Miracle-dz/UniFAR-FTUs huggingface dataset:
A study on rough set-aided feature selection for automatic web-page classification

Due to the recent explosive increase of Web-pages on World Wide Web, it is now urgently required for portal sites like Yahoo! service having directory-style search engines to classify Web-pages into many categories automatically. This paper investigates how rough set theory can help select relevant features for Web-page classification. Our experimental results show that the combination of the rough set-aided feature selection method and the Support Vector Machine with the linear kernel is quite useful in practice to classify Web-pages into multiple categories because not only our experiments give acceptable accuracy but also the high dimensionality reduction is achieved without the need to search for a threshold for feature selection.
Parallel computation of the time-frequency power spectrum: analysis and comparison to the bispectrum

Experiments of large data sets are computationally expensive. Signal processing analysis on a single CPU leads to unacceptably long execution times. The paper presents initial experiments on calculating the time-frequency power spectrum using the coarse-grained parallel programming technique. Experimental speedup factors are given and discussed. The measured speedup factor of the time-frequency power spectrum parallel calculation process is sublinear which indicates that the time-frequency power spectrum is a suitable application for parallel programming. The parallel efficiency is acceptable with the lowest value of 75.1% occurring at N = 10. The maximum speedup factor of 9.1 is obtained when N = 12 at 75.3% of efficiency.
Region of Interest-Based 3D Inpainting of Cultural Heritage Artifacts

In this article, we address the problem of 3D inpainting using an exemplar-based method for point clouds. 3D inpainting is a process of filling holes or missing regions in the reconstructed 3D models. Typically, inpainting methods addressed in the literature fill missing regions due to occlusions or inaccurate scanning of 3D models. However, we focus on scenarios involving naturally existing damaged models, which are partly broken or incomplete in the artifacts at cultural heritage sites. We propose an exemplar-based inpainting technique using the region of interest (ROI)-based method to inpaint the missing regions of the damaged model. The ROI of a 3D model is represented as a set of Riemannian manifolds, and metric tensor and Christoffel symbols are used as geometric features to capture the inherent geometry. We then decompose the ROI into basic shape regions, namely, spherical, conical, and cylindrical components, and identify the best-fit match for inpainting. Instead of using a single similar exemplar for inpainting, we select the most relevant best-fit region to fill the missing region from the basic shape regions library obtained from n similar exemplars. We demonstrate the performance of the proposed inpainting method on artifacts at UNESCO World Heritage site Hampi temples, India with varying complexities and sizes for both synthetically generated holes and real missing regions in 3D objects.
Redesign strategies for digital VLSI circuits with incomplete implementation information

This paper deals with the problem of recovering the design of digital VLSI circuits with incomplete implementation information. A circuit is redesignable if the transfer function of the portion with incomplete implementation information can be derived cost effectively from the partially known information. The derived transfer functions are then used to re-implement the missing portions. We do not intend to discover the exact circuit schematic and components that were present in the circuit originally implemented. Rather, the functions originally intended to be present will be identical. The developed redesign process is comprised of three steps: redesignability check, feasibility check, and reimplementation. Based on the redesign process, experimental results on a number of MCNC benchmark circuits are reported.
The New Trivium

In contrast to Computer Science and Software Engineering, where it’s fundamental role is widely recognized, logic plays a practically non-existent role in information systems (IS) curricula. In this paper we argue that instead of logic’s exclusion from the IS curriculum, a significant adaptation of the contents, as well as teaching methodologies, is required for an alignment with the needs of IS practitioners. We present a vision for such adaptation and report on concrete steps towards its implementation in the design and teaching of a course for graduate IS students at the University of Haifa. We discuss the course plan and present a qualitative analysis of feedback provided by students of the course.
A systematic approach to automatically generate test scenarios from UML activity diagrams

Test scenarios are frequently used in scenario-based software testing. However, generation of the test scenarios is usually a manual and labor-intensive task. It is often desired that test scenarios can be automatically generated. As a semi-formal modeling language, UML is widely used in both academia research and industry practice to describe analysis and design specifications. The UML activity diagrams (ADs) are mainly used in business logic processing at the early stage of software development life-cycle. Obviously, testing scenarios generated from ADs can contribute to test driven development. Nevertheless, it is difficult to automatically generate test scenarios from ADs which contains fork-join pairs mixed with loops and branches. In this paper, a systematic approach is proposed to automatically generate test scenarios from the UML activity diagrams which may contain complicated fork-join structure.
Genie

