Reviewerly/exHarmony huggingface dataset:
Efficient Heavy Hitters Identification Over Speed Traffic Streams

With the rapid increase of link speed and network throughput in recent years, much more attention has been paid to the work of obtaining statistics over speed traffic streams. It is a challenging problem to identify heavy hit... | Find, read and cite all the research you need on Tech Science Press
Kinetic AR

Using Augmented Reality (AR) interfaces, motion paths and tasks for co-bots in the factory can become visible and interactive. We present Kinetic AR, a system to control and manipulate motion of an MIR100 Automated Guided Vehicle (AGV) in Augmented Reality using the Reality Editor platform. The MIR100 robot performs a mapping of the environment using laser scanners. We synchronize the coordinate systems recognized by the smartphone and the AGV by performing spatial mapping. This allows for a seamless interaction where the user can control the motion of the AGV in an intuitive and spatial manner without any further technical requirements than a mobile phone. The user can perform path planning and visualize the motion of the AGV in real-time in AR. The synchronization of both environments allows for a usable manipulation where the AGV is aware of the position of the phone at all times and can perform actions such as following the user or moving towards the position where the phone is pointing on the floor. Moreover, motion checkpoints can be actionable and visually connected to other equipment in order to program the coordinated behavior of multiple systems. The platform is spatially aware and allows for a co-located seamless interaction between machines. We envision this technology as a usable interface for the creation and visualization of manifold AGV operations while maintaining a low entry barrier to complex spatial hardware programming.
Image Classification Using Legendre–Fourier Moments and Artificial Neural Network

The nonlinear structure of the artificial neural network is efficient for the classification; however, the choice of features is a fundamental problem due to their direct impact on the network convergence and performance. In this paper, we present a new method of image classification method based on Legendre–Fourier moments using an artificial neural network. We used LFMs to extract features from images. In result, every image is represented by a descriptor vector; these vectors are inputs of our neural network. We tested this model on Fashion-MNIST database and we got important results; the model’s accuracy exceeds 97%. The validity of this proposed method has provided under different transformations.
Karst Exploration: Extracting Terms and Definitions from Karst Domain Corpus

In this paper, we present the extraction of specialized knowledge from a corpus of karstology literature. Domain terms are extracted by comparing the domain corpus to a reference corpus, and several heuristics to improve the extraction process are proposed (filtering based on nested terms, stopwords and fuzzy matching). We also use a word embedding model to extend the list of terms, and evaluate the potential of the approach from a term extraction perspective, as well as in terms of semantic relatedness. This step is followed by an automated term alignment and analysis of the Slovene and English karst terminology in terms of cognates. Finally, the corpus is used for extracting domain definitions, as well as triplets, where the latter can be considered as a potential resource for complementary knowledge-rich context extraction and visualization.
Maintaining attention state of children during cognitive load

We analyzed EEG signals of children recorded during specific cognitive task - Schulte test. We analyzed behavioural characteristics - time intervals required for subject to find each consecutive number in table as well as frequency characteristics of EEG signal calculated with help of continuous wavelet transform considering the wavelet energies averaged over alpha and beta frequency ranges. We also performed statistical analysis of these characteristics with help of ANOVA to find features that can be used to evaluate level of attention and its dynamics during elementary task completion.
Iris center localization using energy map synthesis based on gradient and isophote

Gaze tracking has wide applications such as in driver fatigue detection, virtual reality, and human-computer interaction. The performance of gaze tracking depends largely on the accuracy of iris center localization. However, most of the existing gaze tracking products are intrusive or require addi tional equipment with a high cost. Therefore, precise localization methods of iris center in low quality images captured in a non-contact way with visible light need to be investigated. This paper proposes a novel localization method of iris center using energy map synthesis based on image gradient, isophote and midpoint of eye ROI (Region of interest). This method combines the advantages of higher localization accuracy based on gradient, invariance to the rotation and linear transformation of light based on isophote, and iris center close to the midpoint of eye ROI. Moreover, a post-processing correction method for the closed eyes and for other large deviations of iris center position is adopted to further improve the localization accuracy. The algorithm is verified on the BioID, Talking Face Video and MUCT Face databases, and the results show that the localization accuracy in the paper has outperformed the listed state-of-the-art methods in varying illuminations.
Software Release Patterns When is it a good time to update a software component?

