Reviewerly/exHarmony huggingface dataset:
Effect of incremental feature enrichment on healthcare text classification system: A machine learning paradigm

Healthcare tweets are particularly challenging due to its sparse layout and its limited character size. Compared to previous method based on "bag of words" (BOW) model, this study uniquely identifies the enrichment protocol and learns how semantically different aspects of feature selection such as BOW (feature F0), term frequency inverse document frequency (TF-IDF, feature F1), and latent semantic indexing (LSI, feature F2) when applied sequentially with classifier improves the overall performance.To study this enrichment concept, our ML model is tested on two kinds of diverse data sets: (i) D1: Disease data with conjunctivitis, diarrhea, stomach ache, cough and nausea related tweets, and (ii) D2: WebKB4 dataset, while adapting three kind of classifiers (a) C1: support vector machine with radial basis function (SVMR), (b) C2: Multi-layer perceptron (MLP) and (c) C3: Random Forest (RF). Partition protocol (K10) was adapted with different performance metrics to evaluate machine learning (ML)-system.Using the combination of F1, C1, D1, K10, ML accuracy was: 94%, while with F2, C1, D1, K10, ML accuracy was 97%. Using the incremental feature enrichment from F0 to F2, K10 protocol gave F1 improvement over F0 by 4.98% on Disease dataset, while F2 improvement over F0 was by 11.78% on WebKB4 dataset. We demonstrated the generalization over memorization process in our ML-design. The system was tested for stability and reliability.We conclude that semantically different aspects of feature selection, when adapted sequentially, leads to improvement in ML-accuracy for healthcare data sets. We validated the system by taking non-healthcare data sets.
Time-Optimal Trajectory Generation for Landing a Quadrotor Onto a Moving Platform

We propose a time-optimal trajectory generation algorithm for landing a quadrotor onto a translating (heaving) and tilting (pitching) platform. The proposed algorithm exploits the differential flatness of the system dynamics and optimizes the trajectory in the space of system flat outputs, i.e., system states and their derivatives. This reformulation of the problem improves computational efficiency and can incorporate input and state constraints during the landing maneuver, such as collision avoidance and time-varying final state constraints. In order to verify the optimality of the solution obtained from the proposed algorithm, a switching time search mechanism is also proposed for a subclass of time-optimal trajectory generation problems. Experimental results demonstrating the landing of a quadrotor onto a moving platform are presented to verify the effectiveness of the proposed trajectory generation algorithm.
A fuzzy-filtered neuro-fuzzy framework for software fault prediction for inter-version and inter-project evaluation

Fault Prediction is the most required measure to estimate the software quality and reliability. Several methods, measures, aspects and testing methodologies are available to evaluate the software fault. In this paper, a fuzzy-filtered neuro-fuzzy framework is introduced to predict the software faults for internal and external software projects. The suggested framework is split into three primary phases. At the earlier phase, the effective metrics or measures are identified, which can derive the accurate decision on prediction of software fault. In this phase, the composite analytical observation of each software attribute is calculated using Information Gain and Gain Ratio measures. In the second phase, these fuzzy rules are applied on these measures for selection of effective and high-impact features. In the last phase, the Neuro-fuzzy classifier is applied on fuzzy-filtered training and testing sets. The proposed framework is applied to identify the software faults based on inter-version and inter-project evaluation. In this framework, the earlier projects or project-versions are considered as training sets and the new projects or versions are taken as testing sets. The experimentation is conducted on nine open source projects taken from PROMISE repository as well as on PDE and JDT projects. The approximation is applied on internal version-specific fault prediction and external software projects evaluation. The comparative analysis is performed against Decision Tree, Random Tree, Random Forest, Naive Bayes and Multilevel Perceptron classifiers. This prediction result signifies that the proposed framework has gained the higher accuracy, lesser error rate and significant AUC and GM for inter-project and inter-version evaluations.
A step toward the standardization of maintenance and training services in C4I military systems with Mixed Reality application

