Reviewerly/exHarmony huggingface dataset:
Dense Trajectory Action Recognition Algorithm Based on Improved SURF

In order to improve the time-consuming and large error problem of camera motion estimation in dense trajectory feature extraction of video, a dense trajectory action recognition algorithm based on Improved Speeded-Up Robust Features (SURF) is proposed. The algorithm mainly performs dense sampling of video images, and then executes camera motion estimation. In the feature point detection stage, the Gaussian pyramid layer was constructed dynamically to improve the real-time and accuracy of feature point extraction. Based on the SURF algorithm, the brightness center algorithm is used to obtain direction of feature. Binary Robust Independent Elementary Feature (BRIEF) is used to generate feature descriptors to determine matching points and optimized images, then to conducts feature tracking and feature extraction on the images to classify features. The experimental results show that the algorithm performs better in terms of speed when removing camera motion, and improves the real-time performance of feature extraction and the accuracy of action recognition.
Mobile Web Design: The Effect of Education on the Influence of Classical and Expressive Aesthetics on Perceived Credibility

Research has shown that the perceived credibility of mobile web design can be largely determined by the two dimensions of visual aesthetics: classical and expressive. However, there is limited research on how users’ education moderates the relationships between both dimensions of visual aesthetics and perceived credibility. To bridge this gap, we conducted an empirical study among 526 subjects to investigate how education moderates the influence of classical and expressive aesthetics on the perceived credibility of mobile website. Specifically, we focused on four visual designs of a mobile website homepage, in which products and/or services are laid out. Our results show that, irrespective of the level of education of users and the visual design of the mobile website, the perception of both dimensions of visual aesthetics has a significant impact on perceived credibility, with classical aesthetics having a stronger impact than expressive aesthetics overall. Moreover, we found that the effect of classical aesthetics on perceived credibility is stronger for higher education users than for lower education users, while the effect of expressive aesthetics on perceived credibility is stronger for lower education users than for higher education users. Our findings suggests that classical aesthetics(perceived visual clarity) is more likely to influence higher education users than lower education users to perceive a mobile website as credible, while expressive aesthetics(perceived visual enrichment) is more likely to influence lower education users than higher education users to perceive a mobile website as credible. Web designers of e-commerce mobile websites can leverage these findings to enhance the perceived credibility of their websites by the respective user groups.
Digital Memorialization in Death-Ridden Societies: How HCI Could Contribute to Death Rituals in Taiwan and Japan

Both Taiwan and Japan are entering an area of hyper aged and death-ridden society. The death rituals in both countries are rapidly changing due to urbanization and secularization, although both cultures have long histories and traditions for funeral and memorialization. Furthermore, some digital technologies have already been adopted in both countries, providing alternative changes for some occasions. This paper principally reports contemporary funeral and memorialization practices in Taipei and Tokyo by introducing insights from our fieldworks and interviews in both cities. These also include examples of digital technology adoption. Comprehensively understanding phenomena being caused by local faiths and traditions, we will discuss how ICT/HCI designers could contribute to the realm of local memorializations and more generally around the world, and what kind of design opportunities for the future death rituals they could be involved in.
Temporally Forward Nonlinear Scale Space with Octave Prediction for High Frame Rate and Ultra-Low Delay A-KAZE Matching System

High frame rate and ultra-low delay matching system is appealing because of its excellent experience for human-machine interactive applications. A-KAZE algorithm is chosen because of its high robustness and high speed. Nonlinear scale space is very important in A-KAZE, but it not only has at least one frame delay and but also is not hardware friendly. This paper proposes temporally forward nonlinear scale space with octave prediction for high frame rate and ultra-low delay A-KAZE matching system. Problems of complex calculations, data dependency and long time delay are solved by HFD based temporally forward nonlinear scale space with octave prediction. Motion estimation prediction is utilized to improve the robustness. It is also processed parallel with keypoint detection part. It finishes processing before the next frame coming and there is no delay as a results. What's more, lower position gray-coded bit-plane motion estimation has been proposed to improve performance. The results show that the proposed method keeps F-score more than 95% for most cases and shows much better performance compared with the current high frame rate and ultra-low delay matching system.
Balanced allocation on graphs: A random walk approach

