Reviewerly/exHarmony huggingface dataset:
Testing for Bugs of Cloud-Based Applications Resulting from Spot Instance Revocations

One of the major advantages of cloud spot instances in cloud computing is to allow stakeholders to economically deploy their applications at much lower costs than that of other types of cloud instances. In exchange, spot instances are often exposed to revocations (i.e., terminations) by cloud providers. With spot instances becoming pervasive, terminations have become a part of the normal behavior of cloud-based applications; thus, these applications may be left in an incorrect state leading to certain bugs. Unfortunately, these applications are not designed or tested to deal with this behavior in the cloud environment, and as a result, the advantages of cloud spot instances could be significantly minimized or even entirely negated. We propose a novel solution to automatically find these bugs and locate their causes in the source code. We evaluate our solution using 10 popular open-source applications. The results show that our solution not only finds more instances and different types of these bugs compared to the random approach, but it also locates the causes of these bugs to help developers to improve the design of the shutdown process for cloud-based applications.
From Technology Studies to Sound Studies

In this paper I put in dialogue two areas of scholarship: Technology Studies and Sound Studies. Within Technology Studies I discuss the influential social construction of technology approach and illustrate it with the history of the moog electronic music synthesizer, the first commercial music synthesizer. I stress the role of standardization of keyboards and the key role played by users in the development of this technology. I examine certain iconic sounds that the moog synthesizer produces and discuss the stabilization of sound. It is argued that just as technologies can be traced as stabilizing over time, sounds also can be traced with certain sounds stabilizing and being taken up by users whilst other sounds fail to stabilize. The technology required to produce a sound, performance practice, and wider cultural concerns such as the naming of sounds are crucial ingredients in the stabilization of sound.
A Comparative Study of SVM and RF Methods for Classification of Alteration Zones Using Remotely Sensed Data

Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, can be substituted for the field study. In this study, the ASTER imagery is used for alteration classification by applying two new methods of machine learning, including Random Forest and Support Vector Machine. The 14 band ASTER and 19 derivative data layers extracted from ASTER including band ratio and PC imagery, are used as training datasets for improving the results. Comparison of analytical results achieved from the two mentioned methods confirmed that the SVM model has sufficient accuracy and more powerful performance than RF model for alteration classification in the study area.
Deep Reinforcement Learning for Video Prediction

This paper introduces a hybrid video prediction scheme that combines the classic parametric overlapped block motion compensation (POBMC) technique with neural networks. Most learning-based video prediction methods rely on a black-box-like model for either direct generation of future video frames or estimation of a dense motion field. The model complexity often increases drastically with frame resolution. Departing from pure black-box approaches, this paper leverages the theoretically-grounded POBMC in a reinforcement learning framework to estimate a sparse motion field for future frame warping. Two neural networks are trained to identify critical points in the motion field for motion estimation. We train our model on 10k unlabeled frames in KITTI dataset and achieve the state-of-the-art SSIM score of 0.923 on CaltechPed and an average SSIM scroe of 0.856 on Common Intermediate Format (CIF) standard sequences.
Construction of Subjective Vehicle Detection Evaluation Model Considering Shift from Ground Truth Position

In order to evaluate the vehicle detection method, it is necessary to know the correct vehicle position considered as “ground truth”. We propose indices considering subjective evaluation in vehicle detection utilizing IoU. Subjective evaluation experiments were carried out with respect to misregistration from ground truth in vehicle detection.
An End to End Framework to High Performance Geometry-Aware Multi-Scale Keypoint Detection and Matching in Fisheye Imag

This paper addresses the problem of robust feature detection and matching across different views of the same scene taken using two (or more) wide angle cameras, assuming that their intrinsic calibration parameters are approximately known. We introduce an end-to-end framework with an aim to enhance both precision and recall in the matching process, i.e. increase the number of correct matches across the fisheye images while reducing the number of false positive matches. Experimental results demonstrate the effectiveness of the proposed work.
Manifold Denoising by Nonlinear Robust Principal Component Analysis.

