Reviewerly/exHarmony huggingface dataset:
Abstract Universal Approximation for Neural Networks.

With growing concerns about the safety and robustness of neural networks, a number of researchers have successfully applied abstract interpretation with numerical domains to verify properties of neural networks. Why do numerical domains work for neural-network verification? We present a theoretical result that demonstrates the power of numerical domains, namely, the simple interval domain, for analysis of neural networks. Our main theorem, which we call the abstract universal approximation (AUA) theorem, generalizes the recent result by Baader et al. [2020] for ReLU networks to a rich class of neural networks. The classical universal approximation theorem says that, given function $f$, for any desired precision, there is a neural network that can approximate $f$. The AUA theorem states that for any function $f$, there exists a neural network whose abstract interpretation is an arbitrarily close approximation of the collecting semantics of $f$. Further, the network may be constructed using any well-behaved activation function---sigmoid, tanh, parametric ReLU, ELU, and more---making our result quite general. The implication of the AUA theorem is that there exist provably correct neural networks: Suppose, for instance, that there is an ideal robust image classifier represented as function $f$. The AUA theorem tells us that there exists a neural network that approximates $f$ and for which we can automatically construct proofs of robustness using the interval abstract domain. Our work sheds light on the existence of provably correct neural networks, using arbitrary activation functions, and establishes intriguing connections between well-known theoretical properties of neural networks and abstract interpretation using numerical domains.
Teaching English: An afresh sophisticated technique to cultivate digital native learners’ vocabulary by utilizing seesaw media as digital literacy

In the modern and digital era, digital native’s learners are conceived into an era of digital networking. It keeps everything connected to technology. Digital natives with digital technical expertise and abilities which can theoretically be utilized for improved learners’ interaction in the classroom. The government has began to vigorously increase the literacy culture in schools by doing reading literacy. Teachers will be willing to provide an precedent by developing a community of literacy at school. Increased classroom reading atmosphere also improves vocabulary for learners studying English. The Seesaw Media implementation may also help learners to insert allusions to enhance scientific knowledge as a regulatory framework for learning. The aim of this research was to discern the use of Seesaw as the media of literacy to cultivate learners’ vocabulary. This research pursued to answer two research questions; (1) does Seesaw Media helps learners to improve their reading ability? And (2) what are the impacts of Seesaw Media application on the learners on developing vocabulary? The methods used by the researcher were; firstly, the researcher distributed the questionnaire with the English learner's. Secondly, the researcher assessed the feedback from the respondents. Thirdly, the researcher sought the response to the learners on the effect of reading ability by using Seesaw. This research has used descriptive qualitative to conduct the research study. This result of the research has shown the Seesaw Media helped learners to improve their reading ability.
Application of Deep Learning in Ocean Big Data Mining

Zhou, X., 2020. Application of deep learning in ocean big data mining. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 614–617. Coconut Creek (Florida), ISSN 0749-0208.Deep learning is an in-depth study originating from artificial neural networks, which simulate the connection structure of human brain nerves by building models. Through deep learning, low-level features can be combined to form more-abstract, high-level representations, attribute categories, features, etc., which can be used for specific problems, such as classification, regression, and information retrieval. Machine learning is a branch of computer science aimed at achieving computer intelligence through a variety of algorithms. Deep learning is a new research field. By learning to automatically build a feature extractor, a neural network can be formed that can simulate a human brain's ability to interpret data, which will achieve the purpose of solving problems. This article analyzes the theory of a deep learning network model. Then, it analyzes the application of deep learning in ocean big data mining.
An Encrypted Traffic Identification Scheme Based on the Multilevel Structure and Variational Automatic Encoder

With the rapid growth of the encrypted network traffic, the identification to it becomes a hot topic in information security. Since the existing methods have difficulties in identifying the application which the encrypted traffic belongs to, a new encrypted traffic identification scheme is proposed in this paper. The proposed scheme has two levels. In the first level, the entropy and estimation of Monte Carlo π value as features are used to identify the encrypted traffic by C4.5 decision tree. In the second level, the application types are distinguished from the encrypted traffic selected above. First, the variational automatic encoder is used to extract the layer features, which is combined with the frequently-used stream features. Meanwhile, the mutual information is used to reduce the dimensionality of the combination features. Finally, the random forest classifier is used to obtain the optimal result. Compared with the existing methods, the experimental results show that the proposed scheme not only has faster convergence speed but also achieves better performance in the recognition accuracy, recall rate, and F1-Measure, which is higher than 97%.
A Framework of Heterogeneous Cloud Service and Multi Attributes Negotiation using Double Auction

