Instance Variables Research Articles

Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks

This paper presents an experimental comparison between two existing methods representative of two categories of 6D pose estimation algorithms nowadays commonly used in the robotics community. The first category includes purely deep learning methods, while the second one includes hybrid approaches combining learning pipelines and geometric reasoning. The hybrid method considered in this paper is a pipeline of an instance-level deep neural network based on RGB data only and a geometric pose refinement algorithm based on the availability of the depth map and the CAD model of the target object. Such a method can handle objects whose dimensions differ from those of the CAD. The pure learning method considered in this comparison is DenseFusion, a consolidated state-of-the-art pose estimation algorithm selected because it uses the same input data, namely, RGB image and depth map. The comparison is carried out by testing the success rate of fresh food pick-and-place operations. The fruit-picking scenario has been selected for the comparison because it is challenging due to the high variability of object instances in appearance and dimensions. The experiments carried out with apples and limes show that the hybrid method outperforms the pure learning one in terms of accuracy, thus allowing the pick-and-place operation of fruits with a higher success rate. An extensive discussion is also presented to help the robotics community select the category of 6D pose estimation algorithms most suitable to the specific application.

Pharo: A reflective language—Analyzing the reflective API and its internal dependencies

Reflective operations are powerful APIs (Application Programming Interface) that let developers build advanced tools and architectures. Reflective operations are used for implementing tools and development environments (e.g., compilers, debuggers, inspectors) or language features (e.g., distributed systems, exceptions, proxies, aspect-oriented programming). In addition, languages are evolving, introducing better concepts, and revising practices and APIs. Since 2008 Pharo has evolved from Squeak and its reflective API has evolved accordingly, diverging consequently from the original Smalltalk reflective API. With more than 500 reflective methods identified, Pharo has one of the largest reflective feature sets ranging from structural reflection to on-demand stack reification. Those operations are often built on top of the other, creating different layers of reflective operations, from low-level to high-level ones.There is a need to understand the current reflective APIs to understand their underlying use, potential dependencies, and whether some reflective features can be scoped and optional. Such an analysis is challenged by new metaobjects organically introduced in the system, such as first-class instance variables, and their mixture with the base-level API of objects and classes.In this article, we analyze the reflective operations used in Pharo 12 and their interdependencies. We propose a classification based on their semantics and we identify a set of issues of the current implementation. Such an analysis of reflective operations in Pharo is important to support the revision of the reflective layer and its potential redesign.

Agricultural Land Cover Mapping through Two Deep Learning Models in the Framework of EU’s CAP Activities Using Sentinel-2 Multitemporal Imagery

The images of the Sentinel-2 constellation can help the verification process of farmers’ declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study’s geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows.

Epistemic virtue in higher education: testing the mechanisms of intellectual character development

Epistemic virtues are character traits conducive to principled ways of thinking, leading to a life of flourishing. Recent years have witnessed an emergence of theoretical accounts describing how they develop. However, few if any studies have conducted rigorous empirical investigation into the mechanisms of intellectual virtue development. In this study, we review several significant frameworks before utilizing a randomized, pretest/posttest control trial design to understand the impacts of a novel thinking disposition intervention on intellectual virtue growth. We ascertain the direct and indirect effects of the intervention on four key intellectual virtues: curiosity, humility, integrity, and tenacity. Additionally, we test theoretical mediators of virtue learning. Clear evidence favoring a theory-inspired mediator is observed, though we observe weaker signals of direct effects, with nuances across the virtues. For instance, tenacity and curiosity variables appear to respond more to the intervention than do humility and integrity. Findings are discussed in light of contemporary theoretical perspectives.

Influence of Fraud Triangle Framework on the Fraud Prevention and Detection Programs in PT. XX

Given the significant variability of fraud instances in businesses of all sizes, it is captivating that despite their complexity, the fraud triangle's three elements can be used to explain all of them. As a result, the researcher was compelled to examine the impact of this framework on fraud programs. This report aims to thoroughly analyze the fraud triangle's influence on fraud prevention and detection programs, particularly at PT. XX. This qualitative research data collection comprises two main target population sources, the internal auditor team and the company's management, through a semi-structured interview approach where data is later processed using the in vivo and pattern coding technique. It is found from the study that each dimension of the fraud triangle which are pressure, opportunity, and rationalization, has a direct causal link to a fraud program described in the company's fraud SOP. The analysis concluded that the internal auditor team used the fraud triangle framework to identify patterns in fraud justifications and then developed several preventive fraud programs that management and users will implement to reduce the amount of fraud occurring in PT. XX. Further findings included the mutually reinforcing roles undertaken by the two divisions under study in creating the SOP and carrying out the company's fraud programs, which the study contends have already been effectively executed.