We propose algorithms for allocating n sequential balls into n bins that are interconnected as a d ‐regular n ‐vertex graph G , where d ≥ 3 can be any integer. In general, the algorithms proceeds in n succeeding rounds. Let ℓ &gt; 0 be an integer, which is given as an input to the algorithms. In each round, ball 1 ≤ t ≤ n picks a node of G uniformly at random and performs a nonbacktracking random walk of length ℓ from the chosen node and simultaneously collects the load information of a subset of the visited nodes. It then allocates itself to one of them with the minimum load (ties are broken uniformly at random). For graphs with sufficiently large girths, we obtain upper and lower bounds for the maximum number of balls at any bin after allocating all n balls in terms of ℓ , with high probability.
AR Assistive System in Domestic Environment Using HMDs: Comparing Visual and Aural Instructions

Household appliances are becoming more varied. In daily life, people usually refer to printed documents while they learn to use different devices. However, augmented reality (AR) assistive systems providing visual and aural instructions have been proposed as an alternative solution. In this work, we evaluated users’ performance of instruction understanding in four different ways: (1) Baseline paper instructions, (2) Visual instructions based on head mounted displays (HMDs), (3) Visual instructions based on computer monitor, (4) Aural instructions. In a Wizard of Oz study, we found that, for the task of making espresso coffee, the helpfulness of visual and aural instructions depends on task complexity. Providing visual instructions is a better way of showing operation details, while aural instructions are suitable for presenting intention of operation. With the same visual instructions on displays, due to the limitation of hardware, the HMD-users complete the task in the longest duration and bear the heaviest perceived cognitive load.
Design of Virtual Cloud Desktop System Based on OpenStack

In this paper, a virtual desktop cloud system based on OpenStack architecture is proposed to specifically satisfy the requirements for teachers and students in experiment classes. We design control and management components for the virtual desktop system, especially considering the schedule management algorithm. Based on OpenStack cloud platform, we implement the architecture of server for integrating the superiority of OpenStack into desktop Virtualization System.
Realtime dehazing using colour uniformity principle

Dehazing is an important process as it can significantly improve the performance of computer vision applications in outdoor environments. The two main requirements that an online dehazing system demands are low processing time and high visual range. The authors present a novel dehazing algorithm based on colour uniformity principle (CUP) which meets the desired requirements of a realtime implementation. Estimation of atmospheric scattering parameter and transmission map forms the key step in dehazing problem. At first, the authors use CUP to generate the transmission map and refine it further by Fast Guided Filter. They estimate the atmospheric scattering parameter with the help of the estimated transmission map. Experimental results show that the quality of dehazed output, produced in real-time using the proposed method, is comparable with the results achieved by the state of the art techniques. The proposed dehazing method produces reliable dehazed output in varying haze conditions, unlike current methods.
Classifying Arabic Farmers’ Complaints Based on Crops and Diseases Using Machine Learning Approaches

In this paper, two models are proposed to automatically categorize the farmers’ complaints on various diseases that may affect crops. In the first model, a complaint which is expressed in Arabic free text is classified into its respective crop and then into a particular disease, but in the second model, the complaint is classified directly into diseases. A dataset of farmers’ complaints described in Arabic script consisting of complaints from five different crops raised by farmers of different parts of Egypt is created. Separate lexicons are created for crops and their diseases by considering all possible technical terms related to a crop and its diseases including their possible slang synonyms. Each preprocessed complaint is represented in the form of a binary vector using the vector space model (VSM) with the help of crop lexicon so that machine learning techniques can be applied. Experiments are conducted on the dataset by varying the percentage of training with multiple trials using SVM and KNN classifiers. It has been observed from the results that the proposed model is performing on par with the human expert and can be deployable for real-time operations.
Experimental Analysis of Machine Learning Algorithms in Classification Task of Mobile Network Providers in Virudhunagar District

Data mining has many classification algorithms with desired features. The machine learning algorithms such as K Nearest Neighbor, Support Vector Machines and Neural Network are some of the most popular algorithms used for classification task. Since classification is gaining importance due to the enormous amount of big data in real world datasets, the choice of a perfect classification algorithm is an ultimate need. For the classification task, the "Mobile Phone network satisfaction" real world dataset has been collected from the mobile phone users. In today's world, mobile network chosen by the users has a greater impact on the individual user's day-to-day activities and also on the business of network providers. Hence, the performance and accuracy of the mentioned machine learning algorithms has been investigated and analyzed in the prior described datasets. The proposed work analyses the performance of the KNN, SVM and Neural network classifiers and also analyses the mobile users' affinity and usage nature based on different age groups.
Hybrid of Filters and Genetic Algorithm - Random Forests Based Wrapper Approach for Feature Selection and Prediction

