Reviewerly/exHarmony huggingface dataset:
Specification and inference of trace refinement relations

The modern software engineering process is evolutionary, with commits/patches begetting new versions of code, progressing steadily toward improved systems. In recent years, program analysis and verification tools have exploited version-based reasoning, where new code can be seen in terms of how it has changed from the previous version. When considering program versions, refinement seems a natural fit and, in recent decades, researchers have weakened classical notions of concrete refinement and program equivalence to capture similarities as well as differences between programs. For example, Benton, Yang and others have worked on state-based refinement relations . In this paper, we explore a form of weak refinement based on trace relations rather than state relations. The idea begins by partitioning traces of a program C 1 into trace classes, each identified via a restriction r 1 . For each class, we specify similar behavior in the other program C 2 via a separate restriction r 2 on C 2 . Still, these two trace classes may not yet be equivalent so we further permit a weakening via a binary relation A on traces, that allows one to, for instance disregard unimportant events, relate analogous atomic events, etc. We address several challenges that arise. First, we explore one way to specify trace refinement relations by instantiating the framework to Kleene Algebra with Tests (KAT) due to Kozen. We use KAT intersection for restriction, KAT hypotheses for A , KAT inclusion for refinement, and have proved compositionality. Next, we present an algorithm for automatically synthesizing refinement relations, based on a mixture of semantic program abstraction, KAT inclusion, a custom edit-distance algorithm on counterexamples, and case-analysis on nondeterministic branching. We have proved our algorithm to be sound. Finally, we implemented our algorithm as a tool called Knotical, on top of Interproc and Symkat. We demonstrate promising first steps in synthesizing trace refinement relations across a hand-crafted collection of 37 benchmarks that include changing fragments of array programs, models of systems code, and examples inspired by the thttpd and Merecat web servers.
The Sequent Systems and Algebraic Semantics of Intuitionistic Tense Logics

In this paper we consider a weak Ewald’s intuitionistic tense logic (wIK.t). We study its sequent system and algebraic semantics. We prove the soundness and the completeness results. We also show that the sequent system for wIK.t introduced in the present paper admits cut elimination. Finally we propose a criterion and prove that all extensions of wIK.t satisfying this criterion have cut free sequent systems.
Hybrid Model of Interval Type-2 Neural Fuzzy Inference System and Mutual Subsethood with Applications

This paper presents the hybrid network of Interval Type-2 Subsethood Neural Fuzzy Inference System (IT2SuNFIS) [1] and Interval Type-2 Mutual Subsethood Neural Fuzzy Inference System (IT2MSFuNIS) [2] and it’s applications in the area of Mackey-Glass time-series (MGTS) prediction, chemical plant control and Hang function approximation. This model introduces the mutual subsethood measure [2] in IT2SuNFIS network [1]. Resulting hybrid network differs from IT2MSFuNIS in its consequent network structure and it is different from IT2SuNFIS as well; it uses mutual subsethood measure instead of subsethood measure for determining correlation between the interval type-2 fuzzy set (IT2 FS) inputs and IT2 FS antecedents. The inputs to the system are fuzzified using IT2 FSs with Gaussian primary membership function (GPMF) having identical mean but different variance. The signal aggregation of type-2 based activation is performed using product operator. This hybrid neuro-fuzzy system learns using different differential evolution (DE) strategies and Artificial Bee colony differential evolution (ABC-DE) framework [3]. Empirical studies are conducted on benchmark data sets of MGTS, a chemical plant and Hang function. Comparisons with other type-1 and type-2 neuro-fuzzy models and in particular with IT2SuNFIS [1], verify the excellent performance of the hybrid network.
Special issue on “Recent Advances on Data Fusion, Estimation in Navigation and Control”

