Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of a Standpoint-based Linked Data Visualization Approach

In this paper, we propose a standpoint-based Linked Data visualization system, as a step in the development of a Linked Data visualization system that receives an entity and visualizes the data related to it in various ways based on multiple “standpoints”. Here, a standpoint denotes the data generated from a user’s perspective of interest in an entity and its visualization. For example, “volcano” and “World Heritage Site” are standpoints with respect to Mt. Fuji. Considering these standpoints, a user interested in “volcano” in our system is shown various types of volcanoes and the history of volcanic eruptions, whereas a user interested in “World Heritage Site” is shown a list of viewing spots and the list of visitor numbers per year. This feature is not found in existing Linked Data browsers which can be defined as “single” standpoint browsers. In this paper, we present our proposed approach and describe a prototype system based on the proposed approach.
Synthesis of exemplar textures by self-similarity matching

Abstract. Texture synthesis is proposed to construct a large digital image from a small sample by taking advantage of its structural content. However, many approaches in texture synthesis are based on the presence of broken features at the overlap of adjacent patches. Due to inaccurate similarity measures, several optimization schemes for patch mergence may fail if these schemes cannot find satisfactory candidates from input samples. Self-similarity of candidate patches was proposed, and an algorithm was developed to perform distance error matching and alignment via the sum of self-similarity. First, when the matching window moves in the texture samples along the scan line, the sum of self-similarity replaces the sum-of-squared difference to decrease broken features or texels of outputs. Next, the synthesis speed was accelerated by eliminating redundancy calculation during matching window sliding. Finally, the proposed method enlarges the search range of the suture from one patch to all patches in the horizontal direction, and the broken features of overlaps are eliminated in the outputs. To demonstrate the performance of the proposed method, the obtained synthesis results were compared with other conventional synthesis methods. In all cases, experimental results demonstrate that the self-similarity matching-based method can decrease the number of feature discontinuities and accelerate synthesis time.
Mental Workload Assessment in Smartphone Multitasking Users: A Feature Selection Approach using Physiological and Simulated Data

When a user of a computer system is performing more than one task at the same time, her error rate increases drastically. In any system this is a critical issue, since the goals of the tasks are not likely to be met. In that sense, the purpose of mental workload assessment is to estimate the mental demand of tasks to take action according to that, avoiding execution errors. In this paper we study two techniques of mental workload assessment, physiological signals and simulation models of mental behavior with the ACT-R cognitive architecture. The contributions of this study are in two folds: validate a positive correlation among physiological and simulated data and, to develop a supervised model of classification with a cost-sensitive feature selection algorithm using the ACT-R simulated data as an input of the model. Results show a positive, significant correlation among the two data sources, and that the model selects features of less cost and classify better than a baseline approach with 93.1% accuracy in average.
Wonderful Design: Applying Appraisal Theory to Procedural Level Generation

textabstractProcedural level generation for games is an active field of research with successful applications. However, how to generate content that embodies design intent is still an open research question. Level designers lack abstractions and tools for authoring generated artifacts for affecting emotion. We propose a novel pattern language for generative level design inspired by Appraisal Theory. Its patterns enable designers to add meaning, depth, and cohesiveness to the resulting content, and modify artifacts to make the content more engaging. We illustrate how these patterns can be implemented in a generative grammar for level generation for an adventure game. Formative evaluation of generated level content demonstrates the feasibility of the approach and suggests points for further improvements. Future work could focus on other elements which seem important for affecting emotions, including pacing, perception, and expectation.
Social-aware incentive mechanism for full-view covered video collection in crowdsensing

Compared with a traditional fixed sensor network, mobile crowdsensing provides an efficient way to collect sensing data. However, conducting sensing tasks consumes the resources of mobile users (e.g. battery, storage memory, time). Therefore, incentive mechanism design plays a key role in efficiently collecting the sensing data in a mobile crowdsensing system. Most of existing works about the incentive mechanism design simply use a constant to describe the data quality. In this study, the authors focus on the collection of video clips and introduce multiple parameters to evaluate the quality of the collected data. By jointly considering the social relationship of mobile users, they propose a social-aware incentive mechanism to achieve the full-view coverage for a target by efficiently collecting video clips. The proposed mechanism satisfies the properties of individual rationality, truthful and computational efficiency. Simulation results show better performance of the proposed mechanism compared with random selection and aspect based selection. In specific, with the number of users n = 80 , the proposed mechanism can improve the data collector's utility by 485% and 33% compared with random selection and aspect based selection, respectively.
Extraction and Visualization of Occupational Health and Safety Related Information from Open Web