Data is too diverse. The diversity of data does not just exist in terms of dimensionality but also of varied datatypes. To extract most useful information from datasets and to improve the prediction accuracy, feature selection is of great importance in data mining. This paper is proposing a hybrid feature selection methodology with the motivation of producing most relevant feature subset and better predicting accuracy. A wrapper composed of Genetic Algorithm (GA), a heuristic search tool and Random Forest (RF) as a predicting model, keeping in view the optimality of Genetic Algorithm and predictive accuracy of Random Forests, is suggested. And for the purpose of creating a reduced search space for GA-RF wrapper, set of filters methods are used, which generate a reduced subset of features by weight assignment and filtration through threshold criteria. The proposed approach has been tested on Breast Cancer dataset from UCI repository and produced 99.04% prediction accuracy. A small comparative study is also carried out to justify that coupling of genetic algorithm and random forests followed by space reduction outperforms other wrapper-based approaches.
Preliminary tests of a real-valued anticipatory classifier system

The paper describes the first attempts toward designing and evaluating anticipatory classifier systems working in a real-valued input domain using interval predicates representation. Promising results are obtained by testing two environments - real-valued multiplexer and checkerboard from the classical XCSR problem domain.
Towards controllable image descriptions with semi-supervised VAE

Image captioning models successfully describe the visual contents of images using natural language. To generate more natural and diverse descriptions, a model must learn style-specific patterns and requires collecting style-specific datasets, which is time-consuming. To address this issue, we propose a semi-supervised deep generative model, Semi-supervised Conditional Variational Auto-Encoder (SCVAE). Our model is capable of leveraging more labelled and unlabelled data in the generative model schema. Extensive empirical results demonstrate that compared with the start-of-art models, our proposed method is able to generate more accurate image captions with more extensive styles.
Tracking Criminal Events through IoT Devices and an Edge Computing Approach

The occurrence of criminal and terrorist activities is one of the biggest problems which is afflicting the current society. Indeed, criminal events are both hard to predict and difficult to handle once they take place. The classic methods applied by Police Forces (PFs) and Law Enforcement Agencies (LEAs) are in fact not effective, in terms of time management, communication and countermeasures, because of gaps among the occurrence of the criminal event, its identification and the intervention of the PFs for its management. In this regard, this paper proposes a solution which aims to enhance the communication and collaboration among citizens and police forces. It is based on an IoT app which exploits the edge computing approach to face with the above mentioned gaps. In particular, the proposed model as well as the algorithm, along with their main features, are described. The operation logic is then exemplified through the implementation of a simulator that shows its functioning.
Horizontal Attacks using K-Means: Comparison with Traditional Analysis Methods

AI means are widely used to detect correlations in large data sets. This makes them an ideal candidate to improve side channel analysis attacks as the core feature if these attacks is to reveal the correlation between measurement values and the key bits processed. In this paper we present an assessment of AI means, i.e. k-means. We investigated the success rate of attacks against three designs with different levels of vulnerability. The result was that even though counter intuitive approaches such as the Pearson correlation coefficient outperform k-means. The highest success rate of the latter was 68.7 per cent of an uncompressed trace and 88.3 per cent for a compressed trace whereas the Pearson correlation coefficient achieved 91.7 per cent for the uncompressed trace and 89.3 per cent for the compressed trace.
Research on the Consumer Reviews of JD Bookstore Based on Text Mining Technology

With the rapid development of the Internet economy, the major online shopping platform has accumulated a large amount of consumer comment data containing rich information. This article takes the JD Bookstore consumer comments as the research object and uses the web crawler technology to collect consumer reviews. Further, depth mining and sentiment orientation analysis are performed on the comment data and the feature word list based on the dictionary and sentiment analysis algorithms. The results of the study show that the majority of users' comments have positive emotional tendencies, and comments with negative emotional tendencies are less than 10%. Although the majority of users maintain a more positive attitude towards JD Bookstore' logistics services, customer service, product quality, product packaging and prices, but logistics services and product quality still need to be further improved. Finally, this paper puts forward some suggestions on the distribution, purchase and pricing of JD Bookstore.
A randomised scheme for secured modular exponentiation against power analysis attacks

This paper presents an approach to compute secured modular exponentiation to mitigate power analysis attacks. Two processes have been designed based on ‘multiply-always’ binary method, which injects dummy multiplications so that the original multiplications can remain intact. The two processes are being invoked based on a random variable, which generated either 0 or 1. With these randomised modular exponentiation, the proposed approach can resist simple and differential power analysis attacks to a large extent. The proposed approach has been implemented using conventional ‘squaring-multiplication’ and ‘Montgomery-Ladder’ methods. Further, the work has also been extended to CRT-RSA, which is widely used for fast computation. The proposed method has been implemented for different key sizes of 1024-bit, 1536-bit and 2048-bit RSA & CRT-RSA. The analysis and results show that with a complexity of O(n), the proposed approach can resist some of the standard DPA attacks on modular exponentiation.
Building Taxonomies based on Human-Machine Teaming

