Reviewerly/exHarmony huggingface dataset:
SAHAR-LSTM: An enhanced Model for Sentiment Analysis of Hotels’Arabic Reviews based on LSTM

Over the last few years, many scientists have paid special attention to the Sentiment analysis (SA) area of research, thanks to its interesting uses in different domains. The most popular studies have tackled the issue of SA in the English language; however, those dealing with SA in the Arabic language are, up to now, limited due to the complexity of the computational processing of this Morphologically Rich Language (MRL). As a matter of fact, Deep learning and especially the use of Recurrent Neural networks (RNN) has recently proved to be an efficient tool for handling SA challenges. The recourse to some approaches, based on the long short-term memory (LSTM) architecture, has provided adequate solutions to the problems of SA in Arabic language. In our paper, we conduct a study on SA in the Arabic language. Therefore, we propose an enhanced LSTM based model for performing SA of Hotels' Arabic reviews, called SAHAR-LSTM. This model is evaluated on a dataset containing Hotels' reviews written in Modern Standard Arabic (MSA), and it is implemented together with two Dimensionality reduction techniques: Latent Semantic Analysis (LSA) and Chi-Square. The experimental results obtained in this work are promising, and demonstrate that our proposed approaches achieve an accuracy of 83.6% on LSA and Chi-Square methods and 92% on LSTM classification Model.
An analysis of aggregated coupling's suitability for software defect prediction

Software Defect Prediction is an important problem during the development of a software system, because it helps to focus the testing effort on those parts of the system which have a high probability of being defective. It is also well-researched, there being many papers presenting Machine Learning-based prediction models for this problem. But most of them use the same object-oriented structural software metrics as features. In this paper we investigate the impact of aggregated coupling, which combines structural and conceptual coupling, on software defects proneness. In this regard, we present three software metrics suites derived from both structural and conceptual coupling and analyze how their different combinations influence the performance of software defect prediction models. We analyze the relative performance of the models when using features extracted with LSI versus Doc2Vec in conjunction with Cosine versus Euclidean similarity for computing the conceptual coupling. The results suggest that all these features are complementary and their usage improves the performance of the machine learning models.
Plural Bodies, Pluriversal Humans: Questioning the Ontology of ‘Body’ in Design

All designing, as well as everything designed, is ontological: things shape and form humans, just as humans shape and give form to them (Willis, 2006, Fry, 2013). However, there is no ontology of the human in the singular sense, but plural, multiple ontologies, and therefore, no human, but only humans. This paper proposes the introduction of a provocation to disturb notions of the ontologically designed body, and in fact, of how we think of what a ‘body’ is, by turning to the insights offered up by a body of literature hitherto relatively unexamined in design research: the ontological turn in anthropology. By turning to a survey of the work done by cultural anthropologists on different cosmologies and cultures, I intend to demonstrate that the Anglo-Eurocentric conception of the ontologically singular body, signified in terms of the “universality” of human biology, is in fact, only one of many ways of bodily being and relating to the body; that matters of the body are locally situated and specific to communities and environments; and therefore, what we mean by ‘the body’ is in fact also plural, multistable, and wrought with incommensurabilities between human communities and cultures. The essay will end with a re-evaluation of ontological designing and speculations on what design could do, through an engagement with examples of ‘other’ ontologies and definitions of body.
Thermal Detection for Free Flight

Abstract Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, or simply to conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of meters of elevation gain. Estimating thermal locations is not an easy task, pilots look for different indicators like color variation on the ground because the difference in the amount of heat absorbed by the ground varies based on the color/composition, birds circling in an area, and certain types of cloud formations (cumulus clouds). The above methods are not always reliable enough and pilots study the conditions for thermals by estimating solar heating of ground (cloud cover and time year/date) and also the lapse rate and dew point of air. In this paper, we present a Machine Learning based solution to forecast thermals. Since pilots in general record many of their flights locally and sometimes upload them to databases, we use the flight data uploaded to determine where the pilot encountered thermals and together with other information (weather and satellite images corresponding to the location and time of the flight) train an algorithm to automatically predict the location of thermals given as input the current weather conditions and terrain information (obtained from Google Earth Engine). Results show that our model is able to converge on the training and validation set with a loss bellow 1%.
Multiple Target Tracking Using KNN-Based Classifier and Probabilistic Data Association

