Reviewerly/exHarmony huggingface dataset:
Compatibility, desirability, and the running intersection property

Compatibility is the problem of checking whether some given probabilistic assessments have a common joint probabilistic model. When the assessments are unconditional, the problem is well established in the literature and finds a solution through the running intersection property (RIP). This is not the case of conditional assessments. In this paper, we study the compatibility problem in a very general setting: any possibility space, unrestricted domains, imprecise (and possibly degenerate) probabilities. We extend the unconditional case to our setting, thus generalising most of previous results in the literature. The conditional case turns out to be fundamentally different from the unconditional one. For such a case, we prove that the problem can still be solved in general by RIP but in a more involved way: by constructing a junction tree and propagating information over it. Still, RIP does not allow us to optimally take advantage of sparsity: in fact, conditional compatibility can be simplified further by joining junction trees with coherence graphs.
Rule-based extraction of family history information from clinical notes

One of the features of Electronic Health Records (EHR) is to store the patient clinical data. Despite the efforts to structure all this data, clinical reports and notes containing essential information about each patient are still stored in free text. Some of this information refers to the family's health history and may be highly relevance for diagnosis and prognosis. We proposed two methodologies to unify this knowledge and extract family history information from clinical notes using rule-based techniques in natural language processing (NLP). With these methods, we intend to collect the family members mentioned in the text as well as associations to diseases and living status. The proposed methods were evaluated into two stages, demonstrating F-scores of 0.72 and 0.74 for the discovery of family members and observations, and 0.62 and 0.52 for the detection of the family relations with the observations, and their living status. Our methodologies raised new strategies to automatically annotate large amounts of EHRs, facilitating the detection of comorbidities within family relations.
Fudge: Fuzzy ontology building with consensuated fuzzy datatypes

An important problem in Fuzzy OWL 2 ontology building is the definition of fuzzy membership functions for real-valued fuzzy sets (so-called fuzzy datatypes in Fuzzy OWL 2 terminology). In this paper, we present a tool, called Fudge, whose aim is to support the consensual creation of fuzzy datatypes by aggregating the specifications given by a group of experts. Fudge is freeware and currently supports several linguistic aggregation strategies, including the convex combination, linguistic OWA, weighted mean and fuzzy OWA, and easily allows to build others in. We also propose and have implemented two novel linguistic aggregation operators, based on a left recursive form of the convex combination and of the linguistic OWA.
Detection of early warning signals for overruns in IS projects: linguistic analysis of business case language

Many Information Systems (IS) projects fail to be completed within budget and on schedule. A contributing factor is the so-called planning fallacy in which people tend to underestimate the resources required to complete a project. In this paper, we propose that signals of the planning fallacy can be detected in a project's business case. We investigated whether language usage in business cases can serve as an early warning signal for overruns in IS projects. Drawing on two theoretical perspectives – the Linguistic Category Model (LCM) and Construal Level Theory (CLT) – two sets of rival hypotheses were tested concerning the relationship between project overruns and whether the language usage in a business case is abstract or concrete. A linguistic analysis of the business cases of large IS projects in the Netherlands suggests that concrete language usage in the business case is associated with bigger budget and schedule overruns. For researchers, our study contributes to the existing literature on the importance of language usage. For practitioners, our study provides an early warning indicator for overruns.
Etherolic

In recent years we have seen a great deal of attention to the topic of security analysis in smart contracts, especially those developed for the Ethereum blockchain. As a result, there seems to be an evergrowing demand for secure smart contracts to protect what could potentially be worth billions. In this paper, we introduce Etherolic as a robust, scalable and efficient fuzzing tool based on concolic testing for performing security analysis on smart contracts. Our approach works based on a successful combination of dynamic taint tracking (DTA) and concolic testing that allows users to analyze the bytecode of smart contracts being run on the Ethereum Virtual Machine. Moreover, Etherolic is not only able to identify a wide spectrum of recent vulnerabilities in smart contracts, but also it generates exploits to trigger unknown errors in the code. In order to demonstrate the usefulness of our approach, we evaluated Etherolic on a crafted benchmark suite, comprising several real-world and synthetic smart contracts along with 98 safety features. The result of our preliminary evaluation reveals 204 security violations in the benchmarks.
Validation of global irradiance derived from INSAT-3D over India

