Reviewerly/exHarmony huggingface dataset:
Robust correlation filter tracking with deep semantic supervision

Traditional correlation filter (CF) tracking has achieved high tracking performance and speed. However, it easily falls into tracking failures in some cases of target occlusion, deformation, rotation etc. Tracking failure also contaminates the CF model and makes it less discriminative. To tackle these problems, the authors propose a deep semantic supervision tracking framework. This framework integrates the advantages of multiple features and tracking methods into an evaluation and redetection tracking mechanism. In this work, customised deep convolutional neural network (CNN) with particle filtering (PF) resampling was employed to alleviate the contamination of the CF model and improve tracking performance. The authors also adopted a mixed decision mechanism for CF tracking results evaluation. Furthermore, based on the observation that most tracking frames can be easily tracked by a CF tracker using handcrafted features, authors’ tracking method achieves real-time performance. It should be noted that the proposed framework is flexible and extensible to improve other existing trackers. In authors’ extensive experiments on large benchmark datasets including OTB2013 and OTB2015, the proposed tracker performed favourably compared to the state-of-the-art methods.
A proposal of calligraphy learning assistant system with letter portion practice function using projection mapping

Purpose For several decades, calligraphy has been popular among people in Japan, China, and even in the world. Traditionally, a teacher teaches how to write letters on a paper with a brush, and a student will imitate them by referring to the model letters. However, if a teacher is not available, this method will not be applicable either. This study aims to propose a calligraphy learning assistant system (CLAS) using projection mapping , which allows a student to learn calligraphy by him/herself. Design/methodology/approach By following the letter writing video of a teacher that is directly projected on the paper, a student is able to learn the stroke order and writing speed in addition to the letter shape. Moreover, the letter portion practice function is incorporated in CLAS to allow a learner to repeat practicing hard portions of each letter. Findings For evaluations, the authors implemented CLAS using Raspberry Pi and open-source software and asked students to use it. The results confirmed that CLAS is effective in improving calligraphy skills of novice students. Originality/value With CLAS, a student can practice calligraphy using a conventional brush, ink and paper at a desk while looking at the model letter writing of a teacher projected on the paper using projection mapping.
Fuzzy Logic Controller for Automation of an Autonomous Irrigation System Designed for Habanero Pepper (Capsicum Chinense Jacq.)

Agriculture is a fundamental economic activity but implies a large consumption of water; in Mexico, the habanero pepper (Capsicum chinense Jacq.) is one of the main agricultural products. This work presents the design of a Sugeno based fuzzy controller with 18 if-then rules for an irrigation system that allows irrigation to be managed in a sustainable manner, reducing the loss of water giving habanero pepper plants a favorable environment for their growth. The controller consists of a set of rules designed to activate or deactivate the irrigation system based on four input variables.
Nonlinear-Feedback Shift Registers for Stream Ciphers

The analysis of shift registers with nonlinear feedbacks was carried out for the possibility of their use as cryptographically strong components of stream ciphers. The use of these registers is particularly relevant for the design of low-resource cryptography and is used to protect the transmitted data on the Internet of Things. Considering the diversity of possible cryptographic attacks on this element, some of the main properties were considered in the work, such as nonlinearity, correlation immunity, and the possibility of choosing NLFSRs optimized for these indicators and at the same time having minimal structural complexity. In addition, such parameters as the balance of the formed sequence and the presence of prohibitions are considered. Full set of possible shift registers with nonlinear feedback has analyzed in this paper. This set is forming the sequence of the maximum period for a register dimension not exceeding six cells, as well as registers with a nonlinearity not higher two with register dimension from seven to nine cells.
An approach to helping developers learn open source projects based on machine learning

Developers usually learn excellent coding methods and design patterns by reading the code from well-known open-source projects, and participate in the development of open-source projects to enhance their programming capabilities. When developers have just joined an existing open-source project development, the first thing to do is to read and understand the project code. However, almost no project will maintain design documentations. Developers can only understand code according to user guide (mainly focus on how to use code but not on how to develop code) or brief code comments, which is relatively difficult for new developers. To help developers learn open-source projects more quickly, we propose an approach to helping developers learn open-source projects based on machine learning. First, we build a code structure graph for the project code by static analysis. Second, we implement a project entries recommendation approach based on clustering and machine learning to recommend project entries suitable for developers to read. Third, we implement a learning path recommendation algorithm. The algorithm recommends learning paths based on function nodes in the code structure graph selected by the developers, helps developers understand open-source projects better. In experiments, we select two famous c++ open-source projects, Lua and Memcache, as examples to perform project learning path recommendation. The experimental results show that our approach save a lot of time for developers to learn open-source projects while maintaining the accuracy of the recommendations.
Dynamic Hashtag Interactions and Recommendations: An Implementation Using Apache Spark Streaming and GraphX

