Reviewerly/exHarmony huggingface dataset:
Multiple Degree Authentication in Sensible Homes basedon IoT Device Vulnerability

Authentication plays an important role by providing security in the Internet of Things. The frequent use of IoT devices in today's world has explored them to more vulnerabilities and things which communicate through the internet for security purpose. Most devices that are used in smart homes are delicately authenticated with much exposure to the security breach, or only one such authentication is been used for them. Thus, the multiple degree Authentication of high vulnerable devices is necessary and important while non-vulnerable devices can have a single degree Authentication aspect in today's technology scenario. The discussed method facilitates the additional security for the IoT devices based on the security parameter which defines the susceptibility of the device, Interaction of user by the device. That can bound to say for single or multiple authentication schemes for particular IoT devices. In this work, for authentication, multiple security mechanisms are used like device authentication based on Password, device authentication based on One Time Password (OTP), device authentication based on certificates. The discussed model uses the lightweight MQTT protocol to send the device data of authentication into the IoT cloud system. Thus, the degree of authentication depends on the devices and their behavior like the type of design and type of data handling along with the connections.
Examination on face recognition method based on type 2 blurry

In recent years, technology of face recognition has developed rapidly, more and more face recognition technologies have been integrated into our work and life. In practical applications, due to influence of various factors, the resolution of the face image is low, the noise interference is large, a nd the illumination changes sharply during the imaging process, which brings difficulties to the face recognition, which seriously affects the accuracy of the face recognition method. This paper aims to introduce two-type fuzzy theory into face recognition and study its extraction and recognition methods of face feature. Firstly, it introduce the face recognition technology simply. Face recognition is a technique that uses a computer to analyze a face image and extract valid identification information to identify the identity. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two methods for extracting features from face recognition. Principal Component Analysis (PCA) is a data analysis method that uses a small number of characterizations to reduce the number of dimensions, which reduces computational complexity greatly. The purpose of linear discriminant analysis is to extract data from high-dimensional feature spaces. Extracted the low-dimensional features with recognition ability, and studied the two-type fuzzy system based on fuzzy sets deeply. Obtained the output function of the two-type fuzzy system by studying the structure of each layer of the two-type fuzzy system. Introduce two types of fuzzy ideas into linear discriminant analysis. Discussed the construction of fuzzy membership functions, the selection of kernel functions and the determination of clustering rules. Finally, the ORL face database of the trained fuzzy face recognition model. As a result, the face recognition method based on the type 2 fuzzy has certain feasibility. The experimental results show that face recognition based on interval two-type fuzzy neural network has good recognition rate and anti-noise ability.
A Conference Paper Exploring System Based on Citing Motivation and Topic

Understanding and maintaining the intended meaning of original text used for citations is essential for unbiased and accurate scholarly work. To this end, this study aims to provide a visual system for exploring the citation relationships and motivations for citations within papers. For this purpose, papers from the IEEE Information Visualization Conference that introduce research on data visualization were collected, and based on the internal citation relationships, citation sentences were extracted and the text were analyzed. In addition, a visualization interface was provided to identify the citation relationships, citation pattern information, and citing motivation. Lastly, the pattern analysis of the citation relationships along with the citing motivation and topic was demonstrated through a case study. Our paper exploring system can confirm the purpose of specific papers being cited by other authors. Furthermore, the findings can help identify the characteristics of related studies based on the target papers.
Aggregation of Convolutional Neural Network Estimations of Homographies by Color Transformations of the Inputs

The standard approach to the estimation of homographies consists in the application of the RANSAC algorithm to a set of tentative matches. More recent strategies based on deep learning, namely convolutional architectures, have become available. In this work, a new algorithm for the estimation of homographies is developed. It is rooted in a convolutional neural network for homography estimation, which is provided with a range of versions of the input pair of pictures. Such versions are generated by perturbation of the color levels of the input images. Each generated pair of images yields a distinct estimation of the homography, and then the estimations are combined together to obtain a final, more robust estimation. Experiments have been designed and carried out to test the validity of our approach, including qualitative and quantitative performance measures. In particular, it is demonstrated that our approach consistently outperforms the baseline approach consisting of using the output of the homography estimation deep network for the original input pair of images.
Partitions of matrix spaces with an application to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e22" altimg="si7.svg"><mml:mi>q</mml:mi></mml:math>-rook polynomials