Over the past decade the industry 4.0 witnessed a trend towards an increasing number of software components, dependencies towards third party software components, and software component release cycles. Industry 4.0 teams building software products are more frequently impacted by third party software component updates. Due to this dependency, updating a single third party software component can break an entire software product. Reasons include parallel conflicting updates of third party software components, updating to an unstable version, or updating to a major stable version without an impact analysis. The objective of this paper is to reduce the risk of breaking updates by reviewing software release patterns and proposing update scheduling recommendations.
Experiences from Extreme Citizen Science: Using Smartphone-based Data Collection Tools with Low-literate People

Citizen science gets increasing recognition for its potential to democratize science and support environmental governance. In this paper we present our experiences and lessons learned from a set of 'extreme' citizen science initiatives in developing countries, where data collection applications are used to support low-literate people in identifying solutions to issues that are of significant local concern. This paper aims to bring to the attention of the HCI community of developments in extreme citizen science and contribute knowledge to the field of HCI4D, especially to research studies which concern the design and use of smartphones for low-literate users.
Pig target detection method based on SSD convolution network

Abstract The behavioral changes of pigs can not only reflect their own health and welfare status, but also have important significance in improving the breeding environment. Rapid and accurate detection of pigs’ targets is the basis of behavioral change analysis.In view of this situation, a target detection algorithm based on deep learning is proposed, and its model structure and working principle are expounded. Then, through the test of pig image data set, the results show that this method can accurately detect pig targets under complex background.
Death Risk Prediction of Intensive Care Unit Patients Combined with Treatment Process Mining

Precise death risk prediction of ICU patients plays an important role in developing follow-up treatment options and reducing rescue cost. Previous work mainly relied on the vital signs of patients to predict the risk of death. In fact, patients’ treatment process also implies valuable information about patient’s condition. To use the treatment process information in patient risk assessment, this paper proposes a new death risk prediction method combined with treatment process mining. Specifically, historical patients are firstly clustered into several classes according to their final treatment outcome and the drugs they took. Patients in one cluster are assumed to share the same treatment process. Then, these treatment processes are mined by means of the LDA topic model and the probability suffix tree training method. Next, for a patient to do death risk prediction, the similarity between his/her treatment procedure and those probability suffix trees is computed. The similarity value is taken as a new feature complementary to those traditional ones in disease severity scoring systems. Finally, the random forest algorithm is used to train the death risk prediction model based on the extended feature set. Taking the prescription data of sepsis patients in the MIMIC-III database as input, an experiment is conduced to evaluate the proposed method. As expected, the proposed method achieves better prediction accuracy, recall rate, and F1 score compared to methods.
Report on the 2019 International Joint Conferences on Artificial Intelligence Explainable Artificial Intelligence Workshop

This article reports on the Explainable Artificial Intelligence Workshop, held within the International Joint Conferences on Artificial Intelligence 2019 Workshop Program in Macau, August 11, 2019. With over 160 registered attendees, the workshop was the largest workshop at the conference. It featured an invited talk and 23 oral presentations, and closed with an audience discussion about where explainable artificial intelligence research stands.
Few-shot learning for defence and security

In Defence and Security, we are often interested in rare events and occurrences where we only have a few examples. This presents a problem for traditional machine learning approaches which typically require thousands of examples per class to learn an effective classifier. Few-shot learning techniques look to use tasks comprising of a small set of labelled images and a novel, unlabelled example, to classify the novel example. These are typically considered as N-way k-shot problems, where we have N distinct classes, with only k labelled examples per class. At the Defence Science and Technology Laboratory (Dstl) in the UK, we are looking to understand the application of few-shot learning techniques to Defence and Security problems, particularly on imagery datasets. In this paper we discuss the application of few-shot learning approaches from literature to Defence and Security problems and discuss meta-learning, one of the key types of approaches to few-shot learning. We also present experimentation on meta-learning models, baselined against a transfer learned ResNet, across a range of few-shot tasks with differing data proportions on the miniImageNet and Caltech-USCD Birds datasets. This experimentation looks to improve our understanding of the behaviour of these few-shot models on a range of data limited problems. We identify a number of challenges that require further research before few-shot approaches can be effectively applied to Defence and Security problems and further research that could increase the range of Defence and Security problems to which few-shot approaches could be applied.
IMPORTANT ELEMENTS FOR A FRAMEWORK IN DESIGNING A MOBILE LEARNING FOR ENGLISH LANGUAGE LISTENING AND SPEAKING SKILLS

