Reviewerly/exHarmony huggingface dataset:
Adaptive Hybrid Projective Models for Aerial Video Stitching

Aerial video stitching has been widely used in many fields such as large-scale videos surveillance, disaster monitoring and unmanned aerial vehicle (UAV) navigation. Ghosting and parallax are challenging problems when the video streams are captured by UAV. This paper presents a novel method based on hybrid projective model which combines local warp alignment and global warp projection for better registration accuracy. The geometric alignment model is used for global registration of images and the content-preserving alignment model is used for local refinement. Simultaneously, reference frame is updated adaptively according to the shift of scenery, which is used to eliminate the cumulative error appearing in long video streams. The proposed approach has been tested under multiple datasets in different scenarios, which shows good performance.
Using affordances to improve AI support of social media posting decisions

Intelligent systems are limited in their ability to match the fluid social needs of people. We use affordances---people's perceptions of the utilities of a target system---as a means of creating models that provide intelligent systems with a better understanding of how people make decisions. We study affordance-based models in the context of social network site (SNS) usage, a domain where people have complex social needs often poorly supported by technology. Using data collected via a scenario-based survey (N=674), we build two affordance-based models about people's multi-SNS posting behavior. Our results highlight the feasibility of using affordances to help intelligent systems support people's decision-making behavior: both of our models are ~15% more accurate than a majority-class baseline, and they are ~33% and ~48% more accurate than a random baseline for this task. We contrast our approach with other ways of modeling posting behavior and discuss the implications of using affordances for modeling human behavior for intelligent systems.
Exploring Anonymous User Reviews: Linkability Analysis Based on Machine Learning

Identity anonymization is believed to be a common mechanism to protect users' privacy in a public review platform, as each user's unique identifier is removed to ensure pseudonymity and unlinkability. However, the usefulness of the mechanism is not explicit, i.e., whether it is possible for an adversary to link anonymous reviews from the same user has not been well studied. In this paper, we attempt to explore this issue by means of machine learning techniques. Specifically, we first extract major features from anonymous reviews and propose some adaptive metrics to measure their effectiveness. Then, we exploit these features and several machine learning methods to link the anonymous reviews created by the same user in a real- world dataset. Considering that different adversaries has different background knowledge, both supervised and unsupervised methods, such as random forest and hierarchical agglomerative clustering, are designed and utilized to perform linkability attacks. The simulation results demonstrate that the supervised methods have a good performance, i.e., almost 40% anonymous reviews can be accurately linked to users if an adversary has the background knowledge. The unsupervised methods, compared with supervised methods, has a bad performance, i.e., it is difficult for an adversary without the background knowledge to link anonymous reviews with a high probability.
Object Detection under Constrained Hardware Scenarios: A Comparative Study of Reduced Convolutional Network Architectures

Recent advances in deep learning point towards the use of computer vision systems based on Deep Neural Networks (DNNs). However, these network architectures are optimized to be executed in specialized hardware, such as in computers with Graphics Processing Units (GPU). Such hardware is rarely available in embedded computers, for instance, those used by mobile robots, so alternatives must be studied in order to guarantee that mobile systems may still benefit from the applications of deep learning. In this work, we investigate the performance of a vision system for ball detection, based on different configurations of the MobileNet Convolutional Neural Network architecture, under a constrained hardware scenario. By gradually reducing the input size and the number of parameters that compose the neural network and comparing their inference time in an Intel NUC Core i7 mini-PC, embedded in a humanoid soccer robot, we have found acceptable values for the width and resolution multipliers to be used in our soccer ball detection system during a robot-soccer match.
Protein Image Classification based on Convolutional Neural Network and Recurrent Neural Network

Proteins are an essential component in the cell where the functions are executed to enable life. At present, the manual evaluation and classification of protein images is not practical given the current situation for generated images on a large scale. Hence, the requirement of automating protein image classification can be quite useful. Until now, classical machine learning and convolutional neural network algorithms have achieved results in image classification without the desired level of accuracy. Under the circumstances, the research aims to propose an accurate classified model for protein image classification by combining a convolutional neural network with a recurrent neural network.
BSA: Enhancing Attribute-Based Encryption in Cloud Computing with Decentralized Specification

Ciphertext-policy attribute-based encryption (CP- ABE) with verifiable outsourced decryption is a mechanism for secure fine-grained access control over encrypted data, and it is suitable for cloud computing applications. However, there exists a risk in CP-ABE with verifiable outsourced decryption that can lead to serious consequences and may limit its wide applications: the key generation center may have misbehavior. In this paper, we present BSA, the blockchain-based specification for ABE to mitigate this risk. We introduce the specification to regulate the data access control and a proof mechanism to supervise whether the key generation center has misbehavior. Also, we can provide decentralized and automated incentives with BSA by smart contracts and blockchain-based consensus.
Transformer Image Recognition System Based on Deep Learning