One of the major problems in target tracking is to identify the measurement of the interested target among the bulk of data. Many algorithms based on identifying single measurement among the received data were presented. However with increase in false alarm rate these models failed. Instead of identifying single measurement among the received signals and discarding others, alternative approach is to associate each measurement with different weights and this approach is called Probabilistic Data association (PDA). However performance of PDA algorithm is visibly degraded for multiple targets. Hence this paper presents the integration of PDA with K-nearest neighbors’ algorithm (KNN) to effectively track multiple targets and reduce the complexity in multiple target tracking. KNN classifies the measurements among targets and then PDA algorithm is applied to each target. Hence the multiple targets tracking problem is split into multiple- single target tracking problem with the help of KNN. Simulations were done using MATLAB and their performances are presented in this paper.
An Empirical-informed Work Process Model for a Combined Approach of Agile, User-Centered Design, and Lean Startup

Organizations worldwide have been adopting software development methods that deviate from the standard Agile approach in order to overcome some of its failings—mostly by mixing Agile with other concepts. It has been reported that combining it with Lean Startup and User-Centered Design results in a very powerful development approach, and academic research has developed some high-level process models for it. However, in-depth, demonstrated work process models for this combined approach do not exist, which makes it hard for inexperienced professionals to start using it. We aim to present a process model that reflects how two industry teams that use the combined approach do their work. We performed a case study where we investigated their day-to-day work using daily observations, semi-structured interviews, and focus group sessions; and mapped a set of the activities, techniques, and work products that both teams use daily. We then had them visually represent their work process with the help of our mapping. The result was a software development process model that encompasses Agile, Lean Startup, and User-Centered Design concepts, which can be used as a starting point for those who want to adhere to such a development approach. We reflect on how the process model was conceived and how it could be used by other organizations.
Avoided emissions resulting from grid-tied solar photovoltaic generation in the Philippines: an energy-based analysis

Abstract One of the motivations of deploying renewable energy is its promise to reduce emissions. The accounting of emissions, however, becomes challenging and complex in the case of grid-tied PV system as it also derives energy from the centralized grid where the energy comes from a mix of resource. The amount of avoided emissions is highly variable. The study focused on the determination of avoided emissions resulting from grid-tied solar photovoltaic (PV) generation of Ateneo de Davao University Jacinto Campus in the Philippines. The results show that the small size of PV systems impacts only the marginal generating plant which is normally a hydro, diesel or natural gas-fired generator and largely depends on which grid the consumer is located. It shows that the energy generation of the PV acts like a peak shaving mechanism that reduces the system load. The solar PV of the university was simulated and the avoided emissions (CO2, SO2, NOx, PM10, PM2.5, NH3, VOCs, and Hg) were quantified through energy-based approach.
Augmentation and Detection of Individual Pose using CUDA

Abstract The collective real-time pose prediction is a leading element in allowing algorithms to understand Individuals in videos and pictures. In this study, we are presenting a strategy to determining the pose of various individuals in a picture in real time. Non-parametric procedure is used as representation that we refer this to Part-Affinity-Fields (PAFs) to understand how to connect parts of the body with individuals. This base up framework accomplishes high exactness and real time execution, paying little mind to the quantity of individuals in the picture. In past work of computer vision researchers, PAFs and body part area estimation were refined at the same time across preparing different steps. We show that PAF just clarifying as opposed to both the PAF and content part area refinement brings about a significant increment in each of runtime execution and precision. We likewise propose the main joined body and foot key point identifier, considering an inner commented on foot dataset that we have freely discharged. The joined finder not just diminishes the derivation time contrasted with running them successively, yet in addition keeps up the exactness of every part separately. The work was completed in appearance to Open Pose, chief opensource continuous structure to multiindividual 2D present disclosure, including foot, body, hand, &amp; facial central issues.
Dynamic Distributed Clustering Approach Directed to Patient-Centric Healthcare System

