Reviewerly/exHarmony huggingface dataset:
Applying Cognitive and Neural Network Approach over Control Flow Graph for Software Defect Prediction

research-article Share on Applying Cognitive and Neural Network Approach over Control Flow Graph for Software Defect Prediction Authors: Kumar Rajnish Birla Institute of Technology, India Birla Institute of Technology, IndiaView Profile , Vandana Bhattacharjee Birla Institute of Technology, India Birla Institute of Technology, IndiaView Profile , Vishnu Chandrabanshi Birla Institute of Technology, India Birla Institute of Technology, IndiaView Profile Authors Info & Claims IC3 '21: 2021 Thirteenth International Conference on Contemporary Computing (IC3-2021)August 2021 Pages 13–17https://doi.org/10.1145/3474124.3474127Online:04 November 2021Publication History 0citation38DownloadsMetricsTotal Citations0Total Downloads38Last 12 Months38Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Track and trace policies in South Korea and UK during covid.19

In responding to Covid-19 pandemic, countries around the world implemented various policies to impede the virus. But the effect of these polices among East Asian and European countries are Utterly different. This paper chose South Korea and the United Kingdom to conduct a case study to explore whether and to what extent did digital track and trace policies make an effect in curbing the virus. This paper mainly utilizes government websites data and peer-reviewed research articles from PubMed and Google Scholar to support the research. This paper illustrates three critical factors why digital contact tracing systems perfrom better in South Korea than the UK, which are cultural notion, movements from government and collaboration with testing respectively.
A Sound Interpretation of Leśniewski's Epsilon in Modal Logic KTB

In this paper, we shall show that the following translation \(I^M\) from the propositional fragment \(\bf L_1\) of Leśniewski's ontology to modal logic \(\bf KTB\) is sound: for any formula \(\phi\) and \(\psi\) of \(\bf L_1\), it is defined as (M1) \(I^M(\phi \vee \psi) = I^M(\phi) \vee I^M(\psi)\), (M2) \(I^M(\neg \phi) = \neg I^M(\phi)\), (M3) \(I^M(\epsilon ab) = \Diamond p_a \supset p_a . \wedge . \Box p_a \supset \Box p_b .\wedge . \Diamond p_b \supset p_a\), where \(p_a\) and \(p_b\) are propositional variables corresponding to the name variables \(a\) and \(b\), respectively. In the last, we shall give some comments including some open problems and my conjectures.
Distributing intelligence for object detection using edge computing

Object detection plays an important role in many artificial intelligence applications such as autonomous driving and video surveillance. However, running object detection models on small edge devices remains computationally expensive and time consuming. In this paper, we present a distributed cloud-edge version of the YOLOv3 model based on split learning. By keeping the data local and sharing only part of the model, both computational and privacy requirements were met. The definition of the cut layer was set by means of a comprehensive analysis of the model architecture. Validation of correctness was established using the COCO dataset and performance comparison was made with client-server basic distribution and federated learning.
Bayesian nonparametric tests for multivariate locations

In this paper, we propose novel, fully Bayesian non-parametric tests for one-sample and two-sample multivariate location problems. We model the underlying distribution using a Dirichlet process prior, and develop a testing procedure based on the posterior credible region for the spatial median functional of the distribution. For the one-sample problem, we fail to reject the null hypothesis if the credible set contains the null value. For the two-sample problem, we form a credible set for the difference of the spatial medians of the two samples and we fail to reject the null hypothesis of equality if the credible set contains zero. We derive the local asymptotic power of the tests under shrinking alternatives, and also present a simulation study to compare the finite-sample performance of our testing procedures with existing parametric and non-parametric tests.
Trans-Attention Multiple Instance Learning for Cancer Tissue Classification in Digital Histopathology Images

The detection of cancerous tissue in histopathological slides is of great value in both clinical practice and pathology research. This paper presents a novel approach that targets automatically classifying cancer tissue by leveraging an attention multiple instance learning scheme; an attention-equivalent neural network-based permutation-invariant aggregation operator applied on the multi-instance learning network. Additionally, we propose a Trans-AMIL approach which is designed to apply Transfer Learning pre-trained models and learn the distribution of the bag label probability using neural networks. We demonstrate experimentally that our approach outperforms several conventional deep learning-based methods on an open BreakHis cancer histopathology dataset.
An End-to-end Attention Transfer Network for Cross-domain Service Recommendation