Engineering adaptive software is an increasingly complex task. Here, we demonstrate Genie, a tool that supports the modelling, generation, and operation of highly reconfigurable, component-based systems. We showcase how Genie is used in two case-studies: i) the development and operation of an adaptive flood warning system, and ii) a service discovery application. In this context, adaptation is enabled by the Gridkit reflective middleware platform.
ARTOO

Intuition is often not a good guide to know which testing strategies will work best. There is no substitute for experimental analysis based on objective criteria: how many faults a strategy finds, and how fast. "Random" testing is an example of an idea that intuitively seems simplistic or even dumb, but when assessed through such criteria can yield better results than seemingly smarter strategies. The efficiency of random testing is improved if the generated inputs are evenly spread across the input domain. This is the idea of adaptive random testing (ART). ART was initially proposed for numerical inputs, on which a notion of distance is immediately available. To extend the ideas to the testing of object-oriented software, we have developed a notion of distance between objects and a new testing strategy called ARTOO, which selects as inputs objects that have the highest average distance to those already used as test inputs. ARTOO has been implemented as part of a tool for automated testing of object-oriented software. We present the ARTOO concepts, their implementation, and a set of experimental results of its application. Analysis of the results shows in particular that, compared to a directed random strategy, ARTOO reduces the number of tests generated until the first fault is found, in some cases by as much as two orders of magnitude. ARTOO also uncovers faults that the random strategy does not find in the time allotted, and its performance is more predictable.
DySy

Dynamically discovering likely program invariants from concrete test executions has emerged as a highly promising software engineering technique. Dynamic invariant inference has the advantage of succinctly summarizing both "expected" program inputs and the subset of program behaviors that is normal under those inputs. In this paper, we introduce a technique that can drastically increase the relevance of inferred invariants, or reduce the size of the test suite required to obtain good invariants. Instead of falsifying invariants produced by pre-set patterns, we determine likely program invariants by combining the concrete execution of actual test cases with a simultaneous symbolic execution of the same tests. The symbolic execution produces abstract conditions over program variables that the concrete tests satisfy during their execution. In this way, we obtain the benefits of dynamic inference tools like Daikon: the inferred invariants correspond to the observed program behaviors. At the same time, however, our inferred invariants are much more suited to the program at hand than Daikon's hard-coded invariant patterns. The symbolic invariants are literally derived from the program text itself, with appropriate value substitutions as dictated by symbolic execution. We implemented our technique in the DySy tool, which utilizes a powerful symbolic execution and simplification engine. The results confirm the benefits of our approach. In Daikon's prime example benchmark, we infer the majority of the interesting Daikon invariants, while eliminating invariants that a human user is likely to consider irrelevant.
SpyWare

Our research is driven by the motivation that change must be put in the center, if one wants to understand the complex processes of software evolution. We built a toolset named SpyWare which, using a monitoring plug-in for integrated development environments (IDEs), tracks the changes that a developer performs on a program as they happen. SpyWare stores these first-class changes in a change repository and offers a plethora of productivity-enhancing IDE extensions to exploit the recorded information.
Marama

We describe the Marama suite of meta-tools. This Eclipse-based toolset permits rapid specification of notational elements, meta-models, view editors and view-model mappings. It has a novel set of behavioural specification tools for both visual and model level behaviours. An integrated mapping tool provides model transformation and code generation support. The toolset has been applied to several significant application development tasks and has undergone a variety of evaluations.
Automatic Life Cycle Management of Network Configurations

Managing the life cycle of network configurations, including the generation, update, transition and diagnosis of the configurations, is the primary task of network operators and a critical process for the reliability and efficiency of the networks. This paper presents NetCraft, a framework which automates the life cycle management of network configurations with a unified network model. Designed for life cycle automation, NetCraft's network model can expressively encode all parts and protocols in the network; It can be converted to or constructed from configurations with interoperability; It is able to perform fine-grained configurations with flexibility to deactivate or undo any configurations for safe configuration updates; And it can work without cooperations from device vendors. We have built and deployed an initial version of NetCraft in Alibaba's global WAN. Evaluations in real environments show that NetCraft can reduce the network incidents caused by configurations by 95% and cut the average time to plan and execute a network update by up to 93%.
Reducing Variance in Gradient Bandit Algorithm using Antithetic Variates Method

