Reviewerly/exHarmony huggingface dataset:
Salient Object Detection for Synthetic Dataset

Salient object detection essentially deals with various image processing and video saliency methodologies such as object recognition, object tracking, and saliency refinement. When image contains diverse object parts with cluttered background then using background prior we perform salient object detection through which we get more accurate and robust saliency maps. This paper introduces the analysis of salient object detection using synthetic dataset which also deals with negative interference of image that contains diverse object parts with cluttered background. Earlier study uses contrast prior but nowadays researchers use mainly boundary connectivity for improving the results. So, for detecting salient object we used four stages: first, we use SLIC superpixel method for image segmentation. Second, we use boundary connectivity which distinguishes the spatial layout of image region by considering image boundaries. Third, we use background measure and for reducing the noise in both foreground and background regions. Lastly, we use optimization framework through which we acquire a clean saliency map.
Shaping the biology of emotion:<i>Emovere</i>, an interactive performance

This paper presents the artistic creation and research work developed for the piece Emovere (2015), an interactive dance and sound performance. Emovere’s creation process focused on configuring artistic material by exploring the biology of emotion in association with physiological signals and the induction of emotional states based on corporeal patterns. The composition methodology drew from a creative interdisciplinary laboratory that used Alba Emoting and interactive technologies with biosensors to generate artistic material. Four designs that propose a relationship between the performers’ bodies and an interactive-sound environment are presented and discussed in order to illustrate an artistic interdisciplinary approach that reflects on the biology of emotion.
Towards Privacy-Preserving Location Sharing over Mobile Online Social Networks

Location sharing services have recently gained momentum over mobile online social networks (mOSNs), seeing the increasing popularity of GPS-capable mobile devices such as smart phones. Despite the convenience brought by location sharing, there comes severe privacy risks. Though many efforts have been made to protect user privacy during location sharing, many of them rely on the extensive deployment of trusted Cellular Towers (CTs) and some incur excessive time overhead. More importantly, little research so far can support complete privacy including location privacy, identity privacy and social relation privacy. We propose SAM, a new System Architecture for mOSNs, and P3S, a Privacy-Preserving Protocol based on SAM, to address the above issues for privacy-preserving location sharing over mOSNs. SAM and P3S differ from previous work in providing complete privacy for location sharing services over mOSNs. Theoretical analysis and extensive experimental results demonstrate the feasibility and efficiency of the proposed system and protocol.
A Survey on Intelligent Face Recognition System

Face recognition system is a computer’s capability which gives it a vision of performing two fundamental operations the detection and the recognition of a human face. With the advancement of machine learning algorithm and image processing techniques the accuracy of face recognition system has been significantly improved. The objective of this paper is to give a detailed survey of a few face recognition algorithm with their features and limitations. The basics of face detection and face recognition techniques along with their approaches are described in the section.
Measuring the Influence of Moods on Stock Market Using Twitter Analysis

It is a well-known fact that sentiments play a vital role and is an incredibly influential tool in several aspects of human life. Sentiments also drive proactive business solutions. Studies have shown that the more appropriate data is gathered and analyzed at the right time, the higher the success of sentiment analysis. This paper analyses the correlation between the public mood and the variation in stock prices towards companies in different domains. For each tweet, scores are assigned to eight predefined moods namely “Joy”, “Sadness”, “Fear”, “Anger”, “Trust”, “Disgust”, “Surprise” and “Anticipation”. A regression model is applied to the mood scores and the stock prices dataset to obtain the R-squared score, which is a metric used to evaluate the model. The paper aims to find the moods that best reflect the stock values of the respective companies. From the results, it is observed that there is a definite correlation between public mood and stock market.
Integrating Social Homophily and Network Lasso for Personalized Sentiment Classification

People often have different opinions toward the same topic, posing challenges for traditional sentiment analysis systems that rely on statistical models estimated with population-level data. Personalization is a solution to the problem, but it suffers from data scarcity issue, as the labeled date for a specific user is usually only available in a small quantity. In this paper, we exploit the theory of social homophily to overcome this difficulty by assuming that social neighbors are likely to express similar opinions with respect to the same topic or document. We present a personalized sentiment analysis system that leverages the social relation information to balance between personalization and overfitting. Our approach is based on network lasso that automatically clusters socially proximate users together, so that users in the same group share the same personalized model. We develop a distributed optimization algorithm that makes our system scale to large networks as well. The experiments on Yelp review datasets show that our approach consistently outperforms competitive methods.
Security-level switchable attribute-based encryption under the strictly weaker assumption family

