Reviewerly/exHarmony huggingface dataset:
Alternate Low-Rank Matrix Approximation in Latent Semantic Analysis

The latent semantic analysis (LSA) is a mathematical/statistical way of discovering hidden concepts between terms and documents or within a document collection (i.e., a large corpus of text). Each document of the corpus and terms are expressed as a vector with elements corresponding to these concepts to form a term-document matrix. Then, the LSA uses a low-rank approximation to the term-document matrix in order to remove irrelevant information, to extract more important relations, and to reduce the computational time. The irrelevant information is called as “noise” and does not have a noteworthy effect on the meaning of the document collection. This is an essential step in the LSA. The singular value decomposition (SVD) has been the main tool obtaining the low-rank approximation in the LSA. Since the document collection is dynamic (i.e., the term-document matrix is subject to repeated updates), we need to renew the approximation. This can be done via recomputing the SVD or updating the SVD. However, the computational time of recomputing or updating the SVD of the term-document matrix is very high when adding new terms and/or documents to preexisting document collection. Therefore, this issue opened the door of using other matrix decompositions for the LSA as ULV- and URV-based decompositions. This study shows that the truncated ULV decomposition (TULVD) is a good alternative to the SVD in the LSA modeling.
How the users find science journal articles in the digital environment

The author discusses the most popular search services for science articles retrieval — which make the main information source for modem researchers. Based on the findings of the global sociological study of over 40,000 respondents, the services most popular with the users are compared. Among the most frequently used are: abstracted bibliographic databases, science retrieval services, global general search services, libraries’ www-sites, publishers’ www-sites, journals www-sites, and social networks. The author analyzes libraries’ potential for tuning popular services for the users to transit from brief article descriptions (bibliographic records) to their full texts available through subscriptions. To unify the exchange mode and formats, the requirements documents by the National Information Standards Organization (NISO) regulate data exchange between content providers (publishers and aggregators), discovery services developers and the libraries. Integration of Russian journal resources into the global exchange infrastructure is hindered by incompleteness and inconsistency of the metadata being provided. Meanwhile such integration is vital for Russian science article visibility and citation. The author also emphasizes the importance of the standardized data exchange between Russian content providers, discovery services, and the libraries.
From planetary to space mining: prospects for sustainable development

The article considers sustainable mining development as one of the most important problems of achieving global stability as a solution to the contradiction between the growing needs of mankind and the presence of limits of the biosphere in their provision. It is expected that sustainable mountain development will continue in both the planetary and space dimensions, and these processes will unfold in close cooperation. Having joined the space industry, sustainable space mining will contribute to the formation of extraterrestrial basis of the future wide space exploration by mankind.
Seam Manipulator: Leveraging Pixel Fusion for Depth-Adjustable Stereoscopic Image Retargeting

To enhance the depth perception for stereoscopic image retargeting, this paper presents a depthadjustable stereoscopic image retargeting method via pixel fusion technique.The traditional methods enforce the depth-preserving constraint on the left and right images with poor depth adaption.To solve this issue, we try to address two critical properties that how to change the widths of unmatched region to control the depth perception, and how to change the alignment of left and right seams to satisfy visual comfort limits, and thus propose a seam manipulator tool to optimize the process for scaling factor determination of stereoscopic seams.The important contribution of our method is to take scaling preservation energy and disparity consistency energy into optimization.The experimental results demonstrate that our method can achieve promising performances on depth perception and visual comfort adjustment for better visual experience.
Rain prediction using fuzzy rule based system in North-West malaysia

&lt;span&gt;The warm and humid condition is the characteristic of Malaysia tropical climate. Prediction of rain occurrences is important for the daily operations and decisions for the country that rely on agriculture needs. However predicting rainfall is a complex problem because it is effected by the dynamic nature of the tropical weather parameters of atmospheric pressure, temperature, humidity, dew point and wind speed. Those parameters have been used in this study. The rainfall prediction are compared and analyzed. Fuzzy Logic and Fuzzy Inference System can deal with ambiguity that often occurred in meteorological prediction; it can easily incorporate with expert knowledge and empirical study into standard mathematical. This paper will determine the dependability of Fuzzy Logic approach in rainfall prediction within the given approximation of rainfall rate, exploring the use of Fuzzy Logic and to develop the fuzzified model for rainfall prediction. The accuracy of the proposed Fuzzy Inference System model yields 72%&lt;/span&gt;
Feature Based Domain Adaptation for Neural Network Language Models with Factorised Hidden Layers

