Reviewerly/exHarmony huggingface dataset:
Discarding jagged artefacts in image upscaling with total variation regularisation

Image upscaling is needed in many areas. There are two types of methods: methods based on a simple hypothesis and methods based on machine learning. Most of the machine learning-based methods have disadvantages: no support is provided for a variety of upscaling factors, a training process with a high time cost is required, and a large amount of storage space and high-end equipment are required. To avoid the disadvantages of machine learning, upscaling images with a simple hypothesis is a promising strategy but simple hypothesis always produces jaggy artifacts. The authors propose a new method to remove these jagged artifacts. They consider an edge in an image as a deformed curve. Removing jagged artefacts is considered equivalent to shortening the full arc length of a curve. By optimising the regularization model, the severity of the artifacts decreases as the number of iterations increases. They compare nine existing methods on the Set5, Set14, and Urban100 datasets. Without using any external data, the proposed algorithm has high visual quality, has few jagged artefacts and performs similarly to very recent state-of-the-art deep convolutional network-based approaches. Compared to other methods without external data, the proposed algorithm balances the quality and time cost well.
Accuracy of statistical machine learning methods in identifying client behavior patterns at network edge

This paper is focused on evaluating the applicability of statistical machine learning methods to identifying flows and user behavior patterns at the source (client) network edge. The research was conducted in a mid-size (covering ca 150 geographically scattered locations) network developed for the Malopolska Educational Cloud (MEC) project. Due to the lack of validation sets we focused on unsupervised learning methods. Modules implementing the methods were fed with the headers of the user-generated packets; payloads were not analyzed due to privacy concerns. The presented research proved that in client edge networks even the simple classification methods yield satisfactory results in flows classification.
Unsupervised Multi-view Learning

Unsupervised multi-view learning is a hot research topic. The main challenge lies in how to integrate information from different views to enhance the unsupervised learning performance. In this paper, we present our research works on multi-view data clustering and multi-view network community detection respectively. The main contributions are summarized by emphasizing the challenges we have addressed. In addition, the ongoing work and the future work are briefly presented.
Mapping Information Asset Profile In The Implementation Of Risk Management Information System Using Octave Allergo

The development of information systems that are very fast nowadays, encourages every organization to follow in order to escalate the competition. The technology that is widely used today is web-based technology. Apart from being easily accessible and used, this technology is vulnerable to any attacks; so using this information system will bring access risks by those who are not authorized. Therefore, risk management is something that needs to be considered in an organization to minimize the risks that will arise. Mapping the information asset profile can help Universitas Internasional Batam to evaluate, manage risks, and protect assets from the risks that will occur. The researcher used OCTAVE Allergo method in mapping and measuring Information Systems at Universitas Internasional Batam, consists of Academic Information Systems, Academic Portals, Registration Information Systems, Financial Information Systems, HR Information Systems. From the results of this study, Universitas Internasional Batam finds out which impact areas are the most effected: the reputation and customers trust, recognize the information of critical assets, mitigate assets that have high risks, in order to minimize unwanted risks.
Research on achieving a meteorological monitoring system to increase efficiency in the execution and operation of solar installations and to reduce environmental pollution

Environmental protection alongside the economic and social cohesion are key pillars in the sustainable development of a country. In this context, the strategy of development of industries in Romania should promote modern technologies with as reduced environmental impact as possible. Therefore, determining how dispersion of the pollutants and identify weather events that cause the stagnation of pollutants in the atmosphere are a necessity in the industrial areas. On the other hand, the increased photovoltaic conversion efficiency by increasing the use of solar radiation, both in terms of execution as well as the operation of photovoltaic installations can be achieved as a result of detailed analysis of parameters and meteorological phenomena that define the area where the photovoltaic park will be located. Thus, this article presents a weather system consisting of six weather stations and a computer for keeping the data acquired. Each weather station acquires from where is assembled, information on the following variables: ambient temperature, solar radiation intensity, humidity, barometric pressure, altitude, wind speed, wind direction and rainfall.
Making Japanese Language Learning Models through Multimedia-Based SCL Implementation

This research is an action research, design in action research designed to looking fo solution to improve japanese teaching process. According to Karwono (2012). The Implementation of action research starting with awareness of the disturbing problem and cause prevent the achievment of education purpose an give dab impact from process and student grade when learning process aplied. Due to those problem then we set the focus of the problem more detail by collecting systematic data with relevan literature review. Things that must be considered is input, process, and output. If in learning process we find incomptability with education purpose or Curiculum then the process must be fixed for education goals, and especially vocational education system. Therefore I try to make a design in the form ofStudent centered learning based on multi media. For technology media using Flash MX, adobe premier, and adobe photoshop program
Learning Simultaneous Face Super-Resolution Using Multiset Partial Least Squares