Policy gradient, which makes use of Monte Carlo method to get an unbiased estimation of the parameter gradients, has been widely used in reinforcement learning. One key issue in policy gradient is reducing the variance of the estimation. From the viewpoint of statistics, policy gradient with baseline, a successful variance reduction method for policy gradient, directly applies the control variates method, a traditional variance reduction technique used in Monte Carlo, to policy gradient. One problem with control variates method is that the quality of estimation heavily depends on the choice of the control variates. To address the issue and inspired by the antithetic variates method for variance reduction, we propose to combine the antithetic variates method with traditional policy gradient for the multi-armed bandit problem. Furthermore, we achieve a new policy gradient algorithm called Antithetic-Arm Bandit (AAB). In AAB, the gradient is estimated through coordinate ascent where at each iteration gradient of the target arm is estimated through: 1) constructing a sequence of arms which is approximately monotonic in terms of estimated gradients, 2) sampling a pair of antithetic arms over the sequence, and 3) re-estimating the target gradient based on the sampled pair. Theoretical analysis proved that AAB achieved an unbiased and variance reduced estimation. Experimental results based on a multi-armed bandit task showed that AAB can achieve state-of-the-art performances.
A Weighted Majority Voting based on NMI for Cluster Analysis

Due to advancements in data acquisition, large amount of data are collected in daily basis. Analysis of the collected data is an important task to discover the patterns, extract the features, and make informed decisions. A vital step in data analysis is dividing the objects (elements, individuals) in different groups based on their similarities. One way to group the objects is clustering. Clustering methods can be divided in two categories, linear and non-linear. K-means is a commonly used linear clustering method, while Kernel K-means is a non-linear technique. Kernel K-means projects the elements to a new space using a kernel function and then group them in different clusters. Different kernels perform differently when they are applied to different data sets. Choosing the right kernel for an application could be challenging, however applying a set of kernels and aggregating the results could provide a robust performance for different data sets. In this work, we address this issue and propose a weighted majority voting to ensemble the results of three different kernels.
Sentiment Analysis of Peer Review Texts for Scholarly Papers

Sentiment analysis has been widely explored in many text domains, including product reviews, movie reviews, tweets, and so on. However, there are very few studies trying to perform sentiment analysis in the domain of peer reviews for scholarly papers, which are usually long and introducing both pros and cons of a paper submission. In this paper, we for the first time investigate the task of automatically predicting the overall recommendation/decision (accept, reject, or sometimes borderline) and further identifying the sentences with positive and negative sentiment polarities from a peer review text written by a reviewer for a paper submission. We propose a multiple instance learning network with a novel abstract-based memory mechanism (MILAM) to address this challenging task. Two evaluation datasets are constructed from the ICLR open reviews and evaluation results verified the efficacy of our proposed model. Our model much outperforms a few existing models in different experimental settings. We also find the generally good consistency between the review texts and the recommended decisions, except for the borderline reviews.
Wi-Foto 2: Heterogeneous device controller using Wi-Fi positioning and template matching

Improving usability of heterogeneous devices is important in pervasive computing. We have developed Wi-Foto, which enables users to control such devices by using a tablet PC as a client. Wi-Foto has a user-friendly interface which has device controllers overlaid on a photo depicting the interior of the room where the user is. In order to find the most appropriate photo, Wi-Foto automatically locates the user by Wi-Fi positioning but the localization would often fail because of in- adequate accuracy. To solve this problem, we propose Wi-Foto 2, which combines the Wi-Fi positioning with template matching based on a SIFT algorithm. We confirm that the proposed method realizes accurate and fast positioning. Wi-Foto 2 also enables users to seamlessly select photos through hierarchical and panoramic views.
Cloud-connected Battery Management System Supporting e-Mobility

Electric vehicles have evolved along with a reduction in the size and weight of batteries. This has led to the development of bicycles, motorcycles, and small vehicles suitable for short distances that are driven by batteries small enough to be replaced by the user. Once batteries become readily replaceable even when away from the home or office, problems such as lengthy charging times and limited running distances will be overcome, and the practicality of electric vehicles will further increase. Since it would be difficult for users to carry replacement batteries with them or to store them at convenient locations, a battery-sharing system is essential. Fujitsu aims to establish a mechanism for grasping the status of shared batteries that will enable users to replace batteries with peace of mind and with no perceived degradation in battery quality. This cloud-connected battery management system will maximize the value of the shared batteries by using a location data cloud to continuously connect to the batteries, manage the state of their charge, and monitor changes in their characteristics. This paper presents the technology to be used to achieve this system and discusses how it will create new value.
M Moth-Eye Antireflective Structures

Moth-eye antireflective (AR) structures are arrays of protuberances, with dimensions smaller than the wavelength of the light incident upon them. They form a region of graded refractive index at the interface between two media, substantially reducing the amount of light reflected by the interface. Such protuberance arrays are found on the eyes of some species of moth, butterfly, and fly, where the reduction in reflectance is thought to increase visual system sensitivity, and on transparent sections of insect wing where they have a camouflaging effect. Biomimetic, artificial motheye AR structures have been fabricated on a range of materials such as polymers, glass, silicon, and GaAs for applications including photovoltaics and display technologies.
A New Evaluation Measure for Feature Subset Selection with Genetic Algorithm