The number of available online services increases sharply with the development of the Internet. These services typically belong to varying service domains. To address the data-sparse issue, cross-domain recommendation techniques are proposed to transfer the information in relevant service domains to improve the recommendation effects. In this paper, we presented a novel end-to-end cross-domain service recommendation learning framework, named EATN, short for End-to-end Attention Transfer Network, which is different from most existing cross-domain step-by-step learning frameworks. To realize this end-to-end framework, we design a workflow to achieve user preferences cross-domain matching procedure. We capture fine-grained and multi-faceted user preferences by using multiple Multi-Layer Perceptron layers. To reasonably integrate multi-faceted transfer preferences, we design a service-level attention module, which learns weight based on the relevance to services. Finally, it can improve the recommendation effect of cold-start users in the target domain. Extensive experiments on the real-world Amazon dataset show the significant improvement of our proposed EATN framework.
Detection of Wheelchair Orientation in Human-Robot Interactions

Autonomous mobile robots are being introduced in human-populated environments with increasing frequency, notably in hospitals and long-term care facilities. Ensuring safe and intuitive human robot interaction (HRI) is becoming a growing need, especially for pedestrians with mobility aids such as wheelchairs. The dynamics of wheelchair users differ from those of foot pedestrians, so accurate characterization of a wheelchair’s location and orientation for state estimation is crucial. The 2D laser scanner is a well-suited sensor for accurate distance measurements with fast computation speeds, but the sparsity of its data is often a hindrance to effective object detection. Despite so, 2D range data from laser scanners is found to be effective in the detection and orientation estimation of wheelchairs, even in cluttered environments. The range data from the scanner is pre-processed by segmenting out objects using density-based clustering. A two-step classification algorithm first identifies wheelchair candidates from segmented objects with the random forest classifier, then estimates the wheelchair’s orientation as one of six classes with a neural network. The models achieve 98% true positive rate for detection and 86% for orientation classification. The outcomes of this research can inform future works in building a real time wheelchair detection and state estimation for mobile robots.
Integration of security and HCI in courses

It is essential to consider the security and privacy (SYP) of information as part of the development of technological solutions and special attention when a large majority of people need to access the internet to carry out their daily activities. Despite the importance of SyP, this article shows that topics related to security and privacy are scarce in HCI courses and that only some universities offer specific courses that integrate usability and security. Additionally, information is presented on topics that the universities include in their courses to have a starting point that serves as a reference to integrate current courses and in this way teach those responsible for generating technological solutions on usable security.
Research on the Development of University Sharing Platform System Based on System Engineering

Based on the idea of system engineering, taking colleges and universities as the research object, using the prototype method and B/S structure, this paper studies the development of the university student goods sharing platform system, and expounds the analysis, design and implementation of the background subsystem. The research on the development of the system reflects the organic combination of computer information technology and real life, which is helpful to solve the waste of resources caused by information asymmetry. At the same time, the realization of the system has certain reference value and guiding significance for the use of computer science and technology to carry out information service and sharing.
Segmentation of Stereo-Camera Depth Image into Planar Regions based on Evolving Principal Component Clustering

In this paper, we present planar segmentation of a depth image using evolving principal component clustering (EPCC). We exploit spatial point locality in ordered data streams to simplify the development of segmentation algorithms and speed up their operation. Experimental work has confirmed that modelling noise and incorporating it into the clustering process increases segmentation performance and accuracy. The evolving approach enables single-pass learning and self-adaptation of linear prototypes used to describe specific data regions. The proposed algorithm enables robust operation and adapts to the data due to the recursive estimation of data variance. The performance of the proposed method is demonstrated on real measurements obtained with a depth camera.
Slicing the Past to Predict Future

In this research, a heuristic approach is proposed and experimented with to detect warning signs based on historical data. The paper makes use of COVID-19 positive count reported by different states of the USA for illustration of the methods. These are data in the form of several single variable time series related to a concept. The basic idea is to divide the total time period into smaller segments and examine changes within and between series in the segments. Algorithms for clustering series and concordance are used as tools of the heuristic. The approach is to observe the behavior of members of clusters in the segments and predictions are formulated based on observed changes which is heuristic part. Hidden Markov Model is used for change detection and series clustering. Concordance is used for comparing similarity in behavior of series in segments.
Automatic Control of Electronic Information Transmission Integrity Considering Gabp Neural Algorithm

