Reviewerly/exHarmony huggingface dataset:
Re-giving pedagogy the interdisciplinary intensity of its voice

In the last two decades the pedagogical cultural and scientific area has been mainly discussing about its statute, its reference subjects, its reference objectives, its reference contents; it was, perhaps, the consequence produced by interdisciplinary approaches, or, perhaps, by the need of adopting interdisciplinary approaches, or, perhaps, by the need of better defining additional directions and designs to pedagogical research. This paper does not aim at giving any contribution to this debate; it aims at stating that several effects of present changes ask pedagogical approaches to be renewed and innovated; its attention focuses on learners, on their present learning, cognitive, social, individual, communicational, expressive, lexical, emotional, affective styles; it focuses on their central position within all educational settings. This paper will shortly comment the effects of some significant changes, whose impact on all the mentioned styles is so deep that the need of re-considering the education’s cores is actually realistic, in terms of a meaningful re-orientation from a pedagogy which supports didactical approaches for competences to a pedagogy which supports didactical approaches for educational objectives.
Politeness Strategy Used in 10th Grade Students’ Anecdote Text

This research was aimed to find out the types of politeness strategy that is used by 10th grade students to express criticism towards public issues through an anecdote text as well as the implication on the teaching and learning process of anecdote text in SMK TI Bali Global Jimbaran. There were fifteen anecdote texts analyzed in this research and they were collected through a writing test conducted in a 10th grade class in SMK TI Bali Global Jimbaran. The data were classified and analyzed based on the politeness strategy theory proposed by Brown and Levinson (1987) and ethnography of communication theory proposed by Hymes (1973). The result of the analysis shows that from the fifteen anecdote texts collected, there were only two types of politeness strategy found to be used in expressing criticism, they are bald on record strategy and off record strategy. There are ten anecdote texts composed by the students found using bald on record strategy, while the other five anecdotes using off record strategy in expressing criticism towards public issues. In other words, more students still used the more risky way of expressing criticisms, therefore it is important for the teacher to choose or design a better model of learning which can improve the students’ pragmatic competence.
Changes in the self-regulation of learning experience after negative self-evaluation of the essay writing task

The establishment of the water corporations in Israel, which are financial companies that took the place of the Water Departments in the local governments, constitutes a turning point in the relation between the local governments and the citizens.If in the past the local government saw itself as a nonprofit organization that served the community, the reform in the water industry converted the community activity of the municipal sector into a totally capitalist business operation.In the first years of the establishment of the State of Israel, it was clear that the State had to provide water to its residents as a source of livelihood and a basis for agricultural development and the conquest of the wilderness, but today in the era of privatization the precious resource, water, has become a "product" from which economic benefit can be derived and which can even be traded.The local governments acquire water "to the city gate" and then they sell it to the city resident at a high mark-up, with the addition of VAT, when the difference goes to the Water Corporations.The neoliberal approach has reached a basic product such as water, without distinction between the socioeconomic situations of people, while placing the burden on the resident.Many Investigation Committees on the topic of the water sector have noted the improper management of the government, which does not prioritize alternative solutions such as desalination, use of treated wastewater, and re-use of water for industry and agriculture.Instead, dozens of water corporations have been established to be used as collection agents and to place the economic burden on the citizen's back.
Point-of-Interest Group Recommendation with an Extreme Learning Machine

With the increasing popularity of location-based social networks (LBSNs), an increasing number of people are sharing their locations with friends through check-in activities. Point-of-interest (POI) recommendation, in which new places are suggested to users, is one of the most important tasks in LBSNs. POI group recommendation aims to suggest the most agreeable places for a group of users. However, the most existing studies, based on collaborative filtering, suffered from various issues, such as: (1) data sparseness, (2) cold start, and (3) scalability. Moreover, many existing schemes are limited in functionality. To address the aforementioned issues, we proposes a POI group recommendation model from a new perspective. The model combines machine learning knowledge and considers POI group recommendation as a classification problem. Extreme learning machine (ELM) is used to train the features. ELM has fast learning speed and ensures the recommendation efficiency. Finally, a series of experiments verify the performance of the model with ELM.
Modeling and Simulation of Self-organized Criticality of Intelligent Optical Network Based on Sand Pile Model

With the continuous development of optical communication technology, the structure of optical network is becoming more and more complex, and complex network theory can analyze the nature of the actual system. From the angle of self-organizing criticality, the “sand pile” model of intelligent optical network is constructed, the model parameters are designed, and the self-organized critical model of intelligent optical network is established. Finally, the computer algorithm is designed according to the model, and the critical value of the whole network load is solved. The research shows that there is a quantitative relationship between the critical value of network load and the number of the betweenness, which has a certain guiding significance for improving the processing ability of key nodes and optimizing the network security.
Word-