Face super-resolution (FSR) is an effective way to solve low-resolution (LR) problems in face analysis. But, most FSR methods only consider that LR face images have a single resolution, which is usually not consistent with practical situations due to the existence of multiple resolutions. To date, simultaneously learning the mappings from multiple LRs to high resolution (HR) has not been given proper attention. To solve this issue, we first propose a multi-set partial least squares (MPLS) approach to jointly deal with multi-set random variables via a recursive optimization. With MPLS, we then present a novel FSR method called MPLS-FH to simultaneously learn multiple resolution-specific mappings for various LR views from the same source. Concretely, MPLS-FH first divides multi-resolution face images into many patches. Then, it jointly learns the latent coherent features of principal-component embeddings of multi-resolution patches. Last, it super-resolves the input LR face by cross-resolution neighborhood search. Experimental results demonstrate the effectiveness of the proposed method in terms of quantitative and qualitative evaluations.
Semi-supervised Three-dimensional Reconstruction Framework with GAN

Because of the intrinsic complexity in computation, three-dimensional (3D) reconstruction is an essential and challenging topic in computer vision research and applications. The existing methods for 3D reconstruction often produce holes, distortions and obscure parts in the reconstructed 3D models, or can only reconstruct voxelized 3D models for simple isolated objects. So they are not adequate for real usage. From 2014, the Generative Adversarial Network (GAN) is widely used in generating unreal dataset and semi-supervised learning. So the focus of this paper is to achieve high quality 3D reconstruction performance by adopting GAN principle. We propose a novel semi-supervised 3D reconstruction framework, namely SS-3D-GAN, which can iteratively improve any raw 3D reconstruction models by training the GAN models to converge. This new model only takes real-time 2D observation images as the weak supervision, and doesn't rely on prior knowledge of shape models or any referenced observations. Finally, through the qualitative and quantitative experiments &amp; analysis, this new method shows compelling advantages over the current state-of-the-art methods on Tanks &amp; Temples reconstruction benchmark dataset.
A-MHA*: Anytime Multi-Heuristic A*

Designing good heuristic functions for graph search requires adequate domain knowledge. It is often easy to design heuristics that perform well and correlate with the underlying true cost-to-go values in certain parts of the search space but these may not be admissible throughout the domain thereby affecting the optimality guarantees of the search. Bounded suboptimal search using several of such partially good but inadmissible heuristics was developed in Multi-Heuristic A* (MHA*). Although MHA* leverages multiple inadmissible heuristics to potentially generate a faster suboptimal solution, the original version does not improve the solution over time. It is an one shot algorithm that requires careful setting of inflation factors to obtain a desired one time solution. In this work, we tackle this issue by extending MHA* to an anytime version that finds a feasible suboptimal solution quickly and continually improve it until time runs out. Our work is inspired from the Anytime Repairing A* (ARA*) algorithm. We prove that our precise adaptation of ARA* concepts in the MHA* framework preserves the original suboptimal and completeness guarantees and enhances MHA* to perform in an anytime fashion. Furthermore, we report the performance of A-MHA* in 3-D path planning domain and sliding tiles puzzle and compare against MHA* and other anytime algorithms.
A Data Quality Evaluation Index for Data Journals

As a new model of data sharing, data publication requires data to be reviewed and published together with a data descriptor. The peer review process of data publication is very different from that of traditional academic papers. It is still in a preliminary stage and awaits further discussion. This study proposes a data quality evaluation index system for data publishing, which aims to enable high-efficiency data sharing and make data accessible, intelligible, evaluable, and reusable.
Deep Learning with Relational Logic Representations

Despite their significant success, all the existing deep neural architectures based on static computational graphs processing fixed tensor representations necessarily face fundamental limitations when presented with dynamically sized and structured data. Examples of these are sparse multi-relational structures present everywhere from biological networks and complex knowledge hyper-graphs to logical theories. Likewise, given the cryptic nature of generalization and representation learning in neural networks, potential integration with the sheer amounts of existing symbolic abstractions present in human knowledge remains highly problematic. Here, we argue that these abilities, naturally present in symbolic approaches based on the expressive power of relational logic, are necessary to be adopted for further progress of neural networks, and present a well founded learning framework for integration of deep and symbolic approaches based on the lifted modelling paradigm.
Using Artificial Vision Techniques for Individual Player Tracking in Sport Events

