Reviewerly/exHarmony huggingface dataset:
HOW SHOULD WE REORGANIZE THE UNIFIED STATE EXAM IN INFORMATICS?

The article presents an analysis of problems that arose in informatics education due to introduction of State Final Attestation in the form of Unified State Exam (USE). Some measures are proposed to overcome the shortcomings of existing systems for evaluating results of education.
Remote Collaborative Live Coding in SuperCollider-Based Environments via Open Sound Control Proxy

Networked live coding as a form of musical ensemble performance is generally limited to having performers coding in the same venue, using the same programming environment. There is an inherent latency issue with any networked musical performance, although to a lesser extent in live coding. Different performers may also be familiar with different live coding languages and environments, thus making collaboration options more limited. We proposeYospanya, Poonna a proxy system that enables different SuperCollider-based live coding environments to collaborate over the Internet via Open Sound Control protocol. The proxy intercepts the communication between a SuperCollider server and its clients at the protocol level and relays it to other participating peer servers. We address the network latency issue with latency-compensated synchronization, allowing each participant to have their performance played back similarly on all participating machines regardless of their physical location and distance from each other.
Discriminatory Target Learning: Mining Significant Dependence Relationships from Labeled and Unlabeled Data

Machine learning techniques have shown superior predictive power, among which Bayesian network classifiers (BNCs) have remained of great interest due to its capacity to demonstrate complex dependence relationships. Most traditional BNCs tend to build only one model to fit training instances by analyzing independence between attributes using conditional mutual information. However, for different class labels, the conditional dependence relationships may be different rather than invariant when attributes take different values, which may result in classification bias. To address this issue, we propose a novel framework, called discriminatory target learning, which can be regarded as a tradeoff between probabilistic model learned from unlabeled instance at the uncertain end and that learned from labeled training data at the certain end. The final model can discriminately represent the dependence relationships hidden in unlabeled instance with respect to different possible class labels. Taking k-dependence Bayesian classifier as an example, experimental comparison on 42 publicly available datasets indicated that the final model achieved competitive classification performance compared to state-of-the-art learners such as Random forest and averaged one-dependence estimators.
Fuzzy Clustering High-Dimensional Data Using Information Weighting

The fuzzy clustering algorithm for high-dimensional data is proposed in this paper. An objective function which is insensitive to the “concentration of norms” phenomenon is also introduced. We recommend using a weighted parameter in the objective function. The proposed fuzzy clustering algorithm is compared with FCM in the experimental part. Dependence of the clustering algorithm’s results on the weighted parameter changes has also been investigated and tested.
Fish Swarm Simulation Fs Virtual Ocean Tourism

As a key technology for the marine visualization, fish swarm simulation plays an important role in the development of marine tourism resources, marine ecotourism and environmental protection, and marine public welfare services. However, the traditional fish swarm simulation methods don’t consider the viewer for marine visualization. The lack of interaction between the fish and the viewer, resulting in viewer cannot reach a deeper marine tourism experience. In this paper, we take marine tourism as an example, the combination of virtual reality technology and tourism, through the establishment of the model of fish swarm, explores and studies the application and future development in VR marine tourism.
Policy Reuse in Reinforcement Learning for Modular Agents

We present reusable policy method for modular reinforcement learning problem in continuous state space. Our method relies on two-layered learning architecture. The first layer partitions the agent's problem space into n-folds sub-agents that are inter-connected with each other with dexterity identical to original problem. It further learns a local control policy for standalone 1-fold sub-agent. The second layer learns a global policy to reuse `already learnt' standalone local policy over each n sub-agents by sampling local policy with global parameters for each sub-agent-parameterizing local policy independently to approximate non-linear interconnections between sub-agents. We demonstrate our method on simulation example of 12-DOF modular robot that learns maneuver pattern of snake-like gait. We also compare our proposed method against standard single-policy learning methods to benchmark optimality.
Hybrid bi-dimensional empirical mode decomposition based enhancement technique for extreme low contrast UAV thermal images

