Reviewerly/exHarmony huggingface dataset:
Improving Software Architecture Design Decision by Selecting Set of Solutions

All software systems are created on the basis of design decisions that are made during software architecture design. The design decisions are taken in earlier phases of software architecture on the architecturally significant requirements (ASRs). Selecting a suitable architecture based on architecture design decision (ADD) is a challenging task. There are several methods, tools, and techniques that have been proposed to address these challenges in selection of software architecture based on ADD. However, they do not thoroughly cover the architectural design decision significantly and systematically. In this paper, we propose an approach that helps the software architects to thoroughly define and trace the architectural design decision significantly and systematically. We conduct online survey to evaluate proposed approach. Result show that the proposed approach would achieve better results in ADDs. The proposed approach helps the software architects in taking decision while software architecting phase. We claim that the approach facilitate the software architects in taking decision in ADDs.
Denoising Hyperspectral Images Using an Improved SSTV Correntropy based Method in the Presence of Non-Gaussian Noise

Denoising hyperspectral images (HSIs) has attracted a lot of attention in remote sensing due to its high significance in the enhancement of the quality of these images. The present methods for denoising HSIs are generally focused on Gaussian noisy environments while in practical cases, non-Gaussian noise usually exists in HSIs. In order to consider this fact, first a general model for noise is introduced. This model constitutes a non-Gaussian noise accompanied with sparse noise. Later, we use information theoretic learning criteria to introduce a cost function based on Correntropy which is formulated as an optimization problem in order to solve the denoising problem. Finally, this problem is solved using the Half-Quadratic algorithm. The results of the experiment with real data show that the proposed method can significantly reduce the noise in these images which is actually a more general form of noise.
Joint Target Tracking and Classification with Scattering Center Model for Radar Sensor

In this paper, a model-based joint tracking and classification (JTC) method for wideband radar is proposed. The method is based on 3D scattering center model (3D-SCM), which is used to generate the predicted high range resolution profile (HRRP) with the information of target aspect angle. The aspect angle is derived from the target state with particle filter. Target classification is implemented through the data match and correlation of observed HRRP with predicted HRRPs. For the continuity of tracking procedure, the estimate uncertainty of target state and class probability decreases as the filter's recursion proceeds, and thus to obtain a stable tracking and classification result. The effectiveness of the proposed method is validated by simulation results for maritime ship targets.
Initialization Vector Generation for AES-CTR Algorithm to Increase Cipher-text Randomness

In the present, the use of communication systems is not a luxury but a necessity. The growing reliance on technology and its uses in all walk of life have realized the need for robust security systems for communication. The Advanced Encryption Standard (AES) in Counter (CTR) mode support high-speed operations due to parallel architecture and having no feedback or chaining mode. The AES-CTR algorithm involves encryption of Initialization Vector (IV) with a secret key for generation of ciphertext. A unique IV is required in the AES-CTR algorithm for maintaining the secrecy of data and generation of unique ciphertext. Previously, the researchers utilized the GEFFE generator for generation of IV as well as right-shift and increment operation were used for IV generation. The combination of these two algorithms can provide an increase in ciphertext randomness as compared to using it individually. In this work, we have proposed an IV generation algorithm based on the GEFFE generator and right-shift and increment operation for the AES-CTR algorithm. The proposed algorithm generates an increase in randomness for data security. The comparative analysis with other techniques shows that our algorithm has better in performance and security.
Metric based reusability analysis of software systems

AbstractAbstractMetric suite is the most discussed term in the software community, when there is a need of quantization of quality to evaluate a software system. Software quality can be estimated by analyzing the key quality attributes of the software system. For a very long time in literature, quality in software products has been deliberately emphasized. But quality can be controlled by making it quantifiable, i.e. by measuring it. Reusability is an influential aspect to enhance the productivity and quality of software systems. Reusability is one of the core software development process. As the technological improvement in the programming languages is done, the scope and awareness to the reusability is increased. Reusability provides an easy form of software development by utilizing existing software projects or components. Toward this goal our previous work has presented a reusability framework for migration of object-oriented system to component-based system and defined a suite of four metrics to assess the reusability. In this paper, we work towards assessing the reusability aspect of these metrics for individual component collected from reputed repositories.Subject Classification: 68N99Keywords: AttributesCorrelationFrameworkSoftware metricsReusabilityQuality
Improved Predictor-Corrector Guidance with Hybrid Lateral Logic for No-fly Zone Avoidance