Geostationary satellites offer the possibility of producing high-frequency time series irradiance datasets, covering a large geographical area. Here, we derive half-hourly global irradiance (GHI) at 4 km spatial resolution from visible channel radiance of INSAT-3D satellite using a semi-empirical model and validate it against 19 ground stations over 2015–16, across India. Our model uses the Advanced Solis clear-sky model with monthly climatological values of modern-era retrospective analysis for research and applications-2 (MERRA-2) aerosol optical depth (AOD) and precipitable water (PW) as inputs. The relative root mean squared error (rRMSE) and relative mean bias error (rMBE) are 24.02% and −0.05%, respectively. Over the validation period, INSAT-3D derived GHI outperformed SARAH-E model and MERRA-2 reanalysis GHI.
Defining agile requirements change management

As opposed to traditional software development, agile development encourages requirements change. Given the importance of requirements change management, there is an increasing number of studies discussing this process in both traditional and agile software developments. By examining existing works, we observed that there are many ways to improve the management of agile requirements change. Aiming to addressing this research gap, our study focus on defining a process to handle Agile Requirement Change Management (ARCM), as well as identifying practices to support ARCM process. We performed systematic study to map the main practices applied to manage agile requirements change. Our primary goal was to seek a better understanding of possible directions in the ARCM research, and foster discussions for future work. We identified 3 steps to handle the ARCM process, and grouped 11 distinct agile practices to carry out these steps. Even though agile practices might be, to some extent, for managing requirement change, we were able to identify practical challenges when adopting these practices for Agile Requirements Changes management.
Multiple-image authentication based on the single-pixel correlated imaging and multiple-level wavelet transform

Single-pixel correlated imaging has been developed as one of the most promising techniques in the past decades. In this paper, a new mechanism of single-pixel correlated imaging is designed for multiple-image authentication. The object wave is sequentially modulated by a series of phase-only mask pairs embedded into two cascaded spatial light modulators. The modulated wave passes through the input image located in the object plane, and its intensity is recorded as one of the ciphertext values by using a single-pixel bucket detector without spatial resolution. The sparse data of plaintext images are obtained with the multiple-level wavelet transform and used to construct an interim image with the help of binary amplitude masks. The interim image with a smaller size than that of plaintext images is considered as the input of single-pixel correlated imaging, which can guarantee the number of measurements reduced. Simultaneously, the phase-only mask pairs are retrieved from the 2D patterns formed with the row vectors of the spatially orthogonal Hadamard matrix by using an efficient iterative phase retrieval algorithm. Because the redundancy between speckle patterns generated with these phase-only mask pairs is greatly restrained, the number of measurements can be further reduced. Moreover, the related binary amplitude masks are employed as the secret keys so that the security level is also extensively enhanced. Given the above, the proposed method can significantly achieve the goal of multiple-image verification and provide a different research approach for optical authentication based on single-pixel correlated imaging.
Preference-driven Pareto front exploitation for bloat control in genetic programming

As one of evolutionary algorithms (EAs), genetic programming (GP) has been applied in a wide range of areas, e.g. bioinformatics and robotics. Different from other EAs, GP can represent problems with variable length (e.g. trees), which makes it more flexible in evolving solutions, yet leads to a serious problem, bloat. It can cause evolving redundant parts and slowing down search. Multi-objective techniques are popularly used for reducing bloat in GP (termed as MOGP). Specifically, MOGP methods evolve trade-off solutions of all objectives, which constitute the so-called Pareto front. Then users select solutions on the front based on their preference for specific tasks. However, existing MOGP methods rarely consider users’ preference during evolution, which wastes computation power and time to search for useless solutions and cannot generate fine-grained interested regions on the Pareto front. Therefore, this paper investigates introducing users’ preference to guide multi-objective techniques to focus on the interested regions on Pareto front during evolution. Specifically, Pareto dominance is an important notion in multi-objective techniques for comparing two solutions. We design two preference-driven Pareto dominance mechanisms, scPd (static constraint Pareto dominance) and dcPd (dynamic constraint Pareto dominance), which are introduced in a base multi-objective technique and then are incorporated with GP respectively to form two new bloat control MOGP methods, i.e. scPd_MOGP and dcPd_MOGP. They are tested on benchmark symbolic regression tasks comparing with GP, two existing bloat control methods (i.e. a parsimony GP method (pGP) and a standard multi-objective GP method (sMOGP)), and four popularly-used symbolic regression methods. Results show that the proposed methods can reduce bloat in GP and outperform pGP in bloat control, and comparison with sMOGP shows that they can search front regions based on users’ preference where the solutions have better functionality, yet relatively larger sizes. In addition, compared with four popularly-used symbolic regression methods, scPd_MOGP is generally better; while dcPd_MOGP achieves varied results, yet it performs better or similar to the reference methods on the majority of the given test functions. Moreover, comparison between the two proposed methods suggests that the constraint in the Pareto dominance of scPd_MOGP is more relaxed than that of dcPd_MOGP.
Hybrid Resampling for Imbalanced Class Handling on Web Phishing Classification Dataset