Procheta Sen, Debasis Ganguly, Gareth Jones. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Emergence of Bitcoin as an Investment Alternative

Bitcoin has witnessed immense popularity and growth since inception, but has been essentially ignored by the market due to its volatile and high speculative nature. Hence, Bitcoin and other cryptocurrencies constitute a highly dynamic but not fully understood field of research. This paper presents a systematic review of the research work conducted so far in the area of Bitcoin and cryptocurrencies. The literature has been searched and selected from the databases of Web of Science and ScienceDirect through the Boolean criteria. The systematic review highlights the major trends, directions and results available from the existing literature, leading us to present a roadmap of the future research agenda for researchers and policymakers.
Image/video annotation and retrieval by taking advantages of both deep learning and traditional approaches

Image/video annotation and retrieval by taking advantages of both deep learning and traditional approaches
The General Procedure of Ensembles Construction in Data Stream Scenarios

During constructing data stream algorithms the following three aspects have to be taken into consideration: accuracy, running time and required memory. However, in many cases, the fastest algorithms are less accurate than methods requiring high computational power and more time for data analysis. Therefore, to enhance the performance of the algorithms, which in data stream scenario must be characterized by low memory requirement and short time of learning, one can use an ensemble approach. Roughly speaking, the decision made by the ensemble of algorithms can be seen as a decision based on an opinion of a few specialists. In real life nobody is infallible, so to improve the decision making process people often take a final decision after consulting with a few various persons. The vivid example is the diagnosis of an illness. When someone gets bad news, he often goes to other doctors for a second, third, fourth opinion and so on, until we are sure about the diagnosis.
Voting-Based Multi-Agent Reinforcement Learning.

The recent success of single-agent reinforcement learning (RL) encourages the exploration of multi-agent reinforcement learning (MARL), which is more challenging due to the interactions among different agents. In this paper, we consider a voting-based MARL problem, in which the agents vote to make group decisions and the goal is to maximize the globally averaged returns. To this end, we formulate the MARL problem based on the linear programming form of the policy optimization problem and propose a distributed primal-dual algorithm to obtain the optimal solution. We also propose a voting mechanism through which the distributed learning achieves the same sub-linear convergence rate as centralized learning. In other words, the distributed decision making does not slow down the global consensus to optimal. We also verify the convergence of our proposed algorithm with numerical simulations and conduct case studies in practical multi-agent systems.
Character-level part-of-speech tagger of Slovene language

Part-of-speech tagger for Slovene language implemented using convolutional and LSTM neural networks. Tagger uses character-level representation of sentences. The tagger has been trained on the ssj500k 2.1 corpus, http://hdl.handle.net/11356/1181.
UTILIZATION OF GENCIL MOBILE APPLICATION IN IMPLEMENTING SMART CITY IN PONTIANAK

Gencil is a mobile application developed in Pontianak to support the implementation of smart city. The aims of this study are; 1)describing the Gencil mobile application, 2)identifying the utilization of Gencil mobile application, 3)identifying the constraining factors in the development of Gencil mobile application in Pontianak. Primary data was obtained through in-depth interviews and questionnaires. The results show that Gencil mobile application consists of six features. The food price feature becomes the most popular one. Constraining factors for the development of Gencil mobile application are the lack of community participation and the inadequate infrastructure for the application’s development
Towards Context-Aware Evaluation for Image Search

Compared to general web search, image search engines present results in a significantly different way, which leads to changes in user behavior patterns, and thus creates challenges for the existing evaluation mechanisms. In this paper, we pay attention to the context factor in the image search scenario. On the basis of a mean-variance analysis, we investigate the effects of context and find that evaluation metrics align with user satisfaction better when the returned image results have high variance. Furthermore, assuming that the image results a user has examined might affect her following judgments, we propose the Context-Aware Gain (CAG), a novel evaluation metric that incorporates the contextual effects within the well-known gain-discount framework. Our experiment results show that, with a proper combination of discount functions, the proposed context-aware evaluation metric can significantly improve the performances of offline metrics for image search evaluation, considering user satisfaction as the golden standard.
Three-Dimensional Structure Measurement and Optimization Method of Indoor Scene Based on Single Image