Asian Journal of ControlVolume 21, Issue 4 p. 1407-1408 EDITORIAL Special issue on “Recent Advances on Data Fusion, Estimation in Navigation and Control” Hassen Fourati, Corresponding Author Hassen Fourati hassen.fourati@gipsa-lab.fr University Grenoble Alpes, CNRS, Inria, Grenoble INP, GIPSA-Lab, 38000 Grenoble, France Correspondence Hassen Fourati, University Grenoble Alpes, CNRS, Inria, Grenoble INP, GIPSA-Lab., 38000 Grenoble, France. Email: hassen.fourati@gipsa-lab.frSearch for more papers by this authorAmit Sanyal, Amit Sanyal Mechanical and Aerospace Engineering, Syracuse University, New YorkSearch for more papers by this authorPedro Batista, Pedro Batista Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa, PortugalSearch for more papers by this authorZebo Zhou, Zebo Zhou School of Aeronautics and Astronautics, University of Electronic Science Technology of China, P.R. ChinaSearch for more papers by this authorNuno Filipe, Nuno Filipe NASA's Jet Propulsion Laboratory, California Institute of Technology, CaliforniaSearch for more papers by this author Hassen Fourati, Corresponding Author Hassen Fourati hassen.fourati@gipsa-lab.fr University Grenoble Alpes, CNRS, Inria, Grenoble INP, GIPSA-Lab, 38000 Grenoble, France Correspondence Hassen Fourati, University Grenoble Alpes, CNRS, Inria, Grenoble INP, GIPSA-Lab., 38000 Grenoble, France. Email: hassen.fourati@gipsa-lab.frSearch for more papers by this authorAmit Sanyal, Amit Sanyal Mechanical and Aerospace Engineering, Syracuse University, New YorkSearch for more papers by this authorPedro Batista, Pedro Batista Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa, PortugalSearch for more papers by this authorZebo Zhou, Zebo Zhou School of Aeronautics and Astronautics, University of Electronic Science Technology of China, P.R. ChinaSearch for more papers by this authorNuno Filipe, Nuno Filipe NASA's Jet Propulsion Laboratory, California Institute of Technology, CaliforniaSearch for more papers by this author First published: 08 October 2019 https://doi.org/10.1002/asjc.2211Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume21, Issue4July 2019Pages 1407-1408 RelatedInformation
Development of Smart Factory Diagnostic Model Reflecting Manufacturing Characteristics and Customized Application of Small and Medium Enterprises

This study is to develop a diagnostic model for the effective introduction of smart factories in the manufacturing industry, to diagnose SMEs that have difficulties in building their own smart factory compared to large enterprise, to identify the current level and to present directions for implementation. IT, AT, and OT experts diagnosed 18 SMEs using the Smart Factory Capacity Diagnosis Tool developed for smart factory level assessment of companies. They analyzed the results and assessed the level by smart factory diagnosis categories. Companies' smart factory diagnostic mean score is 322 out of 1000 points, between 1 level (check) and 2 level (monitoring). According to diagnosis category, Factory Field Basic, R&D, Production/Logistics/Quality Control, Supply Chain Management and Reference Information Standardization are high but Strategy, Facility Automation, Equipment Control, Data/Information System and Effect Analysis are low. There was little difference in smart factory level depending on whether IT system was built or not. Also, Companies with large sales amount were not necessarily advantageous to smart factories. This study will help SMEs who are interested in smart factory. In order to build smart factory, it is necessary to analyze the market trends, SW/ICT and establish a smart factory strategy suitable for the company considering the characteristics of industry and business environment.
Designed to Be Broken: A Reverse Engineering Study of the 3D Secure 2.0 Payment Protocol

3 Domain Secure 2.0 (3DS 2.0) is the most prominent user authentication protocol for credit card based online payment. 3DS 2.0 relies on risk assessment to decide whether to challenge the payment initiator for second factor authentication information (e.g., through a passcode). The 3DS 2.0 standard itself does not specify how to implement transaction risk assessment. The research questions addressed in this paper therefore are: how is transaction risk assessment implemented for current credit cards and are there practical exploits against the 3DS 2.0 risk assessment approach? We conduct a detailed reverse engineering study of 3DS 2.0 for payment using a browser, the first study of this kind. Through experiments with different cards, from different countries and for varying amounts, we deduct the data and decision making process that card issuers use in transaction risk assessment. We will see that card issuers differ considerable in terms of their risk appetite. We also demonstrate a practical impersonation attack against 3DS 2.0 that avoids being challenged for second factor authentication information, requiring no more data than obtained with the reverse engineering approach presented in this paper.
Combined Learning for Similar Tasks with Domain-Switching Networks

We introduce a domain switch for deep neural networks that enables to re-weight convolutional kernels for an input of a known domain. This technique is designed to address re-occurring tasks across multiple domains that are known at runtime and to incorporate them into a single, domain-spanning network. We evaluate this approach in three distinct tasks, namely combined cell nuclei analysis across different stains and fluorescence images, facial landmark detection in grayscale and thermal infrared images, and the BraTS challenge where we treat different recording institutions as domains. We found that conventional U-nets trained on multiple domains perform similar to domain-specific U-nets. Our method improves the results in facial landmark detection significantly, but no change is measured in the other two experiments compared to multi-domain U-nets.
Users as Programmers