Feature selection is one of the most important preprocessing steps for a data mining, pattern recognition or machine learning problem. Finding an optimal subset of features, among all the combinations is a NP-Complete problem. Lot of research has been done in feature selection. However, as the sizes of the datasets are increasing and optimality is a subjective notion, further research is needed to find better techniques. In this paper, a genetic algorithm based feature subset selection method has been proposed with a novel feature evaluation measure as the fitness function. The evaluation measure is different in three primary ways a) It considers the information content of the features apart from relevance with respect to the target b) The redundancy is considered only when it is over a threshold value c) There is lesser penalization towards cardinality of the subset. As the measure accepts value of few parameters, this is available for tuning as per the need of the particular problem domain. Experiments conducted over 21 well known publicly available datasets reveal superior performance. Hypothesis testing for the accuracy improvement is found to be statistically significant.
Speculative Non-Stop : A Novel Symptom-based Fault Detection Architecture for Chip Multiprocessors

Symptom-based fault-tolerant methods are attractive, since they replicate a minor part of instruction to achieve moderate system reliability. Thus, these methods use less power and hardware than dual-module redundancy(DMR). In this paper, we show that by adding more symptom types, symptombased detection methods can reach more satisfactory fault coverage. However, more symptoms will result in more false positive and cause fruitless rolling back and severe performance overhead. In this paper, we propose Speculative Non-stop architecture to off load the symptoms checking jobs from the local processing core to a remote processing core and resume the program execution speculatively on the local core. In addition, the remote slave core can be shared among master cores and amortize hardware overhead. Experimental results show that the proposed architecture can efficiently alleviate false positive problem and reduce performance overhead significantly. Keywords—Soft error; Symptom-based fault detection;
Somaclonal variation in Chrysanthemum morifolium generated through petal cultures

Ray florets of five varieties of Chrysanthemum morifolium were cultured in vitro and the regenerants were evaluated for somaclonal variation. The regenerated shoots exhibited variation in vegetative growth and flowering but limited floral variation. The technique used here could be used to secure naturally occurring colour mutants or sports. This was demonstrated when a yellow coloured floret was successfully isolated and regenerated to exhibit a solid mutant. Such mutants are impossible to isolate vegetatively due to its location and their limited extent. *Strategic, Environment and Natural Resources Research Centre, MARDI Headquarters, P.O. Box 12301, 50774 Kuala Lumpur, Malaysia Authors' full names: Vilasini Pillai and Latipah Zulkifli ©Malaysian Agricultural Research and Development Institute 200
Robust estimation of planar regions for visual navigation using sequential stereo images

In this paper, we propose a robust method to estimate planar regions using sequential stereo images for visual navigation of an autonomous vehicle. The proposed method estimates 2D projective transformations dynamically, which represent planes in space, for both stereo images and sequential images. This can be done robustly by utilizing sequential information, i.e. previous estimation of both the projective transformations and the corresponding planar region. In addition, a method for preventing misdetection due to textureless areas is proposed. The experimental results, using sequential stereo images taken from a moving vehicle, have shown that the proposed method can work robustly even in the conditions of undulation of the road and rolling and pitching of the vehicle.
Munc 18 a Does Not Alter Fusion Rates Mediated by Neuronal

Yunxiang Zhang , Jiajie Diao **, Karen N. Colbert, Ying Lai , Richard A. Pfuetzner **, Mark S. Padolina **, Sandro Vivona , Susanne Ressl , Daniel J. Cipriano **, Ucheor B. Choi **, Niket Shah, William I. Weis , and Axel T. Brunger ** From the Departments of Molecular and Cellular Physiology, Neurology and Neurological Sciences, Structural Biology, and Photon Science and the **Howard Hughes Medical Institute, Stanford University, Stanford, California 94305
Development of Robotic Intelligent Space Using Multiple RGB-D Cameras for Industrial Robots

To create industrial robots that can revise their own work by an autonomous decision, we propose a novel industrial robot sensor system called the "robotic intelligent space (RIS)". The system enables real-time measurement of the industrial robots and the objects manipulated in the working space by using multiple RGB-D cameras. The RGB-D cameras are installed in various places in the RIS to reduce occlusions, and information is merged in a three-dimensional occupancy grid map with real-time updates. Moreover, the RIS provides precise recognition of obstacles, and robot motion planning using the map. We developed a prototype of the RIS with two RGB-D cameras and an industrial robot. Additionally, we constructed an object-handling application including a position and posture recognition function. We demonstrate the efficiency and performance of the RIS through several simulations and experimental results.