Reviewerly/exHarmony huggingface dataset:
Interactive Iconized Grammar‐Based Pailou Modelling

Abstract Pailous are representative Chinese architectural works used for commemoration. However, their geometric structure and semantic construction rules are too complex for quick and intuitive modelling using traditional modelling tools. We propose an intuitive modelling system for the stylized creation of pailous for novices. Our system encapsulates structural components as icons and semantic layouts as topological graphs, using which users create and manipulate icons with topological recommendations. The interpreter automatically and immediately transforms a graph to its corresponding model using built‐in components with the proposed parametric L‐system grammars derived from architectural rules. Using this system to re‐create existing representative pailous and design imaginary ones yields results with the desired visual complexities. In comparison to Maya, a 3D modelling tool, when modelling a pailou and toukung, our system is effective and simple, and eliminates the need to remember and understand complex rules.
Parallel Implementations of CHAM

In this paper, we presented novel parallel implementations of CHAM-64/128 block cipher on modern ARM-NEON processors. In order to accelerate the performance of the implementation of CHAM-64/128 block cipher, the full specifications of ARM-NEON processors are utilized in terms of instruction set and multiple cores. First, the SIMD feature of ARM processor is fully utilized. The modern ARM processor provides $$2\times 16$$ -bit vectorized instruction. By using the instruction sets and full register files, total 4 CHAM-64/128 encryptions are performed at once in data parallel way. Second, the dedicated SIMD instruction sets, namely NEON engine, is fully exploited. The NEON engine supports $$8\times 16$$ -bit vectorized instruction over 128-bit Q registers. The 24 CHAM-64/128 encryptions are performed at once in data parallel way. Third, both ARM and NEON instruction sets are well re-ordered in interleaved way. This mixed approach hides the pipeline stalls between each instruction set. Fourth, the multiple cores are exploited to maximize the performance in thread level. Finally, we achieved the 4.2 cycles/byte for implementation of CHAM-64/128 on ARM-NEON processors. This result is competitive to the parallel implementation of LEA-128/128 and HIGHT-64/128 on same processor.
Multi-step Self-attention Network for Cross-modal Retrieval Based on a Limited Text Space

Cross-modal retrieval has been recently proposed to find an appropriate subspace where the similarity among different modalities, such as image and text, can be directly measured. In this paper, we propose Multi-step Self-Attention Network (MSAN) to perform cross-modal retrieval in a limited text space with multiple attention steps, that can selectively attend to partial shared information at each step and aggregate useful information over multiple steps to measure the final similarity. In order to achieve better retrieval results with faster training speed, we introduce global prior knowledge as the global reference information. Extensive experiments on Flickr30K and MSCOCO, show that MSAN achieves new state-of-the-art results in accuracy for cross-modal retrieval.
Quantification of age-related changes in midsagittal facial profile using Fourier analysis: A longitudinal study on Japanese adult males

The aim of the present study was to use Fourier analysis to quantify and study age-related changes in midsagittal facial profile.Midsagittal facial profiles were extracted as lists of x and y coordinates from 125 pairs of 3D facial scans captured at an average of 10.5 years apart for adult Japanese males aged 23-52 years. These were categorized into three 10-year-long age groups. Files of x and y coordinates underwent Fourier analysis at 30 harmonic levels. Paired t-tests were used to determine statistical significance of differences across corresponding harmonic coefficients. Mean harmonic coefficients were used to construct mean pre and post ageing profiles for each age group for qualitative comparisons.Full detail of facial profile was described by the first 20 harmonics. With increasing age, there was a trend of longitudinal changes involving more midsagittal shape features with increased magnitudes. However, all changes were lower than 1 mm.Fourier analysis is a useful morphometric approach to quantify age-related midsagittal facial changes. The small variations in the study groups prompt for testing Fourier analysis on the elderly and on other parasagittal and transverse facial features.
Generalized and Differential Likelihood Ratio Tests with Quantum Signal Processing

Quantum signal processing invokes the injection of abstract quantum mechanical frameworks into classical signal processing problems. In this work we apply this idea to the notion of optimal likelihood ratio tests within the context of the location verification problem. We first draw parallels with quantum mechanical measurements and the notion of generalized likelihood ratio measurements. As we show, these quite different measurement frameworks are mathematically similar since both can be described in the language of projections into subspaces - the projections removing the nuisance parameters of the underlying system in the latter case. We then show how the imposition of an `artificial' mathematical constraint, borrowed from a similar constraint imposed on quantum mechanics by the uncertainty principle, is likely to assist in machine-learning solutions of the location verification problem - such solutions being more useful in real-world deployments.
Achieving and Maintaining Perceptual Intent with HDR Imaging