Due to the variety of transformer models and the complexity of the image, the traditional method based on machine learning for the artificial design features cannot be large-scale and accurate identification. This paper proposes a transformer image recognition system based on deep learning, which can acquire the target results directly by “end-to-end” learning from the original acquired images. In order to realize the accurate classification of transformer images, this paper selects convolutional neural network algorithm to apply to transformer image recognition, constructing a transformer image recognition system based on convolutional neural network by designing the network structure and classifier of convolutional neural network algorithm. By constructing training set and testing set of the transformer image, the model is trained and its performances are tested. It is shown in the experimental results that compared with BP neural network model and SVM model, convolutional neural network has better effects, reaching 9.17% of the identification error, and realizing the accurate distinction of three types of transformers.
EWS: An Efficient Workflow Scheduling Algorithm for the Minimization of Response Time in Cloud Environment

A workflow comprises of a collection of coordinated tasks aimed to carry out a well-defined systematic process such as planning, arranging, sequencing the jobs and implementing the business process of the enterprises. Our research aims to schedule the workflow, which defines a correct order of execution of jobs. A proper workflow scheduling process helps to enhance the response time, processing time, utilization of resources, performance and quality of service. This proposed approach, EWS: Efficient Workflow Scheduling Algorithm arranges the requests according to the user priority, finds the proficient VMs, maps the tasks to the VMs and manages the execution of tasks within a specified time. The proposed approach helps to deliver the services with the minimum response time and process time as mentioned in the Service Level Agreement (SLA) without any violation. In addition, it also enhances the performance of virtual machines.
Energy Consumption of Hybrid Data Center Networks

Existing data center networks (DCNs) based on only electronic packet or all-optical switching still pose an exponential increase in power consumption and cost due to the current high demand for digital data and sustainability issues. The recent development of hybrid DCN prototypes is a promising solution offering relatively higher data throughput, low latency, reduction in cost and energy consumption. This paper explores the frontier of hybrid DCN with a special focus on energy consumption and cost. We evaluate the energy consumption per bit and the greenhouse gas (GHG) emission per year of three hybrid switching systems as compared with an optical point to point (ptp) network, results show that the hybrid switching systems will consume less energy per bit and are likely to emit less GHG annually. We present feasibility analysis on the energy consumption and cost of some hybrid DCN prototypes. Evaluation results show that Helios-like and Hybrid Optical Switching-like prototypes achieve a power usage effectiveness (PUE) value lower than 1.2, an index which represents a very efficient level of energy performance in a data center network.
Reuse in Agile Development Process

Reusability is a clear principle in software development. However, systematic reuse of software elements is not common in most organizations. Application programmers rarely design and create software elements for possible future reuse. In many agile software development processes, the project teams believe that the development of reusable software elements can slow down the project. This can be a misconception. This chapter examines various ways to reuse software. Three approaches to developing reusable software artifacts from 15 years of experience in the agile development process are presented. The first approach is to create generic programs or configurable frameworks that support similar solutions for a variety of use cases and environments. The reuse of patterns is the second approach presented. Another effective way is to use a model-driven approach with model patterns. These approaches help to speed deployment software. The final product is flexible and can easily be adapted to changes. This is one of the main goals of an agile approach.
An Autonomous Intelligent Ornithopter

The purpose of the system is to provide a powerful and intelligent surveillance tool to the police force so as to reduce crime. The law enforcement agencies have been motivated to use video surveillance systems to monitor and curb these threats. But this becomes a tedious task, prone to human errors. The core module of this system estimates the pose in humans present in the video and a backend capable of understanding the context as a whole. Many AI-powered surveillance systems are good at recognizing violent or malicious activity but fail to understand the context as a whole. We aim to understand the gradual change in human behavior in the given scenario, understand the confidence level of each expression and derive if the given scenario is truly violent or malicious. The Ornithopter is allowed to follow the suspect wherein the direction offsets are given by the server. The system differs from any state-of-the-art surveillance system as it provides aerial surveillance covering larger areas, and since the drone is bird-shaped, it can easily navigate the area without being easily detected. And as mentioned, the recognition of the true violent or malicious activity is context-based.
A Social Platform designed for Music: Learning and Making Compositions Through Collaboration