This paper extends robust principal component analysis (RPCA) to nonlinear manifolds. Suppose that the observed data matrix is the sum of a sparse component and a component drawn from some low dimensional manifold. Is it possible to separate them by using similar ideas as RPCA? Is there any benefit in treating the manifold as a whole as opposed to treating each local region independently? We answer these two questions affirmatively by proposing and analyzing an optimization framework that separates the sparse component from the manifold under noisy data. Theoretical error bounds are provided when the tangent spaces of the manifold satisfy certain incoherence conditions. We also provide a near optimal choice of the tuning parameters for the proposed optimization formulation with the help of a new curvature estimation method. The efficacy of our method is demonstrated on both synthetic and real datasets.
Biometric Identification Using Facial Motion Amplification

We propose a new biometric trait based on facial motion amplification. The main advantage of the new biometric characteristic is that it does not rely on the visibility of critical facial features, such as nose, mouth, iris, or eyebrows. This makes it effective even when the respective areas are covered. Using the proposed system, facial image sequences are captured using an ordinary video camera and facial blood flow is calculated by means of small motion amplification. The calculated blood flow is captured from limited facial areas and is represented as a template that is suitable for identification purposes. Experiments on a new database show promising performance of the proposed approach, and provide evidence of the discriminatory capacity of the proposed biometric.
Fast Groupwise Registration Using Multi-Level and Multi-Resolution Graph Shrinkage

Abstract Groupwise registration aligns a set of images to a common space. It can however be inefficient and ineffective when dealing with datasets with significant anatomical variations. To mitigate these problems, we propose a groupwise registration framework based on hierarchical multi-level and multi-resolution shrinkage of a graph set. First, to deal with datasets with complex inhomogeneous image distributions, we divide the images hierarchically into multiple clusters. Since the images in each cluster have similar appearances, they can be registered effectively. Second, we employ a multi-resolution strategy to reduce computational cost. Experimental results on two public datasets show that our proposed method yields state-of-the-art registration accuracy with significantly reduced computational time.
Semantic Weighted Multi-View Clustering for Web Content

Clustering is a long-standing important research problem. However, it remains challenging when handling large-scale web data from different types of information resources such as user profile, comments, user preferences and so on. All these aspects can be seen as different views and often admit the same underlying clustering of the data. In this paper, we present a novel Semantic Weighted Non-negative Matrix Factorization ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SWNMF$ </tex-math></inline-formula> ) multi-view clustering framework, which can provide an efficient weighted matrix factorization framework, dexterously manipulate multi-view web content, and easily explore the sparseness problem in semantic space of data. Specifically, each view of dataset forming a huge sparse matrix, which results in the non-robust characteristic during the matrix decomposition process, and further influences the accuracy of clustering results. To address above problem, we attempt to use some preference information (e.g. rating values) given by the users as latent semantic information to handle those features that are unobserved in each data point so as to resolve the sparseness problem in all views matrices. To combine multiple views in our large corpus, the overall objective of our proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SWNMF$ </tex-math></inline-formula> is to minimize the loss function of weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-negative matrix factorization</i> (NMF) under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula> -norm and the co-regularized constraint under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> -norm. Extensive experiments on our large-scale multi-view web datasets demonstrate the competitive performance of our solution.
Final Thoughts on Videos in Chemistry Education

Faculty are always looking for new ways to engage their students and improve the learning experience. The book presented here has provided the insight and experiences of several faculty members on their use of technology and multimedia tools in their classrooms.
Data Adaptive HDR Compression in VVC

This paper presents an advanced approach to HDR/WCG video coding developed at FastVDO called FVHDR, and built on top of the Versatile Video Coding (VVC) VTM-5.0 test model of the Joint Video Exploration Team, a joint committee of ITU|ISO/IEC. A fully automatic adaptive video process that differs from a known HDR video processing chain (analogous to HDR10, and herein called “anchor”), is used. FVHDR works entirely within the framework of the VVC software model but adds additional tools. These tools can become an integral part of a future video coding standard or be extracted as additional pre- and post-processing chains. Reconstructed video sequences using FVHDR show an improved subjective visual quality to the output of the anchor. Moreover, the resultant SDR content generated by the data adaptive grading process is backward compatible.
Academic Performance Analysis Supported by a Web-Based Visual Analytics Tool

Understanding the academic performance of students in colleges is an essential topic in Education research field. Educators, program coordinators and professors are interested in understanding how students are learning specific topics, how specific topics may influence the learning of other topics, how students' grades/attendances in each course may represent important indicators to measure their performance, among other tasks. In this paper, we present a visual analytic tool that combines data visualization and machine learning techniques to perform some visual analysis of students' data from program courses. Using this tool, we visually analyzed the students' performance in some Computer Science program courses, and demonstrated that the results of such analysis will help the education experts to understand deficiencies on course structures.
Natural Scene Classification