We study the row-space partition and the pivot partition on the matrix space Fqn×m. We show that both these partitions are reflexive and that the row-space partition is self-dual. Moreover, using various combinatorial methods, we explicitly compute the Krawtchouk coefficients associated with these partitions. This establishes MacWilliams-type identities for the row-space and pivot enumerators of linear rank-metric codes. We then generalize the Singleton-like bound for rank-metric codes, and introduce two new concepts of code extremality. Both of them generalize the notion of MRD code and are preserved by trace-duality. Moreover, codes that are extremal according to either notion satisfy strong rigidity properties analogous to those of MRD codes. As an application of our results to combinatorics, we give closed formulas for the q-rook polynomials associated with Ferrers diagram boards. Moreover, we exploit connections between matrices over finite fields and rook placements to prove that the number of matrices of rank r over Fq supported on a Ferrers diagram is a polynomial in q, whose degree is strictly increasing in r. Finally, we investigate the natural analogues of the MacWilliams Extension Theorem for the rank, the row-space, and the pivot partitions.
A Weighted PageRank-Based Bug Report Summarization Method Using Bug Report Relationships

For software maintenance, bug reports provide useful information to developers because they can be used for various tasks such as debugging and understanding previous changes. However, as they are typically written in the form of conversations among developers, bug reports tend to be unnecessarily long and verbose, with the consequence that developers often have difficulties reading or understanding bug reports. To mitigate this problem, methods that automatically generate a summary of bug reports have been proposed, and various related studies have been conducted. However, existing bug report summarization methods have not fully exploited the inherent characteristics of bug reports. In this paper, we propose a bug report summarization method that uses the weighted-PageRank algorithm and exploits the 'duplicates’, ‘blocks’, and ‘depends-on’ relationships between bug reports. The experimental results show that our method outperforms the state-of-the-art method in terms of both the quality of the summary and the number of applicable bug reports.
Defending Against Stealthy Attacks on Multiple Nodes With Limited Resources: A Game-Theoretic Analysis

Stealthy attacks have become a major threat to cybersecurity. Previous works in this direction fail to capture the practical resource constraints and mainly focus on one-node settings. In this article, we propose a two-player game-theoretic model, including a system of multiple independent nodes, a stealthy attacker, and an observable defender. In our model, the attacker can fully observe the defender's behavior and the system state, whereas the defender has zero feedback information. Furthermore, a strict resource constraint is introduced to limit the frequency of the attacks/defenses for both players. We characterize the best responses for both attacker and defender under both nonadaptive and adaptive strategies. We then study the sequential game where the defender first announces its strategy and the attacker then responds accordingly. We have designed an algorithm that finds a nearly optimal strategy for the defender and provides a full analysis of its complexity and performance guarantee.
RM- RSA algorithm

The RSA public key cryptographic strength (either security of encryption and decryption schemes or efficiency of algorithms) depends upon the complexity in factorizing the largest integer into odd primes. For every chosen integer, the RSA’s strength varies. RSA key generation space is the group of non-negative integers relatively prime to φ(n) and encryption, decryption spaces are both equal to the group of integers relatively prime to n. Operational time and data storage are two of its efficiency aspects. To reduce the operational time along with reduction data storage is a difficult task in RSA public key cryptography. In addition that the Security of RSA public key cryptography for an adversary with polynomial bounds computations is another biggest task. In this paper, we are going to propose a new kind of public key cryptographic system, nearly four times more efficient in data storage and operational time to that of RSA and probably one fourth of security complexity of RSA. This new crypto system is possible only based on one of the strong pseudo prime test :Rabin-Miller test. So, name it as RM-RSA. In this way,we are going to compare the efficiency of that RM-RSA and previously our proposed S-RSA in “Solovay-Strassen test in a RSA Pubic key Cryptosystem”.
REST2Bot: Bridging the Gap between Bot Platforms and REST APIs

With the development of REST (REpresentational State Transfer) APIs, many applications have been designed to harness their potential. As such, bots emerged recently as natural interfaces to facilitate conversations between humans and API-accessible services. Existing bot development platforms (e.g., Dialogflow, Wit.ai) facilitate building bots, but bot developers are still required to provide training data by defining corresponding intents (user’s intention such as booking a hotel) and entities (e.g., hotel location) for each API. Moreover, bot developers are required to build and deploy webhook functions to invoke APIs on intents detection. In this paper, we introduce REST2Bot, a tool that addresses these shortcomings (e.g., translating APIs to Intents, and invoking APIs based on detected Intents) in bot development frameworks to automate several tasks in the life cycle of the bot development process. REST2Bot relies on automated approaches for parsing OpenAPI specifications, generating training data, building bots on desired bot development frameworks, and generating deployable webhook functions to map intents and entities to APIs.
A Comparative Study of Two Complex Ontologies in Air Traffic Management

