Reviewerly/exHarmony huggingface dataset:
Artificial Bee Colony with Mutual Search Strategy and Search Mode Control

The artificial bee colony (ABC) was inspired by bees’ foraging behavior. Bees of the colony will try to find better food source (potential solutions) in solution space. After multiple iterations, the bees in the colony may move closer as a few clusters even are trapped in local optimum. In this paper, the search mode control is proposed; the bees will explore or exploit solution space for finding better solutions. Further, the mutual search strategy is proposed to guide bees move to potential food source. The bees will provide various and useful information to each other for searching potential solutions. In experiments, 10 test functions of CEC 2019 are adopted to test proposed method and compare to related works. The proposed method not only can improve original ABC but also perform better than another ABC variant.
Pose Estimation for Planar Target Based on Monocular Visual Information

Target pose estimation is a key technology in the field of artificial intelligence. This paper focuses on the pose estimation method based on monocular vision. Firstly, a cooperative target composed of five circular patterns is designed to increase the measurement accuracy. To solve the shortcomings of traditional image processing algorithms, we add two constraints, area threshold and ellipse roundness, to achieve more accurate feature extraction and segmentation. Then, the least square method is used to fit ellipses, and an ellipse sorting method is designed. Combined with the established models, the pose estimation of the target can be realized via the PNP problem. A large number of experiments prove the effectiveness and feasibility of the proposed method. Experiments show that the mean error for the attitude angle and position are less than 0.4° and 0.5 mm respectively. In a word, this method has the advantages of simple process, reasonable cost and high precision, and can be applied to engineering practice.
Salient Motion Features for Visual Attention Models

The human vision system (HVS) pays more attention to the moving objects than the static areas. Due to this fact, motion becomes one of the important features of the visual attention model. In this paper, we have integrated motion features with the visual computational model to detect salient moving objects. To implement this objective, several sub-features such as colour, colour change between different frames, optical flow estimation and background subtraction are explored and integrated using arithmetic operation. Here, pyramidal and non- pyramidal approaches are used to analyse these features. Our study concludes that multiplication is a better operation for feature integration. The experiments revealed that the individual colour feature gives low precision-recall scores. However, integrating this colour feature with other features has positive impact on the performance of detecting moving salient objects.
MENJADI ARSIPARIS KOLABORATIF MELALUI PENDIRIAN iSchool

Among others, archivist should be able to provide access to information over time. But it is impossible to do so all alone. By applying literature study to several journals and books, this article aims to urge collaboration among many other recordkeeping professionals, not only archivists, through an establishment of School of Information (iSchool). Due to the growing problems of digital transformation iSchool would become a scientific hub for knowledge and insight sharing.
Sharing Knowledge via Ubiquitous Technology to Enhance Safety Awareness: Willingness and Actual Experience in Hong Kong

Recently, various ubiquitous technological advancements have benefitted knowledge sharing in different sectors. As there is a close relationship between safety knowledge and awareness, this paper examines the use of Web 2.0, Internet of Things (IoT) and mobile applications in construction safety knowledge sharing for enhancing safety awareness at work. The use of such technologies can improve internal and external communication as well as collaboration. A quantitative survey was conducted to investigate the willingness and uptake of advanced technologies in the Hong Kong construction industry. Out of 23 respondents, 16 were eager to use mobile applications to share safety information. Regarding safety knowledge sharing via Web 2.0, respondents perceived a variety of barriers for not using it, such as the need to protect their company’s privacy issues made them hesitant to share knowledge regarding safety issues; others were of the opinion that Web 2.0 was managed by the elite in the organisation. Only three respondents have used Web 2.0 platforms. The willingness to apply IoT was relatively negative due to the perceived extra operation costs.
Features of Interest Points Based Human Interaction Prediction

The recognition and prediction of human interaction based on video has a broad application prospect in intelligent video monitoring and other fields, but the current integration algorithm of it is not mature, which greatly limits the application of the algorithm. A prediction method of human interaction based on the statistical characteristics of interest points is proposed, which established an integrated framework to the recognition and prediction of human interaction initially. First, the spatio-temporal interest points are extracted and performed as 3D-SIFT feature description, then the bag of words is used to represent the action video. In the training stage, Gaussian models are used to establish the action model for each action at different time scales. In the prediction stage, the bag of words representation is extracted and compared with the established prediction models of different time lengths to obtain similar prediction probabilities between the models for an action video of unknown length. Finally, the predictive probability of each action at different time scales is fused by weighted probabilities completing the recognition and prediction of interaction prediction. The experimental result on UT-interaction dataset demonstrated that the proposed approach is easy to implement and has a good predictive effect.
Extracting Keywords on SNS by Successive KeyGraph