Humans are very proficient at perceiving natural scenes and understanding their contents. Everyday image content across the globe is rapidly increasing and there is a need for classifying these images for further research. Scene classification is a challenging task, because in some natural scenes there will be common features in images and some images may contain half indoor and half outdoor scene features. In this project we are going to classify natural scenery in images using Matlab and image processing technique scuh as SURF feature extraction, K-Means clustering,LDA.
Modeling gamma radiation exposure rates using geologic and remote sensing data to locate radiogenic anomalies

Aerial Gamma-Ray Surveys (GRS) are ideal for tracking anthropogenic gamma radiation releases and transport. The interpretation of a GRS can be complicated by natural gamma-ray sources such as atmospheric radon, cosmic rays, geologic materials, and even the survey equipment itself. Some of these complicating factors can be accounted for or corrected by calibration or mathematic techniques. Real-time algorithms that attempt to enhance potential radiogenic anomalies over background are also in use. However, natural geology is a source of significant background gamma-ray production and neither mathematical corrections nor real-time algorithmic approaches directly account for geology and geochemistry. In this study, we advance techniques to predict geologic background exposure rates using rapid and practical methods which can be achieved in the field. In addition we generate models that focus specifically on highlighting radiogenic anomalies for emergency response or further investigation. Predictive models developed in this study were generally able to predict background with medians of ± 1.0 μR/h compared to measured data, and were also able to highlight anomalous areas even where radiation exposure rates were within the range of natural background.
A Survey on Medical Image Encryption

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Topology Graph Pruning for Optical Mapping Methods using Edge Betweenness Centrality

Optical mapping is one of the most widely used application areas of low-cost robotic platforms. These platforms are in favor as they are relatively easy to use, to operate and to maintain. Acquired optical data (in the form of video and/or image) are valuable sources of information for both online (e.g., navigation, localization, mapping, and others) and offline processes (scientific interpretations, change detection, mapping, and others). The amount of data acquired has been continuously growing thanks to the emerging capabilities of mobile platforms in terms of autonomy allowing longer surveying time. This increases the need for fast and efficient methods to process the obtained data. Creating optical 2D maps from acquired data is composed of mainly image matching, trajectory estimation (Global Alignment (GA)) and image blending steps. In this paper, we discuss the usage of Edge Betweenness Centrality (EBC) concept to reduce the total number of overlapping image pairs to be used in the GA step. EBC allows selecting the image pairs that play a relatively key role in the topology graph. We also discuss the usage of graph energy as a decision criterion during image mosaicing iterations. We present experiments with several datasets to show the performance of the proposed method.
Should All Blockchain-Based Digital Assets be Classified Under the Same Asset Class?

The literature is well aware that blockchain-based digital assets would constitute a new asset class. However, it has been rather silent about the distinction among them. This paper discusses the digital tokens’ differences and similarities by their (i) creation and initial distribution; (ii) intended properties; (iii) actual usage; and (iv) behaviors. Although the digital tokens are indistinguishable in some aspects, they differ in the way they are created and initially distributed. Some of them have distinguishable risk and return profiles. Therefore, we take a view that the digital tokens take (or will take) different roles in the financial systems; should be classified under different asset classes; and should be subject to different sets of regulations (although some may overlap).
Understanding Criminal Networks: A Research Guide

Understanding Criminal Networks: A Research Guide University of California Press, 2019, 272, pages. https://www.ucpress.edu/book/9780520297050/understanding-criminal-networks Gisela Bichler Gisela Bichler Search for other works by this author on: Oxford Academic Google Scholar Social Forces, Volume 99, Issue 2, December 2020, Pages e1–e3, https://doi.org/10.1093/sf/soaa061 Published: 16 June 2020 Article history Received: 11 May 2020 Accepted: 14 May 2020 Published: 16 June 2020
Local Normalization Based BN Layer Pruning