Based on a literature search, there are a number of mobile learning frameworks mentioned in a previous study that less emphasis on the major element required for English language (listening and speaking skills) specifically for level 2 students. Therefore, this paper aimed to identify the elements that fulfill their needs to be integrated in the mobile learning framework for listening and speaking skills specifically for level 2 students. This study was used quantitative research method through survey using questionnaires as the instrument to get the data and information for answering the research questions. A sample of 30 English expert teachers in the Batu Pahat district was selected based on purposive sampling method to complete the questionnaire survey given. A questionnaire that consists of four constructs was developed and the administered to obtain information. Data was collected and analyzed descriptively using SPSS (Statistical Package for The Social Sciences).. Besides, the validity was made to ensure the questionnaire can be used. Content validity, face validity and construct validity were used in this study to make sure the instrument's validity. Moreover, the pilot study was carried out to obtain the reliability of the questionnaire. Based on the analysis that has been made, Alpha Cronbach value was 0.87 categorized as a high level. Findings showed that there was an audio, graphics and text elements must be integrated into the mobile learning framework and audio elements help in listening skills while text elements help English speaking skills as well as other elements such as learning anywhere and anytime, easy to use, navigation, other resources for related information are provided, directory and help button. The findings of this study can be used by mobile learning designers as a guide in preparing for learning material and the main contribution of this study is an important element for a framework in designing a mobile learning for english language listening and speaking skills.
One Giant Leap for Computer Security

Today's computer systems trace their roots to an era of trusted users and highly constrained hardware; thus, their designs fundamentally emphasize performance and discount security. This article presents a vision for how small steps using existing technologies can be combined into one giant leap for computer security.
Exploring Explainable Selection to Control Abstractive Generation

It is a big challenge to model long-range input for document summarization. In this paper, we target using a select and generate paradigm to enhance the capability of selecting explainable contents (i.e., interpret the selection given its semantics, novelty, relevance) and then guiding to control the abstract generation. Specifically, a newly designed pair-wise extractor is proposed to capture the sentence pair interactions and their centrality. Furthermore, the generator is hybrid with the selected content and is jointly integrated with a pointer distribution that is derived from a sentence deployment's attention. The abstract generation can be controlled by an explainable mask matrix that determines to what extent the content can be included in the summary. Encoders are adaptable with both Transformer-based and BERT-based configurations. Overall, both results based on ROUGE metrics and human evaluation gain outperformance over several state-of-the-art models on two benchmark CNN/DailyMail and NYT datasets.
Developing AR-based ebook for introducing dynamic process of fuel cell

Due to sustainable energy promising, one technology that has high efficiency and suitable for a friendly environmental case is a Fuel Cell. One type of fuel cells is proton exchange membrane fuel cell (PEMFC), which shows low temperature, high power density, fast response, and zero emissions. A single fuel cell produces enough electricity for only the smallest applications. However, individual fuel cell are typically combine in series into a fuel cell stack. These procesess determaines the kind of chemical reactions taht take place in the cell, the kind of catalysts requaired, the temperature range in which the cell can be operate and other factor are needed. Therefore, we propose that process of the combining procesess is introduced using ebook accomplished with Augmented Reality (AR) technology. AR is a virtual object which can be used for interaction simultaneously, so using this technology, the detail process can be shown in detail when ebook’s user want to explore more the process. This paper described integrating AR to introduce the process and possible interaction of user to explore the dynamic system of the fuel cell. This integrating will be analyzed according to experts justice on the designed of AR ebook.
Adding Noise Trajectory for Providing Privacy in Data Publishing by Vectorization

Since trajectory data is widely collected and utilized for scientific research and business purpose, publishing trajectory without proper privacy-policy leads to an acute threat to individual data. Recently, several methods, i.e., k-anonymity, l-diversity, t-closeness have been studied, though they tend to protect by reducing data depends on a feature of each method. When a strong privacy protection is required, these methods have excessively reduced data utility that may affect the result of scientific research. In this research, we suggest a novel approach to tackle this existing dilemma via an adding noise trajectory on a vector-based grid environment.
Some Multisecret-Sharing Schemes over Finite Fields

A secret sharing scheme is a method of assigning shares for a secret to some participants such that only some distinguished subsets of these subsets can recover the secret while other subsets cannot. Such schemes can be used for sharing a private key, for digital signatures or sharing the key that can be used to decrypt the content of a file. There are many methods for secret sharing. One of them was developed by Blakley. In this work, we construct a multisecret-sharing scheme over finite fields. The reconstruction algorithm is based on Blakley’s method. We determine the access structure and obtain a perfect and ideal scheme.
Photonic discrete-time quantum walks [Invited]

