Reviewerly/exHarmony huggingface dataset:
Publishing CSV Data as Linked Data on the Web

The majority of datasets on Open Government Data (OGD) portals are stored in comma-separated values (CSV) file. Publishing CSV data as a Linked Open Data (LOD) on the Web is an active field of research. However, there are very few effective applications have been developed with this purpose. Linked Data refer many ways for connecting and publishing structured data to data consumers, but available datasets are in CSV format. Therefore, publishing the CSV model on the webpage, it is needed to change CSV in RDF file format. Many methods and tools have been proposed for data mapping and publishing, however, most of them are not followed by the W3C recommendations rules. The contribution and goal of this paper are to develop a Semantic approach that can effectively convert CSV data into RDF data with rich semantics and release RDF data on the web using LOD principles. We utilize Semantic Web resources and W3C recommendation rules in automatic data publishing method, which enables distributed system for scalability. We apply the proposed method to existing CSVW Implementation Report-W3C and U.S Government's application ( data.gov ). Our experimental results indicate that the proposed approach successfully converts CSV to RDF data and publish those RDF as LOD on the Web, with adequate performance on any sized datasets.
Predicting the Degree of Emotional Support in an Online Health Forum for HIV Using Data Mining Techniques

Online Health Community (OHC) services are mostly employed for resolving problems of people with chronic diseases. OHCS also provide social support in addition with solutions to the problems. Social Support is of two forms: Emotional Support, Informational Support. The objective of this work is to analyze online healthcare forum and find the degree of support provided by the forum. In this work, 1200 discussions have been extracted from POZ, an Online HIV Forum. Initially the type of support groups has been identified by application different semi supervised approach. As a result, it has been identified that emotional support is more in the messages of the forum. Further regression has been applied to quantify the amount of emotional support in the discussion of the POZ forum. Various kinds of regression algorithms have been applied to model the degree of support in the messages.
SVD-based state and parameter estimation approach for generalized Kalman filtering with application to GARCH-in-Mean estimation

In this paper, a gradient-based adaptive filtering technique for unknown state and parameter estimation is proposed for some extensions of classical state-space models: (i) linear time-invariant multiple-input, multiple-output (LTI MIMO) systems, and (ii) linear pairwise Markov models (PMMs) with the related pairwise Kalman filter (PKF). The solution to robust adaptive filtering problem is grounded in the singular value decomposition (SVD) recently adopted for the classical Kalman filter. In contrast to our previous result, the new methods are designed for the case of thin matrices (pre-arrays) involved in the filtering recursions and sensitivities calculation. Additionally, the paper discusses a time-varying variance models' calibration by the proposed innovative adaptive filters. In particular, the state-space approach for estimating GARCH-in-Mean(1,1) is explored.
An Angry Birds Level Generator with Rube Goldberg Machine Mechanisms

This study proposes a method for generating Angry Birds-like game levels featuring a domino effect generated based on Rube Goldberg Machine (RGM) mechanisms, which allow them to be completed by one shot of a bird. The proposed method generates a level by selecting predefined segments consisting of several objects arranged in a way that creates a domino effect among them. To increase the variability of generated levels, the proposed method procedurally generates a varying structure on the top of certain blocks in a predefined segment. Our results show that the proposed RGM generator is comparable to two existing generators, including the winner of the 2018 AIBIRDS Level Generation Competition, in terms of stability while it outperforms both baseline generators with respect to running time and an expressivity metric called "dynamic" which is introduced in this work to measure the time period where moving objects, including a shooting bird, reside in a given level. In addition, from the perspective on the destructive power of a shot, the proposed generator can generate levels featuring a successful domino effect with a high probability, in particular for levels with three to four segments.
Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques

Numerous numbers of companies have utilized the web to offer their services and products. Web customers dependably look through the reviews of other customers towards a product or service before they chose to buy the things or viewed the films. The company needs to analyse their customers’ sentiment and feeling based on their comments. The outcome of the sentiment analysis makes the companies easily discover either the expression of their users is more to positive or negative. There are numerous numbers of sentiment analysis techniques available in the market today. However, only three (3) techniques will be used in this research which are the Python NLTK Text Classification, Miopia and MeaningCloud. These techniques used to analyse the sentiment analysis of the reviews and comments from English language in social media. 2400 datasets from Amazon, Kaggle, IMdB, and Yelp were used to analyse the accuracy of these techniques. From this analyses, average accuracy for sentiment analysis using Python NLTK Text Classification is 74.5%, meanwhile only 73% accuracy achieved using Miopia technique. The accuracy achieved when using MeaningCloud technique is 82.1% which is the highest compared to other techniques. This shows that hybrid technique offers a greatest accuracy for sentiment analysis on social reviews.
Pulser: Fast Congestion Response Using Explicit Incast Notifications for Datacenter Networks