Social networking services (SNS) are important sources of news for the world. By summarizing text on SNS, we will be able to grasp what topics in the news are going on. However, because texts keep growing in SNS, we need to process huge amounts of data very efficiently. Therefore, it is necessary to apply a fast and low-memory algorithm. In this investigation, we propose Successive KeyGraph as a method of keywords extraction. The extracted keywords work effectively for news summarization. In Successive KeyGraph, we update keygraph over time. KeyGraph extracts the author's claims and outputs the results immediately. Also, we hold co-occurrence count which we need to make a keygraph current by using Lossy Counting. We apply several preprocessing to manage unknown words and to improve morphological analysis.
Building Private-by-Design IoT Systems

Internet of Things (IoT) devices have revolutionized the way we interact with our physical environment. With a single tap on a smartphone screen or a voice command one can control home lighting, thermostats and cameras, monitor physical activity, and keep track of personal belongings. However, while these devices become more and more embedded in our daily lives, there are growing concerns over the privacy and security of highly sensitive data they collect. Numerous cases of data abuse, unauthorized sharing and leakage have been reported. Unfortunately, existing IoT systems have not only failed to prevent such cases, but often contributed to those. To address this issue, we propose a clean-slate approach to building secure and private-by-design IoT systems, in which users retain full control and ownership of their IoT data. The approach builds upon key design concepts: (1) a dataflow programming model for building IoT apps and services, and (2) a mechanism to track sensitive sensor data flows inside these apps and automatically verify their compliance with user-defined privacy and security preferences.
A Feedback Generation System to Enhance Learning at Primary School

Feedback is an essential aid for the learning process of students. Unfortunately, with an increase in workloads and student numbers, teachers find it time-consuming and difficult to produce valuable and meaningful feedback for students. To maintain an environment in which students can continue to receive beneficial feedback, a feedback generation system in primary schools in Mauritius has been developed. This system is based on English subject where students can perform exercises in the form of multiple-choice questions and fill-in-the- blank questions where immediate feedback is provided to them after it is requested. Natural Language Processing (NLP) techniques has been used to generate feedback to students. The system also provides auto-correction and auto-grading of exercises. The aim of this system is to save teacher's time in providing immediate content-based and meaningful feedback to enhance student learning. The desktop application was tested in some primary schools and positive results were obtained from both instructors and students.
Making Picnic Feasible for Embedded Devices

Picnic is a post-quantum digital signature scheme, where the security is based on the difficulty of inverting a symmetric block cipher and zero-knowledge proofs. Picnic is one of the alternate candidates of the third round of the standardization process. Hence, it could be standardized in case of any weakness found in the round three candidates. Based on our paper at the 23rd Euromicro Conference ([6]), we found an optimization, which reduces memory usage to make it usable on IoT devices. This paper focusses on approaches for the implementation of this optimization. As a proof-of-concept, we implemented our implementation of Picnic on a ST Nucleo-L476RG and measured the cycles of the implementation.
Multi-database Mining

Multiple heterogeneous databases are widely used in many disciplines, such as decision support and medical research. Therefore new data mining approaches are required handling databases coming from multi-database systems which are relational and generally heterogeneous. In this paper, we propose a framework for classification task over multiple relational heterogeneous databases. The proposed framework is composed of two components. The first one resolves the heterogeneity problem using schema matching techniques. The second is a decision tree classification approach using a strategic communication technique based on utility theory. Experiments performed on real databases were very satisfactory and showed that the proposed framework is giving an efficient solution for the multi-database mining.
A Survey on Ranking of Features Using Customer Opinion Reviews

Presently day's web based business is quickly developing which gives office for clients to buy items on the web. Assortments of brands and heaps of items have been offered on the web and quantities of client audits are accessible on web. These audits are significant for the clients just as for the dealers. The majority of the surveys are scattered so it produces trouble for utilizing significant data. So the item viewpoint positioning framework, which distinguishes significant parts of items from online client audits and improve the ease of use of various surveys. The significant item angles are perceived utilizing two perceptions: - 1) the significant perspectives are generally remarked by an enormous number of clients 2) clients conclusions on the significant viewpoints are incredibly impact on the general assessments of the item. This paper gives the review on different methods of viewpoint recognizable proof and conclusion arrangement.
One-Class Support Vector Machine for Data Streams

