Reviewerly/exHarmony huggingface dataset:
Software reusability development through NFL approach for identifying security based inner relationships of affecting factors

&lt;span lang="EN-US"&gt;In component based software reusability development process, the software developers have to choose the best components which are self adaptive future to overcome the functional errors, framework mismatches, violation of user level privacy issues and data leakage feasibilities. The software developers can build high quality software applications by taking the consideration of the reusable components which are more suitable to provide high level data security and privacy. This paper has proposing the neural based fuzzy framework based approach to estimate the reusable components which are directly and indirectly involve the security and privacy to improve the quality of the software system. This approach has considered the twenty effecting factors and fifty three attribute matrices. It has formed with three stages of execution scenarios. The first stage has executed with eleven effecting factors and eighteen attribute matrices for identification of supporting software reusability components, the second stage has executed with four effecting factors and thirty five attribute matrices for identification of sub-internal relationships in terms of security-privacy, and the third stage has executed with eight effecting factors and six attribute matrices for identification of sub of sub-internal relationships in terms of security risk estimation. This analytical finding proposes a fuzzy logic model to evaluate the most feasible effecting factors that influence the enterprise level data security-privacy practices at real time environment.&lt;/span&gt;
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets

Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion.However, they require large batch sizes, which becomes a performance bottleneck when training on large scale datasets due to memory constraints.In this paper we use occurrences of entity-relation pairs in the dataset to construct a joint learning model and to increase the quality of sampled negatives during training.We show on three standard datasets that when these two techniques are combined, they give a significant improvement in performance, especially when the batch size and the number of generated negative examples are low relative to the size of the dataset.We then apply our techniques to a dataset containing 2 million entities and demonstrate that our model outperforms the baseline by 2.8% absolute on hits@1.
Introspective Gan for Meshface Recognition

Majority of face recognition systems can only retrieve protected ID photos from the government agency in China. These facial images covered with mesh-like curves are termed meshface. Meshface could significantly affect the performance of face recognition systems. Although some GAN based methods are proposed to address this issue by translating meshface images to clean face images, their capacities are limited. In this paper, we introduce the introspective modules to encourage the generator to reconstruct face images and accelerate the learning process of the discriminator. Both a private meshface dataset and the public LFW dataset are used for experiments. The quantitative evaluation on both datasets proves that the introspective GAN can recover face image with better quality. Additionally, face recognition performance is also significantly improved.
Efficient IntVec: High recognition rate with reduced computational cost

In many deep neural networks for pattern recognition, the input pattern is classified in the deepest layer based on features extracted through intermediate layers. IntVec (interpolating-vector) is known to be a powerful method for this process of classification. Although the recognition error can be made much smaller by IntVec than by WTA (winner-take-all) or even by SVM (support vector machines), IntVec requires a large computational cost. This paper proposes a new method, by which the computational cost by IntVec can be reduced drastically without increasing the recognition error. Although we basically use IntVec for recognition, we substitute it with WTA, which requires much smaller computational cost, under a certain condition. To be more specific, we first try to classify the input vector using WTA. If a class is a complete loser by WTA, we judge it also a loser by IntVec and omit the calculation of IntVec for that class. If a class is an unrivaled winner by WTA, calculation of IntVec itself can be omitted for all classes.
Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

This paper investigates the optimal design of symmetric switching CMOS inverter using the Symbiotic Organisms Search (SOS) algorithm. SOS has been recently proposed as an effective evolutionary global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the three common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other optimization methods, SOS has no parameters to be tuned, which makes it an attractive and easy-to-implement optimization method. Here, SOS is used to design a high speed symmetric switching CMOS inverter, which is considered the most fundamental logic gate. SOS results are compared to those obtained using several optimization methods, like particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and other ones, available in the literature. It is shown that the SOS is a robust straight-forward evolutionary algorithm that can compete with other well-known advanced methods.
Image stitching by projective transformations

The capture of panoramic images requires the use of complex and specialized cameras. However, high quality panoramic images can be constructed digitally by stitching several images captured with conventional lowcost cameras. In this work, an image stitching method based on projective transformations is proposed. The theoretical principles and computational implementation are presented. Experimental panoramic images are composed to validate the usefulness of our method.
Traffic sign detection and positioning based on monocular camera