Abstract Though displays have undergone dramatic shifts in the last 30 years, many of the imaging pipeline elements needed to create a compelling visual experience have only started to catch up as HDR becomes more prominent. Here, a look at how those new elements are being incorporated and what the changes mean for the industry.
SVM-based Seal Imprint Verification Using Edge Difference

In Asian countries, seals are widely used for authenticating the identity of a person or organization. Therefore, the ability to efficiently verify whether a seal is either genuine or forged is important. We propose an effective method of verification based on Hough transformation to approximate the imprint borders and the four vertexes, and use geometric transformation to align the perspective of the detected imprint image with the genuine imprint. After the edge-difference images between the original image and the detected image are created, distance transformation and connected-component labeling are applied. Finally, the number of edges in the connected component and the distance to the closest point in the edge of original image are used to calculate the input vector for the SVM (support vector machine). The imprint is then determined to be either genuine or forged. The experimental results show the effectiveness of the proposed verification approach.
The Effect of Spatio-temporal Inconsistency on the Subjective Quality Evaluation of Omnidirectional Videos

With the development of immersive media technologies, omnidirectional video services have been launched in many fields. Conducting subjective quality evaluation research becomes a crucial step to benchmark and ensure the quality of omnidirectional video services. As omnidirectional videos record spherical visual scenes that are broader than the visual field of human eyes, the quality scores rated by different observers are based on individual spatio-temporal viewing experience. The potential spatio-temporal inconsistency between observers may impact the reliability of subjective quality evaluation and thus challenge existing experimental methodologies. In this paper, we focus on investigating the effect of spatial-temporal inconsistency on the subjective quality evaluation of omnidirectional videos. A systematic quality evaluation experiment was designed with various viewing methods involved. Experimental results showed that the spatio-temporal inconsistency has a significant impact on the reliability of subjective quality results and the impact is strongly determined by the viewing method. We intend to provide recommendations with respect to the subjective quality evaluation of omnidirectional videos.
Tired of Choosing? Just Add Structure and Virtual Reality

Interactive Evolutionary Computation (IEC) systems often suffer from users only performing a small number of generations, a phenomenon known as user fatigue. This is one of the main hindrances to these systems generating complex and aesthetically pleasing pieces of art. This paper presents two novel approaches to addressing the issue by improving user engagement, firstly through using Virtual Environments and secondly improving the predictability of the generated images using a well-defined structure and giving the user more control. To establish their efficacy, the concepts are applied to a series of prototype systems. Our results show that the approaches are effective to some degree. We propose alterations to further improve their implementation in future systems.
Kernel-based probability measures for generalized interpolations: A deterministic or stochastic problem?

In this article, we propose a kernel-based algorithm for generalized interpolations by kernel-based probability measures on Banach spaces. Different from the classical kernel-based approximation methods, we construct and analyze the deterministic kernel-based estimators based on the generalized data by the stochastic approaches including formulas and convergence. Specially, we do not need the regularization parameter estimations and the noise distribution estimations in the computational processes of the kernel-based estimators based on the noisy data.
Exploration of Blockchain Based Solution for Real-Estate

Blockchain is an evolving technology providing us the ability to design and develop decentralized distributed solutions. It has yields wide-scale impact on finance industry with reducing intermediate authorities. By building digital currency tracking of the transaction has become much easier and immutable nature of block not only give credibility but also is a cheap storage mechanism. So, we decided to explore its feasibility in the real estate industry. Real estate is one of the biggest sectors all across the world and is known to have millions of transactions every day. By making a digital system we can not only reduce redundant paperwork but also increase robustness in the system. It would also help investigating agencies to track transaction like many incidents came where terrorists bought or leased land with fake identities. By developing a system efficient enough to validate citizen and property and provide quick fetching of the transaction would be a revolutionary change for the industry as at present date all transaction are offline at it is impossible to collect data all across the country. This research paper explores one possible model of blockchain in the real estate market particularly rules and regulation followed in India with authentication using AADHAR.
Large-pose Face Alignment via Shape-aware Heatmap

