Reviewerly/exHarmony huggingface dataset:
Prolongation in Good Continuation

Smooth continuation of contours or lines also filling large gaps with illusory structure may be seen as the most stunning example for perceptual grouping. Much of the Gestalt literature on human seeing is about this topic. This must not be confused with the repetition of Gestalten in frieze symmetry. This chapter first presents a proven standard approach to this issue: the tensor voting. This technique is based on a smooth function on the 2D-domain similar to the assessments used elsewhere in this book. However, this function is matrix-valued, a field of symmetric positive semi-definite 2x2 matrices. In its classical use, this tensor field is rasterized and accumulated on the pixel grid, but this chapter shows ways to use the continuous field within the framework of this book. Tensor voting also generalizes well to higher dimensions, but that is beyond the scope of this book. Instead, the chapter also presents an operation for linear prolongation that fits well in the Gestalt-algebra approach and has proven useful in particular for the analysis of remotely sensed images of urban terrain.
Digital Humanities in Singapore

In Singapore, digital humanities (DH) is inclusive of the larger spectrum of the humanities, including not only its traditional disciplines (e.g., languages and literature, philosophy, law, geography, history, art history, musicology) but also anthropology, heritage studies, museum studies, performing arts, and visual arts. Multilingual, interdisciplinary, and audiovisual projects are particularly prominent. A community is growing around an emergent concept of DH, and it is developing results mainly in society-driven research projects. Although the DH label is relatively new, and DH dialogue across Singapore institutions is at its early stages, Singapore-based researchers have carried out digital research for decades. An increasing number of projects are home-grown, but several projects have also migrated to Singapore recently due to the high degree of mobility at Singaporean institutions. Current trends suggest that the next stage of DH history in Singapore will include the development of more formal institutions and more participation in global DH conversations.
Response Analysis of Eulerian Video Magnification

The human eye has a very high optical resolution making it one of the most astonishing curiosities of the world. However, its spatial resolution is not good enough to capture everything happening around it, and it can miss out minor details, which can be termed as hidden movements. The hidden movements can be due to the extremely high speed of the visual, very small movements, long-term physical process, etc. Eulerian video magnification is a spatiotemporal video processing algorithm that can reveal hidden details that are otherwise hidden to naked eyes. In this process, a standard video sequence is spatially decomposed, and temporal filtering of the frames is done. The data so obtained as output can be used in many fields such as biomedical instrumentation, remote surveillance, etc. Here, an analysis has been done on Eulerian Video Magnification (EVM), for different video resolutions to understand its reliability.
Image Encryption Method Using New Concept of Compound Blood Transfusion Rule with Multiple Chaotic Maps

With the advancement in internet technology, a large amount of information in the form of data and image is transferred from one end to the other. The information may be military, defense, medical, etc. which should be kept confidential by providing security.The aim of this article will be to provide security to the image. This is achieved by applying the image encryption method which converts the original information into an unreadable format.This work explores an efficient way of image encryption using a chaotic logistic function. A set of two chaotic logistic functions and 256 bit long external secret key are employed to enhance the security in the encrypted images. The initial condition of first logistic function has been obtained by providing the suitable weights to all bits of the secret key. The initial condition of second logistic function has been derived from the first chaotic logistic function. In this proposed algorithm, ten different operations are used to encrypt the pixel of an image. The outcome of the second logistic map decides the operation to be used in the encryption of the particular image pixel.Various statistical parameters like NPCR, UACI and information entropy were calculated.Results show that the proposed algorithm provides an image encryption method with better security and efficiency for all real-time applications such as medical images.
genieR: An R package for inference of demographic history of phylogenies

genieR is an R package (R Core Team ( 2016)) for the inference of demographic history from reconstructed molecular phylogenies (see Dearlove, Xiang, & Frost (2017) for one application), modeled after the C++ package GENIE (Pybus & Rambaut, 2002).The package processes phylogenetic trees in Newick format, in which taxa may be sampled either at the same time (isochronous) or at different times (heterochronous); the latter are particularly common in the analysis of pathogen sequence data.In addition to extracting basic information from a particular phylogeny such as sampling and coalescent times, genieR also fits a coalescent model (Kingman (1982)) under several demographic scenarios, including constant population size, exponential growth, expansion growth, logistic growth, with both continuous and piecewise variants.The fit of different models can be compared using Akaike's Information Criterion (AIC) (Akaike, 1973) values.genieR uses C++ code as an option for better computational performance, and models can be fit either using maximum likelihood or Markov Chain Monte Carlo (Hastings, 1970).genieR can also simulate phylogenies under different demographic models.
Real-Time Health Monitoring System Implemented on a Bicycle