Datacenter applications frequently cause incast congestion, which degrades short flows' flow completion times and long flows' throughput. Existing congestion control schemes (e.g., DCTCP) do not explicitly detect and isolate incast. Instead, they rely on existing Explicit Congestion Notification (ECN) to react to general congestion. They, therefore, lose performance due to slow, cautious, and inaccurate reaction to incast. We propose a novel algorithm that detects incasts and notifies senders using a new Explicit Incast Notification EIN). We show that our incast detection is fast and accurate. Next, we present our congestion control scheme, called Pulser , which isolates incasts using EIN. Unlike DCTCP, which gradually adjusts sending rate, Pulser drastically backs off during incast and rapidly restores sending rate once incast ends (i.e., like a pulse). Our real experiments and ns-3 simulations show that Pulser outperforms prior schemes, DCTCP and ICTCP, in both flow completion times and throughput.
Social Media in Chemistry: Using a Learning Management System and Twitter To Improve Student Perceptions and Performance in Chemistry

ADVERTISEMENT RETURN TO BOOKPREVChapterNEXTSocial Media in Chemistry: Using a Learning Management System and Twitter To Improve Student Perceptions and Performance in ChemistryMercy Adoma FosuMercy Adoma Fosu Department of Chemistry and Biochemistry, South Dakota State University, Brookings, South Dakota 57007, United StatesMore by Mercy Adoma Fosu, Tanya Gupta*Tanya Gupta Department of Chemistry and Biochemistry, South Dakota State University, Brookings, South Dakota 57007, United States*E-mail: [email protected]More by Tanya Gupta, and Semhar MichaelSemhar Michael Department of Mathematics and Statistics, South Dakota State University, Brookings, South Dakota 57007, United StatesMore by Semhar MichaelDOI: 10.1021/bk-2019-1318.ch013Publication Date (Web):September 26, 2019Publication History Published online26 September 2019Published inprint 1 January 2019RIGHTS & PERMISSIONSTechnology Integration in Chemistry Education and Research (TICER)Chapter 13pp 185-208ACS Symposium SeriesVol. 1318ISBN13: 9780841234390eISBN: 9780841234383 Copyright © 2019 American Chemical SocietyChapter Views35Citations3LEARN ABOUT THESE METRICSChapter Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (4 MB) Get e-Alerts
End-to-End Model for Offline Handwritten Mongolian Word Recognition

This paper proposed an end-to-end model for recognizing offline handwritten Mongolian words. To be specific, a sequence to sequence architecture with attention mechanism is used to perform the task of generating a target sequence from a source sequence. The proposed model consists of two LSTMs and one attention network. The first LSTM is an encoder which consumes a frame sequence of one word image. The second LSTM is a decoder which can generate a sequence of letters. The attention network is added between encoder and decoder, which allow the decoder to focus on different positions in a sequence of frames during the procedure of decoding. In this study, we have attempted two schemes for generating frames from word images. In the first scheme, frames are generated with overlapping. Each adjacent two frames overlap half a frame. In the second scheme, frames are generated without overlapping. In addition, the height of the frame is also taken into consideration in our study. By comparison, the better scheme for generating frames has been determined. Experimental results demonstrate that the proposed end-to-end model outperforms the state-of-the-art method.
Application of Complementary Dual AG Codes to Entanglement-Assisted Quantum Codes

Quantum error correcting codes play the role of suppressing noise and decoherence in quantum systems by introducing redundancy. Some strategies can be used to improve the parameters of these codes. For example, entanglement can provide a way for quantum error correcting codes to achieve higher rates than the one obtained via traditional stabilizer formalism. Such codes are called entanglement-assisted quantum (QUENTA) codes. In this paper, we use algebraic geometry codes to construct two families of QUENTA codes, where one of them has maximal entanglement and is maximal distance separable. In the end, we show that for any asymptotically good tower of algebraic function fields there is an asymptotically good family of maximal entanglement QUENTA codes with nonzero rate, relative minimal distance, and relative amount of entanglement.
Win or Learn Fast Proximal Policy Optimisation

AI agents within video games are often required to compete within an environment shared by many other agents. This problem can be tackled by multi-agent reinforcement learning (MARL). One solution to MARL is to learn a Nash Equilibrium Strategy (NES) that guarantees a known minimum payoff when playing against other rational agents. We focus on one approach for learning a NES, Win or Learn Fast (WoLF), WoLF has been shown to converge towards a NES in a variety of matrix-games and grid based games. Research into Deep MARL has focused on performance against opponent agents and with limited quantitative results regarding learning a NES. We present a systematic empirical investigation into the ability of Proximal Policy Optimisation (PPO) to learn a NES, showing instability in certain matrix games. We then present an extension, WoLF-PPO, that is able to learn a policy that is closer to the NES.
Pseudocodeword-based Decoding of Quantum Stabilizer Codes