In this paper, we have proposed natural language processing and deep learning based techniques for the automatic extraction and curation of occupational health and safety related information from safety-related articles. Such articles typically contain details of the organizations that have been cited for violating the health and safety regulations, safety-related issues and incidents, the location of the incident, and finally details of the penalties incurred. We have done experiments with a collection of 5400 related articles. The end-product of our work is an occupational risk-register that contains details of safety incidents across geographies and time. This register can be further utilized for analytical and reporting purposes. Such information is extremely valuable to industries which see a high occurrence of occupational injuries.
Enforcement in Spectrum Sharing: Crowd-sourced Blind Authentication of Co-channel Transmitters

The harmful interference caused by rogue radios poses a serious threat to spectrum sharing ecosystems. One approach for mitigating this problem is to adopt an enforcement scheme that can be used by an enforcement entity (e.g., Federal Communications Commission’s Enforcement Bureau) to uniquely identify transmitters by authenticating their waveforms. In this approach, the enforcement entity that is authenticating the waveform is not the intended receiver, and hence it has to decode the authentication signal "blindly" with little or no knowledge of the transmission parameters. In real-world scenarios, an enforcement entity may need to cope with additional challenges, including poor signal strength of the received signals and simultaneous co-channel transmissions from multiple transmitters. In this paper, we propose a novel concept that effectively addresses some of these challenges, which we refer to as Crowd-sourced Blind Authentication of co-channel Transmitters (CBAT). We also present a concrete instantiation of this concept called FREquency offset Embedding for CBAT (FREE). Our results show that FREE enables the enforcement entity to blindly authenticate multiple co-channel transmitters with good accuracy by harnessing the power of crowd-sourcing.
Inferring Knowledge Acquisition through Web Navigation Behaviours

As we witness the growing popularity of online learning, we address the problem of knowing if users are actually learning. The traditional assessment approaches involve tests, assignments and peer assessments. We explore if there is a way to measure learning and personalise the user learning experience in an unobtrusive manner. My PhD proposes using data-driven methods to measure learning by mining user interaction data to identify regularities that could be indicators of learning.
Driver Drowsiness Detection in Facial Images

Extracting effective features of fatigue in images and videos is an open problem. This paper introduces a face image descriptor that can be used for discriminating driver fatigue in static frames. In this method, first, each facial image in the sequence is represented by a pyramid whose levels are divided into non-overlapping blocks of the same size, and hybrid image descriptor are employed to extract features in all blocks. Then the obtained descriptor is filtered out using feature selection. Finally, non-linear SVM is applied to predict the drowsiness state of the subject in the image. The proposed method was tested on the public dataset NTH Drowsy Driver Detection (NTHUDDD). This dataset includes a wide range of human subjects of different genders, poses, and illuminations in real-life fatigue conditions. Experimental results show the effectiveness of the proposed method. These results show that the proposed hand-crafted feature compare favorably with several approaches based on the use of deep Convolutional Neural Nets.
From shallow feature learning to deep learning: Benefits from the width and depth of deep architectures

Since Pearson developed principal component analysis (PCA) in 1901, feature learning (or called representation learning) has been studied for more than 100 years. During this period, many “shallow” feature learning methods have been proposed based on various learning criteria and techniques, until the popular deep learning research in recent years. In this advanced review, we describe the historical profile of the shallow feature learning research and introduce the important developments of the deep learning models. Particularly, we survey the deep architectures with benefits from the optimization of their width and depth, as these models have achieved new records in many applications, such as image classification and object detection. Finally, several interesting directions of deep learning are presented and briefly discussed.
Complexity reduction for HEVC encoder using multiplication free one-bit transformation

Abstract. In high-efficiency video coding (HEVC), motion estimation (ME) complexity is high. The ME complexity is greatly reduced by implementing a multiplication free one-bit transform (MF-1BT) in the HEVC encoder. Compared with the other one-bit transforms, the computational load of MF-1BT is less. The implementation of MF-1BT in a HEVC encoder greatly reduces the ME complexity in terms of ME time (MET) and total encoding time (ET). Experimental results show that the MF-1BT along with full search reduces the ME complexity in terms of percentage reduction in MET and ET by 89.03% and 87.53%, respectively, as compared with a conventional full search algorithm with a Bjontegaard delta-rate (BD-rate) of 0.1095% and Bjontegaard delta-peak signal-to-noise ratio (BD-PSNR) of 0.1311% in low delay-P main profile. The performance comparison of test zonal search (TZS) along with MF-1BT is evaluated by comparing it with the TZS algorithm in the fast search mode of the HEVC encoder. Hexagon and diamond search algorithm along with MF-1BT is also proposed in the fast search mode of the HEVC encoder to further reduce the MET and ET compared with the TZS algorithm of the HEVC encoder.
Masked Face Detection using the Viola Jones Algorithm: A Progressive Approach for less Time Consumption