From the previous work related to web phishing, the researchers overlook the imbalanced class problem on the dataset. theoretically, the majority of classification methods would assume that the nature of the class distribution is balanced. It caused the classification's performance of the method will be declining. Therefore, the mechanism of imbalanced class handling is severely needed. In our study, One Sided-Selection and Synthetic Minority Over-Sampling Technique are used to handle the imbalanced class condition. Those algorithms work to balancing the class distribution of the dataset so that the accuracy and the g-mean score of the classification will be enhanced. Based on the result, the combination of those methods (OSS and SMOTE) can enhance the classification's result significantly either on binary type class and multiclass type dataset. Hence, the combination of OSS and SMOTE can be a plausible option to handle the imbalanced class problem on the web phishing classification either on binary class and multiclass datasets.
Towards the Real-Time analysis of Talks

Nowadays people are surrounded always by an interesting virtual world using their smart phones, tablets and laptops. Therefore, it is not so easy -sometimes impossible- to involve students into an old-fashioned learning process. This led us to modernize our classic teaching process using new technologies which are much closer to their needs. To involve and inspire students we started to use two different real-time applications: E-Lection (developed by us) and Veyon (free, ready-made system). We use E-Lection during the lectures to make them interactive and more motivated adding the possibility to ask and sign in real-time. Veyon is working in the time of laboratory work to enforce the students' common work and present the best practices. In this paper we publish the details of this experience and its acception by the students.
Mining micro-influencers from social media posts

Micro-influencers have triggered the interest of commercial brands, public administrations, and other stakeholders because of their demonstrated capability of sensitizing people within their close reach. However, due to their lower visibility in social media platforms, they are challenging to be identified. This work proposes an approach to automatically detect micro-influencers and to highlight their personality traits and community values by computationally analyzing their writings. We introduce two learning methods to retrieve Five Factor Model and Basic Human Values scores. These scores are then used as feature vectors of a Support Vector Machines classifier. We define a set of rules to create a micro-influencer gold standard dataset of more than two million tweets and we compare our approach with three baseline classifiers. The experimental results favor recall meaning that the approach is inclusive in the identification.
Diffusion of Electronic Water Payment Innovations in Urban Ghana. Evidence from Tema Metropolis

Recent advances in mobile technologies, especially in the utility payment space, are having an increasingly profound impact on our daily lives and offer advantageous services in the utility sectors. This paper examines the prevalence and patterns of customer uptake of an electronic water payment (EWP) system and its implications for water delivery in the Tema Metropolitan Area, Ghana. Data for the study comprised a survey of 250 utility customers and the review of a 12-month water use and customer payment database from a water supply company. Results indicate that although customers were aware of EWP’s existence, overall uptake was very low. EWP awareness and intention to use EWP were not significantly associated with customers’ gender, phone/mobile money ownership, educational status, and water usage. However, age, employment status, income, and means of receiving monthly bills were found to be statistically different in relation to the awareness of EWP. These findings offer several implications for water service and utility providers to market mobile payment solutions and to increase the consumer uptake of these services and payment options.
PSSCC: Provably secure communication framework for crowdsourced industrial Internet of Things environments

Summary Internet of things environment is adopted widely in different industries and business organizations with varying capacity. It provides a favorable environment to outsource the crowdsourced data in the cloud to minimize the cost of computation, which is called crowdsourcing. Crowdsourcing is a technique where individuals or organizations obtain goods and services. A professional or industry outsource the crowdsourced data in the cloud, where confidentiality and authenticity of data become essential. Signcryption is the cryptographic technique that serves both the authenticity and the privacy of transmitted messages. This technique ensures secure authentic data transmission and storage. Therefore, this paper proposes an identity‐based signcryption scheme. In the proposed PSSCC framework, the user does pairing free computation during signcryption, which makes efficient calculation on user‐side. Moreover, PSSCC framework is proved secure under modified bilinear Diffie‐Hellman inversion and modified bilinear strong Diffie‐Hellman problems. The performance analysis of PSSCC with related schemes indicates that the proposed system supports efficient communication along with less computation cost.
A detail preserving neural network model for Monte Carlo denoising