Does insurance work? Dynamic assessment of insurance, poverty, and climatic hazard outcomes in Thailand

While the linkage between poverty and climatic hazard outcomes is well documented in the development agenda along with the role of disaster risk financing, dynamic subnational-level statistical assessment of causal effects of formal insurance on climatic hazard outcomes has been scarce to date. This paper investigates the relationship between insurance, poverty, and outcomes of climatic hazards (i.e., flood and drought) using a panel dataset of 76 provinces in Thailand for the period 2009–2018 by applying the panel Granger non-causality test using the half-panel jackknife procedure for deriving bias-corrected estimators. Several bidirectional causalities were revealed between insurance and poverty, insurance and hazard outcomes, and poverty and hazard outcomes, whilst diverse patterns of short-run unidirectional causalities were identified between those variables in several instances. The results suggest that nonlife insurance plays a significant role in alleviating poverty through its rapid effects as well as longer influence. Moreover, insurance consumption had causal effects on hazard outcomes. On the other hand, exposure to flood and drought had significant effects on insurance consumption, particularly nonlife insurance. These findings highlight the relevance of insurance, in particular nonlife insurance, as an ex-ante mechanism for reducing vulnerability to poverty in the aftermath of natural hazards.

Incidence of pain complaints in oropharyngeal squamous cell carcinoma

The purpose of this study was to determine the prevalence of throat pain and otalgia as presenting symptoms in patients with oropharyngeal squamous cell carcinoma (OPSCC) stratified by the cancer being their first or recurrent/second primary head and neck cancer (fHNC or rsHNC). A retrospective analysis of patients operated on for OPSCC was performed. The primary predictor variable was HNC instance (fHNC/rsHNC) with outcome variables of throat pain and/or otalgia. Chi-square analysis was performed to test for significant associations between HNC instance and pain variables. Unadjusted odds ratios were calculated. Eighty-nine patients met the inclusion criteria. Patients with OPSCC as an rsHNC had 4.67 times higher odds of throat pain (95% confidence interval [CI], 1.45-15.06) than those with OPSCC as an fHNC and had 20.22 times higher odds of simultaneous throat pain and otalgia (95% CI, 4.76-85.97) than those presenting with an fHNC. Current smoking status and human papillomavirus (HPV)-negative disease were also significantly predictive of rsHNC. HPV-negative disease was also predictive of pain. In addition to the known associations of smoking and HPV status on recurrence and pain, there is a statistically significant association between cancer instance and pain in patients presenting with newly diagnosed, operable OPSCC.

A New Metric for Class Cohesion for Object Oriented Software

Various class cohesion metrics exist in literature both at design level and source code level to assess the quality of Object Oriented (OO) software. However, the idea of cohesive interactions (or relationships) between instance variables (i.e., attributes) and methods of a class for measuring cohesion varies from one metric to another. Some authors have used instance variable usage by methods of the class to measure class cohesion while some focus on similarity of methods based on sharing of instance variables. However, researchers believe that such metrics still do not properly capture cohesiveness of classes. Therefore, measures based on different perspective on the idea of cohesive interactions should be developed. Consequently, in this paper, we propose a source code level class cohesion metric based on instance variable usage by methods. We first formalize three types of cohesive interactions and then categorize these cohesive interactions by providing them ranking and weights in order to compute our proposed measure. To determine the usefulness of the proposed measure, theoretical validation using a property based axiomatic framework has been done. For empirical validation, we have used Pearson correlation analysis and logistic regression in an experimental study conducted on 28 Java classes to determine the relationship between the proposed measure and maintenance-effort of classes. The results indicate that the proposed cohesion measure is strongly correlated with maintenance-effort and can serve as a good predictor of the same.

Self-generated variability in object images predicts vocabulary growth.