Hashtag, started with Twitter is a keyword with prefix “#” and now being used mostly for all communication on social media. It has been identified as very powerful and effective in organizing communications according to the topic and trend. Hashtag can further help on various analysis, as it links users with their topic of interests. Hashtag aids in building communities of similar interests. With hashtags, we can follow current trend and interest on twitter which can help us in analyzing multiple factors, e.g., sensitivity of the ongoing trend, its spread, people getting affected, its effect on business and so on. Traditionally available approaches help us in analyzing batch data and finding interests and trends on it. Now with the advancements in the field of technology helps us in analyzing a large amount of online data within seconds. In this paper, we will be exploring dynamic hashtag interactions to find correlations among them and propose a methodology which can successfully find relevant hashtags based on the interest in focus. We will propose our methodology of analyzing and exploring tweets in real time with the extent of converting information; we are getting from twitter to knowledge.
Integrating CP-Nets in Reactive BDI Agents

Computational agents based upon the belief-desire-intention (BDI) architecture generally use reactive rules to trigger the execution of plans. For various reasons, certain plans might be preferred over others at design time. Most BDI agents platforms use hard-coding these preferences in some form of the static ordering of the reactive rules, but keeping the preferential structure implicit limits script reuse and generalization. This paper proposes an approach to add qualitative preferences over adoption/avoidance of procedural goals into an agent script, building upon the well-known notation of conditional ceteris paribus preference networks (CP-nets). For effective execution, the procedural knowledge and the preferential structure of the agent are mapped in an off-line fashion into a new reactive agent script. This solution contrasts with recent proposals integrating preferences as a rationale in the decision making cycle, and so overriding the reactive nature of BDI agents.
Linear Equations in Two Variables STEM Education Learning Activities: Developing the Household Power Consumption Calculator App

Abstract The lesson was developed with the collaboration of experienced teachers through brainstorming and small-group conference. Social issue about electric consumption usage and rate were identified as part of the lesson. The content and skills to be learned in this lesson aligned to the K-12 curriculum. The topics identified were linear equation in two variables for mathematics and renewable and nonrenewable sources of energy for science. In this unit, students develop a prototype of a mobile application that calculates actual household electric consumption. The application aims to help household units to closely monitor their electric consumption in order to lessen the said consumption and to promote household budget savings. The outcome of this lesson contributes to globe’s quest to fight against climate change and global warming since electric consumption of households will be lessened. The paper may has implication for STEM education.
Effect of Information Architecture on the Usability of a University Website

A well-planned information architecture (IA) of a website can enhance the end users' efficiency, learnability, controllability and intention to revisit the site. Its significance is even more in the context of academic websites where the generation, management, and distribution of information are among the major activities. However, it remains a neglected issue as designers of academic websites have overlooked the important aspect of ‘intuitive user navigation' and focused primarily on its ‘look and feel.' Thus, the current study aims to analyze and compare the effectiveness of information architectural designs of some randomly selected university websites of Punjab (India) through a usability testing technique. For this purpose, the performance metric measured was the information seeking time. The usability session of each subject was captured through Camtasia Studio software. The findings of this study highlight the shortcomings of presently designed academic websites which adversely affect the usability of a website.
The application of web-3d and augmented reality in e-learning to improve the effectiveness of arts teaching in Vietnam

Abstract Nowadays, the development of information technology in the context of industrial revolution 4.0 has quickly transformed Vietnamese education in general and arts education in particular. Moreover, the process of globalization raises many forms of non-traditional education. E-learning is one of the non-traditional education form that has the ability to bring knowledge across space, time and borders to all over the world. In the context of the industrial revolution 4.0, e-learning is also constantly developing to meet the practical society needs. The paper not only shows e-lecturer roles but also discusses e-learning development in art teaching by applying web-3D technology and augmented reality in innovating art teaching and learning methods in Vietnam. The paper uses interdisciplinary approaching method such as: education, arts, science and technology… Thereby, the article is going to propose solutions to improve the effectiveness of art teaching in Vietnam by implementation e-learning using web-3D and augmented reality in Vietnamese universities.
Lightweight Label Propagation for Large-Scale Network Data

Label propagation spreads the soft labels from few labeled data to a large amount of unlabeled data according to the intrinsic graph structure. Nonetheless, most label propagation solutions work under relatively small-scale data and fail to cope with many real applications, such as social network analysis, where graphs usually have millions of nodes. In this paper, we propose a novel algorithm named SLP to deal with large-scale data. A lightweight iterative process derived from the well-known stochastic gradient descent strategy is used to accelerate the solving process. We also give a theoretical analysis on the necessity of the warm-start technique for label propagation. Experiments show that our algorithm is several times faster than state-of-the-art methods while achieving highly competitive performance.
Robust Heterogeneous C-means