In this paper, we focus on dealing with problems of large-pose face alignment. Recently proposed heatmap-based algorithms have made promising performance on this problem. However, the traditional heatmap is constructed based on Gaussian model with fixed variance, which is inconsistent with the local shape of faces. In this paper, we propose a shape-aware heatmap to efficiently solve the problems of large-pose face alignment. Specifically, we design a novel heatmap based on Gaussian mixture model, where positions of several adjacent landmarks are utilized to construct different components. Thus the probability distribution is modified to fit the shape of the local region. The experimental results on Menpo-3D and AFLW2000-3D databases show that the proposed method outperforms the state-of-the-art algorithms.
Scene Recognition of Surveillance Data using Deep Features and Supervised Classifiers

Precise labeling of an image based on its semantic description is quite challenging task and has its significant applications in surveillance area. Majority of scene classification techniques during past few decades have targeted low level feature by handcraft engineering or unsupervised feature extraction techniques. In this paper, we aim to categorize scene classes for surveillance systems by exploiting deep convolutional features to manifold projection along with supervised classification algorithms. A topology is constructed to depict high dimensions of convolution heat-maps to 128D salient features. Parameters of pre-trained network are tuned to precisely fit with the output of our problem. Experimental results depict that our methodology is more robust and competitive as compared to state of the art methods.
A Unified Framework for Feature-based Domain Adaptation of Neural Network Language Models

An important task for language models is the adaptation of general-domain models to specific target domains. For neural network-based language models, feature-based domain adaptation has been a popular method in previous research. Conventional methods use an adaptation feature providing context information that is calculated from a topic model. However, such a topic model needs to be trained separately from the language model. To unify the language and context model training, we present an approach that combines an extractor network and a domain adaptation layer. The extractor network learns a context representation from a fixed-size window of past words and provides the context information for the adaptation layer. The benefit of our method is that the extractor network can be trained jointly with the language model in a single training step. Our proposed method showed superior performance over conventional domain adaptation with topic features on a dataset of TED talks with respect to perplexity and word error rate after 100-best rescoring.
Principle of Least Expressiveness

I'm always delighted to discover a connection between two ideas that I'm already fond of on their own, so I'd like to share a connection I found recently. The first idea is writing code that expresses my thinking about the problem domain, and the second is the principle of least expressiveness (PLE). The connection is that I can use the PLE to reveal my thinking about the problem domain, and because all ambiguity stops at the code, the act of programming using the PLE can help me simplify and debug the flawed ideas I have in my head.
Recurrent Neural Networks with Grid Data Quantization for Modeling LHC Superconducting Magnets Behavior

This paper presents a model based on Recurrent Neural Network architecture, in particular LSTM, for modeling the behavior of Large Hadron Collider superconducting magnets. High resolution data available in Post Mortem database was used to train a set of models and compare their performance with respect to various hyper-parameters such as input data quantization and number of cells. A novel approach to signal level quantization allowed to reduce a size of the model, simplify tuning of the magnet monitoring system and make the process scalable. The paper shows that RNNs such as LSTM or GRU may be used for modeling high resolution signals with an accuracy over 0.95 and as small number of the parameters ranging from 800 to 1200. This makes the solution suitable for hardware implementation essential in the case of monitoring performance critical and high speed signal of Large Hadron Collider superconducting magnets.
Traffic Analysis and Signal Timer Management Using OpenCV

Traffic has been a major issue at intersections throughout the world, here a technique is proposed to reduce manpower required to handle the traffic and remove static timers from intersections. Proposed system is consisting of simple computer device with CCTV. It works by analyse traffic condition from video input and then count vehicles to manipulate signal timer which avoid traffic collision and maintain traffic flow. Therefor the first step in this process is taking video input from mounted CCTV camera and then detection of cars. The system uses Haar like features, which is mainly made for face detection. Haar feature-based cascade classifier is effective object detection technique. It has data set containing positive and negative image data which help agent to identify target object. Result shows this system is more effective in detection of cars compare to existing systems.
A conceptual model for electronic learning cycle on schoology

The growing up of digital literacy is very important to be integrated into the educational world. Learning in the novelty of technology is the key to achieve learning outcome through digital literacy. However, several types of research focus only on the dimensions of learning outcomes. Therefore, this paper provides learning insight by developing a conceptual model for Electronic Learning Cycle on Schoology (ELCoS) based on the syntax of Technology Learning Cycle (TLC). The cycle of ELCoS model is similar to TLC initiating from awareness, exploration and filtration, learning, personal and professional applications, sharing and reflection. A significant difference within the model is using Schoology. The ELCoS learning model in this paper serves as the basis for other technology-based learning models.
Data Interpolation for Deep Learning based Encrypted Data Packet Classification