It is evident that many systems have been developed and are being developed these days related to health monitoring. The main requirement is the continuous real-time health monitoring where the data, i.e. the body vitals/health parameters, can be easily understood by the user through an application interface, and this would also be shared with the corresponding physician who can be aware of the patient’s vitals at all times. This is a study of methods where a real-time health monitoring system can become an integral part of the society whereby its intention mainly aimed at gaining awareness of each ones health and its implementation leading to focus on a health record which is linked to web also information reaching the doctors in time for continuous monitoring. The obese people are able to control their weight. The project being ‘Real Time Health Monitoring System Implemented On A Bicycle’ aimed at designing and assembly of a non-invasive health monitoring system. The conversion of cycle energy will also be taken into account to supply for the health monitoring devices and charging of the display.
Distributed Representation of Healthcare Text Through Qualitative and Quantitative Analysis

Many healthcare-related applications use pretrained embeddings, but these are often trained over general corpus which is mostly downstreamed to certain particular application. One problem noticed among such embeddings is that these are not efficient across various health text applications and even less number of research describe evaluation of these embedding for health domain. In this paper, distributional embedding model is performed to acquire a word representation on data crawled from Journal of Medical Case Reports. This distributed embedding model is analyzed qualitatively and quantitatively over crawled corpus. Qualitative evaluation is employed by cosine similarity on different categories and is visually represented. Quantitative evaluation performed with parts of speech tagging and entity recognition. The embedding model attained a cross-validation accuracy of 91.70% in parts of speech tagging for GENIA corpus and ensured 83% accuracy in the entity recognition of i2b2 clinical data.
A New Higher Order Differential of BIG

BIG is a 128-bit block cipher proposed by Demeri et al. in 2019. The number of rounds is 18 for high security. The designer evaluated its security against linear cryptanalysis. On the other hand, it has not been reported the security of BIG against higher order differential attack, which is one of the algebraic attacks. In this paper, we focused on a higher order differential of BIG. We found a new 15-round saturation characteristic of BIG using 1-st order differential by computer experiment. Exploiting this characteristic, we show that full-round BIG can be attacked with 10 chosen plaintexts and 2€ encryption operations.
Parallel Implementation of PrePost Algorithm Based on Spark for Big Data

Frequent itemset mining is a fundamental element with respect to many data mining problems directed at finding interesting patterns in data. Recently the PrePost algorithm, a new algorithm for mining frequent itemsets based on the idea of N-lists, which in most cases outperforms other current state-of-the-art algorithms, has been presented. The performance of PrePost algorithm degrades when it comes to processing of big data. However, the existing parallel Prepost algorithms implemented with the MapReduce model are not efficient enough for iterative computation. In view of this, this article proposes a parallel algorithm based on the Spark RDD Framework, which enhances PrePost that uses also a hash table to improve the process of creating N-lists and Excombines the features of Spark in order to process large data efficiently. Experiments show that Our approach algorithm is more superior than MRPrePost in terms of performance, the stability and scalability.
True Color Image Compression and Decompression Using Fusion of Three-Level Discrete Wavelet Transform—Discrete Cosine Transforms and Arithmetic Coding Technique

In this research paper, we have done the implementation and analysis of true color image compression and decompression technique. The implemented paper divides the color image into RGB component then after applying three-level Discrete Wavelet Transform, RGB components are split into nine higher frequency sub-bands and one lower order sub-band. The lower frequency sub-band is compressed into T-Matrix using One Dimension Discrete Cosine Transform. At the same time, higher frequency sub-bands are compressed using scalar quantize and eliminate zero and store data algorithm are applied to remove zeros in sub-band matrixes. Last, the encoded mode adopted arithmetic encoding. This algorithm has use two level of quantization this show significance improve in performance of compression algorithm. The decompression process is reverse process of encoder. The decompression algorithm decoded high-frequency subbands using return zero matrix algorithm and recover low-frequency sub-bands and other sub-bands using applying inverse process.
A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels

Image classification models built into visual support systems and other assistive devices need to provide accurate predictions about their environment. We focus on an application of assistive technology for people with visual impairments, for daily activities such as shopping or cooking. In this paper, we provide a new benchmark dataset for a challenging task in this application - classification of fruits, vegetables, and refrigerated products, e.g. milk packages and juice cartons, in grocery stores. To enable the learning process to utilize multiple sources of structured information, this dataset not only contains a large volume of natural images but also includes the corresponding information of the product from an online shopping website. Such information encompasses the hierarchical structure of the object classes, as well as an iconic image of each type of object. This dataset can be used to train and evaluate image classification models for helping visually impaired people in natural environments. Additionally, we provide benchmark results evaluated on pretrained convolutional neural networks often used for image understanding purposes, and also a multi-view variational autoencoder, which is capable of utilizing the rich product information in the dataset.
Quality and Complexity Measurement of 2D-DCT Archietecture Using Loeffler Algorithm Along with CSD and CSE