Fuzzy c-means is one of the popular algorithms in clustering, but it has some drawbacks such as sensitivity to outliers. Although many correntropy based works have been proposed to improve the robustness of FCM, fundamentally a proper error function is required to apply to FCM. In this paper, we present a new perspective based on the expected loss (or risk) to FCM method to provide different kinds of robustness such as robustness to outliers, to the volume of clusters and robustness in noisy environments. First, we propose Robust FCM method (RCM) by defining a loss function as a least square problem and benefiting the correntropy to make FCM robust to outliers. Furthermore, we utilize the half-quadratic (HQ) optimization as a problem-solving method. Second, inspiring by the Bayesian perspective, we define a new loss function based on correntropy as a distance metric to present Robust Heterogeneous C-Means (RHCM) by utilizing direct clustering (DC) method. DC helps RHCM to have robust initialization. Besides, RHCM will make some robust cluster centers in noisy environments and is capable of clustering the elliptical or spherical shaped data accurately, regardless of the volume of each cluster. The results are shown visually on some synthetic datasets including the noisy ones, the UCI repository and also on real image dataset that was gathered manually from 500px social media. Also , for evaluation of the clustering results, several validity indices are calculated. Experimental results indicate the superiority of our proposed method over the base FCM, DC, KFCM, two new methods called GPFCM and GEPFCM and a method called DC-KFCM that we created for the comparison purpose.
Quantum random numbers from a fiber-optic photon-pair source

We demonstrate a fiber-optic source of random numbers in which random-number generation (RNG) is implemented via an inherently quantum process—detection of correlated photon pairs produced by spontaneous four-wave mixing (FWM) inside an optical fiber. The detection times of individual photon pairs, generated in our RNG scheme via phase-matched FWM in a highly nonlinear photonic-crystal fiber with a suitable dispersion profile, are converted into megabit-size binary sequences, serving as strings of random numbers. The randomness of these bit strings is verified by the National Institute of Standards and Technology Statistical Test Suite. We identify FWM regimes in which mode-locked short-pulse laser sources can provide a high peak power needed to drive photon-pair generation in optical fibers without an excessive loss of randomness due to unwanted patterns in the RNG photon-pair output.
Bit-Shift-Based Accelerator for CNNs with Selectable Accuracy and Throughput

Hardware accelerators for compute intensive algorithms such as convolutional neural networks benefit from number representations with reduced precision. In this paper, we evaluate and extend a number representation based on power-of-two quantization enabling bit-shift-based processing of multiplications. We found that weights of a neural network can either be represented by a single 4 bit power-of-two value or with two 4 bit values depending on accuracy requirements. We evaluate the classification accuracy of VGG-16 and ResNet50 on the ImageNet dataset with weights represented in our novel number format. To include a more complex task, we additionally evaluate the format on two networks for semantic segmentation. In addition, we design a novel processing element based on bit-shifts which is configurable in terms of throughput (4 bit mode) and accuracy (8 bit mode). We evaluate this processing element in an FPGA implementation of a dedicated accelerator for neural networks incorporating a 32-by-64 processing array running at 250 MHz with 1 TOp/s peak throughput in 8 bit mode. The accelerator is capable of processing regular convolutional layers and dilated convolutions in combination with pooling and upsampling. For a semantic segmentation network with 108.5 GOp/frame, our FPGA implementation achieves a throughput of 7.0 FPS in the 8 bit accurate mode and upto 11.2 FPS in the 4 bit mode corresponding to 760.1 GOp/s and 1,218 GOp/s effective throughput, respectively. Finally, we compare the novel design to classical multiplier-based approaches in terms of FPGA utilization and power consumption. Our novel multiply-accumulate engines designed for the optimized number representation uses 9 % less logical elements while allowing double throughput compared to a classical implementation. Moreover, a measurement shows 25 % reduction of power consumption at same throughput. Therefore, our flexible design offers a solution to the trade-off between energy efficiency, accuracy, and high throughput.
From Good Intentions to Behaviour Change