Healthcare data are often obtained from different healthcare providers for improved healthcare services. Healthcare data collection and maintenance became a concern owing to data issues including high-dimensionality irregularity, and sparsity. Several scholars focused on these topics and offered several effective and flexible healthcare solutions. A few other machine learning techniques, such as clustering, are used to evaluate healthcare data. Clustering is one of the well-known and employed data mining methods in health data analysis. However, a major challenge remains on how to scale up and pace up clustering algorithms with a limited loss to clustering consistency. In this paper, a framework is proposed that utilizing an updated hierarchical distributed clustering in biomedical engineering for a large data environment. The complex essence of the method arises from not providing the number of right clusters in advance. The suggested methodology operates through two key phases: the first step constructs local clusters dependent on their portion of the entire dataset; this phase takes maximum advantage of the Spark Framework-based task parallelism paradigm; whereas the second phase aggregates local clusters to render the final clusters lightweight and reliable. The framework can help both doctors and patients. Experimental findings demonstrate that the method is flexible and provides high-quality for Healthcare Cost and Utilization Project (HCUP) dataset.
Exploring the Directives of Communicability for Improving the Quality of Software Artifacts

Software artifacts developed in the early stages of the development process describe the proposed solutions for the software in construction. Thus, these artifacts support team communication. Failures in communication via software artifacts occur because practitioners typically concentrate on the artifacts’ content, without reflecting on how other software development team members will interpret these artifacts. Such communication failures can negatively affect the quality of software artifacts. To support better communication via artifact, we proposed the Directives of Communicability (DCs). The DCs aim to support practitioners in analyzing the characteristics that affect the artifacts’ content on team communication. In this paper, we present an exploratory study carried out to evaluate the practitioners’ perception about the contribution of DCs to the quality of a specific software artifact. The results indicate that DCs contribute to this purpose. We noticed positive perceptions of practitioners about the use of DCs in software artifacts.
Multiple Balance Subsets Stacking for Imbalanced Healthcare Datasets

Accurate prediction is highly important for clinical decision making and early treatment. In this paper, we study the imbalanced data problem in prediction, a key challenge existing in the healthcare area. Imbalanced datasets bias classifiers towards the majority class, leading to an unsatisfied classification prediction performance on the minority class, which is known as imbalance problem. Existing imbalance learning methods may suffer from issues like information loss, overfitting, and high training time cost. To tackle these issues, we propose a novel ensemble learning method called Multiple bAlance Subsets Stacking (MASS) by exploiting a multiple balance subsets construction strategy. Furthermore, we improve MASS with introducing parallelism (Parallel MASS) to reduce the training time cost. We evaluate MASS on three real-world healthcare datasets, and experimental results demonstrate that its prediction performance outperforms the state-of-art methods in terms of AUC, F1-score and MCC. Through the speedup analysis, Parallel MASS reduces the training time cost greatly on large dataset, and its speedup increases as the data size grows.
Kerberos Authentication for Big Data Applications on Cloud Environment

Abstract The large set of User, Sensor, Medical, and Enterprise data can be collected and obtaion big data. Therefore, there is a need for platform for storing, managing and distributing it across multiple nodes. In the paper, we represent approach which is based on Kerberos in Hadoop distributed file system. It is used to access the right blocks of data by the authorized user only. To provide a security in Namenode, Ticket Granting Ticket and Service Ticket have been used for that. Kerberos installation in server and clients are illustrated in details.
A Post-Processing Method for Text Detection Based on Geometric Features

Deep learning text detection is generally divided into two steps: prediction candidate box of depth model and post-processing, and post-processing usually uses NMS or prediction box to merge and connect.At present, many methods of text detection can detect the character position of the document image, but they are often not accurate enough, which affects the effect of subsequent recognition.To solve the problem of inaccurate detection, this article proposes a post-processing method of document detection based on geometric features from the perspective of post-processing.The post-processing method is mainly divided into four modules.Firstly, the background removal (BR) module separates the background information and character information through pixel threshold.Secondly, the candidate box expansion (CBE) module expands the prediction box in all directions by judging whether the boundary of the prediction box is in the character pixel.Then is the non-standard box removal (NBR) module, using the consistency principle of characters and surrounding characters to filter out the error detection of some prediction boxes.Finally, the module of repeating box removal (RBR) is used to remove the repeated prediction box.In order to verify the effectiveness of this method, a large number of experiments have been conducted on Standard yi, Chinese2k, English2k, ICDAR 2015 and ICDAR 2017(CTW-12k) datasets.The experimental results show that the method proposed in this article can improve the effect of text detection.
Diagrammatic mental representation: a methodological bridge