In this paper, an improved predictor-corrector algorithm with hybrid lateral logic is proposed to enhance the maneuverability during re-entry, which lets the reusable launch vehicle (RLV) possess the ability of avoiding a no-fly zone. First, the longitudinal guidance is converted to the quasi-equilibrium guidance algorithm once current states satisfy the joint point condition. During each guidance period in the gliding phase, a quadratic parametric model is devised to adjust the magnitude of the bank angle instead of utilizing the linear one. Then, an artificial potential field based lateral guidance law is designed for no-fly zone avoidance by transforming the problem into finding the reference heading angle. But the bank angle reverses while the RLV is approaching the target, which may aggravate the instability problem and increase the energy consumption. Aiming at above problem, the lateral guidance is combined with conventional heading angle deadband corridor, which works when the RLV leaves the influence area of the no-fly zone. Finally, numerical simulations show that the proposed lateral guidance logic not only is effective for no-fly zone, but also performs well in reducing the times of reversals. The Monte Carlo simulation results further demonstrate the robustness of the above guidance algorithm considering the random initial dispersions and errors.
Optimal Representative Distribution Margin Machine for Multi-Instance Learning

Multi-instance learning (MIL) plays an important role in many real applications, such as image recognition and text classification.The instance-based approach selects instances in each bag to train and has drawn significant attention recently.However, less work took the distribution information in the account and the margin distribution has been proven to be important to the generalization performance.In this paper, we propose an optimal representative distribution margin approach for multi-instance learning (MIORDM).The representative instances are the samples from the instance space and the distribution of them is important for us to find the best separation hyperplane.As the representative instances are selected iteratively, in each iteration, the instances will be more precise by the best hyperplane and the model will be better in the next iteration.In this way, a well-performed method can be derived with better generalization performance.Experiments compared with other types of state-of-the-art approaches on different datasets show that our method outperforms the others and achieves better generalization performance.
Improving Security Control of Text-Based CAPTCHA Challenges using Honeypot and Timestamping

The resistance to attacks aimed to break CAPTCHA challenges and the effectiveness, efficiency and satisfaction of human users in solving them called usability are the two major concerns while designing CAPTCHA schemes. User-friendliness, universality, and accessibility are related dimensions of usability, which must also be addressed adequately. With recent advances in segmentation and optical character recognition techniques, complex distortions, degradations and transformations are added to text-based CAPTCHA challenges resulting in their reduced usability. The extent of these deformations can be decreased if some additional security mechanism is incorporated in such challenges. This paper proposes an additional security mechanism that can add an extra layer of protection to any text-based CAPTCHA challenge, making it more challenging for bots and scripts that might be used to attack websites and web applications. It proposes the use of hidden text-boxes for user entry of CAPTCHA string which serves as honeypots for bots and automated scripts. The honeypot technique is used to trick bots and automated scripts into filling up input fields which legitimate human users cannot fill in. The paper reports implementation of honeypot technique and results of tests carried out over three months during which form submissions were logged for analysis. The results demonstrated great effectiveness of honeypots technique to improve security control and usability of text-based CAPTCHA challenges.
A new parallel galactic swarm optimization algorithm for training artificial neural networks

Metaheuristic algorithms are a family of algorithms that help solve NP-hard problems by providing near-optimal solutions in a reasonable amount of time. Galactic Swarm Optimization (GSO) is the state-of-the-art metaheuristic algorithm that takes inspiration from the motion of stars and galaxies und er the influence of gravity. In this paper, a new scalable algorithm is proposed to help overcome the inherent sequential nature of GSO and helps the modified version of the GSO algorithm to utilize the full computing capacity of the hardware efficiently. The modified algorithm includes new features to tackle the problem of training an Artificial Neural Network. The proposed algorithm is compared with Stochastic Gradient Descent based on performance and accuracy. The algorithm’s performance was evaluated based on per-CPU utilization on multiple platforms. Experimental results have shown that PGSO outperforms GSO and other competitors like PSO in a variety of challenging settings.
Instructional Design and Online Standards

Online education has a foundation so that beneficial practices can leverage online environments effectively. Besides generic instructional design principles, models of good online instructional design are emerging. These practices and models are codified into online instructional design standards that provide research-based criteria that can be used to measure the degree to which such instructional designs meet those standards and can serve as guidelines of factors to consider when designing online instruction. This chapter provides an overview of instructional design as it applies to online teaching and learning. It also discusses how standards can help improve such instructional design in order to optimize student learning and achievement.
Ranking Internet Service Providers using Fuzzy Multi Criteria Decision Making Method