Attribute-Based Encryption (ABE), a special type of public key encryption, efficiently shares sensitive data with fine-grained access control. ABE can be classified into two types: Ciphertext-Policy ABE (CP-ABE) and Key-Policy ABE (KP-ABE). However, the securities of most presented ABE systems were reduced to the q-type DBDH (Dicisional Diffie-Hellman Assumption) assumptions, which are stronger than the DBDH assumption. So, the abovementioned ABE systems become insecure if DBDH is proved to be insecure. We propose a new ABE framework, called security-level switchable ABE (SLS-ABE). In SLS-ABE framework, a series of ABE systems can be generated and their securities are reduced to a k-BDH assumption family proposed by Benson et al. The k-BDH assumption family has the following properties: 1) any assumption in the k-BDH assumption family is associated with a parameter k, and the assumption becomes strictly weaker as the parameter k increases. 2) the 1-BDH assumption is proved to be equivalent to the DBDH assumption. So, all the k-BDH assumptions where k > 1 are weaker than DBDH assumption. We apply the technique of Benson et al. to construct ABE on k-BDH assumption, furthermore, we design a new framework to support the flexible switchable security-level for users. Concretely, the master public key, the master secret key and core keys issued by the system are constant. A User can generate different security-level public key/secret key pairs if it holds the core key. We propose a public key forgery attack model (PKFA) to capture the behaviors of adversary for generating a forged public key. We formally prove the selective-CPA security and PKFA security of our ABE systems. We compare the performances of our systems with Waters’ ABE systems.
Chaos-Based Color Image Encryption with DNA Operations

In this paper, an efficient yet simple color image encryption is proposed based on DNA sequence operation and chaotic maps for Android-based mobile devices. We designed our encryption algorithm based on such constraints. The main idea of the proposed method is different kind of DNA encoding for RGB channels and then Arnold’s cat map shuffling is performed in RGB channel in the combined manner. Then DNA addition is performed to diffuse the values between the RGB channels itself. Finally, DNA decoding is performed to form the color encrypted image. The promising feature of the proposed encryption algorithm is simple operations which comfortably suites for low processing power computing devices like Android mobile phones. The analysis and experiment’s results show the efficiency of the proposed encryption method.
Automatic Segmentation of Malaria Affected Erythrocyte in Thin Blood Films

In today’s world, a highly précised diagnostic method needs to be improved for management of feverish sickness and ensure that medicines are prescribed when necessary. The proposed algorithm is applied to giemsa-stained thin blood films. Using triangle’s thresholding technique, the erythrocytes are segmented, HSV color space based feature extraction is applied on the segmented erythrocytes. Features extracted are given to SVM classifier to identify whether the query sample is affected with a parasite or a normal sample. The performance of this algorithm is evaluated on the database collected from CDC website and 20 samples taken manually from a local hospital.
On the Future of Markets Driven by Blockchain

When an effort is made to understand how the blockchain may influence the evolution of economies, a distinction between marketing promises and neutral assessments is required. This work aims to review the prospective development of three key economic principles under the assumed constitution of a blockchain future. It is analyzed how the concepts of intermediation, economic transparency and economic automation show characteristics under possibly disruptive impact. Their present situation is laid out and extrapolated to gain a reasoned insight into economic development driven by blockchain technology as currently promised. An argument is made that the consumers view of innovative services will be coined by nescience towards the actual data structure in use.
Resources Facilitating International Capstone Experiences

Today it is very common for software systems to be built by teams located in more than one country. For example, a project team may be located in Australia while the team lead resides in the US. Senior design or capstone projects offer students real-world hands-on experience but rarely while working internationally. One reason is that most instructors do not have international business contacts that allow them to find project sponsors in other countries. Another reason is the fear of having to invest a huge amount of time managing an international project.
Efficient accelerator sharing in virtualized environments: A Xeon Phi use-case

High performance computing experts and scientists increasingly pay attention to heterogeneous processing and most systems today are built with some form of heterogeneity. At the same time, a large amount of HPC workloads is executed in cloud computing environments, due to the benefits that cloud offers both for the users and the infrastructure providers. In this context, there is a growing need to bridge the gap between the accelerator ecosystem and the world of virtualization, which is the building block of cloud computing environments. Hence, future accelerator platforms need to be designed in a virtualization-friendly approach, both in the software and in the hardware level. In this paper we extend our previous work and introduce vPHI, a transparent framework that enables sharing of Xeon Phi devices between multiple virtual machines. We design vPHI based on the paravirtualization technique and provide compatibility with the accelerator’s native transport layer to the upper level software stack components. In this way, with vPHI existing applications can be used without any source code or binary modifications. The evaluation of our framework in the popular offload mode of execution shows promising results with vPHI exhibiting minimal overhead for the computational phases in a deployment with multiple (up to 8) VMs. vPHI can enable better accelerator utilization, increasing up to 3.4x the total throughput versus a single host application. We also discuss the applicability of our approach to future accelerator technologies and in this way form a path to bring the accelerator use cases closer to the cloud computing world.
Performance Evaluation of DCT, DWT and SPIHT Techniques for Medical Image Compression