Abstract Monte Carlo based methods such as path tracing are widely used in movie production. To achieve low noise, they require many samples per pixel, resulting in long rendering time. To reduce the cost, one solution is Monte Carlo denoising, which renders the image with fewer samples per pixel (as little as 128) and then denoises the resulting image. Many Monte Carlo denoising methods rely on deep learning: they use convolutional neural networks to learn the relationship between noisy images and reference images, using auxiliary features such as position and normal together with image color as inputs. The network predicts kernels which are then applied to the noisy input. These methods show powerful denoising ability, but tend to lose geometric or lighting details and to blur sharp features during denoising. In this paper, we solve this issue by proposing a novel network structure, a new input feature—light transport covariance from path space—and an improved loss function. Our network separates feature buffers from the color buffer to enhance detail effects. The features are extracted separately and then integrated into a shallow kernel predictor. Our loss function considers perceptual loss, which also improves detail preservation. In addition, we use a light transport covariance feature in path space as one of the features, which helps to preserve illumination details. Our method denoises Monte Carlo path traced images while preserving details much better than previous methods.
Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification

In a real world environment, person re-identification (Re-ID) is a challenging task due to variations in lighting conditions, viewing angles, pose and occlusions. Despite recent performance gains, current person Re-ID algorithms still suffer heavily when encountering these variations. To address this problem, we propose a semantic consistency and identity mapping multi-component generative adversarial network (SC-IMGAN) which provides style adaptation from one to many domains. To ensure that transformed images are as realistic as possible, we propose novel identity mapping and semantic consistency losses to maintain identity across the diverse domains. For the Re-ID task, we propose a joint verification-identification quartet network which is trained with generated and real images, followed by an effective quartet loss for verification. Our proposed method outperforms state-of-the-art techniques on six challenging person Re-ID datasets: CUHK01, CUHK03, VIPeR, PRID2011, iLIDS and Market-1501.
ECC2: Error correcting code and elliptic curve based cryptosystem

Code-based cryptography has aroused wide public concern as one of the main candidates for post quantum cryptography to resist attacks against cryptosystems from quantum computation. However, the large key size becomes a drawback that prevents it from wide practical applications although it performs pretty well on the speed of both encryption and decryption. The use of algebraic geometry codes is considered to be a good solution to reduce the key size, but the special structures of algebraic geometry codes results in lots of attacks including Minder's attack. To cope with the barriers of large key size as well as attacks from the special structures of algebraic codes, we propose a code-based encryption system using elliptic codes. The special structure of elliptic codes helps us to effectively reduce the size of secret key. By choosing the rational points carefully, we build elliptic codes whose minimum weight codeword is hard to sample. Such codes are used in constructing encryption systems such that Minder's attacks can be resisted. More importantly, we apply the list decoding algorithm in the decryption process thus more errors beyond half of the minimum distance of the code could be corrected, which is the key point to resist other known attacks for algebraic geometry codes based cryptosystems. Our implementation shows that the proposed encryption system performs well on the key size and ciphertext expansion rate.
Semantic visual recognition in a cognitive architecture for social robots

Cognitive architectures allow robots to perform their operations by drawing on a process that aims to simulate human reasoning. This paper presents an integrated semantic artificial memory system in cognitive architecture based on symbolic reasoning and a connective representation of the knowledge. This memory system attempts to simulate how humans learn to distinguish instances of particular objects within their class using a convolutional network to detect the relevant elements of an image. We use a vector with the extracted features to learn to discriminate an instance of another element from the same class. A novel feature of our approach is its autonomous learning process during the operation of the robot, integrating a deep learning embedding with a statistical classifier. The usefulness and robustness of this method are demonstrated by applying it to a social robot that learns to differentiate people. Finally, experiments are carried out to validate our approach, comparing the detection results with several alternative methods.
Livelihood information and knowledge needs, access, and exchange in rural communities in the Bunda District, Tanzania

Regardless of deliberate efforts by the government of Tanzania to enhance accessibility and usability of information and knowledge, a divide persists regarding resources between rural and urban areas as evidenced by poverty in rural areas. This article investigates livelihood information and knowledge needs, access, and exchange among rural communities in the Bunda District of Tanzania. A mixed research approach was used to collect, analyse, and present field data. Findings illustrate livelihood information and knowledge needs among rural dwellers, and the sources and mechanisms used to access, exchange, and use it, are diverse. Further, factors impeding effective exchange of information and knowledge include provision of irrelevant and untimely information and knowledge presented in user-unfriendly packages that hamper decision-making on livelihood matters.