We present a research framework for studying the development of programming notation for control interfaces targeted at older consumers of smart connected vehicles in order to support future data collection at the early stages of development using the concept of inclusive design. By analyzing literature from perspectives on mobility/IoT technology, inclusive design and user experience, we hope to establish evidence (in-practice) in the process of developing notations for vehicular interfaces that are inclusive for older consumers. The research framework describes the relationship between the perspectives and relates that to notation development for older consumers. We see notations as a feature that embody "experience-driven' and "interaction-driven' visions with the capacity to help older users better control their driving experience and develop their cognitive ability.
Probabilistic static foreground elimination for background subtraction

Background subtraction is generally used to detect moving objects, because it has low complexity, and it is easy to implement. However, the detection error increases when the background is changing. Therefore, adaptive background subtraction is applied to overcome this problem, and it continuously requires updating the background with a fixed learning rate. The learning rate should be tuned for a consistently evolving background. This paper proposes the Probabilistic Static Foreground Elimination for Background Subtraction (PSFE) algorithm. It consisted of two parameters: the number of frames for static foreground elimination, and the probability of changes in background pixels. These two parameters can tune the learning rate and update background for better detection. The average results of the detection error rate from Wallflower datasets were tested with PSFE and well-known method. They demonstrated that PSFE provides moving object detection with minimum detection error (5.95%), especially in Camouflage, Moved object, and Light switch dataset.
One Theme of the Project Is a Lot of Ideas: on the Example of Designing a Mathematical Calendar

The article shows how a single project topic, formulated on the basis of the “domino principle”, can stimulate creative search and be embodied in several project products — various mathematical calendars. All calendars diff er among themselves by their target audience, form and content. The implementation of such projects is a means of professionalizing students of a pedagogical university in the aspect of improving their methodological, metamathematical and project competencies.
Memberships Networks for High-Dimensional Fuzzy Clustering Visualization

Visualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more complex clustering structures. Here is introduced the concept of membership networks, an undirected weighted network constructed based on the fuzzy partition matrix that represents a fuzzy clustering. This simple network-based method allows understanding visually how elements involved in this kind of complex data clustering structures interact with each other, without relying on a visualization of the input data themselves. Experiment results demonstrated the usefulness of the proposed method for the exploration and analysis of clustering structures on the Iris flower data set and two large and unlabeled financial datasets, which describes the financial profile of customers of a local bank.
An efficient approach for handling degradation in character recognition

Recognition of historical printed degraded Kannada characters is not solved completely and remains as a challenge to the researchers still. In this paper, a scale for measuring degradation of a character is proposed. Further, the degradation is characterised to high, medium and low based on this scale, and use it to study the efficiency of the character restoration technique designed. A new approach, fit discriminant analysis (FDA) for recognition is proposed and compares its recognition accuracy with the existing techniques support vector machines (SVM) and Fisher linear discriminant (FLD) analysis. Through extensive experimentation it is established that rebuilding of characters improves the recognition accuracy of learning-based approaches SVM, FDA, and FLD significantly. Further, it is established that the proposed approach FDA gives the best recognition accuracy for historical printed degraded documents. It is also proved that training-testing set applying the proposed degradation measure is required for better recognition accuracy.
Implementing an Iot Approach to Careawareness in the Home.

There is a need for new thinking in care previsions and IOT is likely to play a major role in achieving transformation of care services at lower cost.Such an approach sits at the heart of this work.Here we present the outcome of an ongoing research programme based on sensor and App interventions (trial) at homes of participants who live independently but require care.The results indicate contrasting views of carers and the persons who receive care regarding (a) the uptake and utilisation of IoT, and (b) the potential impact on the quality of life and independence of both groups.In contrast to current telecare solutions that respond to "hard"acute alerts, our research proposes the introduction of "soft" warnings, based on the continuous evaluation of the occupancy behaviour, care conditions and the home environment.In this context the results suggest that the care needs of a person can be more important determinants of occupancy behaviour than socio-demographic attributes that are typically used in residential occupancy classification.This research under the CareTeam programme, is a non-intrusive IoT approach geared to generate proactive care-awareness to help people who receive care stay independent and active for longer.
Jigsaw method in reading comprehension