We introduce a hybrid approach that can track an individual football player in a video sequence. This solution achieves a good balance between speed and accuracy, combining traditional object tracking techniques with Deep Neural Networks (DNN). While traditional techniques lack accuracy, the main shortcoming of DNN is performance. Both types of techniques complement to each other to provide an accurate and fast object tracking approach that does not require human intervention. The accuracy of our solution has been validated using the SoccerNet Dataset against hand annotated video sequences. For the tracking of 4 different players of 2 different teams our approach has achieved an Area Under Curve (AUC) of 0.66, in terms of accuracy, and a frame rate of 91.75 FPS, in terms of performance, running on a Nvidia GTX 1080Ti GPU.
A Compliance Checking Framework for DNN Models

Growing awareness towards ethical use of machine learning (ML) models has created a surge for the development of fair models. Existing work in this regard assumes the presence of sensitive attributes in the data and hence can build classifiers whose decisions remain agnostic to such attributes. However, in the real world settings, the end-user of the ML model is unaware of the training data; besides, building custom models is not always feasible. Moreover, utilizing a pre-trained model with high accuracy on certain dataset can not be assumed to be fair. Unknown biases in the training data are the true culprit for unfair models (i.e., disparate performance for groups in the dataset). In this preliminary research, we propose a different lens for building fair models by enabling the user with tools to discover blind spots and biases in a pre-trained model and augment them with corrective measures.
A Neural Network Approach for Birds of a Feather Solvability Prediction

Birds of a Feather is a single player, perfect information card game. The game can have multiple board sizes with larger boards introducing larger search spaces that grow exponentially. In this paper, we investigate the solvability of the game, aiming at building a machine learning method to automatically classify whether a given board state has a solution path or not. We propose a method based on image-based features of the board state and deep neural network. Experimental results show that the proposed method can make reasonable predictions of the solvability of a game at an arbitrary stage of the game.
Detecting Inter-Component Vulnerabilities in Event-based Systems

Event-based system (EBS) has become popular because of its high flexibility, scalability, and adaptability.These advantages are enabled by its communication mechanismimplicit invocation and implicit concurrency between components.The communication mechanism is based on nondeterminism in event processing, which can introduce inherent security vulnerabilities into a system referred to as event attacks.Event attack is a particular type of attack that can abuse, incapacitate, and damage a target system by exploiting the system's event-based communication model.It is hard to prevent event attacks because they are administered in a way that does not differ from ordinary event-based communication in general.While a number of techniques have focused on security threats in EBS, they do not appropriately resolve the event attack issues or suffer from inaccuracy in detecting and preventing event attacks.To address the risk of event attacks, I present a novel vulnerability detection technique for EBSs that are implemented by using message-oriented middleware platform.My technique has been evaluated on 25 open-source benchmark apps and eight real-world EBSs.The evaluation exhibited my technique's higher accuracy in detecting vulnerabilities on event attacks than existing techniques as well as its applicability to real-world EBSs.
OPTIMAL AND FAST HAND GESTURE RECOGNITION MODEL USING FASTER R-CNN

Due to the advancements in computer vision, gesture recognition becomes an important research topic and is widely used for human-computer interfaces. Among gesture recognition models, the hand gesture is highly preferable because of its application in various applications like healthcare, gaming, etc. Though numerous hand gesture recognition models exist, none of these methods attained an efficient and faster detection rate in different situations. In this paper, we introduce an optimal and fast hand gesture recognition model using Faster R-CNN. The use of Faster R-CNN leads to efficient recognition at a faster rate. To evaluate the results of the Faster R-CNN model, we employ this model to a set of two benchmark hand gesture dataset. The experimental outcomes demonstrate that the Faster R-CNN model gains enhanced performance over the standard methods in terms of accuracy and computation time.
IDP - OPTICS: Improvement of Differential Privacy Algorithm in Data Histogram Publishing Based on Density Clustering

In recent years, incidents of privacy leaks have frequently occurred. How to protect the group and personal privacy has become a focus issue in the field of information security. Differential privacy technology can protect the privacy of published data by adding noise. In this paper, we propose the IDP-OPTICS algorithm to solve the problem of excessive noise accumulation and low data availability in the histogram data release under the static data set. First, we preprocess the data with a density-based clustering algorithm. Then, we apply differential privacy to the data and finally add noise to the processed data by adding heteroscedasticity. The experimental data shows that IDP-OPTICS achieves a reduction in the problem of excessive noise accumulation and a balance of noise distribution as a whole.
Generalized Diagnostics with the Non-Axiomatic Reasoning System (NARS)

Symbolic reasoning systems have leveraged propositional logic frameworks to build diagnostics tools capable of describing complex artifacts, while also allowing for a controlled and efficacious search over failure modes. These diagnostic systems represent a complex and varied context in which to explore general intelligence. This paper explores the application of a different reasoning system to such frameworks, specifically, the Non-Axiomatic Reasoning System. It shows how statements can be built describing an artifact, and that NARS is capable of diagnosing abnormal states within examples of said artifact.
Model-Driven Integration Testingof Hypermedia Systems