Over the past 25 years, multiple different data models have been introduced to standardize information management and facilitate data exchange and integration in the aviation domain. As a next step in the evolution of aviation data management, ontologies capturing the semantics (concepts, properties, and relationships) have been produced based on those models. In this paper, we describe a study comparing two recently released and independently-developed complex ontologies focused on Air Traffic Management (ATM) - the NASA ATM Ontology and an ontology derived from the ATM Information Reference Model. We develop a methodology for manually comparing two ontologies and identifying what we describe as exact, light, and mismatches between concepts in the two ontologies. We also describe a classification scheme that characterizes mismatches in terms of the general reason for the mismatch. This approach can be applied to improve existing ATM ontologies and foster interoperability, which will benefit aviation stakeholders.
Forensic Analysis as Iterative Learning

This chapter covers the added value of forensic analysis in cybersecurity of critical infrastructure. In the context of the current threat landscape, this chapter details the role of forensic analysis in cybersecurity, concentrating on forensic preparedness, incident scope assessment, forensic intelligence, and an agile cycle for iteratively improving security using insights gathered from scrutinizing prior cyberattacks.
Magnetic Resonance Imaging Visualization in Fully Immersive Virtual Reality

The availability of commercial fully immersive virtual reality systems allows the proposal and development of new applications that offer novel ways to visualize and interact with multidimensional neuroimaging data. We propose a system for the visualization and interaction with Magnetic Resonance Imaging (MRI) scans in a fully immersive learning environment in virtual reality. The system extracts the different slices from a DICOM file and presents the slices in a 3D environment where the user can display and rotate the MRI scan, and select the clipping plane in all the possible orientations. The 3D environment includes two parts: 1) a cube that displays the MRI scan in 3D and 2) three panels that include the axial, sagittal, and coronal views, where it is possible to directly access a desired slice. In addition, the environment includes a representation of the brain where it is possible to access and browse directly through the slices with the controller. This application can be used both for educational purposes as an immersive learning tool, and by neuroscience researchers as a more convenient way to browse through an MRI scan to better analyze 3D data.
Distance-based Bootstrap Sampling in Bagging for Imbalanced Data-Set

In the recent decade, many new technologies and problems have attracted attention from research community/scientists. Some problems like imbalanced data-set, security and privacy concerns in various computing environments like internet connected thing (IoTs), cloud computing, distributed computing, etc., have received many innovative ways as efficient answer. But, some problems are still unsolved. Learning from Imbalance dataset with higher accuracy is an essential task/higher priority work in many applications. For handling the class imbalance problems, many extended approaches have been considered for bagging ensembles. In our study we show that application of distance-based approach (DistBagging) for balancing the distribution of each bag in ensemble bagging provides better results for addressing the class imbalance problems. In this work, we propose distance-based approaches for selecting the group of data for each bootstrap method to improve the performance of the classification in terms of accuracy for minority class in the imbalanced class distribution environment. The experimental results show that our distance-based approach outperforms the other ensemble techniques in the previous studies.
Study and Realization of A Monoaxial Solar Tracker Over an Equatorial Mount

A solar tracker is a mechanism that helps the photovoltaic panel to increase its power while keeping the latter oriented towards the sun. This paper aims to introduce an adjustment that makes our solar tracker efficient, by using a single tracking axis instead of two, in order to minimize the cost of production and the power dissipated, without reducing the efficiency of our photovoltaic panel. We chose a tracking mechanism used for telescopes, called equatorial mount since it allows us to follow perfectly the objects of the sky (the sun, the stars and the planets) with a single axis (this is the tracking axis). We began with a theoretical comparison, and simulation of the solar irradiation amount absorbed by our proposed system, and a perfect tracking system in order to derive the lost amount. The obtained results reveal that the decrease is negligible esteemed to be about 5%. After completing the realization project, we tested it by measuring the production and consumption of our tracker for a period of 24 hours. The results show that our solar tracker has a power gain of 49% compared to the fixed system, with minimal energy consumption.
A Queue Waiting Cost-Aware Control Model for Large Scale Heterogeneous Cloud Datacenter

