy with radial basis kernel 96.67%. Our model can detect any type of helmets in the outdoor video sequences and help security and safety aspects of bike riders.
A literature review on effects of time pressure on decision making in a cyber security context

Shortages of time has become a natural characteristic of the professional environment. Individuals are often pressed to make fast decisions and complete tasks in a timely manner. This element of time pressure is particularly prevalent in the Information Technology (IT) sector due to fast-paced changes in demand, competition, and technology. Reviewing a wide range of journal articles, this paper aims to contribute to a broader discussion on the effects of time pressure on decisions. This paper explores the theoretical and practical considerations of decision-making, considering key decision-making models and the effect of technology on the decision-making process. Subsequently, this paper explores time pressure in a general sense, and then reviews the stress it causes individuals and its presence in technological environments, in particular a cyber-security context. After reviewing both decision-making and time pressure individually, the paper explores the relationship between the two, considering the influence time pressure on decision-making in technological settings. Concluding that time pressure can have an adverse effect on decision-making and hence possibly impacting cyber security services.
Improved Cryptanalysis of a Fully Homomorphic Symmetric Encryption Scheme

Homomorphic encryption is widely used in the scenarios of big data and cloud computing for supporting calculations on ciphertexts without leaking plaintexts. Recently, Li et al. designed a symmetric homomorphic encryption scheme for outsourced databases. Wang et al. proposed a successful key-recovery attack on the homomorphic encryption scheme but required the adversary to know some plaintext/ciphertext pairs. In this paper, we propose a new ciphertext-only attack on the symmetric fully homomorphic encryption scheme. Our attack improves the previous Wang et al.’s attack by eliminating the assumption of known plaintext/ciphertext pairs. We show that the secret key of the user can be recovered by running lattice reduction algorithms twice. Experiments show that the attack successfully and efficiently recovers the secret key of the randomly generated instances with an overwhelming probability.
Deriving and Combining Mixed Graphs from Regulatory Documents Based on Constraint Relations

Extracting meaningful information from regulatory documents such as the General Data Protection Regulation (GDPR) is of utmost importance for almost any company. Existing approaches pose strict assumptions on the documents and output models containing inconsistencies or redundancies since relations within and across documents are neglected. To overcome these shortcomings, this work aims at deriving mixed graphs based on paragraph embedding as well as process discovery and combining these graphs using constraint relations such as "redundant" or "conflicting" detected by the ConRelMiner method. The approach is implemented and evaluated based on two real-world use cases: Austria's energy use cases plus the contained process models as ground truth and the GDPR. Mixed graphs and their combinations constitute the next step towards an end-to-end solution for extracting process models from text, either from scratch or amending existing ones.
Mitigation Techniques for Attacks on 1-Dimensional Databases that Support Range Queries

In recent years, a number of attacks have been developed that can reconstruct encrypted one-dimensional databases that support range queries under the persistent passive adversary model. These attacks allow an (honest but curious) adversary (such as the cloud provider) to find the order of the elements in the database and, in some cases, to even reconstruct the database itself. In this paper we present two mitigation techniques to make it harder for the adversary to reconstruct the database. The first technique makes it impossible for an adversary to reconstruct the values stored in the database with an error smaller than k, for k chosen by the client. By fine-tuning k, the user can increase the adversary’s error at will. The second technique is targeted towards adversaries who have managed to learn the distribution of the queries issued. Such adversaries may be able to reconstruct most of the database after seeing a very small (i.e. poly-logarithmic) number of queries. To neutralize such adversaries, our technique turns the database to a circular buffer. All known techniques that exploit knowledge of distribution fail, and no technique can determine which record is first (or last) based on access pattern leakage.
Context-Based Roles and Competencies of Data Curators in Supporting Research Data Lifecycle Management: Multi-Case Study in China

Abstract Focusing on the main research question of what the critical roles and competencies of data curation are in supporting research data life cycle management, this paper adopts a multi-case study method, with data governance frameworks, to analyze stakeholders and data curators, and their competencies, based on different contexts from cases from enterprises and academic libraries in mainland China. Via the context and business analysis on different cases, critical roles such as data supervisor, data steward, and data custodian in guaranteeing data quality and efficiency of data reuse are put forward. Based on the general factor framework summarized via existing literature, suggestions for empowering data curators’ competencies are raised according to the cases. The findings of this paper are as follows: besides digital archiving and preservation, more emphasis should be placed on data governance in the field of data curation. Data curators are closely related but not equivalent to stakeholders of data governance. The different roles of data curators would play their own part in the process of data curation and can be specified as data supervisor, data steward, and data custodian according to given contexts. The roles, competencies, and empowerment strategies presented in this paper might have both theoretical and practical significance for the fields of both data curation and data governance.