Many double auction frameworks have been proposed for cloud service negotiations [1].However, the frameworks are not able to accommodate both the heterogeneous cloud services and multi-attributes negotiation simultaneously.Therefore, this paper proposed a double-auction framework to accommodate heterogeneous cloud services and multi-attributes negotiation.The study postulated four activities namely literature review, cloud service marketplace investigation, data formulation and double auction-based negotiation framework development.The market requirements, auction requirements and two new mechanisms of a double auction framework have been proposed.
Design and Implementation of High-availability PaaS Platform Based on Virtualization Platform

In view of the problems of the virtual platform failure migration mechanism under the cloud model and microservice architecture platform, the demand for redundant resources is large, and the failure recovery time is long. The key components such as the operating components, resource pools and middleware of the PaaS platform are analyzed, combined with virtualization The platform deployment application is practical. The PaaS platform high-availability architecture is designed to realize the PaaS platform high-availability deployment mode, which effectively improves the robustness of the PaaS platform and guarantees the business continuity of the application system running on the PaaS platform. This paper first analyzes the functional architecture and role positioning of the PaaS platform; secondly, it proposes high-availability design of operating components, resource pools and middleware, and at the same time maps the design to the physical deployment mode, the experimental results show that the design is feasible and effective.
Event-based fault detection for interval type-2 fuzzy systems with measurement outliers

<p style='text-indent:20px;'>This paper investigates the event-based fault detection (FD) problem for a category of discrete-time interval type-2 fuzzy systems with measurement outliers. For the sake of decreasing the utilization of limited communication bandwidth, an event-based mechanism is introduced. Based on the saturation function technique, a novel event-based FD observer is first designed to reduce the influence of outliers in the dynamic systems. Then, on the basis of Lyapunov stability theory, sufficient conditions are provided to ensure that the error system satisfies the <inline-formula><tex-math id="M1">\begin{document}$ \mathcal{H}_{\infty} $\end{document}</tex-math></inline-formula> performance and the <inline-formula><tex-math id="M2">\begin{document}$ \mathcal{H}_{\infty} $\end{document}</tex-math></inline-formula> fault performance in different cases, respectively. In contrast to the existing event-based FD results, the false alarm, which is induced by measurement outliers, can be effectively avoided by the designed FD observer with saturation function. Lastly, some simulation results are given to verify the effectiveness of the method presented in this paper.
Endowing Machines with the Expert Human Ability to Select Representations: Why and How

To achieve efficient human computer collaboration, computers need to be able to represent information in ways that humans can understand. Picking a good representation is critical for effective communication and human learning, especially on technical topics. To select representations appropriately, AI systems must have some understanding of how humans reason and comprehend the nature of representations. In this interdisciplinary research, we are developing the foundations for the analysis of representations for reasoning. Ultimately, our goal is to build AI systems that select representations intelligently, taking users’ preferences and abilities into account.
Leveraging Foreign Language Labeled Data for Aspect-Based Opinion Mining

Aspect-based opinion mining is the task of identifying sentiment at the aspect level in opinionated text, which consists of two subtasks: aspect category extraction and sentiment polarity classification. While aspect category extraction aims to detect and categorize opinion targets such as product features, sentiment polarity classification assigns a sentiment label, i.e. positive, negative, or neutral, to each identified aspect. Supervised learning methods have been shown to deliver better accuracy for this task but they require labeled data, which is costly to obtain, especially for resource-poor languages like Vietnamese. To address this problem, we present a supervised aspect-based opinion mining method that utilizes labeled data from a foreign language (English in this case), which is translated to Vietnamese by an automated translation tool (Google Translate). Because aspects and opinions in different languages may be expressed by different words, we propose using word embeddings, in addition to other features, to reduce the vocabulary difference between the original and translated texts, thus improving the effectiveness of aspect category extraction and sentiment polarity classification processes. We also introduce an annotated corpus of aspect categories and sentiment polarities extracted from restaurant reviews in Vietnamese, and conduct a series of experiments on the corpus. Experimental results demonstrate the effectiveness of the proposed approach.
On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