This paper is a review of the work presented at the 5th IEEE International Workshop on Metrology for Aerospace, MetroAeroSpace 2018 (Piedimonte and Ullo, 2018) [1], enriched with some concepts on standardization for Mixed Reality applications. Nowadays, large organizations are looking for new procedures and configuration management in order to optimize operations for training and maintenance. The idea is to employ advanced techniques in order to handle budget constraints and reduction of human resources. At this aim, Virtual, Augmented and Mixed Realities are getting more and more exploited to support complex systems during their in-service phase. Nevertheless, the Mixed Reality has not formal standards available for the different application fields yet. This problem limits its utilization because it is not simple to deal with all the possible variations that can occur on the apparatus under maintenance or during a training process, which need to be suitably recorded and carried on. These themes are typically managed by the “Configuration Management” and are already harmonized in the norms, typically used in the discipline of the Integrated Logistic Support (ILS). Moreover, the new scenarios, where Virtual, Augmented or Mixed Realities are employed, lead to a maintenance “on condition”, rather than to an “established scheduled” maintenance. In this context, the traditional manuals for maintenance, which present a “constant visualization”, typically a.pdf-file, lose effectiveness. On the contrary, the manuals based on a “variable visualization”, such as the AeroSpace and Defense (ASD) S1000 D norm, integrated with logistic data based on ASD S 3000 L norm, offer a great competitive advantage. This paper aims to present first steps towards the standardization of Maintenance and Training Processes in Command Control Communications Computers and Intelligence (C4I) Military Systems of ITalian Air Force (ITAF) when Mixed Reality is applied. In order to understand the most suitable choice when the C4I systems are involved, ITAF is running a Proof of Concept (PoC) to evaluate and compare the different technologies based on Mixed Reality. It is worth to underline that the benefits of Augmented and Mixed Reality have been extended to other fields such as for instance: industrial, technical, social, medical, cultural, besides the military one. Therefore, the results of this paper and the efforts for implementing standardization procedures will be useful for exploitation actions in all application fields.
Estimation of elastic strain by integrated image correlation on electron diffraction patterns

High-angular-resolution electron backscattered diffraction (HR-EBSD) has been developed to study local elastic strains in crystals. An integrated digital image correlation (DIC) procedure for high resolution diffraction patterns, as recently proposed to bypass several problems of the conventional cross-correlation-based algorithm, was implemented. Through two examples of experimental data where the algorithm was used and compared to conventional means, the current paper illustrates the benefits of the integrated DIC method. It is found that both measurement uncertainty and computation time were simultaneously reduced. Moreover, an enhanced robustness was obtained for relatively high misorientations relative to methods based on cross-correlation. Different computing conditions are explored on experimental data. A number of practical usage conditions are proposed to achieve better precision and speed.
Spectral clustering algorithm using density-sensitive distance measure with global and local consistencies

Spectral clustering algorithm (SC) has recently received great attention for its high performance in large-scale data clustering and simplicity in implementation. However, previous studies have demonstrated that the commonly used distance measures in SC cannot simultaneously consider global and local consistencies and are sensitive to various noises. As a result, the obtained similarity matrices are unable to capture the actual data structure and thus produce poor clustering results, especially when the data exhibits nonlinear and local manifold structures characteristics. In order to address those limitations, we present a spectral clustering algorithm using density-sensitive distance measure with global and local consistencies in this paper. In the presented algorithm, a novel manifold distance with exponential term and scaling factor is introduced as the pairwise similarity measure. By modifying its exponential term and scaling factor, we can flexibly adjust the ratio of the similarities between the data within the same manifold to the similarities between the data across different manifolds. This flexible adjustment is beneficial to the obtained similarity matrix more accurately reflecting the global and local consistencies of data structures. In addition, to eliminate the effect of noises on the clustering performance, we also incorporate the relative density sensitive term into the distance measure to take into account the local distribution characteristics of the data. Finally, to further improve clustering performance, we provide the SC-based k value determination method for k nearest neighbors (KNN) graph. In the experimental part, the effect of parameters on the performance of the proposed technique is discussed and some suggestions about the determination of the parameters are given. Theoretical analysis and experimental results on several synthetic datasets, UCI benchmark datasets and generated large MNIST handwritten digits datasets demonstrate that the proposed approach is superior to other existing spectral clustering techniques with good robustness.
Listening between the Lines: Learning Personal Attributes from Conversations

Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition of such knowledge, for personalization in downstream Web applications, by extracting personal attributes from conversations. This problem is more challenging than the established task of information extraction from scientific publications or Wikipedia articles, because dialogues often give merely implicit cues about the speaker. We propose methods for inferring personal attributes, such as profession, age or family status, from conversations using deep learning. Specifically, we propose several Hidden Attribute Models, which are neural networks leveraging attention mechanisms and embeddings. Our methods are trained on a per-predicate basis to output rankings of object values for a given subject-predicate combination (e.g., ranking the doctor and nurse professions high when speakers talk about patients, emergency rooms, etc). Experiments with various conversational texts including Reddit discussions, movie scripts and a collection of crowdsourced personal dialogues demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.
Estimating the impact of azimuth-angle variations on photovoltaic annual energy production