Behaviour support technology assists people in organising their daily activities and changing their behaviour. A fundamental notion underlying such supportive technology is that of compliance with behavioural norms: do people indeed perform the desired behaviour? Existing technology employs a rigid implementation of compliance: a norm is either satisfied or not. In practice however, behaviour change norms are less strict: E.g., is a new norm to do sports at least three times a week complied with if it is occasionally only done twice a week? To address this, in this paper we formally specify probabilistic norms through a variant of feature diagrams, enabling a hierarchical decomposition of the desired behaviour and its execution frequencies. Further, we define a new notion of probabilistic norm compliance using a formal hypothesis testing framework. We show that probabilistic norm compliance can be used in a real-world setting by implementing and evaluating our semantics with respect to an existing daily behaviour dataset.
An Ontology-Based Data Management Model Applied to a Real Information System

In scenarios where capturing and efficiently managing data from heterogeneous data sources can be an advantage, the development and use of ontologies is increasingly common. In this paper, an ontology-based data management model applied to the Geographic Information System denominated SIGOBE as part of the Business Management System of the Cuban Electric Union (SIGE), is presented. The proposed data management model combines the use of a developed domain ontology (OntoSIGOBE), with an intelligent query answering process based on the case-based reasoning technic. OntoSIGOBE represents the conceptualization associated to the distribution and transmission processes of electrical energy, and the captured knowledge from the heterogeneous data sources of SIGE. OntoSIGOBE allows achieve the semantic interoperability between the data sources and the query answering process. The obtained results evidence that the proposed model provides a flexible and integrated data access in SIGOBE reaching high satisfaction to the end users.
Specifying the visualizing type of multi-dimensional data

Abstract Knowing the type of data is widely required to make better and faster decisions. However, the process requires the user to provide information about the configuration of the data. This paper presents the first attempt to analyze data to extract it is type automatically from multi-dimensional data sets. This is useful not only for experts but also to users, also reduces manual search effort. Layers of multi-dimensional data are formed and evaluated, and the focus is on the most efficient ones. Experiments on experimental and real data demonstrate the efficiency and effectiveness of the proposed method.
Web user preferences and behavior clustering based on BP neural network

The paper uses BP neural network method to analyze the behavior preference of web users to achieve user behavior clustering, help advertisers to find online advertising design and optimize web design. At the same time, the paper analyzes the user preference cluster analysis, which helps users to fi nd the desired webpage and content more conveniently, shorten the retrieval time and improve the retrieval efficiency. Firstly, the paper analyzes the log files of the web server, and then conducts session classification, finds the frequent data from the session vector, and normalizes the generated pattern vector. The BP SOFM model is used to cluster the user behavior preferences to generate users. Clustering. The experimental results show that BP neural network analysis method can effectively analyze user preferences and cluster user behavior.
An Elman Artificial Neural Network for Remaining Useful Life Prediction

In the last decade, the high-speed rail (HSR) has undergone rapid development and is playing a more and more important role in the transportation system of China. However, the currently adopted maintenance policy of HSR is still mainly usage-based preventive maintenance, which is quite conservative and incurs tremendous annual maintenance costs. Thus, it is necessary to conduct predictive maintenance so as to save maintenance cost as well as ensure the reliability of HSR, which requires for predicting the remaining useful life (RUL) as an essential step. As sensor technology and the 5th generation wireless technology advance, condition monitoring has been convenient and cost efficient. Based on the collected condition information data, the RUL prediction becomes possible. In this research, we develop an Elman artificial neural network for the purpose of predicting the RUL of HSR bearings, based on the condition monitoring data. To fulfill this purpose, we firstly propose the concepts of current and cumulative state characteristics for analyzing the state monitoring data to extract and filter features that can reflect the current state of the bearings. Then, we build the Elman artificial neural network, evaluate the role cumulative state characteristics play in the model and obtain the weights and thresholds with optimal prediction performance. This way, the network structure and the neuron number of hidden layers are optimized. Experimentation based on the data set of the 2012 IEEE PHM Data Challenge demonstrates the goodness of the proposed approach.
An Approach to Knowledge Discovery for Fault Detection by Using Compensatory Fuzzy Logic

Failure diagnosis and prevention are crucial areas of interest for the proposal of innovative methods and techniques that can help to increase the availability of industrial machinery and other complex systems. In this work we propose a Knowledge Discovery scheme, based on a Compensatory Fuzzy Logic (CFL), for failure detection and prevention. With an exploratory approach, the proposed methodology includes obtaining a characterization of operating conditions of a system, which can be useful for detecting harmful conditions. As a case of study we obtain data of operating conditions of a direct current (DC) motor. A set of fuzzy predicates are formulated and evaluated using the degrees of membership of the variables of the motor to adequate fuzzy membership functions. The truth values resulting of such evaluations are analyzed in view of the empiric knowledge of failures occurrence of DC motors. The main contribution of this work is to explore the possible advantages of using the compensatory fuzzy logic approach for fuzzy predicate evaluation for fault detection and prevention, which could be applied later to more complex systems.