The performance of automatic target detection and classification systems are typically affected by reduced contrast quality introduced by external interferences. In particular for Unmanned Aerial Vehicle (UAV) captured thermal surveillance images, the effect is more evident. This advises the use of contrast enhancement technique as a solution to enhance the reduced contrast of hot regions for efficient target detection. In this paper, a simple and novel enhancement technique based on singular value decomposition (SVD) using Bi-Dimensional Empirical Mode Decomposition (BEMD) is proposed to enhance the hot regions in extreme low contrast thermal images captured by UAV. In the first step, the technique decomposes the thermal image into Intrinsic Mode Functions (IMFs) and residue by using BEMD. In the second step, it applies Contrast Limited Adaptive Histogram Equalization (CLAHE) in the residue for local contrast enhancement and then calculates the singular value matrix. In the third step, residue component is rescaled for further improvement of hot regions using scaling factor. In the fourth step, a detail enhanced IMF components are generated using gray scale transformation. Finally, the contrast enhanced residue undergoes Inverse BEMD (IBEMD) together with the detailed enhanced IMFs for enhanced image generation. Experimental results demonstrate that the proposed technique effectively enhances the contrast and details in the image with less visual artefacts than other state-of-the-art techniques.
A New Calibration Process for a Homogeneous Cyclorama Illumination in Virtual TV Sets

A virtual TV set combines actors and objects with computer-generated virtual environments in real time. Nowadays, this technology is widely used in television broadcasts and cinema productions. A virtual TV set consists of three main elements: the stage, the computer-system and the chroma-keyer. The stage is composed by a monochrome cyclorama (the background) in front of which actors and objects are located (the foreground). The computer-system generates the virtual elements that will form the virtual environment. The chroma-keyer combines the elements in the foreground with the computer-generated environments by erasing the monochrome background and insetting the synthetic elements using the chroma-keying technique. In order to ease the background removal, the cyclorama illumination must be diffuse and homogeneous, avoiding the hue differences that are introduced by shadows, shines and over-lighted areas. The analysis of this illumination is usually performed manually by an expert using a photometer which makes the process slow, tedious and dependent on the experience of the operator. In this paper, a new calibration process to check and improve the homogeneity of a cyclorama’s illumination by non-experts using a custom software which provides both visual information and statistical data, is presented. This calibration process segments a cyclorama image in regions with similar luminance and calculates the centroid of each of them. The statistical study of the variation in the size of the regions and the position of the centroids are the key tools used to determine the homogeneity of the cyclorama lighting.
Expressive Context Modeling with Description Logics

Modeling and verification of context-aware systems have been proven to be a challenging task mainly because expressive modeling implies, in most cases, expensive verification algorithms. On the other side, description logics have been successfully applied as a modeling and verification framework in many settings, such as in the semantic Web and bioinformatics, just to mention some. The main factor for this success is the delicate balance between expressiveness and computational cost of the corresponding algorithms in description logics. In the current work, we propose the use of an expressive description logics to model the consistency of context-aware systems. We show this expressive modeling language is capable to succinctly express complex properties, such as temporal ones.
Validation of SHACL Constraints over KGs with OWL 2 QL Ontologies via Rewriting

Constraints have traditionally been used to ensure data quality. Recently, several constraint languages such as SHACL, as well as mechanisms for constraint validation, have been proposed for Knowledge Graphs (KGs). KGs are often enhanced with ontologies that define relevant background knowledge in a formal language such as OWL 2 QL. However, existing systems for constraint validation either ignore these ontologies, or compile ontologies and constraints into rules that should be executed by some rule engine. In the latter case, one has to rely on different systems when validating constrains over KGs and over ontology-enhanced KGs. In this work, we address this problem by defining rewriting techniques that allow to compile an OWL 2 QL ontology and a set of SHACL constraints into another set of SHACL constraints. We show that in the general case the rewriting may not exists, but it always exists for the positive fragment of SHACL. Our rewriting techniques allow to validate constraints over KGs with and without ontologies using the same SHACL validation engines.
Bit Standard- Bitcoin between reality and risks of a halfway-money

This work provides an explanation of the market underlying the evolution due to modern technologies and technical advances, especially in transactions.In this regard, the authors specify the aspect related to the creation of virtual currencies like bitcoin that can circulate thanks to the Blockchain system through miners' work.The authors consider areas related to the warnings on the use and exchange of virtual currencies.The aim is to conceptualize in a graphical way the current operational transaction in bitcoin through the existing exchange platforms.The authors try to attest the fickleness of the disintermediation ideal founding Bitcoin.The analysis purposed could be interesting and useful to provide a kind of interpretation of the phenomenon and a general overview about Bitcoin system.
Robustness Attacks on Video Watermarking Using Singular Value Decomposition

In the current digital age, where people are commonly connected within the Internet of Things (IoT) and Internet of People (IoP) environments, videos are easily shared on social media and mobile messaging Apps, so there is a need to determine the ownership of these videos so that they are not illegally distributed without acknowledging the source. Robust video watermarking schemes are commonly applied to solve these copyright ownership problems. These types of robust schemes need to withstand modification attacks attempted on the watermark embedded within the videos. In this paper, we focus on two recent watermarking schemes and analyse their robustness. We present attacks that enable malicious buyers to modify the watermarks embedded by the owner.
Beyond Reason: Uniting Intuition and Rationality in Software Architecture Decision Making