Object names are a major component of early vocabularies and learning object names depends on being able to visually recognize objects in the world. However, the fundamental visual challenge of the moment-to-moment variations in object appearances that learners must resolve has received little attention in word learning research. Here we provide the first evidence that image-level object variability matters and may be the link that connects infant object manipulation to vocabulary development. Using head-mounted eye tracking, the present study objectively measured individual differences in the moment-to-moment variability of visual instances of the same object, from infants' first-person views. Infants who generated more variable visual object images through manual object manipulation at 15months of age experienced greater vocabulary growth over the next six months. Elucidating infants' everyday visual experiences with objects may constitute a crucial missing link in our understanding of the developmental trajectory of object name learning.

Multidimensional appropriate clustering and DBSCAN for SAT solving

PurposeThis paper is an extended version of Hireche and Drias (2018) presented at the WORLD-CIST’18 conference. The major contribution, in this work, is defined in two phases. First of all, the use of data mining technologies and especially the tools of data preprocessing for instances of hard and complex problems prior to their resolution. The authors focus on clustering the instance aiming at reducing its complexity. The second phase is to solve the instance using the knowledge acquired in the first step and problem-solving methods. The paper aims to discuss these issues.Design/methodology/approachBecause different clustering techniques may offer different results for a data set, a prior knowledge on data helps to determine the adequate type of clustering that should be applied. The first part of this work deals with a study on data descriptive characteristics in order to better understand the data. The dispersion and distribution of the variables in the problem instances is especially explored to determine the most suitable clustering technique to apply.FindingsSeveral experiments were performed on different kinds of instances and different kinds of data distribution. The obtained results show the importance and the efficiency of the proposed appropriate preprocessing approaches prior to problem solving.Practical implicationsThe proposed approach is developed, in this paper, on the Boolean satisfiability problem because of its well-recognised importance, with the aim of complexity reduction which allows an easier resolution of the later problem and particularly an important time saving.Originality/valueState of the art of problem solving describes plenty of algorithms and solvers of hard problems that are still a challenge because of their complexity. The originality of this work lies on the investigation of appropriate preprocessing techniques to tackle and overcome this complexity prior to the resolution which becomes easier with an important time saving.

PENGARUH KOMPETENSI DAN PENEMPATAN PEGAWAI TERHADAP KEPUASAN DAN KINERJA PEGAWAI

Organization expects to achieve a good performance. Organizational performance can be measured if the individual or group of employees has had had success criteria or standards set by the organization. Organizational performance is often not be optimal for several reasons including a lack of support for human resources quality. Perceived job satisfaction of the employees, as well as the placement the employees in the organization and work competencies possessed may have impacts on the achievement of employee performance and organizational performance. This study was conducted to access the influence of competency and placement of employees on satisfaction and performance of employees of the Department of Education, Culture, Youth and Sports (PKPO) in Ngada Regency. 66 employees are selected. Data were collected by questionnaires and the analyzed using path analysis. The results of the analysis prove that the competency and placement have positive and significant effect on job satisfaction and that the competency and placement have positive and significant impact on employee performance. It is also proved that job satisfaction has positive and significant impact on employee performance. Validation of the model shows the value of the coefficient of determination of 0.94, which means the performance variable is explained by competence, placements and job satisfaction of 94% and the remaining 6 % is explained by other factors beyond our model instance variables such as work environment, discipline, leadership and culture organization. Competence variable is proven affects the performance through satisfaction, but in influencing the performance, direct competence affect performance is more powerful compared to the competence affect performance through job satisfaction. Placement variable is proven affects the performance through satisfaction, but in influencing the performance, direct placement affect performance is more powerful compared to the placement affect performance through job satisfaction. The analysis concluded that the variable competence, placement and job satisfaction have positive and significant impact on the performance of the officer. In an effort to achieve an optimized organizational performance, it is recommended that further enhance the performance of the officer, the officer job satisfaction in the work should always get the attention of the organization. Placement officers in acting roles and responsibilities of the organization should also base on competence, experience and interest and continue to increase the competency of officers because of the competency of personnel adequate and appropriate organization standard, the performance and the organization's goals can be achieved.