Abstract The performance of a photovoltaic (PV) installation is affected by its tilt and azimuth angles, because these parameters change the amount of solar energy absorbed by the surface of the PV modules. Therefore, this paper demonstrates the impact of the azimuth angle on the energy production of PV installations. Two different PV sites were studied, where the first comprises PV systems installed at –13°, –4°, +12° and +21° azimuth angles in different geographical locations, whereas the second PV site included adjacent PV systems installed at –87°, –32°, +2° and +17° azimuth angles. All the investigated PV sites were located in Huddersfield, UK. In summary, the results indicate that PV systems installed between –4° and +2° presented the maximum energy production over the last 4 years, while the worst energy generation were observed for the PV system installed at an azimuth angle of –87°. Finally, the probability projections for all observed azimuth angles datasets have been assessed. Since PV systems are affected by various environmental conditions such as fluctuations in the wind, humidity, solar irradiance and ambient temperature, ultimately, these factors would affect the annual energy generation of the PV installations. For that reason, we have analysed the disparities and the probability of the annual energy production for multiple PV systems installed at different azimuth angles ranging from –90° to +90° degrees, and affected by different environmental conditions. These analyses are based on the cumulative density function modelling technique as well as the normal distribution function.
Preference Elicitation Strategy for Conversational Recommender System

Traditionally, recommenders have been based on a single-shot model based on past user actions. Conversational recommenders allow incremental elicitation of user preference by performing user-system dialogue. For example, the systems can ask about user preference toward a feature associated with the items. In such systems, it is important to design an efficient conversation, which minimizes the number of question asked while maximizing the preference information obtained. Therefore, this research is intended to explore possible ways to design a conversational recommender with an efficient preference elicitation. Specifically, it focuses on the order of questions. Also, an idea proposed to suggest answers for each question asked, which can assist users in giving their feedback.
BIM Fork: Are Smart Contracts in Construction More Likely to Prosper with or without BIM?

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.
Automatic Detection and Repair Recommendation of Directive Defects in Java API Documentation

Application Programming Interfaces (APIs) represent key tools for software developers to build complex software systems. However, several studies have revealed that even major API providers tend to have incomplete or inconsistent API documentation. This can severely hamper the API comprehension and, as a consequence, the quality of the software built on them. In this paper, we propose DRONE (Detect and Repair of dOcumentatioN dEfects), a framework to automatically detect and repair defects from API documents by leveraging techniques from program analysis, natural language processing, and constraint solving. Specifically, we target at the directives of API documents, which are related to parameter constraints and exception handling declarations. Furthermore, in presence of defects, we also provide a prototypical repair recommendation system. We evaluate our approach on parts of the well-documented APIs of JDK 1.8 APIs (including javaFX) and Android 7.0 (level 24). Across the two empirical studies, our approach can detect API defects with an average F-measure of 79.9, 71.7, and 81.4 percent, respectively. The API repairing capability has also been evaluated on the generated recommendations in a further experiment. User judgments indicate that the constraint information is addressed correctly and concisely in the rendered directives.
Violators versus non-violators of information security measures in organizations—A study of distinguishing factors

The present study analyzes the elements that differentiate violators from non-violators of information security measures. Various elements are derived from established theories and models such as general deterrence theory, theory of planned behavior, theory of reasoned action, protection motivation theory, and social cognitive theory. To examine these factors, the data are gathered through an online study conducted in a Midwestern University, USA. The data are collected using questionnaires, and after scrutiny, 195 questionnaires are selected for final analysis. This data are analyzed using second-level statistical techniques, such as chi-square analysis and ANOVA. Results reveal that violators and non-violators of information security measures differ significantly with respect to many factors. These factors include perceived privacy, subjective norms, perceived information security policy (ISP) scope, perceived severity of penalty, perceived celerity of penalty, management support, organizational security culture, and perceived organizational IT capability. The non-significant factors are trust and work load. Implications for practitioners and researchers are provided.
Towards a Pattern Language for Quantum Algorithms

Creating quantum algorithms is a difficult task, especially for computer scientist not used to quantum computing. But quantum algorithms often use similar elements. Thus, these elements provide proven solutions to recurring problems, i.e. a pattern language. Sketching such a language is a step towards establishing a software engineering discipline of quantum algorithms.
Feasibility assessment of grid-tied rooftop solar photovoltaic systems for industrial sector application in Uganda