To optimize the Discrete Cosine Transform (DCT) in terms of size and to improve the quality of the image in image compression, in this paper, A 2D-DCT using Loeffler algorithm along with Canonical Signed Digit (CSD) and Canonical Sub expression Elimination (CSE) has been proposed. For fast computation 2-DCT/IDCT is executed by using 1D DCT row column method. The performance of the proposed architecture has been evaluated and compared with the other technique.
Model-Based Multi-modal Human-System Interaction

The paper presents a model-based approach to the abstract specification of multi-modal interfaces of assistive systems within the domain of Active and Assisted Living (AAL). We introduce an appropriate metamodel and a related domain specific modeling language that allow for a generic definition of multi-modal, situation dependent interactions. The language has been implemented using the metamodeling framework ADOxx®.
Performance Evaluation of Audio Watermarking Algorithms Using DWT and LFSR

Advancement in Internet technology has compelled encryption, copyright protection and authentication of audios, videos, images, and documents. Watermarking techniques have been widely employed in copyright protection and authentications of digital media. Digital image watermarking techniques are extensively explored and found suitable for copyright protection of images, whereas audio watermarking techniques are less studied and need extensive research due to superior hearing ability of the human beings. In this paper, we present audio watermarking technique using discrete wavelet transform (DWT) and linear feedback shift registers (LFSR). Watermark signal is obtained using LFSR technique before embedding into audio signal. Dispersion of maximum power spectral density property of LFSR is explored that makes it suitable as scramblers. LFSR does not require secret key for scrambling and descrambling of watermark signal. Sequences were embedded into the DWT coefficients of the audio signal. Experimental simulations were performed to evaluate the performance of the audio watermarking technique. Audio watermarking finds applications in the area of ownership protection, tamper detection and authentication of music, military communication, voice-activated machines, and robots.
Short Paper: IoT Context-Driven Architecture: Characterization of the Behavioral Aspect

The contribution of this paper deals with IoT coordination. After a thorough review of the literature, we conclude that the challenge of coordination has not yet been met despite all the contributions already suggested. To address this challenge, we have proposed in a previous work an IoT contextual architecture, while the purpose of this article focuses on the behavior of the proposed multilayer IoT architecture by describing a flood case study. Three aspects are considered: functional aspect, informational aspect and behavioral aspect. When modeling the behavioral aspect, we focus mainly on the control flow throughout the workflow and we put our interest on relative ordering of sub-workflows.
Real-Time Sparse Visual Tracking Using Circulant Reverse Lasso Model

Sparse representation has been successfully applied to visual tracking. Recent progresses in sparse tracking are mainly made within the particle filter framework. However, most sparse trackers need to extract complex feature representations for each particle in the limited sample space, leading to expensive computation cost and yielding inferior tracking performance. To deal with the above issues, we propose a novel sparse tracking method based on the circulant reverse lasso model. Benefiting from the properties of circulant matrices, densely sampled target candidates are implicitly generated by cyclically shifting the base feature descriptors, and then embedded into a reverse sparse reconstruction model as a dictionary to encode a robust appearance template. The alternating direction method of multipliers is employed for solving the reverse sparse model and the optimization process can be efficiently solved in the frequency domain, which enables the proposed tracker to run in real-time. The calculated sparse coefficient map represents the similarity scores between the template and circular shifted samples. Thus the target location can be directly predicted according to the coordinates of the peak coefficient. A scale-aware template updating strategy is combined with the correlation filter template learning to take into account both appearance deformations and scale variations. Both quantitative and qualitative evaluations on two challenging tracking benchmarks demonstrate that the proposed algorithm performs favorably against several state-of-the-art sparse representation based tracking methods.
The Intersection of Digital Humanities and Artistic Augmented/Virtual Reality Research in Hong Kong

Immersive media such as augmented reality/virtual reality (AR/VR) environments are growing in popularity both globally and in Hong Kong. In the artistic research sector, these projects may or may not have systematic digital preservation, data curation, or other digital stewardship components built into the creative process. Although digital multimedia preservation has been a long-standing topic of discussion for archivists, the complexity of immersive media projects raises questions regarding best practices and ensuring future accessibility. The author proposes that artistic AR/VR research in Hong Kong could benefit from the application of digital humanities practices, whether it be for archival, research, or further artistic purposes. This chapter reviews case studies in Hong Kong academic institutions to identify existing needs and makes recommendations for incorporating digital humanities practices into the creative and project management processes.
Digital Forensic Investigation of the Xiyu Pagoda Lighthouse