Compression and acceleration of convolutional neural network (CNN) have raised extensive research interest in the past few years. In this paper, we proposed a novel channel-level pruning method based on gamma (scaling parameters) of Batch Normalization layer to compress and accelerate CNN models. Local gamma normalization and selection was proposed to address the over-pruning issue and introduce local information into channel selection. After that, an ablation based beta (shifting parameters) transfer, and knowledge distillation based fine-tuning were further applied to improve the performance of the pruned model. The experimental results on CIFAR-10, CIFAR-100 and LFW datasets suggest that our approach can achieve much more efficient pruning in terms of reduction of parameters and FLOPs, e.g., $$8.64\times $$ compression and $$3.79\times $$ acceleration of VGG were achieved on CIFAR, with slight accuracy loss.
Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network

Recently, the applications of Artificial Intelligence are widely spread in many areas of research. They almost use tailor made classification engine of Deep Learning, and many of such engines uses Convolutional Neural Networks. In this paper, we propose a method for preprocessing the un-structured data to the 2 dimensional data suitable for CNN using Self Organizing Map. The performance is evaluated with the experiments using KDD cup 99 data as input vectors.
Netgen: A Fast and Scalable Tool for the Generation and Labeling of Networking Datasets

This article presents Netgen, an open-source tool for the generation of large networking datasets through the open-source tool Net2Plan. We show Netgen's features and applicability by generating six large labeled datasets for data analysis and Machine Learning. The datasets comprise 100,000 different data points for different traffic matrices and topologies, labeled after solving IP routing, IP routing with link disjoint 1+1 protection and RWA for IP over WDM networks. Both datasets and Netgen's code are publicly available.
On the Selection of Number of Sensors for a Wearable Sign Language Recognition System

Sign language is the primary language used by the deaf community. The postures and motion of hands of the signer may be captured using wearable sensors to design an electronic interface between a sign language user and a non-signer. In a wearable sign language recognition system, the signals recorded using the sensors are analyzed for identifying which sign is performed. For an accurate and reliable sign language recognition system, the primary requirement is to be able to record such signals that will be able to identify two different signs as distinct. With advancements in sensor manufacturing, surface electromyograms (sEMG) have increasingly been reported in research on human motion analysis. In this paper, an sEMG based system is explored for recognition of basic hand postures and motions used in the Indian Sign Language. A backward search algorithm is proposed to determine the most suitable number and placement of sensors based on their effect on misclassification error. A percentage misclassification error of 8.9% is obtained with 5 sEMG sensors, which degrades as the number of sensors are reduced and reaches up to 30% when only one sensor is used. There is a tradeoff between the classification accuracy and the cost of the system.
Action Recognition from Egocentric Videos Using Random Walks

In recent years, actionSahu, Abhimanyu recognition from egocentric videosBhattacharya, Rajit has emerged as an important research problemBhura, Pallabh. Availability of several wearable camera devices at affordable costs has resultedChowdhury, Ananda S. in a huge amount of first- person/egocentric videos. Recognizing actions from this extensive unstructured data in the presence of camera motion becomes extremely difficult. Existing solutions to this problem are mostly supervised in nature, which require a large number of training samples. In sharp contrast, we propose a weakly supervised solution to this problem using random walk. Our solution requires only a few training samples (seeds). Overall, the proposed method consists of three major components, namely, feature extraction using PHOG (Pyramidal HOG) and a Center-Surround model, construction of a Video Similarity Graph (VSG), and execution of random walk on the VSG. Experimental results on five standard ADL egocentric video datasets clearly indicate the advantage of our solution.
In-memory AES Implementation for Emerging Non-Volatile Main Memory

Non-volatile memories are very promising candidates to be employed as next-generation non-volatile main memory (NVMM), because of their advantages over traditional DRAM main memory such as non-volatility, high density, and low leakage power. However, NVMM suffers a new security vulnerability because the nature of non-volatility allows the data to be retained a long time after power is off. An attacker with physical access to the system can readily scan the main memory content and extract all valuable information from the main memory. To protect the data of the NVMM, the whole memory should be provided with a security mechanism with comparable security level to DRAM. Take mobile devices (e.g. smart phone or laptop) for example, once attackers have physical access to the NVMM will they be able to read out the sensitive information. Therefore, memory encryption is required only when the device is shut down or put into sleep/screenlock mode. One-time encryption, encrypting the whole/most part of the memory only when it is necessary, is an efficient solution in such mobile scenarios. However, one-time memory encryption approach faces two challenges: First, it should be fast enough to maintain a low vulnerability window when locked and provide instant response when unlocked. Second, it should be energy-efficient considering the limited battery life.