Abstract With the rapid development of the economy and society, information has become an essential social resource and creates wealth continuously. However, open electronic information can be easily lost during transmission control. In this paper, the state of the information network is determined through a series of indexes based on the GABP neural algorithm. At the same time, the rate of information transmission is adjusted based on the state of the information network through self-adaptation, and the integrity of information transmission is controlled by the genetic particle swarm algorithm. Based on reasonable control of the integrity of information transmission, the research on the automatic control technology of open electronic information transmission integrity is accomplished under the condition that the bandwidth and response delay indexes are meeting. The study results indicate that the neural algorithm based on the GABP network can automatically control the electronic information transmission integrity and improve the reliability of information transmission effectively.
TrajNet

Discovering anomalous trajectory becomes an essential task in various research and industrial domains in recent years. Unsupervised learning techniques have been used frequently to find an anomaly in trajectory. These methods fail to detect an outlier in highly correlated trajectory data. In general, the supervised approach is found to be more efficient compared to unsupervised learning in many domains. In this article, to detect outliers in vehicle trajectory data, a supervised learning technique is proposed called TrajNet. TrajNet work with a limited number of labelled trajectories exploiting capsule network-based one-shot learning. The experiments are conducted with a publicly available Geolife GPS trajectory dataset, and the preliminary results are very encouraging.
Cryptosystem using Facial Landmark for Authentication Pairing and Key Generation in Bluetooth Security

In this paper, the whole system starts with a real time picture of the sender and the receiver. The pairing is done utilizing the image of the sender and the receiver at the place of the pin used for pairing. The key generation for the encryption also utilizes the facial characteristics of the parties involved. This method will always generate a key of variable length. As the same person taking the snap of his/her face will change the coordinates every time making it is hard to predict the exact key. The text converted into ciphertext is also padded from both sides with variable bits to make it hard to identify the starting point of the ciphertext to start the actual decryption. The overall aim of this research is to develop a cryptosystem that can be used for achieving good encryption even if the length of the plain text is very small(one or two words). It is an encryption system with associated authentication. An electronic device cannot distinguish between its real users and fake one just by looking at the number which is used for the authentication process but if trained to identify the picture of the used the device up to a certain point is secure. Next thing if someone has requested a pairing merely by looking at the numbers one cannot predict that a legal connection is made but by looking at the real-time image of the person the same can be done with satisfaction. Previously used encryption used key and ciphertext of fixed length but here, a variable ciphertext and key are created in every session of transmission as the image of every sender and the receiver is different.
A General Framework of Remote Sensing Epipolar Image Generation

Epipolar images can improve the efficiency and accuracy of dense matching by restricting the search range of correspondences from 2-D to 1-D, which play an important role in 3-D reconstruction. As most of the satellite images in archives are incidental collections, which do not have rigorous stereo properties, in this paper, we propose a general framework to generate epipolar images for both in-track and cross-track stereo images. We first investigate the theoretical epipolar constraints of single-sensor and multi-sensor images and then introduce the proposed framework in detail. Considering large elevation changes in mountain areas, the publicly available digital elevation model (DEM) is applied to reduce the initial offsets of two stereo images. The left image is projected into the image coordinate system of the right image using the rational polynomial coefficients (RPCs). By dividing the raw images into several blocks, the epipolar images of each block are parallel generated through a robust feature matching method and fundamental matrix estimation, in which way, the horizontal disparity can be drastically reduced while maintaining negligible vertical disparity for epipolar blocks. Then, stereo matching using the epipolar blocks can be easily implemented and the forward intersection method is used to generate the digital surface model (DSM). Experimental results on several in-track and cross-track images, including optical-optical, SAR-SAR, and SAR-optical pairs, demonstrate the effectiveness of the proposed framework, which not only has obvious advantages in mountain areas with large elevation changes but also can generate high-quality epipolar images for flat areas. The generated epipolar images of a ZiYuan-3 pair in Songshan are further utilized to produce a high-precision DSM.
Approximate Kalman filtering for Bayesian state estimation in nonlinear measurement models