This study deals with the use of jigsaw method in improving students’ reading at the VIII Grade Students of junior high school PGRI 4 Medan 2017/2018. This research was conducted by applying the Experimental method. The population of this study was the students of the VIII grade of junior high school PGRI 4 Medan. There were four classes which students were 30 in each classes. Totally 120 students. The sample was 60 students in which determined by purposive sampling technique that divided into two (2) groups, (CG) control group and (EG) experimental group. Data was analyzed by t-test formula and used observation sheet and interview sheet to show the result of the process in teaching reading. The result is the data by using t-test found that t- observed (4,8) &gt; t- table ( 2,00) with stage of freedom (df) is 58 and standard sense (0,05). And the end of the alternative hypothesis (Ha) was accepted and the null hypothesis (Ho) was rejected. In make yield means jigsaw significantly increasing reading at the VIII neap tide students of junior high school PGRI 4 Medan.
Modeling and Control of a Two-Axis Solar Tracking System

The present work focuses on the analysis of a two-axis pedestal-type solar tracking system controlled by a Proportional-Integral-Derivative (PID) control law plus feedforward applied to the motors that drive the tracking system. The implementation of a solar positioning algorithm is performed to calculate the trajectories of the solar tracking system to track the movement of the sun. These trajectories are applied as desired trajectories to the PID control loops controlling the motors. Results of real-time experiments are presented, and the energy consumption of the motors is analyzed.
Impact of commercial bank credit on agricultural output in Nigeria

Purpose. The purpose of this study was to investigated the impact of commercial bank credits on agricultural output in Nigeria over the period 1980 to 2015 by setting three specific objectives which are to examine the trend of commercial bank credit and agricultural output in Nigeria; to investigate the effect of commercial bank credit on agricultural output in Nigeria and to investigate the effect of commercial bank credit on subsector of agriculture in Nigeria. The trend analysis and the impact of commercial bank credit on subsector of agriculture in Nigeria make this work unique and different from other studies in this area. Trend analysis was used to achieve the first objective and fully modified ordinary least square (OLS) for objective two and three. Methodology. The study employed Fully Modified Ordinary Least Squares (FMOLS) approach. Findings. It was evidenced that interest rate on commercial banks’ credit to agriculture and deposit money bank’s assets are statistically significant in determine agricultural output in Nigeria within the period considered. Also, commercial bank loan on agriculture and deposit money bank’s assets determine the output of crop production in Nigeria; commercial bank loan on agriculture and interest rate on commercial banks’ credit to agriculture determine the output of livestock production in Nigeria and commercial bank loan on agriculture and interest rate on commercial banks’ credit to agriculture determine the output of forestry in Nigeria while commercial bank loan on agriculture and interest rate on commercial banks’ credit to agriculture determine the output of fishing in Nigeria. Limitations. This study is limited because the study does not include other variables that determine the output of agricultural sector in Nigeria. Also, other theories and methods can still be used by other researcher to make it different from this work. Originality. This is an original work and has neither been published in any other peer-reviewed journal nor is under consideration for publication by any other journal.
Impacts of PCA on Face Recognition System

Face recognition is one of the important applications in the field of security and surveillance. Even though many methodologies are existing for feature extraction and classification, we are in need of dynamic features and classifiers to overcome the emerging challenges in the field of FRS. On analyzing the work of various researchers, PCA feature extractor is found to be more dynamic than other existing techniques. In this paper, the unique performance of PCA with novel methodologies are overviewed and its merits are highlighted.
A Discriminant Two-Dimensional Canonical Correlation Analysis

Two-dimensional canonical correlation analysis (2D-CCA) is an effective method for two-view feature extraction and fusion. It is able to reduce the computational complexity while reserving local data structure of a two-dimensional signal, e.g., image. However, since 2D-CCA only reveals the structure of the input data set without discriminatory information, it cannot effectively extract and represent discriminant representations for recognition and classification. Aiming at providing a method for discriminant feature extraction and fusion, this paper proposes a discriminant 2D canonical correlation analysis (D2DCCA), utilizing descriptor of 2DCCA and scatter information of different classes to improve recognition performance. We conduct experiments on AR face database to evaluate the performance of the proposed method. Experimental results show that the proposed D2DCCA outperforms other related algorithms.
Tutoring implementation in the Psychology bachelor degree program, based on e-learning

This article is part of the results obtained from the tutoring strategy implemented in the Psychology bachelor degree program, based on e-learning, offered by the Autonomous University of the State of Morelos (UAEM), Mexico. The experience of the first generation of the mentioned program is analyzed (2014-2018). The purpose of this research project is to document and evaluate the implementation process of tutoring in this program. The research question was: How effective have been de virtual cubicles for tutoring implemented in the UAEM Psychology bachelor degree program, based on e-learning? The methodological approach has been action research, with data gathered for the first phase based on e-observation in seven tutoring virtual cubicles, in order to document and evaluate the experience throughout a specific generation. Based on the results, changes and improvements are proposed for the tutoring spaces, resources and process.