Reviewerly/exHarmony huggingface dataset:
Detecting inconsistencies in annotated product line models

Model-based product line engineering applies the reuse practices from product line engineering with graphical modeling for the specification of software intensive systems. Variability is usually described in separate variability models, while the implementation of the variable systems is specified in system models that use modeling languages such as SysML. Most of the SysML modeling tools with variability support, implement the annotation-based modeling approach. Annotated product line models tend to be error-prone since the modeler implicitly describes every possible variant in a single system model. To identifying variability-related inconsistencies, in this paper, we firstly define restrictions on the use of SysML for annotative modeling in order to avoid situations where resulting instances of the annotated model may contain ambiguous model constructs. Secondly, inter-feature constraints are extracted from the annotated model, based on relations between elements that are annotated with features. By analyzing the constraints, we can identify if the combined variability- and system model can result in incorrect or ambiguous instances. The evaluation of our prototype implementation shows the potential of our approach by identifying inconsistencies in the product line model of our industrial partner which went undetected through several iterations of the model.
Performance of cloudlets in task implementation using ant colony optimization technique

In general the term fog computing is aimed towards minimization of network congestion and process the data by inheriting as well as applying knowledge. In addition to that it can also facilitate to different data sources linked with sensors as well as actuators. The basic terminologies like cloudlets which are linked with fog computing have the provision of quite similar services as with cloud computing. But in many cases these are associated with large number of resources. Considering its computing capabilities, each component linked with the system has the significance to locally process the data. But sometimes it is required to process the heterogeneous data from multiple sources implementing the available resources in cloud. In many situations, there is a provision of extension of computing resources in cloud with similar as well as dissimilar data along with storage allocation. In such situation, the systems linked with fog computing are quite competent towards processing the data and the platforms will be quite appropriate towards location sensitive applications. The intensification of the applications is measured along with security mechanisms and will impact on the issues with feasible solutions. Also while facilitating the computing in cloud, the security measures cannot be reflected directly towards computation. Particularly it is responsible towards managing as well as enhancing the performance of systems associated with virtual platforms along with virtual clusters. As comparing with the cloud computing, the cloudlets perform better with enhanced security. In this case it is being proposed to focus on linkages of databases with them. Also it is being aimed to analyze the task implementation using ant colony optimization technique prioritizing the operational linkage of databases as well as scheduling mechanisms of tasks in the cloudlets within the virtual machines.
Intelligent Voice Assistant Based on Open Semantic Technology

The paper considers the approach to building a personal assistant based on the open semantic technology (OSTIS). The key idea of our approach is the transition from processing a message in speech form to a formalized representation of the meaning in a knowledge base with the least number of intermediate stages. Knowledge base is built on the basis of a semantic network implemented using OSTIS technology. In this case, many tasks of the syntactic, semantic and pragmatic levels of natural language processing, for example, such as recognition of named entities (NEM) and definition of a part of speech (POS), dialogue management (intentions identifications and directives forming), can be performed directly in the knowledge base of the intelligent assistant. This will make it possible to effectively solve such problems as managing the global and local context of the dialogue, resolving linguistic phenomena such as anaphores, homonymy and eleptical phrases, correctly formulating answers to questions that are complex in structure and content, posed by the user during the dialogue.
Asymmetric Graph-Guided Multitask Survival Analysis With Self-Paced Learning

Recently, multitask learning has been successfully applied to survival analysis problems. A critical challenge in real-world survival analysis tasks is that not all instances and tasks are equally learnable. A survival analysis model can be improved when considering the complexities of instances and tasks during the model training. To this end, we propose an asymmetric graph-guided multitask learning approach with self-paced learning for survival analysis applications. The proposed model is able to improve the learning performance by identifying the complex structure among tasks and considering the complexities of training instances and tasks during the model training. Especially, by incorporating the self-paced learning strategy and asymmetric graph-guided regularization, the proposed model is able to learn the model in a progressive way from "easy" to "hard" loss function items. In addition, together with the self-paced learning function, the asymmetric graph-guided regularization allows the related knowledge transfer from one task to another in an asymmetric way. Consequently, the knowledge acquired from those earlier learned tasks can help to solve complex tasks effectively. The experimental results on both synthetic and real-world TCGA data suggest that the proposed method is indeed useful for improving survival analysis and achieves higher prediction accuracies than the previous state-of-the-art methods.
Sensitive Data Exchange Protocol Suite for Healthcare