Hierarchical object-oriented model (HOOM) for additive manufacturing digital thread

Additive manufacturing (AM) is a high-valued production application for the manufacture of functional parts. Despite significant advances in the AM field, numerous challenges remain regarding the integration of AM processes into a complete, generic, and adaptive digital thread based on standards. These challenges hinder the adoption of AM in traditional manufacturing companies. In this context, this study proposes a methodology for the development of a new AM digital chain model, named the Hierarchical Object-Oriented Model (HOOM). The HOOM is based entirely on object-oriented modeling that uses a collection of objects containing stored values of the instance variables found within an object. It comprises seven levels from the product design stage to post-production and validation stages. In order to assess the HOOM, it was implemented and tested through the manufacture of two test parts in a complete high-level digital chain based on the Standard for the Exchange of Product model data compliant Numerical Control. Results from the application of the HOOM unique data structure demonstrated enhanced transparency, standardization, interoperability, scalability, and modularity of AM project data. Moreover, the HOOM was shown to enable bi-directional data flow and to support cloud manufacturing.

Calibrating Uncertainty: Commonalities in the Estimation of Numeric Variability Versus Spatial Prediction

To assess whether there may be a common ability related to the understanding and calibration of instance variability and mean behavior, participants performed spatial prediction and numeric estimation tasks. In the first task, participants experienced variability in a set of spatial trajectories whose endpoints they predicted along with a central mean. In the second task, they experienced variability in a set of random numbers whose mean and variability they estimated. For both tasks, estimated variability was compared with the true variability of instances to derive measures of bias (e.g., over-or under-estimation) and precision. Correlations between these estimates across the two experiments revealed mixed evidence for a common ability to estimate variability, but suggested similar performance when estimating mean behavior. Implications for individual differences and interventions are discussed.

Software restructuring for enhancing the Cohesion using Backward Slicing

Software structure is characterized by high cohesion among modules. Repeated modification into code can adversely affect cohesive structure of software systems. Efficient code transformation is required to re-engineer the software system for enhancing the cohesion. In this paper we suggest restructuring process objectively for cohesive bond within the module using backward tracing of dependent instance variables. In proposed measure of cohesion output variables produce domain of restructuring. The approach of cohesion measure is further validated experimentally on open source software. Our transformation methodology application evolves from small software to large software. Our approach significantly improves the cohesion and reduces complexity of ill structured code resulting quality software.

On-line self-adaptive framework for tailoring a neural-agent learning model addressing dynamic real-time scheduling problems

The dynamic nature and time-varying behavior of actual environments provide serious challenges for learning models. Thus, changes may deteriorate the constructed control policy over time, which requires permanent adaptation strategies. Changes usually appear as an evolution in the relationship between instance variables composing stream data, known in machine learning under the term concept drift. Several adaptation strategies have been performed to tackle concept drifting data streams, always assuming that arrived instances are labeled, either completely or partially. However, this assumption is violated in many application areas, especially in the manufacturing field. We propose, in this paper, a new framework called Labeling Extraction from the current Model (LEM). LEM is adapted to retrieve learning labels, relying uniquely on unlabeled received instances and without any external supervision, which has never been previously addressed. Hence, to the best of our knowledge, there has been no effort addressing scheduling manufacturing problems for adaptation to data streams with concept drifts. Experiments are conducted to show the effectiveness of LEM. The obtained results demonstrate the ability of LEM to maintain the stability and efficiency of the control policy approximated by the learning model, by significantly improving its prediction performance, compared to its use without adaptation.

Instances of Cinema

This article sketches a commonplace yet neglected epistemic puzzle raised by the diversity of our film-viewing practices. Because our appreciative practices allow for variability in the “instances” of cinematic works we engage, many of our experiential encounters with those works are flawed or impoverished in a number of ways. The article outlines a number of ways in which instances of cinema can vary—including, for example, in terms of color, score, and aspect ratio. This variability of instances of cinema and, hence, the variability in our experiences of a cinematic work raise potential problems around normative questions of interpretation and evaluation.