Intuition and rationality, or System 1 and System 2 when speaking more broadly, are two modes of cognitive processing that all humans use when making decisions. System 1 is a type of thinking that involves faster, more holistic forms of processing that tend to use fewer mental resources, whereas System 2 tends to be slower, more deliberate, and more mentally taxing. Both System 1 and System 2 have been shown to be beneficial in a number of decision making contexts. Although it is clear that both System 1 and System 2 have their place in decision making, how and when to use each processing style in software architecture design decision making is a question that remains largely unanswered. Therefore, our research aims to fill this gap by: (i) determining what is actually known from published research regarding the use of System 1 and System 2 in software architecture design decision making, (ii) evaluating System 1 and System 2 empirically in terms of software architecture decision making performance, and: (iii) developing and validating a decision support model to support the use of System 1 alongside System 2 in software architecture design decision making.
Blockchain Technology in Solar Energy

Two of the important topics concerning scientists and governments are blockchain and climate change. After the paper of Satoshi Nakamoto, blockchains became a global phenomenon. After its usage for cryptocurrencies, blockchain is starting to be used for digital protocols and smart contracts. Blockchain technology is used in many sectors, such as banking, finance, car leasing, entertainment, energy, etc. Climate change leads to global warming, which means the long-term warming of the planet. Therefore, governments have made an effort to decrease global warming or keep it stable. One of the mitigation ways of global warming is to use renewable energy. Solar energy is one of the most used types of renewable energy sources, and also blockchain technology is widely used in this sector. In this chapter, the authors investigate the use of blockchain technology in the solar energy sector.
Managing training data from untrusted partners using self-generating policies

When training data for machine learning is obtained from many different sources, not all of which may be trusted, it is difficult to determine which training data to accept and which to reject. A policy-based approach for data curation, where the policies are generated after examining the properties of the offered data, can provide a way to only accept selected data for creating a machine learning model. In this paper, we discuss the challenges associated with generating policies that can manage training data from different sources. An efficient policy generation scheme needs to determine the order in which information is received, must have an approach to determine the trustworthiness of each partner, must have an approach to decide how to quickly assess which data subset can add value to a complex model, and must address several other issues. After providing an overview of the challenges, we propose approaches to solve them and study the properties of those approaches.
Fault-Proneness Prediction of Object-Oriented Classes Using Reusability Measures

Fault-proneness is a key external software-quality attribute of interest for software developers and practitioners. The fault-proneness of an object-oriented class indicates the extent to which the class is prone to faults. Because software engineers cannot measure fault-proneness before they release the software, they apply prediction models to estimate the fault-proneness of the software classes. These models are based on measures that require data that are extracted from design or code artifacts, and therefore, the models are applicable before the software's release date. In this paper, we report an empirical study that explores the ability of models that are based on reusability measures to predict fault-proneness. We considered three reusability measures that account for two reusability aspects: class inheritance and class instantiation. The study involves four open-source Java systems.
Extracting actionable information from Security Forums

The goal of this work is to systematically extract information from hacker forums, whose information would be in general described as unstructured: the text of a post is not necessarily following any writing rules. By contrast, many security initiatives and commercial entities are harnessing the readily public information, but they seem to focus on structured sources of information. Here, we focus on the problem of analyzing text content in security forums. A key novelty is that we use user profiles and contextual features along with transfer learning approach and also embedding space to help us identify and refine information that we could not get from security forum with trivial analysis. We collect a wealth of data from 5 different security forums. The contribution of our work is twofold; (a) we develop a method to automatically identify through the forums malicious IP addresses (b) we also propose a systematic method to identify and classify user-specified threads of interest into four categories. We further showcase how this information can inform knowledge extraction from the forums. As the cyber-wars are becoming more intense, having early accesses to useful information becomes more imperative to remove the hackers first-move advantage, and our work is a solid step towards this direction.
Strategi Pembelajaran Menulis Puisi Reflektif untuk Siswa SMA Kelas X