Language models are a key technology in various tasks, such as, speech recognition and machine translation. They are usually used on texts covering various domains and as a result domain adaptation has been a long ongoing challenge in language model research. With the rising popularity of neural network based language models, many methods have been proposed in recent years. These methods can be separated into two categories: model based and feature based adaptation methods. Feature based domain adaptation has compared to model based domain adaptation the advantage that it does not require domain labels in the corpus. Most existing feature based adaptation methods are based on bias adaptation. We propose a novel feature based domain adaptation technique using hidden layer factorisation. This method is fundamentally different from existing methods because we use the domain features to calculate a linear combination of linear layers. These linear layers can capture domain specific information and information common to different domains. In the experiments, we compare our proposed method with existing adaptation methods. The compared adaptation techniques are based on two different ideas, that is, bias based adaptation and gating of hidden units. All language models in our comparison use state-of-the-art long short-term memory based recurrent neural networks. We demonstrate the effectiveness of the proposed method with perplexity results for the well-known Penn Treebank and speech recognition results for a corpus of TED talks.
Reconstruction of Sports Competition Video Using Fixed Camera Based on Receding Horizon Strategy

At the sports and games for elementary and junior high school students, their parents often become personal photographer for the game. They see their child over the frame despite being in the playing field. In addition, problems such as trying to take a good place to compete may occur. We can solve these problems by the conference management side distributing the sports competition videos. So in this paper, we show a method to automatically reconstruct movies using competition movies taken by fixed point cameras and to present high quality movies to viewers.
Supporting job mediator and job seeker through an actionable dashboard

Job mediation services can assist job seekers in finding suitable employment through a personalised approach. Consultation or mediation sessions, supported by personal profile data of the job seeker, help job mediators understand personal situation and requests. Prediction and recommendation systems can directly provide job seekers with possible job vacancies. However, incorrect or unrealistic suggestions, and bad interpretations can result in bad decisions or demotivation of the job seeker. This paper explores how an interactive dashboard visualising prediction and recommendation output can help support the dialogue between job mediator and job seeker, by increasing the "explainability" and providing mediators with control over the information that is shown to job seekers.
Rescuing Epistemic Objects from Speculative Design

This article explores the role of epistemic objects in speculative design, proposing an overview that goes from the origins of this practice in the ‘90s to its consolidation as a research method, through the emergence of design fiction in the last decade. In considering different critical accounts and the limits to this practice pointed out in recent years, it seeks to rescue and analyze the role of diegetic prototypes as research sites. This paper will argue that prototypes are a possible embodiment of what Rheinberger calls ‘epistemic objects’ or ‘epistemic things’. Conceived as such, these prototypes allow us for generation of knowledge by designing and to revisit the experimental character of a method that leads us to explore and materialize what we presently do not know.
Prospective Studies for the Implementation of a Remote Access Earthquake Damage Detection System for High-Rise Buildings in Romania

The rapid increase of the number newly-erected high-rise buildings in Romania during the last two decades brought up the importance of the continuous monitoring of their state. For seismic actions, a post-event modification of the dynamic characteristics of a building can provide a rapid indicator about its potential damage. The paper presents the methods, procedures and steps by which a system providing online remote access to data resulting from building vibration monitoring could be implemented and used for buildings in Romania. The system would allow rapid identification of building damage, based on the processing of recorded data. Identified issues, as well as potential bottlenecks are presented, together with the proposed solutions. The system is planned to be implemented by using the infrastructure available at NIRD URBAN-INCERC, Romania. The paper also presents a case study concerning the structural health monitoring of a multi-story reinforced concrete building, carried out by the project team.
Bayesian consistency for a nonparametric stationary Markov model

We consider posterior consistency for a Markov model with a novel class of nonparametric prior.In this model, the transition density is parameterized via a mixing distribution function.Therefore, the Wasserstein distance between mixing measures can be used to construct neighborhoods of a transition density.The Wasserstein distance is sufficiently strong, for example, if the mixing distributions are compactly supported, it dominates the sup-L 1 metric.We provide sufficient conditions for posterior consistency with respect to the Wasserstein metric provided that the true transition density is also parametrized via a mixing distribution.In general, when it is not be parameterized by a mixing distribution, we show the posterior distribution is consistent with respect to the average L 1 metric.Also, we provide a prior whose support is sufficiently large to contain most smooth transition densities.
An Exploratory Study on the Effect of Petroleum Hydrocarbon on Soils Using Hyperspectral Longwave Infrared Imagery