In various information systems, application learning algorithms have to act in a dynamic environment where the acquired data is in data streams. In contrast to static data mining, processing streams introduce an array of computational and algorithmic stipulations. With the continuous input of data in data streams, one would like a mechanism that automatically identifies unusual events in the time series. The topic has been in the limelight as it has huge potential for real-time activities. To show the algorithm's robustness, we have trained the classifier to multiple activities and its success in identifying each activity. The paper explores the possibility of using the One-Class Support Vector Machine (OCSVM) for novelty detection in data streams.
Towards a Distributed Framework for Multi-Agent Reinforcement Learning Research

Some of the most important publications in deep reinforcement learning over the last few years have been fueled by access to massive amounts of computation through large scale distributed systems. The success of these approaches in achieving human-expert level performance on several complex video-game environments has motivated further exploration into the limits of these approaches as computation increases. In this paper, we present a distributed RL training framework designed for super computing infrastructures such as the MIT SuperCloud. We review a collection of challenging learning environments-such as Google Research Football, StarCraft II, and Multi-Agent Mujoco- which are at the frontier of reinforcement learning research. We provide results on these environments that illustrate the current state of the field on these problems. Finally, we also quantify and discuss the computational requirements needed for performing RL research by enumerating all experiments performed on these environments.
Mammography Registration for Unsupervised Learning Based on CC and MLO Views

Mammography image usually contains two views in different orientations---Cranial Caudal (CC) and Mediolateral Oblique (MLO). In clinical decision making, the location of the lesions on the CC and MLO views are usually different. And the shape of breast varies greatly among patients, therefore, two views are necessary for evaluating the information in a comprehensively manner. In this paper, we propose an unsupervised registration algorithm based on CC and MLO views of mammography, which learns the deformation function through a Convolutional Neural Network (CNN). This function maps the input image to the corresponding deformation field and generates an image with the same shape as the template image after deformation, so that the doctor can better observe the two views. According to the radiologist's assessment, our work can contribute to medical image analysis and processing while providing novel guidance in learning-based registration and its applications.
Graph Embedding Discriminant Analysis and Semi-Supervised Extension for Face Recognition

Considering graph embedding based dimension reduction methods are easily affected by outliers and the fact that there exist insufficient labeled samples in practical applications and thus degrades the discriminant performance, a novel graph embedding discriminant analysis method and its semi-supervised extension for face recognition is proposed in this paper. It uses the intraclass samples and their within-class mean to construct the intrinsic graph for describing the intraclass compactness, which can effectively avoid the influence of outliers to the within-class scatter. Meanwhile, we build the penalty graph through the local information of interclass samples to characterize the interclass separability, which considers the different contributions of interclass samples within the neighborhood to the between-class scatter. On the other hand, we apply the low-rank representation with sparsity constraint for semi-supervised learning, aiming to explore the global low-rank relationships of unlabeled samples. Experimental results on ORL, AR and FERET face datasets demonstrate the effectiveness and robustness of our method.
Person Re-Identification in Movies/Dramas

In this paper, we present a system for person re-identification in movies and dramas. Our person re-identification method simultaneously learns differences and commonalities between two pairs of images. Features are extracted using three images, and the relationship between the extracted features is constructed into two pairs of feature maps to learn. Our method significantly outperforms on a large data set (CUHK03), and our own re-identification dataset generated from video contents such as movies and dramas. We also show that the proposed model learned on our own data set can be applied to actor re-identification in video content.
A Cascade Collaborative Offloading Framework for Video Analytics Based on Online Learning

Offloading has already become an essential component of the video analytics system due to its low latency and energy saving. The generic traffic offloading strategy allocates video tasks according to computation resources without considering video information, which leads to incorrect decisions that can not guarantee bandwidth reduction and accuracy in video analytics. Therefore, to alleviate the problem, we propose a Cascade Collaborative Offloading Framework (CCOF) in the paper. CCOF is a cross-edge framework, and it coordinates mobile devices, edge servers, and cloud servers to complete video analytics tasks. First, we model the traffic offloading problem in a video analytics scenario as an optimization problem and propose the CCOF as a solution. Then, we propose a periodic online learning algorithm that can update the model parameters with stream data. Afterwards, we adopt the online algorithm and the sharing-based classifier on the mobile devices and edge servers to filter out unnecessary frames and reduce the bandwidth cost. Furthermore, we feed the recognition results back to the edge and mobile devices for online learning. Evaluation of dataset Jackson shows that the proposed method reduces the bandwidth cost by an order of magnitude and achieves 10x speed-ups on video analysis tasks.
Towards text-based domain-specific modeling language for representational state transfer compliant services