The use of CCTV surveillance is today’s need inpublic and private sector for ensuring security against terrorismand robbery. Regular expressions are used to signify enormoussets of motion attributes captured in video. The video vigilanceis popular system without using human interference to captureimportant scenes. The motive of the work is to introduce automaticrevelation of masked objects in real time with a surveillancecamera. The main aim is to detect masked person automaticallyin less time period. In this paper,the researcher proposes a systemthat consists methods which uses four variant steps that are thesteps of calculating distance range of person from the camera,eye or vision line detection and face part detection such asmouth detection and face detection. Performance of proposedalgorithm is carried out on various real time inputs. Experimentalevaluation shows that proposed algorithm exceeds better in termsof time consumption. This unique approach for the problemhas created a method transparent and easier in complexity sothat the real time implementation can be made beneficial andworkable. Analysis of the algorithms fulfillment on the test videotrack gives appropriate judgments for additional improvementsin the masked face detection performance. Finally, based on theresearch, the axioms were useful for the work which can beusually accessible from available algorithms.
Rapid handling of outliers in dense sampling descriptor correspondence fields

Abstract. The sparse-to-dense approach is considered to be the standard method of capturing the long-range motion of small objects during large displacement optical flow. Despite progress in the matching and interpolation of this approach, little work has focused on improving the handling of outliers after dense sampling descriptor matching. We propose an improved grid-based statistical matching method that can quickly remove outliers without calculating backward flow. First, a multigrid statistical matching method is developed to remove the most outliers of the dense sampling descriptor correspondence field. Second, to improve the accuracy of outliers handling, the misjudgment match in the edge grid is corrected based on the statistical matching constraint. The results of extensive experiments on public optical flow datasets demonstrate the effectiveness of the proposed method.
The Identification of Marketing Performance Using Text Mining of Airline Review Data

We are aim firstly to extract major keywords using text mining method, secondly to identify prominent keyword from the keywords extracted from text mining analysis, and then to confirm differences in influences of the keywords which affect corporate performance. Results were as following. First, keywords have been found to show distinctive features. Since the keywords posted from the clients showed certain tendency, airlines accordingly need service management by identifying the service property through keyword analysis. Second, prominent keywords have been found out of the keyword extracted from text mining. Some of the keywords have significantly correlated with marketing performance, but others not. This implies that the company could uncover consumers’ needs through the prominent keywords and managing the properties related to the prominent keywords would help with improving corporate performance. Third, “recommend” should be treated distinctively with “satisfaction” in terms of service management through the keywords. Results suggest strategic implications to the practical business environment by analyzing keywords around the industry using text mining. We believe this work, which aims to establish common ground for understanding these analyses across multiple disciplinary perspectives, will encourage further research and development of service industry.
A Semantic Similarity Based Topic Evaluation for Enhancing Information Filtering

Topic Modelling has been applied in many successful applications in data mining, text mining, machine learning and information filtering. The limitation is that the quality of topics generated from modelled corpus are not always good because many topics contain intrusive and ambiguous words. This negative drawback would affect the performance of text based application systems based on topic models. Hence, topic evaluation to assess and to rank the topics is really important for the good quality topics before applying those topics to text based applications. In this study, we proposed an ontology-based topic evaluation method for enhancing information filtering, named as STRbTCM. This new model assesses the quality of topics by matching topic models with headings in Library Congress Subject Heading (LCSH) ontology. To evaluate the effectiveness of our proposed model, we compare the model with two existing topic evaluation methods applied to information filtering system. In addition, we also compare our proposed model to term-based model BM25 and two other models based on topics: TNG and LDA_words. Through extensive experiments, we find that our proposed model performed better than other baseline models according to four main evaluating measures.
Modified L-shaped Decomposition Method with Scenario Aggregation for a Two-Stage Stochastic Programming Problem