Manmade crude oil contamination, which has negative impacts on the environment and human health, can be found in various ecosystems all over the globe. Hyperspectral remote sensing (HRS) is an efficient tool to investigate this crude oil contamination where its electromagnetic spectrum is analyzed. This exploratory study used an innovative HRS imagery sensor to study the effect of petroleum hydrocarbon (PHC), found in crude oil, on the spectrum of soils across the longwave infrared (LWIR 8–12 μm) spectral region. This contrasts with previous studies that focused on shortwave and midwave infrared (SWIR 1–2.5 and MWIR 3–8 μm, respectively) regions. An outdoor HRS image of three different types of soils, contaminated with 11 PHC concentrations, was processed and analyzed. Since PHC is spectrally featureless in the LWIR region, the analysis focused on the spectral alteration of the dominant minerals in the soils. Good evaluation metrics of R2 &gt; 0.83 and a root-mean-squared-error (RMSE) between 1.06 and 1.33 wt % showed that the PHC level can be predicted with relatively good accuracy, even without direct spectral features of crude oil PHC, using an airborne LWIR camera in field conditions. This study can be used as a proof of concept for future airborne remote sensing of PHC-contaminated soils.
Human Action Recognition using Multi-Kernel Learning for Temporal Residual Network

Deep learning has led to a series of breakthrough in the human action recognition field.Given the powerful representational ability of residual networks (ResNet), performance in many computer vision tasks including human action recognition has improved.Motivated by the success of ResNet, we use the residual network and its variations to obtain feature representation.Bearing in mind the importance of appearance and motion information for action representation, our network utilizes both for feature extraction.Appearance and motion features are further fused for action classification using a multi-kernel support vector machine (SVM).We also investigate the fusion of dense trajectories with the proposed network to boost up the network performance.We evaluate our proposed methods on a benchmark dataset (HMDB-51) and results shows the multi-kernel learning shows the better performance than the fusion of classification score from deep network SoftMax layer.Our proposed method also shows good performance as compared to the recent state-of-the-art methods.
On Cross-Lingual Text Similarity Using Neural Translation Models

Accurately computing the similarity between two texts written in different languages has tremendous value in many applications, such as cross-lingual information retrieval and cross-lingual text mining/analytics. This paper studies the important problem based on neural networks. Specifically, our focus is on the neural machine translation models. While translation models are utilized, we pay special attention not to the translation itself but to the intermediate states of given texts stored in the translation models. Our assumption is that the intermediate states capture the syntactic and semantic meaning of input texts and are a good representation of the texts, avoiding inevitable translation errors. To study the validity of the assumption, we investigate the utility of the intermediates states and their effectiveness in computing cross-lingual text similarity in comparison with other neural network-based distributed representations of texts, including word and paragraph embedding-based approaches. We demonstrate that an approach using the intermediate states outperforms not only these approaches but also a strong machine translation-based one. Furthermore, it is revealed that intermediate states and translated texts work complementarily each other despite the fact that they are generated from the same NMT models.
Model for Object Detection using Computer Vision and Machine Learning for Decision Making

This paper gives the efficient model of the process of detecting the object and analyzing the gesture of an object using the machine learning and computer vision for decision making after surveying various research in the field of pattern recognition.*It is based on the objects behavioural activities, Gesture recognition is very essential for making a decision.This research paper deals with Human-Computer Interaction (HCI).The object is may be hand, face, vehicle etc.In our research work we will extract 3D information about the captured image and calculate the distance of an object from the camera, detect the object gesture further we perform edge detection and then apply vision based and data based object gesture recognition.The implementation of the methodology will be done on the MATLAB tool.
Partition-based optimization model for generative anatomy modeling language (POM-GAML)

This paper presents a novel approach for Generative Anatomy Modeling Language (GAML). This approach automatically detects the geometric partitions in 3D anatomy that in turn speeds up integrated non-linear optimization model in GAML for 3D anatomy modeling with constraints (e.g. joints). This integrated non-linear optimization model requires the exponential execution time. However, our approach effectively computes the solution for non-linear optimization model and reduces computation time from exponential to linear time. This is achieved by grouping the 3D geometric constraints into communities.Various community detection algorithms (k-means clustering, Clauset Newman Moore, and Density-Based Spatial Clustering of Applications with Noise) were used to find communities and partition the non-linear optimization problem into sub-problems. GAML was used to create a case study for 3D shoulder model to benchmark our approach with up to 5000 constraints.Our results show that the computation time was reduced from exponential time to linear time and the error rate between the partitioned and non-partitioned approach decreases with the increasing number of constraints. For the largest constraint set (5000 constraints), speed up was over 2689-fold whereas error was computed as low as 2.2%.This study presents a novel approach to group anatomical constraints in 3D human shoulder model using community detection algorithms. A case study for 3D modeling for shoulder models developed for arthroscopic rotator cuff simulation was presented. Our results significantly reduced the computation time in conjunction with a decrease in error using constrained optimization by linear approximation, non-linear optimization solver.
Analytic semi-device-independent entanglement quantification for bipartite quantum states

