Reviewerly/exHarmony huggingface dataset:
Improving Software Engineering Research Through Experimentation Workbenches

Experimentation with software prototypes plays a fundamental role in software engineering research. In contrast to many other scientific disciplines, however, explicit support for this key activity in software engineering is relatively small. While some approaches to improve this situation have been proposed by the software engineering community, experiments are still very difficult and sometimes impossible to replicate. In this paper, we propose the concept of an experimentation workbench as a means of explicit support for experimentation in software engineering research. In particular, we discuss core requirements that an experimentation workbench should satisfy in order to qualify as such and to offer a real benefit for researchers. Beyond their core benefits for experimentation, we stipulate that experimentation workbenches will also have benefits in regard to reproducibility and repeatability of software engineering research. Further, we illustrate this concept with a scenario and a case study, and describe relevant challenges as well as our experience with experimentation workbenches.
Multimedia Analysis on User-Generated Content for Safety-Oriented Applications

An important factor that boosts the rapid penetration of smartphone devices is the increasing incorporation of sensors, which have stimulated a new type of content, the so-called user-generated content. The huge amount of media information accumulating every day presents an opportunity to incorporate image analysis methods and applications for a safer and more secure environment for citizens. This chapter proposed an anomaly detection mechanism for video streams, especially from social media. The methodology employs low-level feature extraction over non-overlapping frame patches and density-based clustering. The core idea consists of two steps: cluster the image patches and observe the difference in the number of clusters for successive images. A threshold-based approach triggers the detection mechanism by investigating the change in the number of clusters. The proposed unsupervised approach runs smoothly on ordinary desktop computers and operates in real time. This chapter outlines the approach and underlying methodology together with an evaluation based on YouTube videos depicting car explosions.
Client-side Bandwidth Estimation Technique for Adaptive Streaming of a Browser Based Free-Viewpoint Application

High bandwidth demands in interactive streaming applications pose challenges in efficiently utilizing the available bandwidth. Well known standards like MPEG-DASH and Apple HTTP streaming use buffer control mechanisms at the client for bandwidth estimation and segmentation at the server, which can add latency and complex- ity. We present a prototype of a real-time interactive free-viewpoint rendered application, that can be applied as an alternative to the existing buffering and segmentation-based approaches. We employ a novel approach in passive bandwidth estimation and implemented a weighed bit rate algorithm in JavaScript and HTML5 that utilizes browser statistics. The video quality fluctuations at the client are signaled to the server via WebSocket. Using open source technolo- gies and client-side bandwidth estimation techniques, the feasibility of the proposed algorithm is demonstrated. The evaluation results show that the prototype quickly responds to fluctuations in the available bandwidth under varying network conditions.
Efficient Nucleus Detection in Digital Pathology Images using Multi-task and Multi-scale Instance Segmentation Network

Automatic cell spotting is a challenging problem in biomedical image analysis. Efficient detection of the nuclei in these cells is important for both clinicians and researchers. Technically, this problem suffers from background clutters of the pathological tissue and has difficulty in tiny objects recognition. To solve these, we tend to apply deep learning based method and treat this as a nucleus instance segmentation task. Structurally, we modify the widely used Mask R-CNN structure in region proposal networks with multi-scale, resulting in the aware of tiny objects. And for adapting to our specific task, we take advantage of the “anchor” mechanism. Compared with traditional detection methods, the learned features are more robust to massive tissue backgrounds. Moreover, our model can simultaneously detect and segment the nucleus through an end to end training strategy, which of great application values. To our best knowledge, this work is the first time to the nucleus detection task. Experimentally, we test our method on the dataset of 2018 Data Science Bowl. As a result, both visualization and quantization indicators show the effectiveness of our method.
Towards Translation of Semantics of Automotive Interface Description Models from Franca to AUTOSAR Frameworks : An Approach using Semantic Synergies

The automotive industry is eventually evolving into a complex network of services. The heterogeneous and distributed nature of automotive software systems demands flexible software components which can operate in different environments. Because of heterogeneous automotive development environments, the domain experts, must cope with too many diversities, adaption layers, and incompatibilities to design applications for the current generation of autonomous driving vehicles. In this context, interface adaptation is a promising approach to achieve flexibility without directly changing the respective components. AUTOSAR, which is the de-facto standard for describing automotive system architecture and is a hugely comprehensive standard allowing designers full control from abstract system description to bare metal level deployment. However, the vehicle subsystems have still evolved to include multifarious high-level domains not covered by AUTOSAR e.g. Infotainment, Telematics etc. Therefore, it seems beneficial to bridge the semantic gaps between AUTOSAR applications and other standards of automotive application domains. The goal of this paper is to investigate interface semantic mapping and achieve transparent integration of domain-specific applications using the translation of semantics among the AUTOSAR platform software component models and other software components models of open source development platforms e.g. GENIVI. A key goal of such a modelling approach is the reuse of existing interface description languages and respective code generators. This will enhance future interoperability and decrease in incompatibility among these platforms.
Poster