Learning Healthcare System (LHS) is an increasingly deployed approach in health to improve patient care. For the successful implementation of this approach, communications must become cross-cutting between research and primary care. To meet this need, standardized protocols for health data exchange, such as Fast Healthcare Interoperability Resources from Health Level Seven organization, are massively used in healthcare organizations. However, these protocols don’t meet new security needs and they don’t natively integrate anonymization mechanisms for data sources and patients while maintaining individuation. In this paper, a new protocol suite is proposed for sensitive health data exchange. Thus, an architecture is presented: it integrates proxies and anonymizers for the extraction and transmission phases of sensitive data. Then, requirements on several new protocols are detailed to meet the exchanges needs between the learning health system entities. Finally, a comparison of security properties and a vulnerability analysis are carried out between the Fast Healthcare Interoperability Resources protocol and the protocol suite proposed. These analyses show that the protocol suite integrates most of the defenses against common protocol attacks and that anonymization, confidentiality, authentication and logging requirements are met.
Stratified Sampling Based Experience Replay for Efficient Camera Selection Decisions

Target tracking across a network of cameras has various applications in surveillance and forensics. These networks typically have cameras with non-overlapping fields of view, which necessitates target handovers involving approaches like target re-identification that are robust to illumination and pose variations of the target. Re-identification based target handovers are susceptible to false alarms, more so in a high data volume setting like a surveillance camera network. In this work, we learn to decide when to make a re-identification query and to which camera in the network. We model this camera selection problem as a sequential decision making problem and solve it using a Reinforcement Learning (RL) based policy, which selects one of the cameras for querying or decides to not query. Using a Deep Q-Network (DQN) type approach, we observed that existing experience replay (ER) methods with DQN are inadequate for learning an optimal policy when the actions are imbalanced. Employing standard ER techniques result in learning policies that are likely to be biased towards selecting a more frequent action and result in a poorer performance of the end task, in our case multi-camera tracking. To address this problem, we segregate the experiences from agent-environment interaction into multiple replay memories and sample independently from these to create a diverse minibatch. We demonstrate the performance of the proposed method on the NLPR MCT dataset and DukeMTMC dataset along with its computational benefits.
Micro Loans for Farmers

As per 2018, agriculture provides employment to more than 50% of the Indian workforce and contributes 17-18% to the country's GDP. Every farmer possesses a requirement of credibility or other services during the time of cultivation. Due to unavailability of bank loans for low-income farmers, they have no other option but to look up to private lenders or micro-finance institutions. The interest rate charged by microfinance institutions consists of various subjective factors which forces MFIs to provide microcredits to farmers at high-interest rates. Relevant to this context, this paper proposes a blockchain-based system which eliminates the need of MFIs. The system handles microloans on a distributed ledger which increases transparency among stakeholders and also enables small/large scale investors to lend microcredits to farmers at interest rate and repayment schedule decided by the farmer. The proposed system facilitates low-cost, secure and real-time payments with the help of smart contracts.
Mobile Augmented Reality Application with Virtual Buttons as User Interaction and Stereoscopic View for Medical Learning

This paper presents an interaction method for wearable AR (augmented reality) by using VR box with virtual buttons. It is introducing an Android application that could help medical students in their learning. Anatomy education is traditionally performed by the dissection of cadavers. This AR technology could offer an additional teaching method for anatomy education, depending on how it is implemented. Studying AR in medical education could provide benefits for medical education and provide students a more personalized and exploration in learning experiences. Virtual buttons interaction in this application let learning experience more interesting for the medical students.
Sentiment Analysis of BPJS Kesehatan’s Services Based on Affective Models

BPJS Kesehatan is a corporation in Indonesia which aim organizing health insurance program. By increasing the number of BPJS Kesehatan's members every year, BPJS Kesehatan should be able to do all its services well so that its members can get their rights. BPJS Kesehatan performance can be assessed from the public response, one of the social media used by public to share their responses to BPJS Kesehtaan's service is Twitter. BPJS Kesehatan can use these responses to find out people's opinions on their services. Therefore, this study proposes a new approach to analyzing public opinion using the field of scientific computational linguistics. Specifically by making a computing system with features, 1) Sentiment analysis using the effective models method which sees a different degree for each adjective in the commentary. Affective model is a new approach in Indonesian Language that evaluates each adjective has a different level of pleasure and arousal. This method collects adjectives in Indonesian into a context and assigns different values to each adjective. This value is obtained from the adjective mapping results from Russel's Circumplex model of affect, we also sees words that have affect polarity in a sentence and words that affect the degree of affection in a sentence. 2) Categorization, this feature is to categorize comments into types of BPJS Kesehatan's services. There are 10 service categories, each of categories has keywords. System identified the keywords in each comment and calculated similarity with existing categories. Total data that has been obtained is SS3S2 tweets. For each data obtained sentiment value will be calculated and categorized, this system will show which service category has positive or negative sentiment. The test method uses data that has been labeled manually before and then is tested using a program. From 211 tweets that have been labeled manually, the sentiment analysis program has succeeded in achieving an accuracy of 83.4% and the categorization program produces an accuracy of 81.05%.
Comparison of Text Classification Methods for Government Documents