We define a property called nondegeneracy for Bell inequalities, which describes the situation that in a Bell setting, if a Bell inequality and involved local measurements are fixed, any quantum state with a given dimension and its orthogonal quantum state cannot violate remarkably the inequality simultaneously. By choosing a proper nondegenerate Bell inequality, we prove that for an unknown bipartite quantum state of given dimension, based on the measurement statistics only, we can provide an analytic lower bound for the entanglement of formation or even the distillable entanglement, making the whole process semi device independent. We characterize the mathematical structure of nondegeneracy, and prove that quite a lot of well-known Bell inequalities are nondegenerate. We demonstrate our approach by quantifying entanglement for qutrit-qutrit states based on their violations of the Collins-Gisin-Linden-Masser-Popescu inequality.
Professional Gamer: The Meaning on ‘Point Blank Online’ Play

&lt;p class="Abstract"&gt;&lt;em&gt;&lt;span lang="IN"&gt;The growth rate of online game players in Indonesia is 33% average per year on 2010 and 35% on 2013. Majority of game players are not able to control game play activities and only waste time. On the other hand there is a group of online game players who are capable of achieving and succeeding in the online gaming world especially in playing Point Blank. &lt;/span&gt;&lt;span&gt;This research use qualitative analysis&lt;/span&gt;&lt;span lang="IN"&gt; with descriptive approach&lt;/span&gt;&lt;span&gt; to investigate the professional online game player on ‘Team Fun’ Group that consist of 6 player with different ability in Point Blank. This research show that the &lt;/span&gt;&lt;span&gt;meaning on Point Blank &lt;/span&gt;&lt;span lang="IN"&gt;give a chance for &lt;/span&gt;&lt;span&gt;the &lt;/span&gt;&lt;span lang="IN"&gt;player&lt;/span&gt;&lt;span&gt;to become professional gamers&lt;/span&gt;&lt;span lang="IN"&gt; as long as&lt;/span&gt;&lt;span lang="IN"&gt;t&lt;/span&gt;&lt;span&gt;he players able to determine their goals and dreams in playing online games&lt;/span&gt;&lt;span lang="IN"&gt;. Professional game player&lt;/span&gt;&lt;span&gt;also must join a solid team &lt;/span&gt;&lt;span lang="IN"&gt;to&lt;/span&gt;&lt;span&gt; clear goal in play.&lt;/span&gt;&lt;/em&gt;&lt;/p&gt;
Video Surveillance-Based Intelligent Traffic Management in Smart Cities

Visualization of video is considered as important part of visual analytics.Several challenges arise from massive video contents that can be resolved by using data analytics and consequently gaining significance.Though rapid progression in digital technologies resulted in videos data explosion that incites the requirements to create visualization and computer graphics from videos, a state-of-the-art algorithm has been proposed in this chapter for 3D conversion of traffic video contents and displaying on Google Maps.Time stamped visualization based on glyph is employed efficiently in surveillance videos and utilized for event detection.This method of visualization can possibly decrease the complexity of data, having complete view of videos from video collection.The effectiveness of proposed system has shown by obtaining numerous unprocessed videos and algorithm is tested on these videos without concerning field conditions.The proposed visualization technique produces promising results and found effective in conveying meaningful information while alleviating the need of searching exhaustively colossal amount of video data.
A BPMN Extension for Business Process Outsourcing to the Cloud

The trend of business process outsourcing (BPO) has been intensified with the advent of Cloud computing that brings a new way of paying and consuming resources. Nevertheless, ensuring the compliance of outsourced business processes (BPs), protecting sensitive data, and making the right choices of offers, remain among the most important challenges in BPO to the Cloud. In this context, it is crucial to clearly specify the awaited requirements when modelling BPs. BPMN (Business Process Model and Notation) is the most used standard for BP modelling. However, it is a generic language and does not support BPO characteristics. This paper proposes a BPMN extension (called BPOMN) that allows specifying the requirements of BP activities in terms of security, compliance, cost, and performance. This enables specifying and considering all relevant information for effective Cloud BPO.