In music, the score has always been the main instrument used for the transcription, performance, and composition. There exist several systems for transcribing and composing music scores. Although there are a number of evolved systems for composing music scores, they generally lack of (1) collaborative mechanisms, to allow composers to share their contributions and look for other author's compositions, and (2) communication mechanisms, to allow composers to become an active part of the composition process of other composers, by messaging and sharing contents. Generally, musicians take advantage of the most social media systems to implement these features, such as Facebook. Twitter. Instagrarm, and Linkedin. In this work, we introduce SoMusic, a Social platform designed for collaboration and learning. In addition to the main features provided by the most important social networks, SoMusic allows musicians to collaborate, simultaneously create a musical composition (in a concurrent manner), and to find other composers with a similar composition style. Social collaboration is the key aspect for increasing the musical quality productivity, where musicians participate in the discussions, learn in the web classrooms, cocreate music compositions, and share musical ideas. Furthermore, SoMusic proposes itself as an educational support tool allowing teachers to create online classes, to monitor the performance of students, and to interact with them in real-time. We involved 20 participants to analyze SoMusic effectiveness, usability, and the overall user satisfaction. We split the people involved in the evaluation study in two classes: one class with a traditional teaching approach and the other one using SoMusic. On average, participants that tested SoMusic produced music with fewer errors.
Target Tracking Algorithm Based on CUDA Parallel Acceleration

TLD is a single-target visual tracking algorithm with good robustness and high accuracy, which can solve the problems of occlusion and deformation of the target during the tracking process. However, due to the high complexity of the algorithm, it can't meet application requirements. Based on the analysis of the parallelism and complexity of the improved TLD algorithm, a parallel implementation algorithm is proposed and implemented with CUDA to improve the real-time performance of the improved TLD algorithm for the foreground detection classifier and integrated classifier in the algorithm detection module. The results show that the improved TLD algorithm based on CUDA improves the average speed of the detection module by 8.8 times and the speed is 181 frames per second. For the test videos with different resolutions, the average execution speed of TLD algorithm is 56 frames per second, which satisfies the requirement of intelligent video monitoring.
Semantic Similarity and Word-Net Based Web News Classification

One of the most significant applications of Natural Language Processing is text classification. Classification is required to search and manage data in information repository. With the huge popularity of Internet, various types of information and news articles on a wide range of domains are floating over different online news blogs and websites. To fetch them quickly based on user requirement for different purposes, these are required to be classified and categorized. In this paper, a technique for identifying and categorizing the class of online news articles has been proposed based on semantic similarity and Word-Net. At first, we identify some keywords (noun, verb, and adjective) from both title and body of the news article; then we extract semantically similar words for each of the keywords from Word-Net. Next we perform a user-annotation based technique to find the threshold value which is used to categorize the news articles.
Variable Macropixel Spectral-Spatial Transforms With Intra- and Inter-Color Decorrelations for Arbitrary RGB CFA-Sampled Raw Images

A raw image captured by a color filter array (CFA), such as a Bayer pattern, is usually compressed after demosaicing with some processings (denoising, deblurring, tone-mapping, and so on). However, since photographers, designers, and high-end users prefer to work with the raw image sampled by CFA (referred to as “raw image”) directly, a raw image should be compressed before demosaicing. For effective raw image compression, this study introduces variable macropixel spectral-spatial transforms (VMSSTs), that can successfully decorrelate not only Bayer raw images but any other pure-color (RGB) ones. The proposed VMSSTs are designed by the following two steps: 1) intra-color decorrelation and 2) inter-color decorrelation. In lossless compression with JPEG 2000, compared with methods which do not use transforms, the VMSSTs reduced the average bitrates of three types of CFAs: from approximately 0.09 to 0.12 bpp for the modified Bayer CFA, from 0.25 to 0.65 bpp for the diagonal stripe CFA, and from 0.33 to 0.70 bpp for the Fujifilm X-Trans CFA due to their high color decorrelation efficiency. In addition, in lossy compression with JPEG 2000, compared with a rearranged method, the VMSSTs improved the average bitrates of the Bjøntegaard delta by around 3.97%, 14.95%, and 18.65% for each CFA model, respectively. Although a data-dependent adaptive transformation, the Karhunen-Loève transform (KLT), showed the best performance in lossy compression, the introduced VMSSTs have shown performances comparable to those of the KLT in lossless compression, despite their simple structures.
The North Indian Sarode and Questions Concerning Technology