Medical imaging is, visible illustration of internal of the human body in digital form. There exists a need for compression of these medical images for storage and transmission purposes with high image quality for error free diagnosis of diseases. It is necessary to develop new techniques for compression of images, resulting into reduction in cost of data storage and transmission. This paper proposes a progressive and DWT-based Set Partition in Hierarchical Tree (SPIHT) algorithm for achieving better image compression while maintaining the nature of restored image. The performance of SPIHT algorithm is evaluated and compared with simple DCT and DWT using the parameters like Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE) and compression ratio (CR). From the results it is concluded that SPIHT algorithm produces better PSNR and MSE values compared to DCT and DWT with high image quality.
Invisible BlockChain and Plasticity of Money – Adam Smith Meets Darwin to Buy Crypto Currency

This paper will attempt to compare evolution in nature (based on Darwin’s theory of evolution by natural selection), with similar processes happening in money, especially in the current iteration of money (cryptocurrencies), and the underlying blockchain technology. This paper will review the history and evolution of money up to this point, shortly explain Darwin’s theory of evolution by natural selection, compare the similarities between evolution by natural selection in nature with evolution in blockchain technology and cryptocurrencies, and will examine the potential paths for the blockchain technology and cryptocurrencies to evolve in the future.
Enhancing Security of Double Random Phase Encryption Schemes Based on Discrete Fractional Fourier Transforms

Existing double random phase encryption (DRPE) schemes in the DFT, discrete fractional Fourier transform (DFRFT), and multiple-parameter DFRFT domains are all linear and vulnerable to the known-plaintext attack. In this brief, we first propose a random nonlinearizing block, which is then added to and nonlinearizes the encryption process of the conventional linear DRPE scheme. Besides, from computer experiments, we show the resulting random nonlinearized DRPE schemes are very sensitive to decryption key errors. Consequently, the proposed random nonlinearized DRPE schemes can resist the known-plaintext attack with enhanced security.
The Need for Automatic Detection of Uncommon Behaviour in Surveillance Systems: A Short Review

Surveillance system used for monitoring existed since decades. However, these surveillance systems required human intervention for the monitoring of suspicious behaviors. With the rapid revolution in technology, automatic detection of uncommon behaviors is gaining much attention from researchers. Being rapidly accepted in most public places to ensure transparency and security, surveillance systems are contributing in many applications like live traffic monitoring, crime scenes, and old people care. The deployment of automatic detection is very complex since it requires complex algorithms that should accurately detect uncommon behaviors, which is context-sensitive. In addition, it involves a lot of incoming data from various cameras, making it more challenging. In this perspective, the factors affecting surveillance systems and the techniques devised so far to detect uncommon behavior from these systems are analyzed and discussed. Robust automatic detection applications may yield to proactive decisions to be taken to prevent any harms/damages that would be caused by any uncommon/suspicious behaviors. Thus, it is important to explore techniques that can be used to implement automatic surveillance systems.
A high fidelity haze removal algorithm for optical satellite images using progressive transmission estimation based on the dark channel prior

In this paper, a high fidelity haze removal algorithm is proposed for optical satellite images by using progressive transmission estimation based on the dark channel prior (DCP). The transmission is estimated adaptively according to the histogram of the dark channel image and the constraint of maximum transmission. Then, the guided filter is used to refine the transmission to obtain a continuous transmission map in which the clean areas are retained as much as possible. The refined transmission is applied to each visual band to obtain the initial de-hazing image. Then, the transmission is re-evaluated for the initial de-hazing image, and a guided filter with a small window size is used to refine the re-evaluated transmission. Furthermore, the transmission is stretched with the power-law transformation (PLT). To ensure fidelity in hazy areas, the optimal stretched transmission is estimated according to the artificially selected samples, from which the final haze removal results can be achieved. Several optical satellite images are collected and tested to validate the effectiveness of the proposed method. The evaluation results demonstrate that the proposed method is superior to the traditional methods and can recover a haze-free image with high fidelity.
A Text Classification Algorithm Based on Improved Multidimensional–Multiresolution Topological Pattern Recognition