Geospatial and geometric states of traffic signs are indispensable information for traffic facility maintenance. This study applies a low-cost single camera system to locate and identify traffic signs and reflects their on-site conditions for assisting in maintenance. Referring to official regulations, a traffic sign can be identified and classified based on its color and shape attributes. The poses and states of the sign planes can also be evaluated according to their geometric shape ratios and the discrepancy with respect to traffic sign regulations, in which a least-square consistency check is proposed to ensure assessment reliability and accuracy. Validation with day and nighttime image sequences was performed to verify the effectiveness of the proposed method and the robustness to illumination changes, color deterioration, and motion blur. Considering the promising results, this study can be deemed as an alternative to promote routine maintenance of traffic facilities.
The Current Status and the Future Tasks of Online Training Courses for Korean Language Teachers - Focusing on Quality Management of Korean Practicum Subjects in Degree Programs -

Journal of the International Network for Korean Language and Culture 16-2, 199-229. After reviewing the aspects of cyber universities and Academic Credit Bank System (ACBS) distance institutions which provide online training courses for Korean language teachers, this study clearly called for more thoroughness in the quality management of Korean practicum subjects. The common aspect shared between cyber universities and ACBS distance institutions is that both are online training institutions for Korean language teachers. However, a huge difference between these institutions can be noted as cyber universities are classified as university institutions under Higher Education Act, whereas ACBS distance institutions are subject to Lifelong Education Act. For this reason, as stated in Lifelong Education Act, National Institute for Lifelong Education oversees ACBS distance institutions in their course establishment and management, as well as their credit management in degree programs. This difference causes great concern, since it can weaken National Institute of Korean Language’s supervising role for ACBS distance institutions. Therefore, as the Institute’s quality management system for Korean practicum subjects is set to be introduced in 2020, this study proposed the need of reinforcing National Institute of Korean Language’s interests and supervising role for actual Korean practicum (class observation and teaching practice) under ACBS distance institutions, most of which do not have Korean education centers.
Hide-CAM: Finding Multiple Discriminative Regions in Weakly Supervised Location

Weakly supervised localization is a more challenging task due to the absence of an object's annotation. Because the depth convolution feature can well represent the spatial information of the object, the position of the object can be located by the saliency study of the image. However, the most discriminative area tends to focus too much on the details of the object and lacks the perception of the object's overall structure, whereas the information of the complementary object regions is the complement of the most discriminative area. The combination of these information types can fully express the global information of the object. Therefore, our method takes into account the entire area of the object rather than the most discriminative area. In this paper, the hide strategy is used to locate the most discriminative and the complementary object regions of the object. First, we use CAM to extract the most discriminative area. Next, we mask the most discriminative area and use CAM to extract complementary object regions in the masked image. Finally, the two areas are integrated to complete the task of location. Our method only needs the classification label of the image instead of a detailed object annotation. The operation is simple and convenient, and does not require training a complex model or additional annotation. Experiments show our method achieves good results in ILSVRC 2012 validation.
A Reputation Capital and Blockchain-based Model to Support Group Formation Processes in the Internet of Things

The “Internet of Things” (IoT) is requiring increasing computational capabilities which can be obtained from Machine-to-Machine cooperation among smart objects. The convergence of IoT and Multi-Agent Systems gives the opportunity to benefit of their social attitude to interact and cooperate for services. In a mobile and federated context, selecting reliable partners for cooperation is a complex task because the most part of devices are not referenced with respect to their trustworthiness. The aggregation of agents in groups, in each IoT environment, on the basis of reputation information can provide a solution to this problem. In order to spread device/agent reputation information in our distributed scenario without using a central repository, we propose to rely on the blockchain technology to support a reputation approach to form groups. In particular, information about the reputation capital, certified by the blockchain, is used by an algorithm to drive group formation processes of agents in IoT environments.
Application of reflectance transformation imaging for the display of handwriting traces