&lt;div align="center"&gt;&lt;table width="645" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="439"&gt;&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; Writing poetry learning in school tends to be mechanistic and less direct students in creative process that cause difficulty and decrease students’ motivation on writing poetry. Therefore, the researcher develops poetry writing learning strategy, that is learning strategy of writing reflective poetry which is packaged in a guidebook designed for teachers. Based on those explanations, this development aims to (1) developing products in the form of learning strategies to write reflective poems, (2) develop a guide of the implementation of a reflective poetry writing strategy that designed for teachers, and (3) reported the product feasibility test of reflective poetry writing strategy.&lt;/p&gt;&lt;p class="Abstract"&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt; Selama ini, pembelajaran menulis puisi di sekolah cenderung bersifat mekanistik dan kurang mengarahkan siswa untuk berproses kreatif. Hal tersebut menjadikan siswa mengalami kesulitan dan penurunan motivasi dalam menulis puisi. Oleh karena itu, dikembangkan strategi pembelajaran menulis puisi, yaitu strategi pembelajaran menulis puisi reflektif yang dikemas dalam bentuk buku panduan dan ditujukan untuk guru. Berdasarkan paparan tersebut, penelitian pengembangan ini bertujuan untuk (1) mengembangkan produk berupa strategi pembelajaran menulis puisi reflektif, (2) mengembangkan panduan penerapan strategi pembelajaran menulis puisi reflektif yang penggunaannya ditujukan untuk guru, dan (3) melaporkan uji kelayakan produk strategi pembelajaran menulis puisi reflektif.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;
Aurora Image Search With Saliency Deep Features

Although the convolutional neural networks have obtained amazing performance in the area of image search, most of the existing methods are applied for natural images collected via normal lens without anamorphic distortion.Actually, there are great amounts of images collected with a circular fisheye lens to obtain larger field-of-view (FOV), especially in the area of natural science study.This paper aims to present a novel image search method with saliency deep features for those images, especially the aurora images used in solar-terrestrial space research.Our method exploits the advanced Mask R-CNN framework to extract semantic features.To utilize the unique physical characteristics of aurora and focus on the most informative local regions, we present a saliency proposal network (SPN) to take place in the region proposal network (RPN).In our SPN, different from the conventional rectangular gridding way, the proposed anchors show spherical distortion determined by imaging principle and magnetic information.In addition, instead of the horizontal directions, our anchor boxes direct perpendicular to the physical magnetic meridian, and thus ensure them to include the auroral structures within minimum areas.We perform numerous experiments on the big aurora image dataset, and the results prove the superiority of the proposed method over the state-ofthe-art methods on both search accuracy and efficiency.
A Probe into the Reform of Math Education under the Mode of Internationalized Talents Training in Engineering School

Based on the guidance of the State and Sichuan Province's Medium and Long-term Education Reform and Development Plan (2010-2020), and the needs of economic construction in Sichuan Province and the western region, and on the basis of fully investigating of bilingual teaching models and methods at home and abroad, this paper studies the mathematics ability and foreign language level of the "international" talents needed by China's social economy.To meet this demand, some reform proposals are designed, such as the university mathematics curriculum system, bilingual curriculum teaching mode, after-school supervision and assessment methods, and the combination of discipline competition.This reform proposal lays the foundation for improving the quality of undergraduate teaching and cultivating innovative talents who can be integrated with the international community.
Measuring interpretable semantic similarity of sentences using a multi chunk aligner

This work focuses on bolstering the pre–existing Interpretable Semantic Textual Similarity (iSTS) method, that will enable a user to understand the behaviour of an artificial intelligent system. The proposed iSTS method explains the similarities and differences between a pair of sentences. The obj ective of the iSTS problem is to formalize the alignment between a pair of text segments and to label the relationship between the text fragments with a relation type and relatedness score. The overall objective of this work is to develop a 1:M multi chunk aligner for an iSTS method, which is trained on SemEval 2016 Task 2 dataset. The obtained result outperforms many state–of–art aligners, which were part of SemEval 2016 iSTS task.
Techno-economic Feasibility Assessment of Implementation of Solar Tracking for Utility Scale Solar Photovoltaic Plants in Sri Lanka

Solar tracking is a method for enhancing the energy yield of a solar photovoltaic array by orienting the solar panels to align with the path of the sun using an electro mechanical drive system. The details of different tracking systems, their performances and their extra yields tend to exhibit high variances strongly depending on the location where it is implemented. Based on this, there is an argument that there is no significant requirement for implementing solar tracking for solar parks in equatorial regions like Sri Lanka, that experience a good solar irradiance throughout all months of the year. The paper presents the results of a techno-economic feasibility analysis of implementing solar tracking, carried out for a utility scale solar photovoltaic plant of a selected site location in Sri Lanka. During the analysis, the energy yields of different tracking systems were estimated by incorporating the weather data for the selected site location. The results obtained were incorporated with a sound cost benefit analysis to justify the techno economic feasibility of solar tracking strategy for Sri Lankan context.The paper also contains some proposals for future directions for improving tracking systems to enhance their ability to harness more solar energy.