Over the past few decades, Vehicular Ad hoc Networks have attracted the attention of numerous researchers from both academia and industry. Today, this promising wireless communication technology plays an indispensable role as vehicles exchange low-latent safety critical messages with one another in a bid to make the road traffic more safer, efficient, and convenient. However, dissemination of malicious messages within the network not only significantly reduces the network performance but also becomes a source of threat for the passengers and vulnerable pedestrians. Accordingly, a number of trust models have been recently proposed in the literature to ensure the identification and elimination of malicious vehicles from the network. These trust models primarily rely on the aggregation of both direct and indirect observations and evict the malicious vehicles based on a particular threshold set on this composite trust value. Nevertheless, setting-up of this threshold poses a significant challenge especially owing to diverse influential factors in such a dynamic and distributed networking environment. To this end, in this manuscript, machine learning has been employed to compute the aggregate trust score for flagging and evicting of the malicious vehicles from a vehicular network. It is evident from the simulated results that the devised method is both accurate and scalable.
Breast Mass Detection in Mammograms via Blending Adversarial Learning

Deep learning approaches have recently been proposed for breast cancer screening in mammograms. However, the performance of such deep models is often severely constrained by the limited size of publicly available mammography datasets and the imbalance of healthy and abnormal images. In this paper, we propose a blending adversarial learning method to address this issue by regularizing the imbalanced data with synthetically generated abnormal samples. Unlike most existing data generation methods that require large-scale training data, our approach is carefully designed for augmenting small datasets. Specifically, we train a generative model to simulate the growth of mass on normal tissue by blending mass patches into healthy breast images. The resulting synthetic images are exploited as complementary abnormal data to make the training of deep learning based mass detector more stable and the resulting model more robust. Experimental results on the commonly used INbreast dataset demonstrate the effectiveness of the proposed method.
Hand Sign Language Feature Extraction Using Image Processing

Sign language is a method of communication, especially with people in special needs such as those that are deaf and dumb, but this language is not easily understood by everyone. Many articles and researches have focused on learning hand language and converting hand signals into meaningful signs, by using signal processing methods to translate or to convert sign language to speech or texts, for example, showing the number or the text on the screen or converting it to a spoken language. In this paper, an image processing technique has been proposed, to extract the hand signal feature of English numbers and convert them into written text. Specific gloves were used to simplify the extraction of features using two symbols- a circle and a triangle that was printed on each glove. An algorithm was applied to each image with its feature extracted by MATLAB programs. The process was applied to each PNG image by converting it to a binary image and using object detection, sobole filter for edge detection, image resizing, calculating the number of circles and triangle. The correlation between each image feature was measured to identify the specific sign language and then to convert it to texts or audio.
DAFO: An Ontological Database System with Faceted Queries

We present an ontology-based data access provided with a faceted interface. The underlying relational database is perceived through an ontological schema. Queries against the ontology are formulated using a hierarchical faceted interface. We show main ideas of DAFO and discuss how it relates to well-known OBDA systems.
Crucial-Resource Scheduling Strategy in Edge Computing

Internet of Things (IoT) is flourished in recent years, and a large of IoT devices are in our life. In general, processing time is an important issue for real-time IoT devices. Due to the characteristic of edge computing, edge computing could process the tasks with real-time requirements, and reduce the burden of clouds. Because the computing resource is limited and heterogeneous in edge nodes, how to assign tasks to suitable edge nodes is an important issue. So, this work proposes a new scheduling approach, which is called Crucial-Resource Scheduling Strategy (CRSS), for task allocation. The proposed approach adopts the multi-dimensional packing algorithm to increase the resource utilization in heterogeneous edge nodes. Experimental results show that CRSS effectively increases resource utilization in edge nodes, resulting in placing more tasks on heterogeneous nodes.
FlashFreeze: low-overhead JavaScript instrumentation for function serialization

Object serialization is important to a variety of applications, including session migration and distributed computing. A general JavaScript object serializer must support function serialization as functions are first-class objects. However, JavaScript offers no built-in function serialization and limits custom serializers by exposing no meta operator to query a function’s captured variables. Code instrumentation can expose captured variables but state-of-the-art instrumentation techniques introduce high overheads, vary in supported syntax and/or use complex (de)serialization algorithms.
Knowledge-Now and Knowledge-All