The Kalman filter is known to provide a tractable solution to the Bayesian state estimation process as it computes the underlying probability densities exactly. However, the Kalman filter cannot be derived for nonlinear models. This limitation is overcome by its other variants and the particle filter. The particle filter, however, requires a large number of samples to obtain an accurate representation of the densities thus leading to high computational complexity. In this paper, we propose an approximate solution to the Kalman filter for applicability to nonlinear observation models. The key rationale in this proposition is to represent the predicted density using a set of samples and then weigh them according to the measurement density. This weighted sample set simply becomes representative of the updated posterior probability density from which any required statistical feature like the mean and variance can be calculated directly. This approach hence bypasses the need for deriving exact densities as in the Kalman filter and does not suffer from computational burden as does the particle filter. Using simulations, we test the proposed method in terms of faithfully representing of the probability densities, filtering accuracy and computational efficacy.
Deleuze and the Digital: On the Materiality of Algorithmic Infrastructures

In his short and often quoted essay ‘Postscript on the Societies of Control’, Gilles Deleuze famously describes the structures of power in the dawning twenty-first century as driven by ‘machines of a third type, computers’, as novel and predominantly digital infrastructures. In fact, from a Deleuzian perspective the entire ecosystem of the digital transformation can be described as a larger shift in modes of production and the political economy. This essay proposes to read this ‘technological evolution’ as the power of algorithms and their material substance – digital infrastructures that entail a different mode of interaction between humans and technology. In looking at these infrastructures from a materialist position, my essay reconceptualises the digital as the unfolding logic of assemblages that have been shaping a ‘long now’ of technological modernity. In bringing a Deleuzian reading of infrastructures to the study of technology and society, this essay seeks to shed a new light on the political function – and the increasing abstraction – of infrastructures in the realm of the digital.
An Automatic Thresholding Approach to Gravitation-Based Edge Detection in Grey-Scale Images

This paper presents an optimal auto-thresholding approach for the gravitational edge detection method in grey-scale images.The goal of this approach is to enhance the performance measures of the edge detector in clean and noisy conditions.To this aim, an optimal threshold is automatically found, according to which the proposed method dichotomizes the pixels to the edges and non-edges.First, some pre-processing operations are applied to the image.Then, the vector sum of the gravitational forces applied to each pixel by its neighbors is computed according to the universal law of gravitation.Afterwards, the force magnitude is mapped to a new characteristic called the force feature.Following this, the histogram representation of this feature is determined, for which an optimal threshold is aimed to be discovered.Three thresholding techniques are proposed, two of which contain iterative processes.The parameters of the formulation used in these techniques are adjusted by means of the metaheuristic grasshopper optimization algorithm.To evaluate the proposed system, two standard databases were used and multiple qualitative and quantitative measures were utilized.The results confirmed that the methodology of our work outperformed some conventional and recent detectors, achieving the average precision of 0.894 on the BSDS500 dataset.Moreover, the outputs had high similarity to the ideal edge maps.
Processing Time Reduction for Non-local Median Filter

In this paper, we propose an algorithm that uses a switching process to reduce the processing time of nonlocal median filters with retaining its advantages. The non-local median filter provides noise reduction performance with excellent structure preservation performance, but it has been recognized that the method cannot be applicable to real-time applications due to the slow processing speed caused by the nature of the algorithm. The goal of the proposal is to provide a solution to the issue by switching to a simpler filter in the flat regions of the image. Achieving both high structure-preserving performance and short process time in video noise reduction systems becomes very important because applying real-time video processing to IoT devices such as vehicles and embedded devices is now spreading widely.
Imposters Among Us: A Supervised Learning Approach to Anomaly Detection in IoT Sensor Data

Internet of Things (IoT) technology has made smart homes more prevalent in everyday lives. However, anomalies in IoT data may be emblematic of potential cybersecurity risks like false data injection attacks or physical security risks like house fires. In this paper, we propose a segmentation based anomaly detection method that converts unsupervised time-series data into a supervised format, and then trains a Long-Short Term Memory (LSTM) neural network to detect anomalies. The LSTM network is trained to predict un-sound statistical properties, which gets combined with sound statistical properties, to detect anomalies in IoT sensor data. Data smoothing using Holt Winters Exponential Smoothing is also performed, without loss of information, to improve anomaly detector performance. Using Precision, Recall, and F-Measure scores as metrics, results show efficient anomaly detection performance on IoT temperature sensor data. We, additionally, test performance by varying specific parameters. Lastly, results also show that performing data smoothing, to a certain extent, can improve anomaly detection performance over data that didn’t undergo any smoothing.