This paper proposes an alternative approach to determination of rooftop area suitable for solar PV deployment by using validated open source tools that require minimal computational know-how. The paper goes ahead to evaluate the feasibility of implementing grid-tied rooftop solar PV systems in the industrial sector in Uganda. For the considered sample space of 36 industries, a total suitable rooftop area for solar PV application was found to be around 164594.56 m2 and would be able to accommodate about 25.4 MWp capacity. The investigation of energy generation potential showed the possibility of annual energy yield in the range of 1046 kWh/kW–1344 kWh/kW for all roof orientations and roof tilt angles in the range 0°–45°. The economic analysis for PV system in Uganda resulted in an LCOE of 5.75 US cent/kWh, internal rate of return of 7% and BCR of 1.27 for a system implemented entirely on equity. The sensitivity to debt of economic analysis indicated that the project with short loan period of 5 years was still feasible when the loan share of investment cost reached up to 75%.
KLD sampling with Gmapping proposal for Monte Carlo localization of mobile robots

The paper proposes an algorithm for mobile robot navigation that integrates the Gmapping proposal distribution with the Kullback–Leibler divergence for adapting the number of particles. This results in a very effective particle filter with adaptive sample size. The algorithm has been evaluated in both simulation and experimental studies, using the standard KLD—sampling MCL as a benchmark. Simulation results show that the proposed algorithm achieves higher localization accuracy with a smaller number of particles compared to the benchmark algorithm. In a more realistic scenario using experimental data and simulated robot odometry with drift, the proposed algorithm again has greater accuracy using a lower number of particles.
Video synopsis: A survey

Video synopsis is an activity-based video condensation approach to achieve efficient video browsing and retrieval for surveillance cameras. It is one of the most effective ways to reduce the inactive density of input video in order to provide fast and easy retrieval of the parts of interest. Unlike frame-based video summarization methods, the interested activities are shifted in the time domain to obtain video representation that is more compact. Although the number of studies on video synopsis has increased over the past years, there has still been no survey study on the subject. The aim in this article is to review state-of-the-art approaches in video synopsis studies and provide a comprehensive analysis. The methodology of video synopsis is described to provide an overview on the flow of the algorithm. Recent literature is investigated into different aspects such as optimization type, camera topology, input data domain, and activity clustering mechanisms. Commonly used performance evaluation techniques are also examined. Finally, the current situation of the literature and potential future research directions are discussed after an exhaustive analysis that covers most of the studies from early on to the present in this field. To the best of our knowledge, this study is the first review of published video synopsis approaches.
Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques

Computer-aided diagnosis (CAD) approach is presented as strong frameworks to solve the inaccuracy problems. The major purpose of this paper is to improve a CAD system depended on supervised classification that can be useful in diagnosing and detecting the changes of breast cancers in digitized mammograms earlier, accurately and faster than standard examination programs by applying CAD according to image processing techniques beginning with preprocessing step, segmentation, feature extraction and finally classification stage. The work presented in this study is based on the integration of different features such as shape, texture and invariant moment features. This integration achieved best results for sensitivity and specificity rather than using the one type of features in breast cancer classification. The accuracy of our integration system reached 96% in the automatic mode of ANN while best accuracy accomplished by features result according to invariant moments that reached 97% by ANN in an automatic way.
Evidence gathering for hypothesis resolution using judicial evidential reasoning

Realistic decision-making often occurs with insufficient time to gather all possible evidence before a decision must be rendered, requiring an efficient process for prioritizing between potential action sequences. This work aims to develop a rigorous framework for gathering evidence to resolve hypotheses notwithstanding ambiguous, incomplete, and uncertain evidence. Studies have shown that decision-makers demonstrate several biases in decisions involving probability judgment, so decision-makers must be confident that the evidence-based hypothesis resolution is strong and impartial before declaring a resolution. The proposed Judicial Evidential Reasoning framework encodes decision-maker questions as rigorously testable hypotheses to be interrogated through evidence-gathering actions. Dempster–Shafer theory is applied to model hypothesis knowledge and quantify ambiguity, and an equal-effort heuristic is proposed to balance time-efficiency and impartiality. Adversarial optimization techniques are used to make many-hypothesis resolution computationally tractable. This work includes derivation of the generalized formulation, computational tractability considerations for improved performance, several illustrative examples, and application to a space situational awareness sensor network tasking scenario. The results show strong hypothesis resolution and robustness to fixation due to poor prior evidence.