This paper introduces a method which is developed to solve two-stage stochastic programming problems in which first-stage region is unbounded and cannot be solved using traditional decomposition. We are using our proposed L-shaped decomposition method modification to solve such problems. In order to achieve a more accurate result, the number of scenarios generated in the optimization process must be large enough. If there is a large number of target variables, optimization takes a long time and uses a lot of resources. Thus, in order to reduce the number of iterations of the optimization process, the amount of resources used, and the calculating time needed to get the optimal solution, the aggregation approach is applied. This paper also presents results of our research on optimal parameters setting of the proposed method. DOI: http://dx.doi. org/10.5755/j01.itc. 47.4. 20103
Multi-Label Classification for Past Events

Study and analysis of past events can provide numerous benefits. While event categorization has been previously studied, it was usually assigned only one event category to an event. In this work we focus on multi-label classification for past events that is a more general and challenging problem than the previous studies. We categorize them into 13 event categories using a range of diverse features and report micro-average F1 score is improved approximately by 10% compared with the state-of-the-art algorithm.
A Novel Metric Combination Approach for Verification Problems

In this paper we propose and test a novel approach, namely Supervised Asymmetric Metric Extraction (SAME), that learns from the supervised metric data and extracts the best single metric from a given set of metrics. It takes up large space to represent the metric-based descriptions, so the approach is specifically crafted to allow for a computationally effective solution. The proposed learning model is scaleindependent and hence rescaling of any metric does not affect the learning. Another advantage of metric extraction is the way of training set annotation which specifically suits verification problems. In this metric extraction approach, we separate intraclass and interclass distances, simplifying the metric extraction problem to linear programming problem which can use optimization techniques effectively. Here, the number of variables needed in the computation remains small and hence it eliminates the need of any soft constraint extension resulting in reduced computational time complexity. The experimental results on offline and online signature data demonstrate that the proposed approach yields better performance and time complexity compared to other metric extraction technique as computational complexity in the proposed approach depends mainly on the calculation of original distances.
Investigating Learning Resources Precedence Relations via Concept Prerequisite Learning

The identification of prerequisite relationships among concepts is a fundamental step toward the organization of knowledge for educational purposes. In the context of a learning process, simplest concepts that are requirements to understand and address more complex concepts should be presented first. Therefore, the identification of prerequisite relationships is a fundamental step for effective course design and automatic learning path generation systems. Although there have been recent advances in machine learning methods for the automatic identification of prerequisite relationships between concepts, little research has been done on whether these automatic strategies can be extended to establish precedence relationships among learning resources. The precedence relation between two learning resources establishes which of the resources must be presented first. In this paper, we approach this problem and propose a strategy to identify the precedence relation. Given two learning resources our strategy analyzes prerequisites among the concepts addressed by the learning resources to estimate the precedence relation. A set of 1588 pairs of learning resources extracted from MOOCs refined by human experts is used to evaluate the strategy. The experimental results show that it is possible to identify the precedence relation between learning resources through the automatic identification of prerequisite relationships between concepts.
A Python Library for Memory Augmented Neural Networks

A Memory Augmented Neural Network (MANN) is an extension to an RNN which enables it to save large amount of data to a memory object which is dimensionally separated from the Neural Network. This paper introduces a new Python library based on TensorFlow to define MANNs as Python objects. In addition to the standard implementation of the MANN, this contribution proposes a modification to the head calculation which decreases the noise while searching through the memory. The paper presents two experiments concerning the proposed implementation.FirsttheMANNistrainedtobeabletostoreand reproduce a piece of data (a task with linear data connectivity), and second the MANN is trained to find a Minimum Vertex Cover of a Graph (MVCG). This task was chosen because the connectivity of the vertex in the graph, that would pose a challenge to the MANN. The tests show that he MANN has no problem learning the first task, and that it is able to find an optimal solution for the MVCG problem in most cases.
A Pragmatic Introduction to Secure Multi-Party Computation

Since its introduction by Andrew Yao in the 1980s, multi-party computation has developed from a theoretical curiosity to an important tool for building large-scale privacy-preserving applications. Secure multi-party computation (MPC) enables a group to jointly perform a computation without disclosing any participant’s private inputs. The participants agree on a function to compute, and then can use an MPC protocol to jointly compute the output of that function on their secret inputs without revealing them. This monograph provides an introduction to multi-party computation for practitioners interested in building privacy-preserving applications and researchers who want to work in the area. The authors introduce the foundations of MPC and describe the current state of the art. The goal is to enable readers to understand what is possible today, and what may be possible in the future. It provides a starting point for building applications using MPC and for developing MPC protocols, implementations, tools, and applications. Those seeking a concise, accessible introduction to the topic which quickly enables them to build practical systems or conduct further research will find this essential reading.