Classification of documents is a task which is rapidly gaining popularity in machine learning application. I can be used for such tasks as detection of spam, detection of phishing attempts or for detection of fake messages. One of the areas where document classification is of growing importance is automatic assessment of civic, legal and government documents. Using automated classification, a governmental institution might scan all its incoming correspondence, and automatically assign it to the proper departments. In this paper results of various classification approaches to classification of official documents are presented. Presented results are based upon classification of two sets of document: first, where given class of documents is derived from their common origin; and second, where class is determined by the common destination of the documents.
Reconciling taxonomies of electoral constituencies and recognized tribes of indigenous Taiwan

Abstract Over the years, information science professionals have been studying biases in Knowledge Organization Systems (KOS), for example, bibliographic classifications. The robustness of classifications has been examined in diverse measures, ranging from the representation of race, gender, ethnic minorities, to indigenous peoples. In this study, we aim at (a) uncovering implicit assumptions about minorities in everyday taxonomies; (b) comparing and reconciling these different taxonomies. Specifically, we study the use case of Taiwanese Indigenous Peoples' tribe classifications and the indigenous constituencies of the legislature electoral representation. We compare four finer‐grained taxonomies for indigenous people with the coarse‐grained indigenous peoples' electoral constituencies that only recognize two regions (Lowland, Highland). The four taxonomies are: the recognized tribes in the past, the recognized tribes in the present, other possible tribes, and re‐scaled groups based on population. We employ a logic‐based taxonomy alignment approach using Region Connection Calculus (RCC‐5) relations to align these taxonomies. Our results show different options when modeling and interpreting the use case of Indigenous Taiwan constituencies, and also demonstrate that multiple perspectives can be preserved and co‐exist in our merged taxonomic representations.
Evaluation of the Peak-Sun Hour on a Tilted Surface in the City of Juliaca

The solar energy that reaches a certain surface depends directly on the orientation of the surface and the position of the sun. The calculation of the Peak-Sun Hour is the main objective of this work, for this, the global solar radiation that affects a sloping surface is considered, and from this it is determined for periods, daily, monthly and annual. For validation, the global solar radiation data from NASA was used, obtaining a Pearson correlation of 0.84. An annual average of optimum tilt angle of -15.44° was also obtained, which is equivalent to the latitude of the study area. Likewise, an annual average value of 5.41 peak solar hours was obtained. The result of our work will help users and engineers to determine the optimal performance of solar collectors in the city of Juliaca and the Region.
Improved Feature Generalization in Smaller Datasets with Early Feature Fusion of Handcrafted and Automated Features for Content Based Image Classification

Diverse techniques of feature extraction are carefully crafted by researchers in the domain of content based image classification for improved categorization of image data. These are handcrafted techniques of descriptor definition which have mostly emphasized in representing a particular feature of the query image. Recent advancements in deep neural networks have enabled extraction of features in automated manner without any human intervention. The process considers multiple dimensions of image properties in designing content based descriptors. However, deep features have shown limitations for generalization of feature vectors when number of training instances are small. The pre-trained CNN networks have also revealed limitations in generalization of extracted features from smaller datasets. This work has attempted to address this issue by designing fusion based architecture. In the process, the authors have experimented with features extracted with individual techniques comprising of handcrafted extraction and automated neural network based extraction from a small dataset. The process of feature extraction is carried out on an imbalanced dataset for which the classification evaluation is done with F1 Score. The experimental process has revealed better feature generalization with enhanced classification performances by application of early fusion of extracted features compared to that of features extracted with single techniques.
Docker environment based Apache Storm and Spark Benchmark Test

With the development of various technologies such as high-speed Internet and SNS dissemination, there have been many fields that require processing of big data generated in real time. Accordingly, real-time streaming data processing technology has been developed, and representative platforms include Apache Storm, Apache Spark, and Hadoop. These processing technologies provide scalability to configure distributed systems using multiple servers because they vary in performance, such as throughput and processing speed, depending on the server environment, but the more the number of servers, the more difficult it is to manage. To solve this problem, a problem can be solved by using a docker, a kind of virtualization system that provides ease of expansion. However, there is a place to maintain a native environment without using Docker due to the problem that performance may be reduced, which is a disadvantage of all virtualization systems. In this paper, we build Apache Storm and Apache Spark, which are real-time data processing systems in Docker and Native environments and conduct performance measurements through experiments processing JSON-format data to verify how much performance decreases in Docker environments.
A Metric Learning Approach to Anomaly Detection in Video Games