In our Letter, two kinds of handwriting traces, colored and colorless, are studied by means of reflectance transformation imaging. The illumination direction and rendering mode can be changed alternatively to obtain two-dimensional and three-dimensional details of the traces that are not recognized easily by naked eyes. Furthermore, an objective evaluation method without reference is applied to evaluate the reconstructed images, which provides a basis for setting the illumination direction and rendering mode. Therefore, the handwriting trace information including the written content, the writing features, and the stroke order features can be obtained objectively and accurately.
Employee Performance as Function of Performance Management System: An Empirical Study Information Technology Enabled Services Companies around Hyderabad

In this manuscript we report the results outcome of an empirical study carried out on, how the employee performance is a function of performance management system in Information Technology Enabled Service Companies around Hyderabad. A survey was carried on the employees of IT Enabled Services companies around Hyderabad using a sample of 924 employees, consisting of 545 men and 379 women employees. The influence on performance management system was measured against the a) Factors responsible for improved employee performance and b) Factors that impact on performance management system. Using a structured survey instrument a five-point Likert-type scale, 13 factors of dependent variable performance management system was measured against the two independent variables 9 factors responsible for improved performance and 5 factors that impact the performance management applying multiple regression analysis. The survey instrument reliability and internal consistency was measured using reliability statistic Cronbach Alpha which is 0.84, 0.76 and 0.73 respectively, for performance management system, factors responsible for improvement performance management and factors that impact performance management system. The results reveal that both the independent variables significantly influencing the performance management system and there were statistically significant gender differences observed in the sample. However, there were no significant differences in different age groups of the employees.
INVITED: Protecting RISC-V against Side-Channel Attacks

Software (SW) implementations of cryptographic algorithms are vulnerable to Side-channel Analysis (SCA) attacks, basically relinquishing the key to the outside world through measurable physical properties of the processor like power consumption and electromagnetic radiation. Protected SW implementations typically have a significant timing and code size overhead as well as a substantially long development time because hands-on testing the result is crucial. Plenty of scientific publications offer solutions for this problem for all kinds of algorithms but they are not straightforward to implement as they rely on device assumptions which are rarely met, nor do these solutions take micro-architecture related leakages into account. We present a solution to this problem by integrating sidechannel analysis countermeasures into a RISC-V implementation. Our solution protects against first-order power or electromagnetic attacks while keeping the implementation costs as low as possible. We made use of state of the art masking techniques and present a novel solution to protect memory access against SCA. Practical results are provided that demonstrate the leakage results of various cryptographic primitives running on our protected hardware platform.CCS CONCEPTS•Security and privacy $\rightarrow$ Embedded systems security; Side-channel analysis and countermeasures;ACM Reference format:Elke De Mulder, Samatha Gummalla, and Michael Hutter. 2019. INVITED: Protecting RISC-V against Side-Channel Attacks. In Proceedings of The 56th Annual Design Automation Conference 2019, Las Vegas, NV, USA, June 2-6, 2019 (DAC ’19), 4 pages. https://doi.org/10.1145/3316781.3323485
Geometric calibration of a camera and a turntable system using two views

A calibration algorithm for a system comprising a camera and turntable is provided, in which the camera captures three-dimensional point clouds while a target object rotates on the turntable to three-dimensionally scan the entire object. A mathematical approach is used to obtain the geometry between the coordinate systems of the camera and the turntable. As a minimal setup, the camera captures two views of a chessboard calibration pattern placed on the turntable. The coordinate transformation from the camera to the turntable is then calculated through geometric reasoning. First, two camera poses are computed with respect to the chessboard images taken before and after rotation. The direction of the rotation axis of the turntable coordinate system can be calculated without any ambiguity. However, there is one degree of freedom in the choice of the other two directions and one degree of freedom in choosing the location of the origin of the turntable coordinate system. We provide a practical method for these choices. Experimental results with a stereo-vision system and a turntable validate the proposed method.
Cost Aggregation for Stereo Matching Using Total Generalized Variation With Fusion Tensor

Stereo matching methods have achieved remarkable improvements by exploiting various attempts. However, most stereo matching algorithms still suffer from problems such as ambiguous region and inherent ambiguities. In particular, some problems affecting cost aggregation step have the greatest impact on depth results. To resolve the above-mentioned problems, we propose a new cost aggregation method using the modified total generalized variation with fusion tensor. First, two kinds of diffusion tensors are extracted from the guidance color image and the guidance depth map. They are incorporated into an energy functional to obtain the total generalized variation. After formulating the final energy functional, it is optimized via a primal-dual energy minimization method. The performance of the proposed method is experimentally verified by qualitatively and quantitatively comparing the results to those of other algorithms.
Discovering Dialog Rules by Means of an Evolutionary Approach