Internet service providers (ISPs) are companies that generally provide Internet-related services to customers in multiple forms. They can be classified as Access providers, Mailbox providers, Transit ISPs, Virtual ISPs, Free ISPs or Wireless ISPs. ISPs offer their services at variable costs and such offerings come with varied service levels. Different organizations and individuals pay for the acquired service or pay well in advance to acquire the same. The number of service providers grows day by day and each provider offers plenty of plans with many service levels to cater the need of different customers. Customers have to make a right selection so that they get the complete benefit out of it. The focus of this paper is to help customers to make a judicious choice using a hierarchical estimation framework. This framework uses various QoS based parameters to rank the currently available ISPs in the market using Fuzzy based Multi-criteria decision-making (MCDM) methods like Fuzzy-AHP and Fuzzy-TOPSIS.
Joint image restoration and matching method based on distance-weighted sparse representation prior

Image matching is a classic problem in the field of image processing, aims to locate the unique position of the real-time images and has been widely used in visual-based navigation systems. However, most of the works on image matching simply assume the ideal inputs without considering the degradation of the real world, such as image blur. In the presence of such a situation, the traditional matching methods usually first resort to image restoration and then perform image matching with the restored image. However, by treating the restoration and matching separately, the accuracy of blurred image matching will be reduced by the defective output of the image restoration. In this paper, we propose a joint blurred image restoration and matching method based on distance-weighted sparse representation (JRM-DSR), which utilizes the sparse representation prior to exploit the correlation between restoration and matching. This prior assumes that the blurred image, if correctly restored, can be well represented as a sparse linear combination of the dictionary constructed by the reference image. In order to achieve more accurate matching results to help image restoration, we consider both local and sparse information as well as adopt distance-weighted sparse representation to obtain better representation coefficients. By iterative restoring the input image in pursuit of the sparest representation, our approach can achieve restoration and match simultaneous, and these two tasks can benefit greatly from each other. Moreover, a coarse-to-fine matching strategy is proposed to further improve the matching accuracy and search efficiency. Experiments demonstrate the effectiveness of our method compared with conventional methods.
Grouper: Accelerating Hyperparameter Searching in Deep Learning Clusters With Network Scheduling

Training a high-accuracy model requires trying hundreds of configurations of hyperparameters to search for the optimal configuration. It is common to launch a group of training jobs (named cojob) with different configurations at the same time and stop the jobs performing worst every stage (i.e., a certain number of iterations). Thus deep learning requires minimizing stage completion time (SCT) to accelerate the searching. To quickly complete the stages, each job in the cojob typically uses multiple GPUs to perform distributed training. The GPUs exchange data per iteration to synchronize their models through the network. However, data transfers of DL jobs compete for network bandwidth since the GPU cluster hosts a number of cojobs from various users, resulting in network congestion and consequently a large SCT for cojobs. Existing flow schedulers aimed at reducing flow/coflow/job completion time mismatch the requirement of hyperparameter searching. In this paper, we implement a system Grouper to minimize average SCT for cojobs. Grouper adopts a well-designed algorithm to permute stages of cojobs and schedules flows from different stages in the order of the permutation. The extensive testbed experiments and simulations show that Grouper outperforms advanced network designs Baraat, Sincrona, and per-flow fair share.
A Bayesian nonparametric model and its application in insurance loss prediction

Predicting insurance losses is an eternal focus of actuarial science in the insurance sector. Due to the existence of complicated features such as skewness, heavy tail, and multi-modality, traditional parametric models are often inadequate to describe the distribution of losses, calling for a mature application of Bayesian methods. In this study we explore a Gaussian mixture model based on Dirichlet process priors. Using three automobile insurance datasets, we employ the probit stick-breaking method to incorporate the effect of covariates into the weight of the mixture component, improve its hierarchical structure, and propose a Bayesian nonparametric model that can identify the unique regression pattern of different samples. Moreover, an advanced updating algorithm of slice sampling is integrated to apply an improved approximation to the infinite mixture model. We compare our framework with four common regression techniques: three generalized linear models and a dependent Dirichlet process ANOVA model. The empirical results show that the proposed framework flexibly characterizes the actual loss distribution in the insurance datasets and demonstrates superior performance in the accuracy of data fitting and extrapolating predictions, thus greatly extending the application of Bayesian methods in the insurance sector.
Design of Pipelined Parity Preserving Double Precision Reversible Floating Point Multiplier Using 90 nm Technology