In the paper, we detect 2D surfaces and recognize 3D structures in images, and generate occluded part of three-dimensional structure. We detect objects vanish points and geometric segmentation lines, and generate the vector and normal plane. We measure the objects boundaries which have been detected. In the process, we have found the best linear segmentation positions, generated buildings, and other 3D models in room. We propose some effective assumptions to recognize the object model from images by geometric reasoning. At the same time, we put forward structure prediction technology combined with the volume reasoning by parameter representation of spatial objects. We detect scene model relationships of each other by combining image-rich appearance with geometric features. We proposed image geometric modeling grammar framework according to previous discriminative classifier. It is used to represent the physical structure of the visual component. The framework has broken the traditional probability texture context grammar tree model. We proposed spatial context theory and model generation rules. Finally, on the public existing data sets, we proved that we only use structure prediction and linear segmentation of scene to recover 3D structure by a lot of experiments, comparing to restoration algorithm using whole image structure, ours method can produce more credible scene entity models and space constraint relationships.
Statistical Characteristics of Driver Accelerating Behavior and Its Probability Model.

The naturalistic driving data are employed to study the accelerating behavior of the driver. Firstly, the question that whether the database is big enough to achieve a convergent accelerating behavior of the driver is studied. The kernel density estimation is applied to estimate the distributions of the accelerations. The Kullback-Liebler divergence is employed to evaluate the distinction between datasets composed of different quantity of data. The results show that a convergent accelerating behavior of the driver can be obtained by using the database in this study. Secondly, the bivariate accelerating behavior is proposed. It is shown that the bivariate distribution between longitudinal acceleration and lateral acceleration follows the dual triangle distribution pattern. Two bivariate distribution models are proposed to explain this phenomenon, i.e. the bivariate Normal distribution model (BNDM) and the bivariate Pareto distribution model (BPDM). The univariate acceleration behavior is presented to examine which model is better. It is identified that the marginal distribution and conditional distribution of the accelerations approximately follow the univariate Pareto distribution. Hence, the BPDM is a more appropriate one to describe the bivariate accelerating behavior of the driver. This reveals that the bivariate distribution pattern will never reach a circle-shaped region.
Deep Convolutional Neural Network for Motion Deblurring

Removing blur caused by camera shake in images has always been a challenging problem in computer vision literature due to its ill-posed nature. So it is very difficult to accurately predict blur kernels. In this paper, we propose deep convolutional neural networks for this debluring task to avoid calculating blur kernels. Compared with others approaches, we used the L2 norm to the loss function. L2 regularization is generally used to optimize the regular term in the objective function, which guarantees the restoration image quality and prevents overfitting caused by too many parameters. The model proposed in this paper realizes image deblurring directly by learning the intrinsic relationship between blurred image and target image.
Demonstrating Requirement Search on a University Degree Search Application

In many domains of information retrieval, we are required to retrieve documents that describe requirements on a predefined set of terms. A requirement is a relationship between a set of terms and the document. As requirements become more complex by catering for optional, alternative, and combinations of terms, efficiently retrieving documents becomes more challenging due to the exponential size of the search space. In this paper, we propose RevBoMIR, which utilizes a modified Boolean Model for Information Retrieval to retrieve requirements-based documents without sacrificing the expressiveness of requirements. Our proposed approach is particularly useful in domains where documents embed criteria that can be satisfied by mandatory, alternative or disqualifying terms to determine its retrieval. Finally, we present a graph model for representing document requirements, and demonstrate Requirement Search via a university degree search application.
Lossy Compression for Large Scale PDE Problems.

Solvers for partial differential equations (PDE) are one of the cornerstones of computational science. For large problems, they involve huge amounts of data that needs to be stored and transmitted on all levels of the memory hierarchy. Often, bandwidth is the limiting factor due to relatively small arithmetic intensity, and increasingly so due to the growing disparity between computing power and bandwidth. Consequently, data compression techniques have been investigated and tailored towards the specific requirements of PDE solvers during the last decades. This paper surveys data compression challenges and corresponding solution approaches for PDE problems, covering all levels of the memory hierarchy from mass storage up to main memory. Exemplarily, we illustrate concepts at particular methods, and give references to alternatives.
Geometrical Transformation Invariant Approach for Classification of Signatures Using k-NN Classifier

Signature-based authentication of human is still very popular approach. Manual checking is not always accurate and it depends on expertise. The need is an automated and accurate system for signature classification. The signatures do not necessarily comprise of well-formed letters. It can be a random combination of curves and lines. The written signature may be of variable sized, inclined in arbitrary angle or misplaced. This makes the classification task more challenging. This paper proposes an automated approach of handwritten signature classification addressing those problems. The binarized version of the input image is pre-processed in various ways to compensate translation, rotation and noise removal. The four features, which does not vary due to scaling, are selected from the pre-processed image for the classification using k-NN classifier. Overall system accuracy of the proposed approach is 92% on a dataset of 100 images.
Protest against Pakistan's proposed cybercrime bill; threatens civil liberties on the pretext of security – South Asia Citizens Web

The Pakistan National Assembly's Standing Committee on Information Technology (IT) on Thursday passed a controversial cybercrime bill that industry leaders and civil society members have been…
Proxy Certificates: The Missing Link in the Web's Chain of Trust.