In this paper, we propose a logical framework extending the standard epistemic logic with a new knowledge operator which captures the knowledge about (physically) necessary facts, e.g., scientific knowledge. Semantically, the truth of depends on not only the epistemically indistinguishable worlds from the current real world but also the relevant (physically) possible worlds which are clearly distinguishable. Essentially, is a bundle of the standard epistemic modality and a necessity-like modality. We axiomatize the corresponding epistemic logic completely in single- and multi-agent cases with interesting interaction axioms between the two epistemic operators.
WeUp: Wireless User Perception Based on Dimensional Reduction and Semi-Supervised Clustering

Wireless user perception (WeUP) is considered one of the most important factors in designing next-generation wireless communications systems. The recognition of WeUP involves lots of labor cost up to now. In order to solve this problem, this paper proposes an accuracy recognition algorithm for WeUP based on dimensional reduction and semi-supervised clustering. Usually, the WeUP is highly reflected in the key quality indicator (KQI). we build up a database of KQI including more than 1000 cells to train a deep belief autoencoder (DBA) for dimensional reduction (DR). Then we feed the historical unlabeled and manual-labeled negative data set after dimensional reduction into semi-supervised clustering model. After that, we find out a recognition range, which is the most similar to manual-labeled objects with unsatisfied WeUP. Simulation results show that our proposed method achieves an accurate recognition of unsatisfied WeUP over 93%.The study indicates that dimensional reduction and semi-supervised machine learning method is effective in recognizing unsatisfied WeUP in wireless networks.
Constructive Modeling of Conservative DBMS

Commercial OLAP-systems are quite expensive and therefore inaccessible to organizations with limited financial capabilities. Analytical processing of significant amounts of data in these organizations can be carried out using open source software systems on low price cluster platform. Previously made Clusterix-like conservative-type DBMSs (with occasional data updates) using a regular query processing plan were not sufficiently effective. Therefore, research on such systems has been developed with a focus on the full load of processor cores and the use of dynamic segmentation of intermediate and temporary relations on the GPU (Clusterix-N, N - from New) up to the development of a system comparable in efficiency to the promising open Spark system. The development methodology was based on the constructive system modeling methodology. According to this methodology, the simulation of the synthesis process of the considered DBMS was carried out iteratively. Each iteration involved a complete software development of the corresponding state of the model and analysis of the results of testing on the GPU cluster platform. The required simulation quality was obtained at the fourth iteration.
Virtual Object Grasping in Augmented Reality: Drop Shadows for Improved Interaction

This paper presents the use of rendered visual cues as drop shadows and their impact on overall usability and accuracy of grasping interactions for Augmented Reality (AR). We report on two conditions; grasping with drop shadows and without drop shadows and analyse a total of 910 grasps. We report on the accuracy of Grasp Aperture (GAp), Grasp Displacement (GDisp), completion time and usability metrics from 30 participants. A comprehensive statistical analysis of the results is presented giving comparisons of the inclusion of drop shadows in AR grasping. Findings showed that the use of drop shadows increases usability of AR grasping while significantly decreasing task completion time. Furthermore drop shadows also significantly improve user's depth estimation of AR object position. However, this study also shows that using drop shadows does not improve user's object size estimation, which remains as a problematic element in grasping AR interaction literature.
Anisotropy‐based image smoothing via deep neural network training

An anisotropy-based image smoothing method is proposed to remove image details from various images. Image details appear as the textures in the image. After removing them, the remaining part is known as image structure. To effectively distinguish image structures and textures, the authors present an anisotropy-based measurement which depicts the anisotropy degree of local gradients for each edge pixel. The pixels with larger anisotropy are more likely to be the ones on structural edges. Then, the anisotropy-based measurement is embedded in a regularised objective function. To achieve the image smoothing, the objective function is finally optimised by training a deep network. Visual results demonstrate that the proposed method is powerful to keep the edges in the smoothed images sharp and eliminate trivial details simultaneously.
User-Based Cognitive Model in NGOMS-L for the Towers of Hanoi Algorithm in the Metacognitive Architecture CARINA