In semiconductor industries, speed of operation and the size of the computational devices are playing a major role. Size of a single transistor may limit the scaling of semiconductor devices. In turn, an alternative technology is needed for faster computation and to reduce the size of computational devices. One such technology is Reversible Logic. Parity preserving is a property that is added to reversible gates with the help of which, presence of errors can be detected. In this Paper, Parity Preserving Reversible Floating point multiplier (RFPM) which uses IEEE 754 standard representation is proposed using P2RG and Fredkin Reversible gates. This RFPM uses Carry select adder for Exponent addition and Carry save Multiplier for Mantissa multiplication. The architecture also includes a multiplexer, Bit slicer, rounding and normalization register. This Pipelined RFPM architecture is coded using Verilog and the same has been simulated using Modelsim 10.7c. Parameters such as Area, Power, Cell Utilization and Maximum operating frequency have been analyzed using Cadence Genus Synthesis 14.25. On Comparison of Pipelined RFPM with Non Pipelined RFPM between 180nm and 90nm technology, the results show that Pipelined RFPM using 90nm technology, occupies lesser Area and uses lesser number of cells by 67.17% and 4.42% respectively whereas, the Maximum operating frequency has increased by a factor of 2.73.
Intelligent Vision with TensorFlow using Neural Network Algorithms

Computer vision and video analytics are the torrid research area in Machine learning and their establishment process traditionally starts with object detection and eventually tracking. In recent years, there is a tremendous growth in performing comprehensive study based on the field of object detection and Pattern Analysis. In our system we have improvised and experimented with detection method based on machine learning and deep learning approach in object recognition and pattern analysis. We assume object detection as a retrogression problem to spatially separated corresponding class probabilities and bounding boxes. Many prominent algorithms have been designed for object detection, Pattern Analysis and tracking, which also includes edge tracking, color segmentation and pattern matching. A single neural network is capable of predicting class probabilities and bounding boxes directly from the full image per cycle. Therefore we have used various neural network algorithms such as YOLOv3, Single Shot Multiple detection algorithm to carry out video analysis using object detection and drowsiness detection using pattern or behavior analysis with the help of Tensorflow. The framework will recognize object continuously, from the input perceived through camera where it can apparently capture a required frames to predict the object and also to match the pattern. It has been accomplished using real-time video processing and a single camera. The proposed system is versatile to operate in complex, real time, non-plain environment.
User Behavior Prediction using A Novel Sentence N-Gram Model

Humans normally use language contextually and make their decisions. This intelligence if transferred to a computer can assist in effective decision making. There is a need to replicate the contextual behavior of humans using web mining techniques. The aim of this work is to ingest real time data from social media data sources and harvest in a data warehouse. The raw data is preprocessed to eliminate the anomalies present raw data. The data is then parsed for creating user profiles. The N-Grams vocabulary is constructed and mapped with the synonym and abbreviation vocabularies. The user behavior is analyzed contextually using the user comments and their corresponding action in the past. The model is trained using the historical data extracted from the same sources. Various supervised machine learning techniques are used for predicting the succeeding comment and their performance is assessed. Conditional probability is assigned to each N-gram using the frequency of occurrence. The comment that has the highest probability value is predicted as the next comment. The performance analysis demonstrates that the prediction using ensemble approaches yield better results.
Extracting Principal Smartness Dimensions of Smart Speakers Using Topic Modeling and Sentiment Analysis

Although the smart speaker market has experienced massive growth in recent years, there is a lack of research on what consumers really consider important for so called "smart" speakers, which are supposedly distinguished from traditional products. Therefore, this study aims at identifying key smartness dimensions that are related to the satisfaction of smart speaker users. First, a total of seven topics were extracted from the Amazon's review data through Latent Dirichlet Allocation, and the topics were mapped, through group discussion, to three smartness elements defined from the literature review. Then, sentiment scores of each topic were calculated using SentiWordNet, which were then used as variables to develop star rating classifiers. The feature importance of the classifiers revealed that the connectivity issue is the most influential factor in determining the customer satisfaction of smart speakers. The next important topics are sound quality and its use as a media player. The study findings have direct practical implications on smart speaker development.
Efficiency measurement of data centers: An elucidative review

Most enterprises host their critical data in data centers to have a secure, robust environment supporting the highest uptime for their IT operations. As per industry best practices, standards, conference papers and journals; there are various ways suggested through which efficiency of data center is calculated. This review paper provides information on the efficiency calculation factors relatum to data center loads and its classification. It describes how measurement of data center efficiency is to be done and which are the crucial components which need to be considered for calculating the true values accurately for power usage efficiency (PUE) and data center infrastructure efficiency (DCiE).Basis on researchfindings & working towards data center efficiency measurement, the future research scope has been identified that how various techniques can be applied on data center major components - Power & Cooling which further can be helpful towards improvement of Data Center efficiency.