Traditional pattern recognition is based on “optimal partition” and the goal is to find an optimal classification interface based on the distribution of each category in high-dimensional space, thus has its inherent shortcomings and deficiencies. While topology pattern recognition can effectively compensate for the shortcomings of traditional pattern recognition, topological pattern recognition is based on “cognition” and the goal is to find the appropriate cover according to the “complex set cover” in high-dimensional space to achieve cognitive effect. Topological pattern recognition can effectively consummate the characteristics of high error rate, low recognition rate and repetitive training in the existing recognition system with low training sample number. At present, topology pattern recognition has been applied in many areas of social life. However, one problem that can’t be ignored is that topological pattern recognition requires a long training time and low fault tolerance rate. Therefore, this paper proposes an improved multidimensional–multiresolution topological pattern recognition, and applies it to text classification and recognition. The results show that the improved multidimensional–multiresolution topological pattern recognition method can effectively reduce the training time of text classification and improve the classification efficiency.
Application of the Usability Metrics of the ISO 9126 Standard in the E-Commerce Domain: A Case Study

Quantitative metrics are one of the few tools that allow specialists in the field of Human-Computer Interaction (HCI) to obtain numerical values about the level of usability of a software system. Although the advantages that software metrics provide, there is not enough evidence about their use in the literature. In this paper, we describe the evaluation process and the application of the usability metrics proposed by the ISO 9126 standard to evaluate a specific software product in the E-Commerce domain. The purpose of this study is to establish a guide that can be used by professionals in Software Engineering and related areas, to determine the correct level of usability of a set of graphical user interfaces that are designed as part of the front end of a system. In the same way, we describe the example of an application in which the entire inspection process is followed.
Feedback Control on Einstein-Podolsky-Rosen Steering of Dissipative System

In this article, we investigate the behaviors of Einstein-Podolsky-Rosen (EPR) steering for two uniformly accelerated atoms coupled with electromagnetic vacuum fluctuation. We firstly analyze the solving process of master equation that describes the system evolution. Unlike the complicated behaviors of entanglement, EPR steering decays with decoherent time and presents sudden death, the critical points of which can be located. In addition, small acceleration and interatomic distance can decrease the degradation of EPR steering. Since the measure of EPR steering adopted in this paper is equivalent to that of Bell non-locality, the EPR steering dynamics discussed in the article is applied equally to Bell non-locality.
Contexts of Digital Humanities in Japan

This chapter describes the brief history and recent trends in digital humanities in Japan, which had been led within the context of IT (information technology) and recently has strongly involved humanities researchers. According to the analysis of 991 technical reports by the Special Interest Group for Computers and Humanities (SIG-CH), the fields of linguistics and literary studies have been dominant while recently the history field has been increasing its number of the presentations, and many other fields in the humanities have been treated in a small percentage. Japanese texts have some difficulties in the digital environments. Although the recent developments in IT partially solve them, other attempts to improve the DH research environment have been activated. The policy of Japanese government to promote open science and open data will make DH in Japan more fruitful in the future.
Development of a computationally efficient voice conversion system on mobile phones

Voice conversion aims to change a source speaker's voice to make it sound like the one of a target speaker while preserving linguistic information. Despite the rapid advance of voice conversion algorithms in the last decade, most of them are still too complicated to be accessible to the public. With the popularity of mobile devices especially smart phones, mobile voice conversion applications are highly desirable such that everyone can enjoy the pleasure of high-quality voice mimicry and people with speech disorders can also potentially benefit from it. Due to the limited computing resources on mobile phones, the major concern is the time efficiency of such a mobile application to guarantee positive user experience. In this paper, we detail the development of a mobile voice conversion system based on the Gaussian mixture model (GMM) and the weighted frequency warping methods. We attempt to boost the computational efficiency by making the best of hardware characteristics of today's mobile phones, such as parallel computing on multiple cores and the advanced vectorization support. Experimental evaluation results indicate that our system can achieve acceptable voice conversion performance while the conversion time for a five-second sentence only takes slightly more than one second on iPhone 7.
Hexagonal Image Generation by Virtual Multi-grid-Camera

The process of capturing of an image is realized by a two-dimensional plane composed of photosensitive elements of almost entirely rectangle shape in technical solutions. However, biological visual systems use almost entirely hexagonal shapes and theoretical research shows the advantages of them but also the lack of usable capturing devices. We address this problem and create a virtual multi-grid-camera to overcome the problem and make further research possible. We create some scenes with common known content to demonstrate the use and show some effects being the result of the different shapes.