While computer-mediated user studies tend to rely on surveys, observations, focus groups, and ethnography, participant-generated mental model diagrammatic representation can yield valuable data involving complex and abstract user practices and behaviour. Computer-mediated research has struggled with eliciting users' beliefs of concrete representations in technology interactions. Using an investigation of authentication and user perceptions of security, we tested a method to elicit user mental model representations. We asked this overarching question: What are user perceptions of security when logging in to third-party apps and services via existing Facebook, Google, and Twitter accounts? Drawing from approaches to researching technology from the sub-fields of human-computer interaction, user studies, visual and risk communication, while incorporating methodological techniques from psychology on mental models, we offer a methodological innovation. We found evidence suggesting that a diagrammatic approach to representing mental models is feasible in addressing some of the challenges faced by existing computer-mediated approaches.
A Comprehensive Survey of Different Phases for Involuntary System for Face Emotion Recognition

Involuntary recognition of basic and non-basic emotions has started great curiosity in research area. Developing a fully automated computational model for face emotion analysis is quite difficult a human face is complex, multidimensional and has different meaningful visual messages to pass. A big research augmented in Human Computer Interaction over decades but still many queries are still clueless as in what should be the important signs and expressions to be evaluated for meaning encoding of messages. In this paper fundamental components, as face image acquisition, registration, pre-processing or dimensionality reduction, feature extraction and learning, classifiers, recognition, datasets and new designs for involuntary expression identification is been reviewed in detail. The paper also enlightens some core problems like illumination variations, registration faults, occlusions due to head movements or glasses, age and any disorders.
Technological Review on Integrating Image Processing, Augmented Reality and Speech Recognition for Enhancing Visual Representations

There is still a lack of research focus on augmenting user engagement by AR-based applications. As technology has grown, the movie industry has been undergoing a remarkable change in the past few years. Visually attractive movie posters play a major role in advertising even in the era of digital promotion. This paper is a review of the use of technologies such as Image processing, Augmented Reality and Speech Recognition to interactively present movie information to users in a more convenient method.
Combining JSON-LD with First Order Logic

This paper describes a specification, JSON-LD-LOGIC, for using JSON as a language for first order logic. It is compatible with both the JSON-LD and TPTP languages, the latter being the de facto standard first order logic language for theorem provers. The paper also describes the GKC toolkit for proving theorems using the JSON-LD-LOGIC language, and for converting between the JSON-LD-LOGIC and TPTP languages.
SQL2X

A key challenge in designing a database course is how to introduce students to the great variety of data models, query languages, databases, and data processing systems available now. To address this challenge, we propose SQL2X, a novel SQL-centric learning model that teaches students SQL, NoSQL, and MapReduce via translation. For example, translating SQL queries into MapReduce programs to gain insights on how aggregation and join are performed in parallel in MapReduce, and translating SQL queries into REST requests to Firebase to help understand the differences between the query capability of SQL and NoSQL databases. We have applied the model to a graduate database course in our applied data science program. The evaluation and feedback from the students with diverse background indicate the effectiveness of the model in developing students' modeling, querying, and analytical skills over diverse data systems.
Spatial variability analysis of the solar energy resources for future urban energy applications using Meteosat satellite-derived datasets

The proper management and operation of any energy resources require accurate analysis and assessment of the available resources. In this context, the variability analysis of solar energy resources is gaining attention due to its prospects for future thermal/heating and photovoltaic applications. But, availability of any energy resources depends on the topographic orientation of the region and temporal availability of the resources. In this context, the current work offers an approach for spatial variability analysis of the available solar energy resources at the selected southern region of India with the help of geostatistical models and time series studies to identify the availability of energy potential for the future urban energy applications. The work requires Meteosat satellite-derived datasets acquired from the National Renewable Energy Laboratory (NREL) data catalogue. After performing the geostatistical operations on the datasets, it classifies the selected region into five classes comprising “most”, “highly”, “moderately”, “marginally”, and “least” suitable region for solar energy resources. The results obtained from this research result specifies that the region has an annual average variability of DNI as 3.757 to 5.797 kWh/m2/day and GHI as 4.4 to 6.083 kWh/m2/day respectively and total availability at selected 56 urban locations having footprint area more 10 km2 over the selected southern region has average annual GHI as 690.051 GWh/m2 (total GHI of 38642.870 GW) and annual average DNI as 617.580 GWh/m2 (total DNI of 32523.653 GW). The research outcomes infer that the region has sufficient solar energy resource, and these can be harvested for urban solar photovoltaics or solar heating/thermal installations. The work will provide an ideal solution for sustainable energy resources towards the urban applications in the region along with infrastructural development with natural resource management.