With the aim of designing automated tools that assist in the video game quality assurance process, we frame the problem of identifying bugs in video games as an anomaly detection (AD) problem. We develop State-State Siamese Networks (S3N) as an efficient deep metric learning approach to AD in this context and explore how it may be used as part of an automated testing tool. Finally, we show by empirical evaluation on a series of Atari games, that S3N is able to learn a meaningful embedding, and consequently is able to identify various common types of video game bugs.
An Experimental Approach for Estimating Cyber Risk: a Proposal Building upon Cyber Ranges and Capture the Flags

Current approaches to estimate the risk of compromise are based on either historical data or pure technical assessments, such as the number and severity of vulnerabilities in the target network. We propose a novel experimental approach for estimating the risk of compromise based on experimental data, as opposed to observational data, by leveraging on cyber ranges and capture the flag exercises. We identify the key design principles in terms of response and explanatory variables, specification of how they can be measured, and the overall block design from related experiments and approaches as well as assess their suitability and limitations.
A PREDICATIVE VARIANT OF HYLAND’S EFFECTIVE TOPOS

Abstract Here, we present a category ${\mathbf {pEff}}$ which can be considered a predicative variant of Hyland's Effective Topos ${{\mathbf {Eff} }}$ for the following reasons. First, its construction is carried in Feferman’s predicative theory of non-iterative fixpoints ${{\widehat {ID_1}}}$ . Second, ${\mathbf {pEff}}$ is a list-arithmetic locally cartesian closed pretopos with a full subcategory ${{\mathbf {pEff}_{set}}}$ of small objects having the same categorical structure which is preserved by the embedding in ${\mathbf {pEff}}$ ; furthermore subobjects in ${{\mathbf {pEff}_{set}}}$ are classified by a non-small object in ${\mathbf {pEff}}$ . Third ${\mathbf {pEff}}$ happens to coincide with the exact completion of the lex category defined as a predicative rendering in ${{\widehat {ID_1}}}$ of the subcategory of ${{\mathbf {Eff} }}$ of recursive functions and it validates the Formal Church’s thesis. Hence pEff turns out to be itself a predicative rendering of a full subcategory of ${{\mathbf {Eff} }}$ .
A multi-sensorial approach for the protection of operational vehicles by detection and classification of small flying objects

Due to the high availability and the easy handling of small drones, the number of reported incidents caused by UAVs both intentionally and accidentally is increasing. To be capable to prevent such incidents in future, it is essential to be able to detect UAVs. However, not every small flying object poses a potential threat and therefore the object not only has to be detected, but also classified or identified. Typical 360◦ scanning LiDAR systems can be deployed to detect and track small objects in the 3D sensor data at ranges of up to 50 m. Unfortunately, in most cases the verification and classification of the detected objects is not possible due to the low resolution of this type of sensor. In high-resolution 2D images, a differentiation of flying objects seems to be more practical, and at least cameras in the visible spectrum are well established and inexpensive. The major drawback of this type of sensor is the dependence on an adequate illumination. An active illumination could be a solution to this problem, but it is usually impossible to illuminate the scene permanently. A more practical way would be to select a sensor with a different spectral sensitivity, for example in the thermal IR. In this paper, we present an approach for a complete chain of detection, tracking and classification of small flying objects such as micro UAVs or birds, using a mobile multi-sensor platform with two 360◦ LiDAR scanners and pan-and-tilt cameras in the visible and thermal IR spectrum. The flying objects are initially detected and tracked in 3D LiDAR data. After detection, the cameras (a grayscale camera in the visible spectrum and a bolometer sensitive in the wavelength range of 7.5 µm to 14 µm) are automatically pointed to the object’s position, and each sensor records a 2D image. A convolutional neural network (CNN) realizes the identification of the region of interest (ROI) as well as the object classification (we consider classes of eight different types of UAVs and birds). In particular, we compare the classification results of the CNN for the two camera types, i.e. for the different wavelengths. The high number of training data for the CNN as well as the test data used for the experiments described in this paper were recorded at a field trial of the NATO group SET-260 (“Assessment of EO/IR Technologies for Detection of Small UAVs in an Urban Environment”) at CENZUB, Sissonne, France.