The proper design of Representational State Transfer (REST) APIs is not trivial because developers have to deal with a flood of recommendations and best practices, especially the proper application of the hypermedia constraint requires some decent experience. Furthermore, testing RESTful APIs is a missing topic within literature. Especially hypermedia testing is not mentioned at all. Manual hypermedia testing is time-consuming and hard to maintain. Testing a hypermedia API requires many test cases that have similar structure, especially when different user roles and error cases are considered. In order to tackle this problem, we proposed a Model-Driven Testing (MDT) approach for hypermedia systems using the metamodel within our existing Model Driven Software Development (MDSD) approach. This work discusses challenges and results of hypermedia testing for RESTful APIs using MDT techniques that were discovered within our research. MDT allows white-box testing, hence covering complete program structure and behavior of the generated application. By doing this, we are able to achieve a high automated test coverage. Moreover, any runtime behavior deviated from the metamodel reveals bugs within the generators.
An Agglomerative-adapted Partition Approach for Large-scale Graphs

In recent years, an increasing number of knowledge bases have been built using linked data, thus datasets have grown substantially. It is neither reasonable to store a large amount of triple data in a single graph, nor appropriate to store RDF in named graphs by class URIs, because many joins can cause performance problems between graphs. This paper presents an agglomerative-adapted approach for large-scale graphs, which is also a bottom-up merging process. The proposed algorithm can partition triples data in three levels: blank nodes, associated nodes, and inference nodes. Regarding blank nodes and classes/nodes involved in reasoning rules, it is better to store with an optimal neighbor node in the same partition instead of splitting into separate partitions. The process of merging associated nodes needs to start with the node in the smallest cost and then repeat it until the final number of partitions is met. Finally, the feasibility and rationality of the merging algorithm are analyzed in detail through bibliographic cases. In summary, the partitioning methods proposed in this paper can be applied in distributed storage, data retrieval, data export, and semantic reasoning of large-scale triples graphs. In the future, we will research the automation setting of the number of partitions with machine learning algorithms.
Perbandingan Metode Klasifikasi Analisis Sentimen Tokoh Politik Pada Komentar Media Berita Online

General elections are an important part of the political process so that many political figures participate in the process. Electability is one of the concerns, various things are done to be able to increase the electability of political figures who participate in general elections. Media has become one of the important tools used to increase electability, one of which is online news media. Reader comments can be used as an assessment of political figures in the form of sentiment analysis. However, it is not easy to analyze sentiments from comments on online news media, because comments contain unstructured text, especially in Indonesian text. Text pre-processing in text mining is an important part of getting the basic information contained in the comments. This research uses Indonesian text pre-processing using the Gata Framework Tetmining. Then proceed with extracting information using the Naïve Bayes classification algorithm and Support Vector Machine which are optimized using Particle Swarm Optimization. Tests carried out with both methods get the results that, Particle Swarm Optimization based on Support Vector Machine is the best method with an accuracy of 78.40% and AUC 0.850. This study found an algorithm that was effective in classifying positive and negative comments related to political figures from online news media.
PEMANFAATAN INFORMATION AND COMMUNICATIONS TECHNOLOGY SEBAGAI SUMBER BELAJAR DI ERA DIGITAL

The purpose of this research is to illustrate that ICT can be used as a source of learning that is very relevant in the digital age. The research method used is a qualitative research method with the type of literature study. The results obtained from research conducted that ICT has many impacts in the development of the learning process. Impacts generated by ICT are not only positive, but also negative impacts. However, in this research that was studied was one of the positive impacts of ICT, which was to become a source of learning in the digital age. Utilization of ICT in learning can optimize lifelong learning through distance and asynchronous mode where students can communicate independently at different times whenever they are online, as well as close the professional isolation space that has often been felt in learning.
Meta-Path Augmented Response Generation

We propose a chatbot, namely MOCHA to make good use of relevant entities when generating responses. Augmented with meta-path information, MOCHA is able to mention proper entities following the conversation flow.
Converging on Common Knowledge

Common knowledge, as is well known, is not attainable in finite time by unreliable communication, thus hindering perfect coordination. Focusing on the coordinated attack problem modeled using dynamic epistemic logic, this paper discusses unreliable communication protocols from a topological perspective and asks "If the generals may communicate indefinitely, will they then *converge* to a state of common knowledge?" We answer by making precise and showing the following: *common knowledge is attainable if, and only if, we do not care about common knowledge*.