It has been shown that graph-cover pseudocodewords can be used to characterize the behavior of sum-product algorithm (SPA) decoding of classical codes. In this paper, we leverage and adapt these results to analyze SPA decoding of quantum stabilizer codes. We use the obtained insights to formulate modifications to the SPA that overcome some of its weaknesses.
Level Graph – Incremental Procedural Generation of Indoor Levels using Minimum Spanning Trees

Procedural generation of virtual worlds is an important aspect of game development since decades, typically for increasing replayability or for speeding up the level creation process. However, the utilization of this potential has always been a great challenge due to the difficult controllability of the underlying algorithms or limitations to specific level geometries, like 2D regular structures. In this paper, we present a novel approach for semi-automatic generation of a wide variety of 2D/3D corridor and room systems. The underlying processing pipeline is based on a separation between a user-guided generation of a graph-based abstract level structure and a fully-automatic construction of the corresponding geometry using a pre-modeled component library. The core algorithm is built on the computation of an extended minimal spanning tree, which can be controlled by a set of intuitive vertex and edge parameters. First experimental results have shown that our incremental generation pipeline allows the efficient creation of complex indoor levels, minimizing limitations on level and game designers' creativity.
Identifying and Evaluating Information Technology Baccalaureate Degree Programs per IT2017

SIGITE 2012 included a report1 of Information Technology (IT) bachelor's degree programs in the USA as compared to the IT2008 Model Curriculum2. New IT curriculum guidelines were released by the ACM in late 20173. This paper describes the process for identifying and evaluating IT bachelor's programs in the United States per the new 2017 IT curriculum guidelines. Due to widespread variation in the names of academic degree programs, one cannot simply count those named "Information Technology." This research, funded by SIGITE, first defined a framework for evaluating the compliance of an IT program to the standard of the IT2017 document. This framework was then applied throughout this research. We first compiled a list of prospective IT programs to research. Each institution on the list was then researched by looking at their webpage in search of a list of computing majors listed at that institution. If, at a glance, these program's required courses looked similar to those required in an IT program, the program was evaluated and given a numerical compliance factor. This compliance factor was calculated using the assessment form that is included in this document. The results of this analysis are presented.
An LSTM-based Approach for Overall Quality Prediction in HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) has become a popular solution for multimedia delivery nowadays. In HAS, video quality is generally varying in each streaming session. Therefore, a key question in HTTP Adaptive Streaming is how to evaluate the overall quality of a streaming session. In this paper, we propose a machine learning approach for overall quality prediction in HTTP Adaptive Streaming. In the proposed approach, each segment is represented by four features of segment quality, stalling durations, content characteristics, and padding. The features are fed into a Long Short Term Memory (LSTM) network that is capable of exploring temporal relations between segments. The overall quality of the streaming session is predicted from the outputs of the LSTM network using a linear regression module. Experiment results show that the proposed approach is effective in predicting the overall quality of streaming sessions. Also, it is found that our proposed approach outperforms four existing approaches.
A Practical Lattice-Based Sequential Aggregate Signature

In this work, we construct a lattice-based efficient Sequential Aggregate Signature (SAS) scheme that is provably secure in standard ideal cipher model with some slight changes. This framework is inspired by the scheme of Gentry et al. at PKC 2018 which presented trapdoor-permutation-based sequential aggregate signatures. Since to present, there is no known method to construct a lattice-based trapdoor permutation, we use lattice-based trapdoor function instead to design SAS scheme. In particular, our scheme is history-free, where the sequentially-executed aggregation operation does not need to take the previous messages in order as one part of its input. We also give software implementation of our SAS scheme using FALCON based trapdoor function, which originates from the provably secure NTRUSign signature scheme proposed by Stehlé and Steinfeld at Eurocrypt 2011. The experiment results show our scheme is efficient and practical.
Gaussian Transfer Convolutional Neural Networks

In deep convolutional neural networks (DCNN), the pooling operation is usually adopted to produce condensed and transformation invariant feature maps for the input image. However, it will inevitably induce information loss, which has not be addressed yet in designing filters of DCNNs. In this paper, we propose Gaussian transfer convolutional neural networks (GT-CNN), which introduce Gaussian filters to pool convolutional filters of DCNNs. In our GT-CNN, the pooling on features can be transferred to the pooling on filters, which are achieved in the same end-to-end framework. More importantly, the Gaussian filters of multiple scales and orientations further improve the capability of GT-CNN, leading to a more robust feature representation for the input image. We evaluate our GT-CNN on various datasets, including MNIST, CIFAR-10, and CIFAR-100, and achieve the best performance compared with the state of the arts. Moreover, we also apply GT-CNN to the remote sensing image dataset, NWPU-RESISC45 dataset, and validate the superiority of GT-CNN on the task.
Secret key distillation over a pure loss quantum wiretap channel under restricted eavesdropping