This chapter describes a lighthouse heritage experiential education project conducted by Run Run Shaw Library, City University of Hong Kong. The project initiative aims to connect students to archival collections with the aid of digital technology. The research, or “digital forensic investigation,” was conducted on the Xiyu Pagoda Lighthouse, a demolished lighthouse that once existed on Xiyu Island, Penghu, Taiwan. The first stages of research have been framed within a six-step “forensic investigation” model. Proceeding research developed from and beyond the model and focused on resolving the inconsistencies encountered in the initial research. Digital humanities tools were utilized for reconstruction and representation purposes, accumulating evidence through an iterative process between primary and authoritative sources, and first-hand observation. Beyond demonstrating the way in which technology can be employed to enhance historical research, the aim of this chapter is to illustrate how library initiatives can serve to connect students to archival collections.
Video Activity Recognition Using Sequence Kernel Based Support Vector Machines

This paper addresses issues in performing video activity recognition using support vector machines (SVMs). The videos comprise of sequence of sub-activities where a sub-activity correspond to a segment of video. For building activity recognizer, each segment is encoded into a feature vector. Hence a video is represented as a sequence of feature vectors. In this work, we propose to explore GMM-based encoding scheme ot encode a video segment into bag-of-visual-word vector representation. We also propose to use Fisher score vector as an encoded representation for a video segment. For building SVM-based activity recognizer, it is necessary to use suitable kernel that match sequences of feature vectors. Such kernels are called sequence kernels. In this work, we propose different sequence kernels like modified time flexible kernel, segment level pyramid match kernel, segment level probability sequence kernel and segment level Fisher kernel for matching videos when segments are represented using an encoded feature vector representation. The effectiveness of the proposed sequence kernels in the SVM- based activity recognition are studied using benchmark datasets.
A Lucrative Sensor for Counting in the Limousine

In developing countries like India, the governments are in dire need of funds for all its projects. Hence it relies on a number of external factors such as exports, trading and FDI. On the other hand, the government also depends on internal factors such as tax from its citizens and indirectly from departments such as transportation. It is the responsibility of the government to provide transportation facilities like buses and trains to its citizens at a nominal charge and at the same time make a profit to run the government and its aided projects. To start from the lowest level, the sad truth is that the number of people travelling without tickets in buses is increasing at an alarming rate. This leads to a big loss for the transportation department and indirectly to the government which falls short of funds. When such losses are incurred due to defaulters in a large scale the government is forced to increase prices of other commodities to look at other sources of income from its citizens though hike in tax, interest rates, fuel price, VAT, etc. The proposed system reduces manpower by excluding checking inspector’s role and their responsibilities. As we are all facing a lot of problems in identifying the number of passengers travelling without tickets the design of an automation system emerges with an in-built lucrative sensor that can count the number of passengers entering inside and leaving the limousine. Further, it can also detect the number of passengers who have taken tickets and who have not taken tickets, which will be handy to track down defaulter and make the bus ticketing system more efficient.
Application of Neural Networks in Image Processing

In today’s world, the need for computers and their logics is critical for the development of any system. This paper is for those people who have little or no knowledge about Artificial Neural Networks (ANNs). Various advances have been made in creating intelligent systems, some enlivened by biological neural networks. Analysts from numerous logical orders are outlining artificial neural networks (ANNs) to take care of a variety of problems in design recognition, prediction, optimization, associative memory, and control. And will be discussing some of the algorithms of ANNs such as image segmentation using edge detection, image enhancement using multiscale retinex and technique combining sharpening and noise reduction and some practical applications such as diagnosing liver disease and face detection systems.
Modelling Sparse Saliency Maps on Manifolds: Numerical Results and Applications

Saliency detection is an image processing task which aims at automatically estimating visually salient object regions in a digital image mimicking human visual attention and eyes fixation. A number of different computational approaches for visual saliency estimation has recently appeared in Computer and Artificial Vision. Relevant and new applications can be found everywhere varying from automatic image segmentation and understanding, localization and quantification for biomedical and aerial images to fast video tracking and surveillance. In this contribution, we present a new variational model on finite dimensional manifolds generated by some characteristic features of the data. A Primal-Dual method is implemented for the numerical resolution showing promising preliminary results.