The Towers of Hanoi is a mathematical problem, which consists of three pegs, and a number "n" of disks of distinct sizes which can slide onto any peg. A cognitive model is a theoretical, empirical and computational representation of mental processes which belong to a cognitive function. A cognitive model generates a human-like performance for developing tasks, correcting errors, using strategies, and acquiring knowledge. A cognitive model constructed in a cognitive architecture is characterized to be runnable and producing specific behaviors. A cognitive architecture is a general-purpose control framework based on scientific theories to specify computational models of human cognitive performance. CARINA is a metacognitive architecture for artificial intelligent agents, obtained from the MISM Metacognitive Meta model. The objective of this paper is to present a cognitive model based on NGOMS-L to solve the algorithm of the Towers of Hanoi that can be runnable in the metacognitive architecture CARINA. The methodology used for the analysis of the cognitive task was: pre-processing stage, processing stage, classification of subjects, description of cognitive task in natural language and finally description of the cognitive task in NGOMS-L. The results obtained showed that of the four subjects originally selected, three of them were able to solve the problem and only one abandoned the problem. In the classification made, a successful and an unsuccessful subject was selected, to represent the cognitive task in natural language and finally express in the NGOMS-L notation the different Goals, Operators, Methods, and Selection Rules.
Content And Concept Indexing For High-Dimensional Multimedia Data

Although the semantic understanding of multimedia content is immediate for humans, it is far from it for a computer. This problem is commonly called the semantic gap and is one of the major problems in multimedia retrieval. Therefore, to achieve better retrieval performance, low-level content features must be associated with semantic features effectively. In this study, we focus on the retrieval of multimedia data by combining semantic information with data content in an attempt to effectively solve the semantic gap problem. The main idea behind the combining content and concept descriptors of multimedia data is to represent the content information with the semantic information together by adding content descriptor as a new dimension to our index structure. This new dimension is constructed using a fuzzy cluster algorithm called Array Index. Thus, a new index structure which supports multimedia data querying, including fuzzy querying, is presented in this paper. The construction and query algorithms of this proposed index structure are explained throughout this paper. Experiments show that our new index structure is better than an index mechanism that stores content and concept descriptors in separate structures when the size of the data is large.
First-class dynamic types

Since LISP, dynamic languages have supported dynamically-checked type annotations. Even in dynamic languages, these annotations are typically static: tests are restricted to checking low-level features of objects and values, such as primitive types or membership of an explicit programmer-defined class.
Semantic Representation Models of Sensor Data for Monitoring Agricultural Crops

This paper shows the development semantic representation models of sensor data for monitoring agricultural crops. The purpose of this research article is explore the possible application of methodologies for ontologies in the process related to the agricultural environment and the information collected from the crop growth variables. therefore, improving interoporability becomes an important element in the treatment of large volumes of data, which will eventually help to make the right decisions and improve production in different types of crops.
4 Systems Perspectives into Human-Centered Machine Learning

Machine learning (ML) has had a tremendous impact in across the world over the last decade. As we think about ML solving complex tasks, sometimes at super-human levels, it is easy to forget that there is no machine learning without humans in the loop. Humans define tasks and metrics, develop and program algorithms, collect and label data, debug and optimize systems, and are (usually) ultimately the users of the ML-based applications we are developing. In this talk, we will cover 4 human-centered perspectives in the ML development process, along with methods and systems, to empower humans to maximize the ultimate impact of their ML-based applications. In particular, we will cover: 1. Developer tools for ML that allow a wider range of people to create intelligent applications?, focusing on mobile devices. 2. Learning to optimize the performance and power of ML models on a wide range of hardware backends and mobile devices. 3. Closing the gap between the loss function we optimize in ML and the product metrics we really want to optimize. 4. Helping humans understand why ML models make each prediction, when these models will break, and how to improve them.
Satellite Image Processing for Retrieving Historical Solar Irradiance Data Within the Mexican Territory

Solar resource assessment is a key subject for the viability and implementation of solar power plants. Solar sensors measure irradiance locally, but high cost of these specialized sensors makes their implementation through all the Mexican territory difficult. Satellite images provide a valuable tool for the determination of solar irradiance through image processing. In this work, an algorithm for the estimation of global solar irradiance based on determining the cloud index through pixel analysis is implemented for the central region of Mexico.
Image Enhancement Technique Based on Human Visual Perception and Multi-exposure Fusion for a Landslide-prone Area Monitoring System

In this paper, we propose a new technique for enhancing image quality and generating a representative image from a set of input images taken from a landslide-prone area monitoring camera at different times of a day. Thus, less-visible areas in the input images are different from one another. First, the proposed technique enhances each input image by deploying a scaling function based on human visual perception. Then, it fuses all input images and all enhanced images by using Gaussian and Laplacian pyramid-based blending. Experimental results show that the resulting image can improve the visibility of some shadowed details and that the objective evaluation results regarding image enhancement metric, universal image quality index, and perceptual similarity index are satisfying.