Summary Screen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. User terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience. Previous studies tried to link mean opinion score (MOS) to video bitrate for different screen types (e.g., Common Intermediate Format [CIF], Quarter Common Intermediate Format [QCIF], and High Definition [HD]). We leverage such studies and formulate a Quality of Experience (QoE)‐driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE over all users of a network service provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on datasets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and Karush Kuhn Tucker (KKT) conditions to efficiently solve the optimization problem. Our numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with a TCP look‐alike strategy implementing max‐min fairness.
Development of a Method for Data Dimensionality Reduction in Loop Closure Detection: An Incremental Approach

SUMMARY This article proposes a method for incremental data dimensionality reduction in loop closure detection for robotic autonomous navigation. The approach uses dominant eigenvector concept for: (a) spectral description of visual datasets and (b) representation in low dimension. Unlike most other papers on data dimensionality reduction (which is done in batch mode), our method combines a sliding window technique and coordinate transformation to achieve dimensionality reduction in incremental data. Experiments in both simulated and real scenarios were performed and the results are suitable.
Wavelet approximation of a function using Chebyshev wavelets

Abstract In this paper, we estimate the best wavelet approximations of a function f having bounded second derivatives and bounded higher-order derivatives using Chebyshev wavelets of third and fourth kinds.
Static Analysis for Improved Modularity of Procedural Web Application Programming Interfaces

Despite their rapid growth, the utilisation of application programming interfaces (APIs) poses challenges for companies under pressure to yield productive systems integration. APIs of larger systems tend to be large, complex and have reduced modularity and quality, which makes them cumbersome to comprehend and use. These challenges can be addressed by static API analysis that focuses on studying API code itself and deriving business entities and dependencies from operational signatures. However, existing techniques for static analysis of APIs face the challenges in deriving a sufficient coverage of business entity relationship types from implementation-oriented API operational signatures carrying limited semantic insights. The paper aims to address such problems by supporting static analysis techniques for APIs that improve their modularity. Our approach adopts an object-oriented paradigm where the concept of “object” is exemplified by the notion of business entity. It systematically applies interface analysis methods and techniques for eliciting knowledge of business entities and their attributes, for deriving the temporal order of calling operations across multiple business entities, and for learning and extracting various ways of invoking a service via APIs. The approach is implemented as an open-source tool and applied to a group of widely-deployed services in practice for validation. The research contributes to identifying key aspects of both the structure and behaviour of APIs, which will lead to building a simplified but comprehensive interface (presentation) layer to assist service users in understanding complex and overloaded interfaces as well as to facilitate efficient and effective service integration.
Modelling of Language Syntax and Semantics: The Case of the Assembler Compiler.

Application of software language technologies, whether analytical, transformational, or generational, in an industrial context is usually a taxing endeavour, with high demands in qualification levels of developers involved in it.Yet, if applied successfully, in the right places and with the right amount of effort, they promise high returns in terms of optimisation, effectiveness, validity and verifiability.In this paper, we report on our experience on writing a compiler for a complex second generation legacy programming language originally intended to be used on a mainframe.The business case for this product deals with companies migrating their software systems off the mainframe to cloud native or PC.Leveraging the documentation, available domain knowledge, several sample projects and a test suite, as well as several proprietary DSLs, we successfully modelled syntax and semantics of hundreds of instructions of that language, to the point of producing a compiler with a very limited group of compiler developers in limited time.The compiler is currently deployed at some of our customers and has received a top technology award from Microsoft.This report is meant to serve as a sample snapshot of how compilers can be built in the industry with software language engineering techniques.Traditional problems of compiler construction such as parsing or code optimisation either did not present a noticeable challenge or did not manifest themselves altogether in the course of this project, but MDE matters such as model transformation, modular design, the use of DSLs and meta-tools, were a constant concern.The focus of the report is in truthful representation of the domain as well as the details of the project, on reflection of the choices that were taken or could have been taken in the meantime, and on lessons learnt during the project.
Prediction of Recidivism in Thefts and Burglaries Using Machine Learning