Taxonomies and ontologies are handy tools in many application domains such as knowledge systematization and automatic reasoning. In the cyber security field, many researchers have proposed such taxonomies and ontologies, most of which were built based on manual work. Some researchers proposed the use of computing tools to automate the building process, but mainly on very narrow sub-areas of cyber security. Thus, there is a lack of general cyber security taxonomies and ontologies, possibly due to the difficulties of manually curating keywords and concepts for such a diverse, inter-disciplinary and dynamically evolving field.
Recommender Systems Based on Autoencoder and Differential Privacy

Recommender systems are widely applied in practice. However the process of recommender involves the users' sensitive and privacy information inevitably. The privacy protection of recommender systems must be taken into account. In this paper, a recommender system model based on autoencoder and differential privacy is proposed. Two methods of applying differential privacy to autoencoder are designed: input perturbation and objective function perturbation. Both theoretical analysis and experimental results show that the proposed methods, as well as related algorithms, can provide reliable privacy preservation while maintaining high prediction accuracy.
The Architecture of a Driverless Robot Car Based on EyeBot System

Abstract This paper presents the system architecture of a driverless robot car, designed to participate in the Carolo-Cup, a competition regarding automated model vehicles. We describe an implementation that completed the different tasks in this competition on our EyeBot platform in detail. EyeBot has one RGB camera as well as three infrared distance sensors, and it’s powered by Raspberry Pi 3B. We developed the lane detection algorithm using the OpenCV library and completed the traffic sign recognition task based on SVM which can be used offline. Experimental and simulation results recorded in real-time are also reported. The test result showed that our programs can run at high speed to achieve stable motion control in real time and complete all the tasks in the competition. The highlight of our work is that the whole system can run on a platform with limited computing resources.
Gaussian process variance reduction by location selection

A key issue in Gaussian Process modeling is to decide on the locations where measurements are going to be taken. A good set of observations will provide a better model. Current state of the art selects such a set so as to minimize the posterior variance of the Gaussian process by exploiting submodularity. We propose two techniques, a Gradient Descent procedure and an heuristic algorithm to iteratively improve an initial set of observations so as to minimize the posterior variance directly. Results show the clear improvements that can be obtain under different settings.
STRNN: End-to-end deep learning framework for video partial copy detection

Abstract The task of partial copy detection in videos aims at determine if one or more segments of the query video are already present in the data-set, while giving the information of similar portion time period. At present, most effective algorithms of partial copy detection in videos are designed as three steps: feature extraction, feature matching and time alignment. The separation of feature matching and time alignment module ignores the spatio-temporal information of partial copy to some extent. Therefore, satisfactory performance is not obtained. In order to reduce this loss, this article does not decompose it into two separate tasks, but using a single convolution neural network to solve these two aspects. First, we sample video frames and extract CNN features, calculate the spatio-temporal relationship matrix of the source video and the query video, and then graphically map the matrix and train the convolution neural network based on the object detection task of the RefineDet model. Finally, in the query phase, the time period of the partial copy is deduced based on the detection result. In this paper, we evaluate the performance of the algorithm on the real complex video copy detection data-set VCDB which is significantly improved compared with the state-of-the-art partial copy detection framework.
A Conversation-Based Intelligent Tutoring System Benefits Adult Readers with Low Literacy Skills

This article introduces three distinctive features of a conversation-based intelligent tutoring system called AutoTutor. AutoTutor was designed to teach low literacy adult learners comprehension strategies across different levels of discourse processing. In AutoTutor, three-way conversations take place between two computers agents (a teacher agent and a peer agent) and a human learner. Computer agents scaffold learning by asking questions and providing feedback. The interface of AutoTutor is simple and easy to use and addresses the special technology needs of adult learners. One of AutoTutor’s strengths is that it is adaptive and as such can provide individualized instruction for the diverse population of adult literacy students. The adaptivity of AutoTutor is achieved by assessing learners’ performance and branching them into conditions with different difficulty level. Data from a reading comprehension intervention suggest that adult literacy students benefit from using AutoTutor. Such learning benefits may be increased by enhancing the adaptivity of AutoTutor. This may be accomplished by tailoring instruction and materials to meet the various needs of the individuals with low literacy skills.