Optimal load distribution and speed scaling in a heterogeneous data center that takes account of the tradeoff between the maintenance and operation costs and system performance are crucial issues of cloud computing. Due to the changing states of cloud centers and diversity of the task arrival rate, a static control model is infeasible for cloud computing. In this article, we aim to provide a novel multi-server control model with dynamic feedback to acquire dynamic states of the cloud system, and with queue waiting cost-awareness to optimize the queue wait time and load distribution in task assignment and server configuration management. Using the technology of speed scaling, each server in a data center is configured as an <inline-formula><tex-math notation="LaTeX">$M/M/1$</tex-math></inline-formula> queue system with variable service rate, and the service rate is a function of the length of the task queue. We formulate two optimization problems, the optimal load distribution problem and the optimal service rate controlling problem, and provide algorithms to solve these problems, facilitating load distributions and service rate adjustments. We also present numerical simulations to validate our model. The results show our model to be efficient in multi-server dynamic configurations and task assignments according to the feedback information for the tradeoff between the system cost and performance.
Automatic Transformation of a Video Using Multimodal Information for an Engaging Exploration Experience

Exploring the content of a video is typically inefficient due to the linear streamed nature of its media and the lack of interactivity. While different approaches have been proposed for enhancing the exploration experience of video content, the general view of video content has remained basically the same, that is, a continuous stream of images. It is our contention that such a conservative view on video limits its potential value as a content source. This paper presents An Alternative Representation of Video via feature Extraction (RAAVE), a novel approach to transform videos from a linear stream of content into an adaptive interactive multimedia document and thereby enhance the exploration potential of video content by providing a more engaging user experience. We explore the idea of viewing video as a diverse multimedia content source, opening new opportunities and applications to explore and consume video content. A modular framework and algorithm for the representation engine and template collection is described. The representation engine based approach is evaluated through development of a prototype system grounded on the design of the proposed approach, allowing users to perform multiple content exploration tasks within a video. The evaluation demonstrated RAAVE’s ability to provide users with a more engaging, efficient and effective experience than a typical multimedia player while performing video exploration tasks.
Robust Evaluation of Language–Brain Encoding Experiments

Language–brain encoding experiments evaluate the ability of language models to predict brain responses elicited by language stimuli. The evaluation scenarios for this task have not yet been standardized which makes it difficult to compare and interpret results. We perform a series of evaluation experiments with a consistent encoding setup and compute the results for multiple fMRI datasets. In addition, we test the sensitivity of the evaluation measures to randomized data and analyze the effect of voxel selection methods. Our experimental framework is publicly available to make modelling decisions more transparent and support reproducibility for future comparisons.
Chinese Relation Extraction Using Lattice GRU

Most of the existing Chinese entity relation extraction models have adopted methods which are based on character or word input. However, the word-based input method ignores potential word information in the sentence, and the method based on word input will be affected by the spread of word segmentation errors. In order to solve these problem, we introduce a lattice model that can merge words and characters input and use BiGRU instead of BiLSTM. After comparative experiments, our model achieves better results.
Comparison of SVM And BPNN Methods in The Classification of Batik Patterns Based on Color Histograms And Invariant Moments

Currently, various types and patterns of batik in Indonesia have been widely documented. Nevertheless, there are many obstacles in classifying batik's data, because the classification of batik has not been based on standard types of motifs. In this research, classification of batik's motif patterns based on color histograms and invariant moments was carried out. For the batik's pattern recognition method, two methods are tested and compared, namely the Backpropagation Neural Network (BPNN) method, and the Support Vector Machine (SVM) method. The speed of the pattern recognition process of batiks' motif using SVM method requires an average processing time of 0.77 milliseconds, while the BPNN method requires an average time of 3.59 milliseconds. The accuracy of the classification of batik's pattern test data using the SVM method has an accuracy of 88.33 percent, while the BPNN method has an accuracy of 76.25 percent. The development of this research can be done by improving methods and variations in image data of batik's motif pattern.
TNT: How to Tweak a Block Cipher

In this paper, we propose Tweak-aNd-Tweak ( $$\mathsf {TNT}$$ for short) mode, which builds a tweakable block cipher from three independent block ciphers. $$\mathsf {TNT}$$ handles the tweak input by simply XOR-ing the unmodified tweak into the internal state of block ciphers twice. Due to its simplicity, $$\mathsf {TNT}$$ can also be viewed as a way of turning a block cipher into a tweakable block cipher by dividing the block cipher into three chunks, and adding the tweak at the two cutting points only. $$\mathsf {TNT}$$ is proven to be of beyond-birthday-bound $$2^{2n/3}$$ security, under the assumption that the three chunks are independent secure n-bit SPRPs. It clearly brings minimum possible overhead to both software and hardware implementations. To demonstrate this, an instantiation named TNT-AES with $$6 $$ , $$6 $$ , $$6 $$ rounds of AES as the underlying block ciphers is proposed. Besides the inherent proven security bound and tweak-independent rekeying feature of the $$\mathsf {TNT}$$ mode, the performance of TNT-AES is comparable with all existing TBCs designed through modular methods.