Background/objectives: Theft and burglary are two crimes against property that have a great social impact.Their prevention drastically lowers victimization rates and the feeling of insecurity in the population.The objective of this investigation is to obtain an index that allows the prediction of repeat offenses by criminals in these types of crimes, in order to support decision-making with respect to preventative actions.Methodology: In order to obtain the index, a group of machines learning was trained, with information provided by the Criminal Analysis and Investigative Focus System (CAIFS) from the Regional Public Prosecutor's Office in Biobío, Chile.The information provided was from thefts and burglaries committed between 2012 and 2017 in the city of Concepción.Findings/application: The results show a characterization of repeat offenders in these types of crime and a recurrence index that allows for a greater assertiveness in the prediction of recidivism than the method that is currently being used.
Text Mining Untuk Analisis Sentimen Pelanggan Terhadap Layanan Uang Elektronik Menggunakan Algoritma Support Vector Machine

Electronic money is a cashless payment instrument whose money is stored in media server or chip that can be moved for the benefit of payment transactions or fund transfers. In Indonesia, there are already many electronic money products, one of which is OVO. OVO is very popular with the people of Indonesia because it offers many promos such as discounts and cashback. But over time, that much promotion is detrimental to OVO shareholders, so the portion of promo given by OVO to its customers is finally reduced. That incident caused many pros and cons opinions about OVO, one of them is on social media Twitter. Sentiment analysis can be used as a solution to process the opinions of OVO customers on Twitter. This study aims to classify the customer opinions on OVO services into positive and negative classes. This study uses the Support Vector Machine algorithm with 3852 data taken from Twitter with keyword @ovo_id using web scraping techniques. The dataset divided into two classes, 2034 positive and 1818 negative sentiment data. The classification process is carried out with four splitting data scenarios, with 60:40, 70:30, 80:20, 90:10 data ratio and with four kernel such as linear, rbf, sigomid, and polynomial. The final results show that the greatest accuracy value obtained by linear kernel with 90:10 data ratio which gets an accuracy value of 98.7%.
Klasifikasi Jenis Kacang-Kacangan Berdasarkan Tekstur Menggunakan Jaringan Syaraf Tiruan

Classification of types of beans is done on red beans, green beans, and peanuts. Texture features are obtained using the Gray Level Co-occurrence Matrix (GLCM) algorithm. The algorithm used to do the classification is Artificial Neural Networks (ANN). Experiments carried out with 3 different numbers of neurons in the hidden layer. Also, there are 17 types of training functions used. Each experiment scenario was repeated 5 times. Based on the experimental scenario, the best results are 99.8% for accuracy, 99.6% for precision and 99.8% for recall using 20 neurons in the hidden layer.
THE COMPARISON STUDY ON TWO-TYPES OF STUDENTS` PERSONALITY: SANGUINE AND PHLEGMATIC TOWARD THEIR ENGLISH SPEAKING ABILITY

The study is an attempt to find out the difference between Sanguine and Phlegmatic personality toward their speaking ability. The design in this study was comparative analysis design. The samples of this study were 44 students from Faculty of Law. Personality test was carried out to decide whether the students were categorized to the group of Sanguine or Phlegmatic. Furthermore, the researcher used personal description for monologue speaking technique for testing the students speaking skills. The data were analyzed by using t-test formula to investigate whether there was significant difference result between the Sanguine and the Phlegmatic students in their speaking skill. From the result of the study, it could be concluded that there was no significant different result between the Sanguine and the Phlegmatic students toward their speaking skill. It means, there were not any better students in speaking between both Sanguine and Phlegmatic students. Those two personalities, the Sanguine and the Phlegmatic students could perform better English speaking skill through their own personal way.Keywords: Comparison Study, Sanguine, Phlegmatic, Speaking Ability
High-fidelity Rendering of Physical Colour References for Projected-based Spatial Augmented Reality Design Applications

Serial plates are not reasonable in material saving and stress dispersion.To design orthopedic plates ideally and conveniently, this paper proposes a method to optimize plates through editing semantic parameters based on average bone model.Firstly, for the reasonable distribution of serial plates in number and size, an average bone model is created from the existing bones, among which each bone has a contribution to the average model.Secondly, a common orthopedic plate with semantic parameters is constructed on average bone model and it can be conveniently modified.Lastly, optimizing the thickness of the plate through finite element analysis and genetic algorithm to meet the stress condition and use as little material as possible.The simulation results indicate that the method can save material and disperse the stress of the plates so that it can effectively optimize the orthopedic plates.