Quantum cryptography provides absolute security against an all-powerful eavesdropper (Eve). However, in practice Eve's resources may be restricted to a limited aperture size so that she cannot collect all paraxial light without alerting the communicating parties (Alice and Bob). In this paper we study a quantum wiretap channel in which the connection from Alice to Eve is lossy, so that some of the transmitted quantum information is inaccessible to both Bob and Eve. For a pureloss channel under such restricted eavesdropping, we show that the key rates achievable with a two-mode squeezed vacuum state, heterodyne detection, and public classical communication assistance-given by the Hashing inequality-can exceed the secret key distillation capacity of the channel against an omnipotent eavesdropper. We report upper bounds on the key rates under the restricted eavesdropping model based on the relative entropy of entanglement, which closely match the achievable rates. For the pure-loss channel under restricted eavesdropping, we compare the secret-key rates of continuous-variable (CV) quantum key distribution (QKD) based on Gaussian-modulated coherent states and heterodyne detection with the discrete variable (DV) decoystate BB84 QKD protocol based on polarization qubits encoded in weak coherent laser pulses.
GIMME: Group Interactions Manager for Multiplayer sErious games

Serious games are moving towards multiplayer environments which aim to concurrently train and educate people. Therefore, we believe there is a growing need to construct systems which account for this tendency in order to ease learning and training. We here propose a model named Group Interactions Manager for Multiplayer sErious games (GIMME) which aims to improve the collective ability of players interacting in environments such as these. Our method organizes the players in groups and computes the types of interactions that should be promoted for each group. These interactions are then promoted by generating adequate game mechanics. We validate the most important aspects of the model by performing several agent-based simulations. The simulations suggest that learning can be improved when applying our strategy, as the average ability of the agents rapidly converged to high, near optimal values, as opposed to a random baseline (a strategy possibly implemented by real teachers when they do not know the students) which maintained low values. Moreover, unlike the random strategy, GIMME considerably approximated the promoted interactions profiles to the agents’ computed preferences.
Art Scene Investigation: discovering and supporting cultural heritage conservation through Mobile AR

The current availability and the wide diffusion of ICT technologies are driving us into a scenario where objects and environments intelligence, together with their enhanced capabilities, can play a strategic role in different aspects and activities of our everyday life. A significant one is represented by the case of cultural heritage, where ICT-based strategies can be exploited to disseminate and communicate information about a specific piece of art or a specific historical site. Digital guides on mobile devices are already used during touristic visits in several museums and cultural institutions, but there is an additional and hidden treasure behind the scenes that could be discovered by the audience thanks to smart objects: understanding of the cultural heritage coming from diagnostic analyses. To reach this purpose, this paper presents a prototype of a digital Augmented Reality (AR) mobile app designed and developed for revealing scientific information about a specific historical and cultural site to a broader and non-specialized audience. The case study we took into account is the Koguryo tombs, located in the northern and central parts of the Korean Peninsula, characterized by painted tumuli with a fragile indoor environment and by difficult access.
What Is Cloud Computing?

Cloud computing is the practice where computing services such as storage options, processing units, and networking capabilities are exposed for consumption by users over the Internet (the cloud). These services range from free to pay-as-you-use billing.
A Factored Relevance Model for Contextual Point-of-Interest Recommendation

The challenge of providing personalized and contextually appropriate recommendations to a user is faced in a range of use-cases, e.g., recommendations for movies, places to visit, articles to read etc. In this paper, we focus on one such application, namely that of suggesting 'points of interest' (POIs) to a user given her current location, by leveraging relevant information from her past preferences. An automated contextual recommendation algorithm is likely to work well if it can extract information from the preference history of a user (exploitation) and effectively combine it with information from the user's current context (exploration) to predict an item's 'usefulness' in the new context. To balance this trade-off between exploration and exploitation, we propose a generic unsupervised framework involving a factored relevance model (FRLM), comprising two distinct components, one corresponding to the historical information from past contexts, and the other pertaining to the information from the local context. Our experiments are conducted on the TREC contextual suggestion (TREC-CS) 2016 dataset. The results of our experiments demonstrate the effectiveness of our proposed approach in comparison to a number of standard IR and recommender-based baselines.