Designing the rules for the dialog management process is one of the most resources-consuming tasks when developing a dialog system.Although statistical approaches to dialog management are becoming mainstream in research and industrial contexts, still many systems are being developed following the rule-based or hybrid paradigms.For example, when developers require deterministic system responses to keep total control on the decisions made by the system, or because the infrastructure employed is designed for rule-based systems using technologies currently used in commercial platforms.In this paper, we propose the use of evolutionary algorithms to automatically obtain the dialog rules that are implicit in a dialog corpus.Our proposal makes it possible to exploit the benefits of statistical approaches to build rule-based systems.Our proposal has been evaluated with a practical spoken dialog system, for which we have automatically obtained a set of fuzzy rules to successfully manage the dialog.
Energy-Based Modelling for Dialogue State Tracking

The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing.To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process.To this end, we present an energybased approach to dialogue state tracking as a structured classification task.The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including input features and output labels.We demonstrate that the energy-based approach improves the performance of a deep learning dialogue state tracker towards state-of-the-art results without the need for many of the other steps required by current state-of-the-art methods.
Modeling and Evaluating of Human 3d+t Activities in Virtual Environment

This paper studies the problem of evaluation of human 3d+t activities in Virtual Environments (VE) for Learning (VEL). Current evaluation methods focus mostly on: (i) the automatic identification of an ordered sequence of actions and/or (ii), an empirical analysis made by experts through the VE. In many cases, the learner’s activity can be represented by some specific time series made of geometrical data of 3D artefacts. For the extraction and analysis of such Motions Of Interest (MOI), one can manually segment them among the traces, and/or use automatic approaches requiring a database of annotated examples. Both cases usually require too many resources to design such environments. Consequently, this work presents a method allowing teachers to quickly build, compare and evaluate a 3d+t learning activity in VE. This method is based on a semi-automatic approach combining the Dynamic Time Warping algorithm, with 3D reference shapes and few expert’s demonstrations of the task to learn.
A new approach to Color Edge Detection

Most edge detection algorithms deal only with grayscale images, and the way of adapting them to use with RGB images is an open problem.In this work, we explore different ways of aggregating the color information of a RGB image for edges extraction, and this is made by means of well-known edge detection algorithms.In this research, it is been used the set of images from Berkeley.In order to evaluate the algorithm's performance, F measure is computed.The way that color information -the different channels-is aggregated is proved to be relevant for the edge detection task.Moreover, post-aggregation of channels performed significatively better than the classic approach (pre-aggregation of channels).
New Approach for Context-dependent Learner Model for Technology Enhanced Learning System

Learner modeling still the object of very active research in the technology-enhanced learning system.It makes it possible to represent a complete description of the learner.In this paper, we have proposed a new learner modeling approach which is independent on the learning system and dependent on the learning context.The proposed model involved four information categories: Personal data describing the general information about the learner; Cognitive data representing the learner performances and knowledge; Activity data providing details about learner activities during the learning process.Finally, contextual data presenting a description about learner context such as location, device, accessibility, connectivity, etc.To ensure a better representation of these characteristics, we have proposed an ontology-based learner model in order to benefit of advantages gained from the use of ontological technology, like extensibility, usability, exchanging information, inferring new knowledge by reasoning on an existing one.
A method to recognize entering and leaving person based on door opening and closing movement using angular velocity sensor

In our daily lives, we use places shared by multiple people such as homes and offices. Although many methods for individual recognition have been proposed, when they are used in such places, they have some problems in terms of psychological and physical burden. Therefore, in this research, we propose a method to recognize a person who entered or left the room by using the opening and closing movement of a door. In the proposed method, an angular velocity sensor is installed on the doorknob, and the user is identified based on personal characteristics of door's motion that is naturally accompanied with entering and leaving a room. We implement the proposed system on a door with a lever handle typed doorknob for a target scene of general household. From experimental results that F value of user's leaving motion was 0.90 and user's entering motion was 0.73, we confirmed the effectiveness of our method.