This paper discusses the development of text-based domain-specific modeling language. The covered domain is representational state transfer (REST) architectural style. With domain-specific modeling language, we can develop software in a higher level of abstraction than general-purpose programming language. Domain analysis is performed to classify different kinds of resources into several resource types, namely resource, individual resource, projection resource, container resource, list resource, collection resource, transitional resource, and subresource. The design of modeling language is represented in metamodel and the implementation of modeling language is represented in EBNF-like grammar using Xtext domain-specific language (DSL) framework. In conclusion, we have successfully developed a domain-specific modeling language and its validators. The problem faced lies in the code generator in the behavioral modeling aspect, especially the business logic. It is very difficult to generalize the various business logic at a high level of abstraction. So that in the future, the modeling language needs to be limited to a narrower domain so that it can be fully executable.
Zero-Shot Classification by Large-Margin Distance Learning

Zero-Shot Learning (ZSL) has increasingly attained a lot of attentions due to the scalability it provides to recognition models for classifying instances from new classes of which no training data have been seen before. This scalability is achieved by providing semantic information about new classes, which could be obtained remarkably easier, with a lower cost, in comparison with gathering a new training set. In other words, ZSL can be seen as a subset of transfer learning. In this paper, a distance function is learnt in order to discriminate the classes with a customized large-margin loss function. We also propose a nonlinear prototype learning approach by defining a theoretical-based, but simple mapping function to generate the class prototypes from associated semantic information. The instances are compared with the generated prototypes for classification considering the learnt distance function. The evaluations on five widely-used ZSL datasets, illustrate the effectiveness and superiority of the proposed method in comparison with the state-of-the-art ZSL approaches, despite of its simplicity.
An Approach to Automatic Recognition of Web Advertising Focused on Different Languages

Technology has revolutionized marketing models in recent years. This paper presents the operation of an algorithm for the identification of advertisements on the Web-focused in different languages. The tests were carried out in four languages: English, French, Portuguese, and Italian. It was found that advertisements on the Web distract, steal or inconvenience the attention of users, so identifying them, and if possible blocking them, is essential. For the test, three aspects to be evaluated were considered: brand, product, or service, and how to capture the attention of users. Based on this identification, we sought to provide the user with privacy and comfort in their continuous navigation.
Object Texture Transform Based on Improved CycleGAN

Image style transfer is a kind of image-to-image conversion method, which has been widely used in the field of computer vision and is an important research direction. In the application of target deformation, the traditional algorithm has the disadvantages of unclear positioning and difficult separation from the background. In this paper, an improved cyclic generative antagonistic network (CycleGAN) is proposed. The model adds a layer of self-attention algorithm to the discriminator structure, and uses clues from all feature locations to establish global dependence, to obtain global geometric features of images, and to achieve long correlation. Compared with CycleGAN algorithm, the results show that the algorithm in this paper can learn to generate a style transfer image with clear texture and full rendering, and at the same time the image has no obvious loss in details. Compared with the original algorithm, the convergence speed in the learning process of the algorithm in this paper is obviously improved, the oscillation amplitude of loss function is small, and the number of steps required for network convergence is reduced. In the process of testing, the algorithm stylizes 1163 images, and the consumption time of each generation only increases by 17 s.
End-to-end Learned Image Compression with Conditional Latent Space Modeling for Entropy Coding

The use of neural networks in image compression enables transforms and probability models for entropy coding which can process images based on much more complex models than the simple Gauss-Markov models in traditional compression methods. All at the expense of higher computational complexity. In the neural-network based image compression literature, various methods to model the dependencies in the transform domain/latent space are proposed. This work uses an alternative method to exploit the dependencies of the latent representation. The joint density of the latent representation is modeled as a product of conditional densities, which are learned using neural networks. However, each latent variable is not conditioned on all previous latent variables as in the chain rule of factoring joint distributions, but only on a few previous variables, in particular the left, upper and upperleft spatial neighbor variables based on a Markov property assumption for a simpler model and algorthm. The compression performance is comparable with the state- of-the-art compression models, while the conditional densities require a much simpler network and training time due to their simplicity and less number of parameters then its counterparts.