The ability to quickly revoke a compromised key is critical to the security of a public-key infrastructure. Regrettably, most certificate revocation schemes suffer from latency, availability, or privacy issues. The problem is exacerbated by the lack of a native delegation mechanism in TLS, which increasingly leads domain owners to engage in dangerous practices such as sharing their private keys with third parties. We investigate the utility of to address long-standing revocation and delegation shortcomings in the web PKI. By issuing proxy certificates, entities holding a regular (non-CA) certificate can grant all or a subset of their privileges to other entities. This fine-grained control on delegating privileges requires no further actions from a CA, yet does not require trust on first use (TOFU). The lifetime of a proxy certificate can be made almost arbitrarily short to curb the consequences of a key compromise. We analyze the benefits of this approach in comparison to alternatives, discussing various use cases and technical implications. We also show that combining short-lived proxy certificates with other schemes constitutes an attractive solution to several pressing problems. Overall, we make the case that the benefits obtained from incorporating proxy certificates into the current PKI substantially outweighs the changes required in practice. Such changes are minimal, and would only be required on the browser end, should a domain owner opt to use proxy certificates.
From the ground up: Building a roadmap of research priorities for a national research data management community

Presented at RDAP2019: Building Communities, May 15-17, 2019. (Research Data Access and Preservation Summit)
An ELM-Based Ensemble Strategy for POI Recommendation

The prosperity of Location-Based Social Networks (LBSNs) facilitates a promising focus on personalized POI recommendation. The check-in activity is a multiple criteria decision making process. In this paper, we devise a novel personalized POI recommendation system called Multiple Criteria Ensemble Recommendation System (MCERS) to integrate the effect of geographical influence, temporal influence and categorical influence in a unified way to model dynamic user preferences for context-aware query. Specifically, we propose an ELM-based ensemble strategy to provide a more sophisticated framework for integrating different criteria to improve the quality of POI recommendations. Extensive experiments on three real-world datasets show that MCERS achieves better performance than existing state-of-the-art methods.
Multistage Clustering Based Automatic Modulation Classification

Automatic modulation classification (AMC) is the problem of identifying the modulation type of a given radio frequency (RF) signal. This operation is one of the key steps in a cognitive radio based spectrum sharing communication network. It is known that the optimal classification algorithms for AMC are computationally intensive which renders real-time implementation almost impossible. In this paper, we propose a practical AMC algorithm that employs multiple stages of clustering to identify the modulation type of the received RF signal. Here, we consider the communication signals to be modulated using the most common digital modulation types: phase shift keying (PSK) or quadrature amplitude modulation (QAM). First, we present a novel algorithm that performs multiple stages of clustering to identify the clusters present in the received data and classifies it to one of the several possible modulation types. Second, we validate our proposed algorithm through practical implementation using software defined radios (SDR). Our results show that the proposed multistage clustering based AMC algorithm works well in practical conditions.
Introduction and Overview of the Main Results of the Book

In recent decades we are observing an exponential increase in the available digital data, generated in various areas of human activity. This growth is much faster with respect to the increase in the available processing capabilities. Apart from large volumes, the data produced by modern data sources are often dynamic and generated at very high rates. Therefore, there is a big challenge to design new data mining algorithms able to deal with such a streaming nature of data. Data stream mining became a very important domain of computer science and finds applications in many areas, e.g. in engineering and industrial processes, robotics, sensor networks, social networks, spam filtering or credit card transaction flows. In this book we present a unique approach to data stream mining problems, putting emphasis on the theoretical backgrounds of considered algorithms. Contrary to the vast majority of the previously presented in the literature heuristic methods, this book focuses on algorithms which are mathematically justified. However, it should be noted that the heuristic solutions cannot be completely abandoned since they often lead to satisfactory practical results. Therefore, the mathematically justified algorithms presented in this book are sometimes slightly tuned and modified in a heuristic way to increase their final accuracy.