In three previous issues of OS (10/1, 2005, 13/3, 2008 and 19/2, 2014) a range of scholars explored non-Western instrumentation in electroacoustic music. These issues addressed concerns about sensitive cultural issues within electroacoustic music. This article builds upon this discussion through an examination of a number of electroacoustic composer-performers using non-Western instrumentation. This discussion will include the voices of ‘Western’ electroacoustic composers using non-Western instruments or sounds sources. It will also document some of the work of non-Western electroacoustic composers who incorporate traditional material or indigenous instruments in their music. Special attention will be given to the complexity of being in-between musical cultures through a critical engagement with theories relating to hybridity, orientalism and self-identity. In particular, this article will focus on my own practice of composing and performing electroacoustic music with the North Indian lute known as the sarode. It will discuss both cultural and artistic concerns about using the sarode outside the framework of Indian classical music and question whether Indian classical music can ever be ‘appropriately appropriated’ in an electroacoustic context. Two of my recent compositions will be explored and I will outline the development of my practice leading to the creation of a new ‘hybrid’ instrument especially for playing electroacoustic music.
Medical Image Retrieval Using Efficient Texture and Color Patterns with Neural Network Classifier

Presently, the usage of medicinal images has drastically increased and it provides extensive details related to the patient’s health status. It shows the applicability of diagnosing the disease and stored in a memory for examination purposes. For the retrieval of medical images in a real world environment, significant need is present in the designing of an effective medical image indexing and retrieval technique. This paper offers an efficient medical image retrieval (MIR) model through feature extraction based classification model. Here, Directional local ternary quantized extrema patterns (DLTerQEPs) and autocorrelogram (AC) based feature extraction process takes place to extract texture and color features. Next, neural network (NN) based classification process takes place. The investigation of the simulation results takes place to showcase the betterment of the presented mode. During experimentation process, it is noticed that the presented model is superior to compared methods.
Adversarial Examples Identification in an End-to-End System With Image Transformation and Filters

Deep learning has been receiving great attention in recent years because of its impressive performance in many tasks. However, the widespread adoption of deep learning also becomes a major security risk for those systems as recent researches have pointed out the vulnerabilities of deep learning models. And one of the security issues related to deep learning models is adversarial examples that are an instance with very small, intentional feature perturbations that cause a machine learning model to make a wrong prediction. There have been many proposed defensive methods to combat or detect adversarial examples but still not perfect, powerful and still need a lot of fine-tuning in the process of installing security systems. In this work, we introduce a completely automated method of identifying adversarial examples by using image transformation and filter techniques in an end-to-end system. By exploring the adversarial features that are sensitive to geometry and frequency, we integrate the geometric transformation and denoising based on the frequency domain for identifying adversarial examples. Our proposed detection system is evaluated on popular data sets such as ImageNet or MNIST and gives accurate results up to 99.9% with many optimizations.
Securing Trust in Online Social Networks

Trust in Online Social Networks (OSN) is a contentious topic. On one hand, there is an increasing reliance on them for trustworthy information and on the other, wariness to believe anything on it. Although the providers of OSNs have tried multiple ways to boost the trustworthiness of the information posted on their websites and weed out millions of fake accounts, the problem is largely unsolved and poses a formidable challenge. This paper examines the problem is some detail, discusses existing solutions to the problem using Machine Learning and other techniques and concludes by discussing some more ideas on enhancing the trustworthiness of the OSNs.
Edge detection methods and key parameters study

Abstract Edge is essential for diverse vision processing tasks and it constitutes a crucial part in the image of objects. There has already been a huge amount of researches about edge detection algorithms. The Canny method and the Sobel method are the most widely used techniques because of their relative advantages. Some parameters can drastically affect the performance and results of the two methods, which will be discussed in this paper. In addition, noise is also a factor affecting the number of the extracted edges. Experiment results have demonstrated that the noise filter will achieve better edge detection performance. Further improvements with suitable parameters of different applications for the two methods are possible.
Proposal of a support system for learning brushstrokes in transcription for beginners

The purpose of this research is to propose a calligraphy learning support system focusing on brushstrokes. In conventional calligraphy, the learner imitates and writes characters while looking at a model placed next to the writing paper. However, it is difficult for the learner to learn the brushstroke characteristics, such as correct pressure and speed of brush movement, just by looking at a model. Therefore, we added an onomatopoeia utterance function to the conventional system to solve these problems. The onomatopoeia represents the brushstroke characteristics, such as pressure and speed. We compare the proposed method with the function to the comparative method without the function. As a result, it became clear that, when using the proposed method, correct brushstroke method could certainly be learned in a shorter time than with the comparison method, thus confirming the possibility of improving the calligraphy of proposed method.