Applying Extended DMAIC methodology to optimize weakly structured business processes

Alexander I. Gromov - Professor, Head of Department of Modeling and Optimization of Business Processes, National Research University Higher School of EconomicsAddress: 20, Myasnitskaya Street, Moscow, 101000, Russian FederationE-mail: agromov@hse.ruYulia A. Bilinkis - Lecturer, Department of Modeling and Optimization of Business Processes, National Research University Higher School of EconomicsAddress: 20, Myasnitskaya Street, Moscow, 101000, Russian FederationE-mail: ybilinkis@hse.ruNikolay S. Kazantsev - Lecturer, Department of Modeling and Optimization of Business Processes, National Research University Higher School of EconomicsAddress: 20, Myasnitskaya Street, Moscow, 101000, Russian FederationE-mail: nkazantsev@hse.ruAnastasia G. Zueva - Lecturer, Department of Modeling and Optimization of Business Processes, National Research University Higher School of EconomicsAddress: 20, Myasnitskaya Street, Moscow, 101000, Russian FederationE-mail: zueva_ag@mail.ru The paper is focused on DMAIC methodology, which is currently widely used in projects to optimize routine business processes by implementing 6 Sigma methodologies. The article analyzes the applicability of DMAIC methodology to weakly structured non-linear business processes characterized by uncertainty of the input, output and variability of process instances, primarily dependent on content and user behavior. First, it describes the main steps of the methodology: Define, Measure, Analyze, Improve and Control. These steps are used for a routine documentation approval process. Routine process is regulated and has few exceptions; its instances rarely differ from each other. Standard statistical methods can be used to analyze it, such as control charts. Second, the paper shows approaches to the definition of weakly structured processes with the use of the information field and subject-oriented interaction to achieve the goal. Third, tools and techniques that extend the DMAIC methodology for weakly structured process are proposed using the example of ad-hoc weakly structured operational risk management processes. The main differences were identified in the Define, Measure and Analyze steps. These recommendations can be used in projects to optimize weakly structured processes.

Special Issue on Autonomic Clouds

Cloud computing continues to grow in complexity due to both the increasing availability of configuration options from public cloud providers and the increasing variability and types of application instances that can be deployed on such platforms. This complexity is also evident in the enterprise-scale datacenters that dominate computing infrastructures in industry, leading to complex business applications and workflows. Autonomic computing enables self-management of systems and applications. The underlying concepts and mechanisms of autonomics can be applied to each component within a cloud (resource manager/scheduler, power manager, and so on) as well as to the cloud as a whole, or they can be applied within an application that makes use of such a cloud. Additionally, autonomics can play a critical role as applications explore dynamic federations of cloud infrastructure and services. This special issue explores the role of autonomic computing concepts and practices in managing cloud infrastructure and applications.

Predicting Quantitative Functional Dependency Metric based upon the Interface Complexity Metric in Component based Software Systems: A New Approach

ABSTRACT One of the major issues in component based software systems structuring and quality prediction is the interdependencies of system components. This paper proposes a novel technique for determining the strength of functional coupling in component based software systems. Authors propose Strength of Functional Dependency (SFD) metric, which is based upon two new metrics Operational Coupling complexity Index (OCI), and Instance Coupling complexity Index (ICI). It allows us to quantify the functional dependencies, formed by different kinds of operations and instances between these components. Compared to other existing dependency metrics, which are often based on number of operations or instance variables between the components only, authors consider operational complexity and instance variables complexity as a measure to how strong this dependency is and therefore promote a more systematic approach to the reasoning about modularity in component based software systems. This paper can be divided broadly into two parts. The first part quantifies interface operations and instance variables. The quantification is performed by considering the number of input, output parameters and their types. Based upon these factors of operations and instance variables, authors used analytical hierarchy approach (AHP) to assign weights to these factors and outcomes OCI, ICI and SFD. The second part shows the experimentation and validation of the proposed metrics. The advantages of the proposed method are discussed as well through a case study in this paper.

Verification of object-oriented programs: A transformational approach

We show that verification of object-oriented programs by means of the assertional method can be achieved in a simple way by exploiting a syntax-directed transformation from object-oriented programs to recursive programs. This transformation suggests natural proofs rules and its correctness helps us to establish soundness and relative completeness of the proposed proof system. One of the difficulties is how to properly deal in the assertion language with the instance variables and aliasing. The discussed programming language supports arrays, instance variables, failures and recursive methods with parameters. We also explain how the transformational approach can be extended to deal with other features of object-oriented programming, like classes, inheritance, subtyping and dynamic binding.