Quantum walks, a counterpart of classical random walks, have many applications due to their neoteric features. Since they were first proposed, quantum walks have been explored in many fields theoretically and have also been demonstrated experimentally in various physical systems. In this paper, we review the experimental realizations of discrete-time quantum walks in photonic systems with different physical structures, such as bulk optics and time-multiplexed framework. Then, some typical applications using quantum walks are introduced. Finally, the advantages and disadvantages of these physical systems are discussed.
A gamma correction method based on constant-intensity images in phase-measuring profilometry

Phase-measuring profilometry (PMP) is an important technique for image analysis in optical non-destructive testing (NDT). Its measurement accuracy is significantly affected by gamma distortion in the projector, which makes the projected sinusoidal fringe patterns non-sinusoidal. In order to address this issue, a generic gamma non-linearity model based on constant-intensity images is proposed in this paper. In the proposed model, system defocus and noise are considered and analysed. It is demonstrated through theoretical derivation that the constant-intensity images remain unchanged, with no additional frequency being produced. For this reason, system defocus is not modelled in the proposed gamma calibration model and the related defocus parameters need not be calculated, which reduces the complexity of the calculation. Any noise that exists in the optical measurement system is another main factor influencing the greyscale levels of the image. A mutual information (MI)-based denoising method is proposed to reduce the noise and improve the accuracy of the gamma calibration. Furthermore, with the defocus analysis and the noise reducing method, a robust pixel-wise gamma calibration method is introduced. The experimental results in this paper show that the proposed gamma calibration method is able to accurately calibrate the gamma non-linearity of the system. Moreover, the phase precision is significantly improved and a higher quality measurement is achieved for the measured surfaces.
Investigating Students’ Attitude and Motivation in Learning English as a Second Language among Four Higher Institutions in Malaysia

The aim of this study is to examine the students’ motivation and attitude towards learning English as a second language from four Malaysian higher institutions. A random sampling of 471 respondents participated in this study. Using 28 items instrument, adapted from Gardner’s Attitude/Motivation Test Battery (AMTB), an online questionnaire was distributed in getting the data. Students’ motivational level is found to be high with (M=6.37, SD=0.77, M=6.10, SD=0.82). Additionally, the respondents are having positive attitudes in learning English (M=4.07, SD=0.48). With the findings, it is a hope that this can be a guidance for instructors to design a suitable lesson and approach to be used in the class.
Application of blockchain in integrated energy system transactions

Blockchain technology is the underlying technology of Bitcoin, which is fair, transparent and decentralized. The integrated energy system has the characteristics of open interconnection, user-centered and distributed peer-to-peer sharing, and its energy trading model will also be developed centrally to distributed. The characteristics of blockchain technology make it naturally applicable to energy transactions in integrated energy systems. This article first analyzes the characteristics of the integrated energy system market and summarizes the participants in the market. Then, based on the existing research and analysis, a blockchain-based energy transaction architecture is designed, and a weakly centralized management method is introduced. finally, the problems and challenges faced by the application of blockchain in energy transactions are analyzed.
A Polynomial-Time Algorithm for Solving the Hidden Subset Sum Problem

At Crypto ’99, Nguyen and Stern described a lattice based algorithm for solving the hidden subset sum problem, a variant of the classical subset sum problem where the n weights are also hidden. While the Nguyen-Stern algorithm works quite well in practice for moderate values of n, we argue that its complexity is actually exponential in n; namely in the final step one must recover a very short basis of a n-dimensional lattice, which takes exponential-time in n, as one must apply BKZ reduction with increasingly large block-sizes. In this paper, we describe a variant of the Nguyen-Stern algorithm that works in polynomial-time. The first step is the same orthogonal lattice attack with LLL as in the original algorithm. In the second step, instead of applying BKZ, we use a multivariate technique that recovers the short lattice vectors and finally the hidden secrets in polynomial time. Our algorithm works quite well in practice, as we can reach $$n \simeq 250$$ in a few hours on a single PC.
Twitter Sentiment Analysis Using Elasticsearch, LOGSTASH And KIBANA

In the era of technology, where for almost everything we are dependent on the internet, has been successful enough to dominate our social lives too. So, with excess usage of internet, creating huge log files containing hidden valuable information in it. Hence efforts have been made to overcome the shortcomings of log management and perform effective search to extract the real crux from pool of data which is needed for analysis. Further we require an efficient graphical visualizer which can completely express any shape of data. This paper demonstrates the working of open source tools i.e. elastic search, logstash, and kibana which has been clubbed together to have complete insight and visualization of data. Elastic search is used for searching and indexing, Logstash for slicing and dicing the raw data, maintaining and managing events whereas kibana is a graphical front end for data held in elastic search. By implementing these tools we are performing sentiment analysis of data taken from social networking blogging service like twitter.