In this paper, we propose to apply deep learning for encrypted data packet classification. Due to the complexity of deep learning, it is inefficient to process the entire data packet, which is usually padded with many zeros. In order to reduce data size for classification, while maintaining high accuracy, we develop data interpolation schemes to process data packets with various lengths to a fixed size. In particular, three data interpolation schemes based on nearest value, bilinear and bicubic interpolation are proposed. Experiments are conducted using an open source dataset. The evaluation results demonstrate that our data interpolation schemes can be applied to process input data for higher computational efficiency without losing classification accuracy.
The Research of Data Center Construction for Smart City

Basic support is provided for system operation and analysis by Data Center, which is used as an important infrastructure of Smart City.Data Center is related to network, system integration, information security, data base, data mining, etc.the quality of Resource sharing and integration is influenced directly by construction effect of Data Center.The connotation and the composition of Data Center are researched, and according to the construction request of Data Center, a certain architecture of Smart City is put forward, and the content of data center is discussed, and the approach of construction idea is researched, and the application of data center for Smart City is discussed in this paper.
Method of recognition of the blasts nuclei structure by using light microscopy and computer data processing

Method for recognizing the structure of blood and bone marrow blast nuclei using light microscopy and computer data processing is proposed to improve the accuracy of diagnosis of acute leukemia, including reducing the subjectivity of microscopic analysis of blood and bone marrow preparations. The paper presents a model of automated recognition of blood cells during microscopic analysis based on objective criteria of classification of images of pathological cells, focused on the differential diagnosis of acute leukemia. The application of the proposed method allows to clarify the variant of the disease and reduce the time of the diagnostic process.
An Extended Control Framework for Privacy-Preserving Photo Sharing across Different Social Networks

Photo sharing across different social networks has become one of the most popular social activities in our daily life. However, users lost the control totally once they upload their photos to the current social network. In this paper, we propose the design, implementation and evaluation of PrivacyJPEG, a framework to preserve users’ privacy on their uploaded photos, especially when these photos are forwarded to other social networks. PrivacyJPEG acts as a third-party plugin to existing photo sharing-supported social networks, it enables: 1) to encrypt several privacy areas of photos and bind the access control polices before users uploading their photos, 2) each user’s privacy policy could restrain the following users on a dissemination chain, 3) any privacy areas on the photos could be hidden away from unwanted viewers even across different social networks. Besides, PrivacyJPEG presents a carefully designed embedding method, which binds the policies to the uploaded JPEG file without disturbing their displays. We implement a prototype of PrivacyJPEG, design and perform a set of useful real life experiments. The evaluation results indicate the effectiveness and efficiency of PrivacyJPEG.
Energy Trading through Blockchain

Energy Trading through Blockchain is an innovative way to trade solar energy across different places. It enables the owners of solar plants to trade the solar energy that is been produced by their plants in an easy and utmost secure manner using Blockchain technology. The core point of the project is to pay for what is being served. The source and destination stations will be abiding to a smart contract which allows them to pay for exactly the served amount of energy. Each and every transaction is stored in a ledger. Operating the entire system in Ethereum platform provides transparency, security and credibility to the process. The process entirely runs on the transfer of cryptocurrencies and not any other mode of money.
BBGDASH: A Max-Min Bounded Bitrate Guidance for SDN Enabled Adaptive Video Streaming

The increase in video traffic and the end-user demands for high-quality videos have triggered academia and industry to find novel mechanisms for media distribution. Among the available streaming services, HTTP adaptive streaming (HAS) is being the de facto standard for multi-bitrate streaming. Recent studies show that the bitrate adaptation of client-driven HAS applications is challenging due to the fact that they are based on locally taken decisions for adapting the quality of the received video. Software-defined networking (SDN) has emerged as a new network paradigm to provide centralised management. The programmability and flexibility of SDN can be utilised to enhance the delivery of video over the Internet. In this paper, we present a novel and scalable network-assisted approach (denoted BBGDASH) that identifies the boundary range of the requested bitrate levels while preserving the final quality adaptation at the client. Experimental results demonstrate the potential of the proposed approach for delivering the video over SDN-enabled networks.
Embeddings.jl: easy access to pretrained word embeddings from Julia

Embeddings.jl is a tool to help users of the Julia programming language (Bezanson, Edelman, Karpinski, & Shah, 2017) make use of pretrained word embeddings for natural language processing.Word embeddings are a very important feature representation in natural language processing.The use of embeddings pretrained on very large corpora can be seen as a form of transfer learning.It allows knowledge of lexical semantics derived from the distributional hypothesis-that words occurring in similar contexts have similar meaning-to be injected into models which may have only limited amounts of supervised, task oriented training data.